From 637e0fc7eb930bf4d95dfd2a4eab64411097387f Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 16 Jul 2026 18:15:42 +0000
Subject: [PATCH 01/41] factoring out in-place edge sort

---
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 122 ++++++++++--------
 1 file changed, 67 insertions(+), 55 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 52b4542798..00fb0de36b 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 #pragma once
@@ -37,6 +37,7 @@
 
 #include <climits>
 #include <iostream>
+#include <limits>
 #include <memory>
 #include <random>
 
@@ -174,6 +175,24 @@ __global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size,
   }
 }
 
+template <typename DataT, typename IdxT>
+using sort_kernel_type =
+  void (*)(DataT const*, IdxT, uint32_t, IdxT*, uint32_t, uint32_t, cuvs::distance::DistanceType);
+
+template <typename DataT, typename IdxT>
+auto select_sort_kernel(uint64_t degree) -> sort_kernel_type<DataT, IdxT>
+{
+  if (degree <= 32) { return kern_sort<DataT, IdxT, 1>; }
+  if (degree <= 64) { return kern_sort<DataT, IdxT, 2>; }
+  if (degree <= 128) { return kern_sort<DataT, IdxT, 4>; }
+  if (degree <= 256) { return kern_sort<DataT, IdxT, 8>; }
+  if (degree <= 512) { return kern_sort<DataT, IdxT, 16>; }
+  if (degree <= 1024) { return kern_sort<DataT, IdxT, 32>; }
+  RAFT_FAIL(
+    "The degree of input knn graph is too large (%lu). It must be equal to or smaller than 1024.",
+    degree);
+}
+
 template <typename IdxT, typename OutputMatrixView>
 __global__ void kern_make_rev_graph_k(
   OutputMatrixView output_graph,                                // [graph_size, degree]
@@ -971,6 +990,51 @@ void make_reverse_graph_gpu(
   }
 }
 
+/** Sort a device-resident graph in place without staging the dataset or graph. */
+template <typename DataT, typename IdxT = uint32_t>
+void sort_knn_graph_device_inplace(
+  raft::resources const& res,
+  cuvs::distance::DistanceType metric,
+  raft::device_matrix_view<const DataT, int64_t, raft::row_major> dataset,
+  raft::device_matrix_view<IdxT, int64_t, raft::row_major> knn_graph)
+{
+  RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
+               "dataset size is expected to have the same number of graph index size");
+  RAFT_EXPECTS(
+    metric == cuvs::distance::DistanceType::InnerProduct ||
+      metric == cuvs::distance::DistanceType::CosineExpanded ||
+      metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::BitwiseHamming ||
+      metric == cuvs::distance::DistanceType::L1,
+    "Unsupported metric. Only InnerProduct, CosineExpanded, L2Expanded, BitwiseHamming and L1 are "
+    "supported");
+
+  auto const rows   = static_cast<uint64_t>(dataset.extent(0));
+  auto const dim    = static_cast<uint64_t>(dataset.extent(1));
+  auto const degree = static_cast<uint64_t>(knn_graph.extent(1));
+  RAFT_EXPECTS(rows <= static_cast<uint64_t>(std::numeric_limits<IdxT>::max()),
+               "Dataset size must fit in the graph index type");
+  RAFT_EXPECTS(rows <= static_cast<uint64_t>(std::numeric_limits<uint32_t>::max()) &&
+                 dim <= static_cast<uint64_t>(std::numeric_limits<uint32_t>::max()),
+               "Dataset extents must fit in uint32_t");
+  RAFT_EXPECTS(degree > 0 && degree <= 1024, "Graph degree must be in [1, 1024]");
+
+  auto kernel = select_sort_kernel<DataT, IdxT>(degree);
+
+  constexpr uint32_t block_size = 256;
+  auto const warps              = block_size / raft::WarpSize;
+  auto const blocks             = static_cast<uint32_t>((rows + warps - 1) / warps);
+  kernel<<<blocks, block_size, 0, raft::resource::get_cuda_stream(res)>>>(
+    dataset.data_handle(),
+    static_cast<IdxT>(rows),
+    static_cast<uint32_t>(dim),
+    knn_graph.data_handle(),
+    static_cast<uint32_t>(rows),
+    static_cast<uint32_t>(degree),
+    metric);
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
 template <typename DataT,
           typename IdxT       = uint32_t,
           typename d_accessor = raft::host_device_accessor<cuda::std::default_accessor<DataT>,
@@ -985,21 +1049,11 @@ void sort_knn_graph(
 {
   RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
                "dataset size is expected to have the same number of graph index size");
-  RAFT_EXPECTS(
-    metric == cuvs::distance::DistanceType::InnerProduct ||
-      metric == cuvs::distance::DistanceType::CosineExpanded ||
-      metric == cuvs::distance::DistanceType::L2Expanded ||
-      metric == cuvs::distance::DistanceType::BitwiseHamming ||
-      metric == cuvs::distance::DistanceType::L1,
-    "Unsupported metric. Only InnerProduct, CosineExpanded, L2Expanded, BitwiseHamming and L1 are "
-    "supported");
   const uint64_t dataset_size = dataset.extent(0);
   const uint64_t dataset_dim  = dataset.extent(1);
-  const DataT* dataset_ptr    = dataset.data_handle();
 
   const IdxT graph_size             = dataset_size;
   const uint64_t input_graph_degree = knn_graph.extent(1);
-  IdxT* const input_graph_ptr       = knn_graph.data_handle();
 
   auto large_tmp_mr = raft::resource::get_large_workspace_resource_ref(res);
 
@@ -1018,51 +1072,9 @@ void sort_knn_graph(
 
   raft::copy(res, d_input_graph.view(), knn_graph);
 
-  void (*kernel_sort)(const DataT* const,
-                      const IdxT,
-                      const uint32_t,
-                      IdxT* const,
-                      const uint32_t,
-                      const uint32_t,
-                      const cuvs::distance::DistanceType);
-  if (input_graph_degree <= 32) {
-    constexpr int numElementsPerThread = 1;
-    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
-  } else if (input_graph_degree <= 64) {
-    constexpr int numElementsPerThread = 2;
-    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
-  } else if (input_graph_degree <= 128) {
-    constexpr int numElementsPerThread = 4;
-    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
-  } else if (input_graph_degree <= 256) {
-    constexpr int numElementsPerThread = 8;
-    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
-  } else if (input_graph_degree <= 512) {
-    constexpr int numElementsPerThread = 16;
-    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
-  } else if (input_graph_degree <= 1024) {
-    constexpr int numElementsPerThread = 32;
-    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
-  } else {
-    RAFT_FAIL(
-      "The degree of input knn graph is too large (%lu). "
-      "It must be equal to or smaller than %d.",
-      input_graph_degree,
-      1024);
-  }
-  const auto block_size          = 256;
-  const auto num_warps_per_block = block_size / raft::WarpSize;
-  const auto grid_size           = (graph_size + num_warps_per_block - 1) / num_warps_per_block;
-
   RAFT_LOG_DEBUG(".");
-  kernel_sort<<<grid_size, block_size, 0, raft::resource::get_cuda_stream(res)>>>(
-    d_dataset.data_handle(),
-    dataset_size,
-    dataset_dim,
-    d_input_graph.data_handle(),
-    graph_size,
-    input_graph_degree,
-    metric);
+  sort_knn_graph_device_inplace<DataT, IdxT>(
+    res, metric, raft::make_const_mdspan(d_dataset.view()), d_input_graph.view());
   raft::resource::sync_stream(res);
   RAFT_LOG_DEBUG(".");
   raft::copy(res, knn_graph, raft::make_const_mdspan(d_input_graph.view()));

From 7972d3a6ada42bdbb0827f8ec65f677ac6e130e3 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 00:03:08 +0000
Subject: [PATCH 02/41] core merge logic

---
 .../detail/cagra/cagra_merge_scaffold.cuh     | 1396 +++++++++++++++++
 1 file changed, 1396 insertions(+)
 create mode 100644 cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
new file mode 100644
index 0000000000..3342d06530
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -0,0 +1,1396 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+#pragma once
+
+#include "../neighbors_device_intrinsics.cuh"
+
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <raft/core/copy.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/resource/cublas_handle.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+
+#include <algorithm>
+#include <bit>
+#include <cmath>
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail::merge_scaffold {
+
+// ----------------------------------------------------------------------------
+// Constants and size limits
+// ----------------------------------------------------------------------------
+
+inline constexpr uint32_t MAX_FANOUT         = 32;
+inline constexpr uint32_t MAX_LEADERS        = 8192;
+inline constexpr uint32_t MAX_LEAF_SIZE      = 256;
+inline constexpr int MAX_LEAF_DEGREE         = 8;
+inline constexpr int ASSIGNMENT_TILE_ROWS    = 2048;
+inline constexpr uint64_t DETERMINISTIC_SEED = 0x4c616e6472756d;
+inline constexpr size_t GEMM_WORKSPACE_BYTES = size_t{2} * 1024 * 1024 * 1024;
+inline constexpr int WARP_SIZE               = 32;
+/** Warps per block in the one-row-per-warp kernels; their launch geometry derives from this. */
+inline constexpr int ROW_WARPS_PER_BLOCK = 4;
+// Centered INT8 dot products accumulate into INT32. Overflow requires more than 131,071
+// dimensions even at the worst-case magnitude of 128, which is not realistic.
+inline constexpr int64_t MAX_INTEGER_LEAF_DIMENSION =
+  std::numeric_limits<int32_t>::max() / (128 * 128);
+
+/** Grid size for the grid-stride kernels: blocks of 256 threads covering `items`, capped so
+ * oversized workloads loop within resident threads instead. */
+inline auto strided_grid_size(int64_t items) -> int
+{
+  return static_cast<int>(std::min<int64_t>((items + 255) / 256, 1048576));
+}
+
+// ----------------------------------------------------------------------------
+// Build parameters and partition data structures
+// ----------------------------------------------------------------------------
+
+/** Internal controls for deterministic multi-level ball carving and leaf neighbor construction. */
+struct build_params {
+  uint32_t levels        = 2;
+  uint32_t root_fanout   = 2;
+  uint32_t lower_fanout  = 3;
+  double leader_fraction = 0.02;
+  uint32_t max_leaders   = 1000;
+  uint32_t leaf_size     = 256;
+  uint32_t leaf_degree   = 4;
+};
+
+/** Controls for one invocation of the reusable many-way split boundary. */
+struct split_params {
+  uint32_t fanout            = 1;
+  double leader_fraction     = 0.02;
+  uint32_t max_leaders       = 1000;
+  uint32_t leaf_size         = 256;
+  uint32_t level             = 0;
+  uint32_t occurrence_stride = 1;
+};
+
+/** Device state and tuning knobs shared by every split level: the precomputed row norms, the
+ * tiling and workspace capacities, and the seed feeding the deterministic leader samples. */
+struct split_context {
+  split_context(rmm::cuda_stream_view stream, int64_t rows)
+    : norms(static_cast<size_t>(rows), stream)
+  {
+  }
+
+  rmm::device_uvector<float> norms;
+  int assignment_tile_rows    = ASSIGNMENT_TILE_ROWS;
+  size_t gemm_workspace_bytes = GEMM_WORKSPACE_BYTES;
+  uint64_t seed               = DETERMINISTIC_SEED;
+};
+
+struct partition_membership {
+  uint32_t id         = 0;
+  uint16_t occurrence = 0;
+  uint16_t padding    = 0;
+};
+
+struct partition_range {
+  uint32_t key  = 0;
+  int64_t start = 0;
+  int64_t end   = 0;
+};
+
+struct partition_set {
+  rmm::device_uvector<partition_membership> memberships;
+  std::vector<partition_range> ranges;
+};
+
+// ----------------------------------------------------------------------------
+// Many-way partitioning
+// ----------------------------------------------------------------------------
+
+/** Describes a contiguous tile of a parent partition to assign to child leaders. */
+struct assignment_tile {
+  int64_t input_start     = 0;
+  int64_t group_start     = 0;
+  int64_t group_size      = 0;
+  int64_t output_start    = 0;
+  int32_t rows            = 0;
+  int32_t leader_count    = 0;
+  uint32_t child_key_base = 0;
+  uint32_t leader_offset  = 0;
+};
+
+/** Describes a completed parent partition copied unchanged into the next split level. */
+struct carry_span {
+  int64_t input_start  = 0;
+  int64_t output_start = 0;
+  int32_t rows         = 0;
+  uint32_t child_key   = 0;
+};
+
+/** Leader count selection logic */
+inline int select_leader_count(int64_t rows, split_params const& params)
+{
+  auto sampled =
+    static_cast<int64_t>(std::ceil(params.leader_fraction * static_cast<double>(rows)));
+  sampled = std::max<int64_t>(sampled, params.fanout);
+  sampled = std::min<int64_t>(sampled, params.max_leaders);
+  sampled = std::min<int64_t>(sampled, rows);
+  return static_cast<int>(sampled);
+}
+
+/** Return the bucket index for a leader count: ceil(log2(leaders)), so that
+ * `1 << leader_bucket_index(leaders)` is the smallest power of two covering it. Requires
+ * leaders >= 1. */
+inline int leader_bucket_index(int leaders)
+{
+  return static_cast<int>(std::bit_width(static_cast<unsigned>(leaders - 1)));
+}
+
+/** Host-side decision for one parent partition: where its children and output rows land. */
+struct parent_plan {
+  partition_range parent;
+  int64_t output_start    = 0;
+  uint32_t child_key_base = 0;
+  int32_t leader_count    = 0;  // 0 => carried unchanged as one child
+  uint32_t leader_offset  = 0;  // deterministic leader sample start; meaningful only for splits
+
+  auto carried() const -> bool { return leader_count == 0; }
+  auto size() const -> int64_t { return parent.end - parent.start; }
+  auto child_count() const -> uint32_t
+  {
+    return carried() ? 1 : static_cast<uint32_t>(leader_count);
+  }
+  auto output_rows(uint32_t fanout) const -> int64_t
+  {
+    return carried() ? size() : size() * static_cast<int64_t>(fanout);
+  }
+};
+
+struct split_plan {
+  std::vector<parent_plan> parents;
+  int64_t output_rows  = 0;
+  uint32_t child_count = 0;
+};
+
+/**
+ * Decide on host how every parent partition maps into the next level.
+ *
+ * Parents within the leaf size are carried: their memberships pass through unchanged under one
+ * child key. Larger parents are split: a deterministic leader sample of `leader_fraction` of
+ * their rows, clamped to [fanout, max_leaders], is taken at evenly strided member positions
+ * starting from an offset hashed from the seed, level, and parent identity, so reruns select the
+ * same leaders. Child keys and output rows are dense in parent order.
+ */
+inline auto plan_split(std::vector<partition_range> const& ranges,
+                       int64_t membership_count,
+                       split_params const& params,
+                       uint64_t seed) -> split_plan
+{
+  RAFT_EXPECTS(params.fanout >= 1 && params.fanout <= MAX_FANOUT,
+               "Fastener split fanout must be between 1 and %d",
+               MAX_FANOUT);
+  RAFT_EXPECTS(params.leader_fraction > 0.0 && params.leader_fraction <= 1.0,
+               "Fastener leader fraction must be in (0, 1]");
+  RAFT_EXPECTS(params.max_leaders >= params.fanout && params.max_leaders <= MAX_LEADERS,
+               "Fastener leader cap must cover the fanout and not exceed %d",
+               MAX_LEADERS);
+
+  split_plan plan;
+  plan.parents.reserve(ranges.size());
+  int64_t covered     = 0;
+  int64_t output_rows = 0;
+  int64_t child_keys  = 0;
+
+  for (size_t parent_index = 0; parent_index < ranges.size(); ++parent_index) {
+    auto const& parent = ranges[parent_index];
+    RAFT_EXPECTS(parent.start == covered && parent.end > parent.start,
+                 "Fastener parent ranges must compactly cover all memberships");
+    covered = parent.end;
+
+    parent_plan entry{.parent         = parent,
+                      .output_start   = output_rows,
+                      .child_key_base = static_cast<uint32_t>(child_keys)};
+    if (entry.size() > params.leaf_size) {
+      entry.leader_count  = select_leader_count(entry.size(), params);
+      entry.leader_offset = static_cast<uint32_t>(
+        cuvs::neighbors::detail::device::xorshift64(
+          seed ^ (static_cast<uint64_t>(params.level) << 48) ^
+          (static_cast<uint64_t>(parent.key) << 1) ^ static_cast<uint64_t>(parent_index)) %
+        static_cast<uint64_t>(entry.size()));  // mixing in all the relevant state
+    }
+
+    child_keys += entry.child_count();
+    output_rows += entry.output_rows(params.fanout);
+    plan.parents.push_back(entry);
+  }
+  RAFT_EXPECTS(covered == membership_count,
+               "Fastener parent ranges must compactly cover all memberships");
+  RAFT_EXPECTS(output_rows <= static_cast<int64_t>(std::numeric_limits<uint32_t>::max()),
+               "Fastener membership count must fit in uint32_t");
+  RAFT_EXPECTS(child_keys <= static_cast<int64_t>(std::numeric_limits<uint32_t>::max()),
+               "Fastener child key count must fit in uint32_t");
+  plan.output_rows = output_rows;
+  plan.child_count = static_cast<uint32_t>(child_keys);
+  return plan;
+}
+
+/** Render the device-facing descriptor for one tile of a split parent. */
+inline auto make_tile(parent_plan const& entry, int64_t start, uint32_t fanout, int tile_rows)
+  -> assignment_tile
+{
+  return assignment_tile{
+    .input_start = start,
+    .group_start = entry.parent.start,
+    .group_size  = entry.size(),
+    .output_start =
+      entry.output_start + (start - entry.parent.start) * static_cast<int64_t>(fanout),
+    .rows           = static_cast<int32_t>(std::min<int64_t>(tile_rows, entry.parent.end - start)),
+    .leader_count   = entry.leader_count,
+    .child_key_base = entry.child_key_base,
+    .leader_offset  = entry.leader_offset};
+}
+
+/** Group the split parents by padded leader count, so each bucket shares one GEMM shape. */
+inline auto bucket_split_parents(split_plan const& plan, split_params const& params)
+  -> std::vector<std::vector<parent_plan const*>>
+{
+  std::vector<std::vector<parent_plan const*>> buckets(
+    leader_bucket_index(static_cast<int>(params.max_leaders)) + 1);
+  for (auto const& entry : plan.parents) {
+    if (!entry.carried()) { buckets[leader_bucket_index(entry.leader_count)].push_back(&entry); }
+  }
+  return buckets;
+}
+
+/** One candidate assignment of a row to a leader. Defaults to the invalid sentinel, which sorts
+ * after every real candidate. */
+struct leader_candidate {
+  float distance  = std::numeric_limits<float>::max();
+  uint32_t leader = std::numeric_limits<uint32_t>::max();
+
+  __device__ auto valid() const -> bool { return leader != std::numeric_limits<uint32_t>::max(); }
+};
+
+/** Nearer candidates order first. Equal distances order by the lower leader index, keeping the
+ * selection deterministic. */
+__device__ inline auto operator<(leader_candidate a, leader_candidate b) -> bool
+{
+  return a.distance < b.distance || (a.distance == b.distance && a.leader < b.leader);
+}
+
+/** Replace the worst entry in the best list if the new candidate is better. */
+__device__ inline void manyway_insert_local(leader_candidate candidate,
+                                            int fanout,
+                                            leader_candidate* best)
+{
+  int worst = 0;
+  for (int j = 1; j < fanout; ++j) {
+    if (best[worst] < best[j]) { worst = j; }
+  }
+  if (candidate < best[worst]) { best[worst] = candidate; }
+}
+
+/** Sort the selected candidates in place from the nearest to the farthest.
+ *
+ * This is hand-rolled because thrust::sort has an allocation and I can't get a cuda::std::sort to
+ * compile. */
+__device__ inline void manyway_sort_selected(int fanout, leader_candidate* selected)
+{
+  for (int i = 1; i < fanout; ++i) {
+    leader_candidate candidate = selected[i];
+    int j                      = i;
+    while (j > 0 && candidate < selected[j - 1]) {
+      selected[j] = selected[j - 1];
+      --j;
+    }
+    selected[j] = candidate;
+  }
+}
+
+/** Write the identity membership for each dataset row. */
+static __global__ void initialize_root_memberships_kernel(partition_membership* memberships,
+                                                          int64_t rows)
+{
+  int64_t row = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  if (row < rows) { memberships[row] = {static_cast<uint32_t>(row), uint16_t{0}, uint16_t{0}}; }
+}
+
+/** Represent the full dataset as one identity partition without copying any vectors. */
+inline auto make_root_partition(raft::resources const& res, int64_t rows) -> partition_set
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+  partition_set root{rmm::device_uvector<partition_membership>(static_cast<size_t>(rows), stream),
+                     {{uint32_t{0}, int64_t{0}, rows}}};
+  int blocks = static_cast<int>((rows + 255) / 256);
+  initialize_root_memberships_kernel<<<blocks, 256, 0, stream>>>(root.memberships.data(), rows);
+  RAFT_CUDA_TRY(cudaGetLastError());
+  return root;
+}
+
+/** Copy the memberships of each completed parent to the output and write its one child key. */
+static __global__ void carry_completed_parents_kernel(partition_membership const* input,
+                                                      carry_span const* spans,
+                                                      partition_membership* output,
+                                                      uint32_t* output_keys)
+{
+  auto span = spans[blockIdx.x];
+  for (int row = threadIdx.x; row < span.rows; row += blockDim.x) {
+    output[span.output_start + row]      = input[span.input_start + row];
+    output_keys[span.output_start + row] = span.child_key;
+  }
+}
+
+/** Copy the vectors of each tile row into a dense float buffer. Unused rows become zero. */
+template <typename T>
+__global__ void manyway_gather_tile_points_kernel(T const* dataset,
+                                                  int64_t dim,
+                                                  partition_membership const* memberships,
+                                                  assignment_tile const* tiles,
+                                                  int batch_size,
+                                                  int tile_rows,
+                                                  float* output)
+{
+  int64_t linear = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  int64_t stride = static_cast<int64_t>(blockDim.x) * gridDim.x;
+  int64_t total  = static_cast<int64_t>(batch_size) * tile_rows * dim;
+  for (; linear < total; linear += stride) {
+    int64_t d   = linear % dim;
+    int64_t row = (linear / dim) % tile_rows;
+    int batch   = static_cast<int>(linear / (dim * tile_rows));
+    auto tile   = tiles[batch];
+    float value = 0.0f;
+    if (row < tile.rows) {
+      uint32_t id = memberships[tile.input_start + row].id;
+      value       = static_cast<float>(dataset[static_cast<int64_t>(id) * dim + d]);
+    }
+    output[linear] = value;
+  }
+}
+
+/** Copy the leader vectors of each tile into a dense float buffer and record the leader IDs. */
+template <typename T>
+__global__ void manyway_gather_tile_leaders_kernel(T const* dataset,
+                                                   int64_t dim,
+                                                   partition_membership const* memberships,
+                                                   assignment_tile const* tiles,
+                                                   int batch_size,
+                                                   int padded_leaders,
+                                                   float* output,
+                                                   uint32_t* leader_ids)
+{
+  int64_t linear = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  int64_t stride = static_cast<int64_t>(blockDim.x) * gridDim.x;
+  int64_t total  = static_cast<int64_t>(batch_size) * padded_leaders * dim;
+  for (; linear < total; linear += stride) {
+    int64_t d   = linear % dim;
+    int leader  = static_cast<int>((linear / dim) % padded_leaders);
+    int batch   = static_cast<int>(linear / (dim * padded_leaders));
+    auto tile   = tiles[batch];
+    float value = 0.0f;
+    if (leader < tile.leader_count) {
+      int64_t relative = (tile.leader_offset +
+                          (static_cast<int64_t>(leader) * tile.group_size) / tile.leader_count) %
+                         tile.group_size;
+      uint32_t id = memberships[tile.group_start + relative].id;
+      value       = static_cast<float>(dataset[static_cast<int64_t>(id) * dim + d]);
+      if (d == 0) { leader_ids[static_cast<int64_t>(batch) * padded_leaders + leader] = id; }
+    }
+    output[linear] = value;
+  }
+}
+
+/** Select the nearest leaders for each tile row from the dot products. Write the child keys and
+ *  memberships. One warp does one row. */
+static __global__ void manyway_select_tiles_kernel(float const* dots,
+                                                   int batch_size,
+                                                   int tile_rows,
+                                                   int padded_leaders,
+                                                   int fanout,
+                                                   int occurrence_stride,
+                                                   float const* norms,
+                                                   uint32_t const* leader_ids,
+                                                   partition_membership const* input_memberships,
+                                                   assignment_tile const* tiles,
+                                                   uint32_t* output_keys,
+                                                   partition_membership* output_memberships)
+{
+  int lane           = threadIdx.x % WARP_SIZE;
+  int warp           = threadIdx.x / WARP_SIZE;
+  int64_t linear_row = static_cast<int64_t>(blockIdx.x) * ROW_WARPS_PER_BLOCK + warp;
+  int64_t total_rows = static_cast<int64_t>(batch_size) * tile_rows;
+  if (linear_row >= total_rows) { return; }
+  int batch = static_cast<int>(linear_row / tile_rows);
+  int row   = static_cast<int>(linear_row % tile_rows);
+  auto tile = tiles[batch];
+  if (row >= tile.rows) { return; }
+
+  int64_t input_index = tile.input_start + row;
+  auto membership     = input_memberships[input_index];
+  leader_candidate local_best[MAX_FANOUT];
+
+  for (int leader = lane; leader < tile.leader_count; leader += WARP_SIZE) {
+    uint32_t leader_id = leader_ids[static_cast<int64_t>(batch) * padded_leaders + leader];
+    float dot = dots[(static_cast<int64_t>(batch) * tile_rows + row) * padded_leaders + leader];
+    float distance = fmaxf(0.0f, norms[membership.id] + norms[leader_id] - 2.0f * dot);
+    manyway_insert_local(
+      {.distance = distance, .leader = static_cast<uint32_t>(leader)}, fanout, local_best);
+  }
+
+  extern __shared__ unsigned char shared_bytes[];
+  auto* shared_candidates = reinterpret_cast<leader_candidate*>(shared_bytes);
+  int shared_base         = (warp * WARP_SIZE + lane) * fanout;
+  for (int j = 0; j < fanout; ++j) {
+    shared_candidates[shared_base + j] = local_best[j];
+  }
+  __syncwarp();
+
+  if (lane == 0) {
+    leader_candidate selected[MAX_FANOUT];
+    int warp_base = warp * WARP_SIZE * fanout;
+    for (int candidate = 0; candidate < WARP_SIZE * fanout; ++candidate) {
+      if (shared_candidates[warp_base + candidate].valid()) {
+        manyway_insert_local(shared_candidates[warp_base + candidate], fanout, selected);
+      }
+    }
+    manyway_sort_selected(fanout, selected);
+    int64_t output_base =
+      tile.output_start + (input_index - tile.input_start) * static_cast<int64_t>(fanout);
+    for (int rank = 0; rank < fanout; ++rank) {
+      output_keys[output_base + rank]        = tile.child_key_base + selected[rank].leader;
+      output_memberships[output_base + rank] = {
+        membership.id,
+        static_cast<uint16_t>(membership.occurrence + rank * occurrence_stride),
+        uint16_t{0}};
+    }
+  }
+}
+
+/** Read the sorted keys and make one contiguous range for each unique partition key. */
+inline auto collect_partition_ranges(raft::resources const& res,
+                                     uint32_t const* keys,
+                                     int64_t count,
+                                     uint32_t key_count) -> std::vector<partition_range>
+{
+  auto stream          = raft::resource::get_cuda_stream(res);
+  auto output_capacity = static_cast<size_t>(std::min<int64_t>(count, key_count));
+  rmm::device_uvector<uint32_t> device_unique_keys(output_capacity, stream);
+  rmm::device_uvector<uint32_t> device_counts(output_capacity, stream);
+  auto reduced_end = thrust::reduce_by_key(thrust::cuda::par.on(stream),
+                                           thrust::device_pointer_cast(keys),
+                                           thrust::device_pointer_cast(keys + count),
+                                           thrust::make_constant_iterator(uint32_t{1}),
+                                           thrust::device_pointer_cast(device_unique_keys.data()),
+                                           thrust::device_pointer_cast(device_counts.data()));
+  auto group_count =
+    static_cast<size_t>(reduced_end.first - thrust::device_pointer_cast(device_unique_keys.data()));
+
+  std::vector<uint32_t> unique_keys(group_count);
+  std::vector<uint32_t> counts(group_count);
+  raft::copy(unique_keys.data(), device_unique_keys.data(), group_count, stream);
+  raft::copy(counts.data(), device_counts.data(), group_count, stream);
+  raft::resource::sync_stream(res);
+
+  std::vector<partition_range> groups;
+  groups.reserve(group_count);
+  int64_t cursor = 0;
+  for (size_t index = 0; index < group_count; ++index) {
+    RAFT_EXPECTS(unique_keys[index] < key_count, "Many-way group key is out of range");
+    groups.push_back({unique_keys[index], cursor, cursor + counts[index]});
+    cursor += counts[index];
+  }
+  RAFT_EXPECTS(cursor == count, "Many-way partition membership histogram lost entries");
+  return groups;
+}
+
+/** Copy every carried parent's memberships unchanged. */
+inline void carry_parents(raft::resources const& res,
+                          partition_set const& parents,
+                          split_plan const& plan,
+                          rmm::device_uvector<uint32_t>& keys,
+                          rmm::device_uvector<partition_membership>& memberships)
+{
+  std::vector<carry_span> carries;
+  for (auto const& entry : plan.parents) {
+    if (entry.carried()) {
+      carries.push_back({entry.parent.start,
+                         entry.output_start,
+                         static_cast<int32_t>(entry.size()),
+                         entry.child_key_base});
+    }
+  }
+  if (carries.empty()) { return; }
+
+  auto stream = raft::resource::get_cuda_stream(res);
+  rmm::device_uvector<carry_span> device_carries(carries.size(), stream);
+  raft::copy(device_carries.data(), carries.data(), carries.size(), stream);
+  carry_completed_parents_kernel<<<static_cast<int>(carries.size()), 256, 0, stream>>>(
+    parents.memberships.data(), device_carries.data(), memberships.data(), keys.data());
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+/** Compute every pairwise dot product between the rows of A_i and the rows of B_i for a strided
+ * batch of row-major matrices with rows of length `row_width`:
+ * out_i[b * a_rows + a] = A_i[a] . B_i[b].
+ *
+ * Pretty much a wrapper around `cublasGemmStridedBatchedEx`. */
+template <typename DataT, typename AccT>
+void batched_row_dot_products(raft::resources const& res,
+                              DataT const* a,
+                              int a_rows,
+                              long long a_stride,
+                              DataT const* b,
+                              int b_rows,
+                              long long b_stride,
+                              AccT* out,
+                              long long out_stride,
+                              int row_width,
+                              int batch_count)
+{
+  static_assert((std::is_same_v<DataT, float> && std::is_same_v<AccT, float>) ||
+                  (std::is_same_v<DataT, int8_t> && std::is_same_v<AccT, int32_t>),
+                "Unsupported batched dot product type combination");
+  constexpr auto data_type = std::is_same_v<DataT, float> ? CUDA_R_32F : CUDA_R_8I;
+  constexpr auto out_type  = std::is_same_v<AccT, float> ? CUDA_R_32F : CUDA_R_32I;
+  constexpr auto compute_type =
+    std::is_same_v<AccT, float> ? CUBLAS_COMPUTE_32F : CUBLAS_COMPUTE_32I;
+  AccT alpha         = 1;
+  AccT beta          = 0;
+  auto cublas_handle = raft::resource::get_cublas_handle(res);
+  RAFT_CUBLAS_TRY(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_HOST));
+  RAFT_CUBLAS_TRY(cublasGemmStridedBatchedEx(cublas_handle,
+                                             CUBLAS_OP_T,
+                                             CUBLAS_OP_N,
+                                             a_rows,
+                                             b_rows,
+                                             row_width,
+                                             &alpha,
+                                             a,
+                                             data_type,
+                                             row_width,
+                                             a_stride,
+                                             b,
+                                             data_type,
+                                             row_width,
+                                             b_stride,
+                                             &beta,
+                                             out,
+                                             out_type,
+                                             a_rows,
+                                             out_stride,
+                                             batch_count,
+                                             compute_type,
+                                             CUBLAS_GEMM_DEFAULT));
+}
+
+/**
+ * Assign every row of one bucket's split parents to its `fanout` nearest leaders.
+ *
+ * Parents are cut into tiles of `assignment_tile_rows` rows; every tile in a bucket shares the
+ * same padded leader count, so one strided batched GEMM per batch produces all point-leader dot
+ * products. Tiles are processed in batches sized to the GEMM workspace: each batch gathers its
+ * tile vectors and its parents' leader vectors into dense float buffers, and the selection kernel
+ * converts dots to distances with the precomputed row norms (|x|^2 + |l|^2 - 2 x.l), keeps the
+ * `fanout` nearest leaders per row (ties broken by lower leader index), and writes the child keys
+ * and memberships.
+ */
+template <typename T>
+void assign_bucket(raft::resources const& res,
+                   raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+                   partition_set const& parents,
+                   std::vector<parent_plan const*> const& bucket,
+                   int padded_leaders,
+                   split_params const& params,
+                   split_context& context,
+                   rmm::device_uvector<uint32_t>& keys,
+                   rmm::device_uvector<partition_membership>& memberships)
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+  auto dim    = dataset.extent(1);
+
+  // Cut every parent in the bucket into fixed-height tiles
+  int tile_rows = context.assignment_tile_rows;
+  std::vector<assignment_tile> tiles;
+  for (auto const* entry : bucket) {
+    for (int64_t start = entry->parent.start; start < entry->parent.end; start += tile_rows) {
+      tiles.push_back(make_tile(*entry, start, params.fanout, tile_rows));
+    }
+  }
+
+  // Size the batch so all per-tile buffers fit in the GEMM workspace budget
+  size_t point_elements  = static_cast<size_t>(tile_rows) * dim;
+  size_t leader_elements = static_cast<size_t>(padded_leaders) * dim;
+  size_t dot_elements    = static_cast<size_t>(tile_rows) * padded_leaders;
+  size_t bytes_per_batch = (point_elements + leader_elements + dot_elements) * sizeof(float) +
+                           static_cast<size_t>(padded_leaders) * sizeof(uint32_t) +
+                           sizeof(assignment_tile);
+  RAFT_EXPECTS(bytes_per_batch <= context.gemm_workspace_bytes,
+               "Fastener assignment workspace is too small for one tile");
+  size_t batch_capacity = std::max<size_t>(
+    1, std::min<size_t>(tiles.size(), context.gemm_workspace_bytes / bytes_per_batch));
+
+  rmm::device_uvector<assignment_tile> device_tiles(batch_capacity, stream);
+  rmm::device_uvector<float> tile_points(batch_capacity * point_elements, stream);
+  rmm::device_uvector<float> tile_leaders(batch_capacity * leader_elements, stream);
+  rmm::device_uvector<float> tile_dots(batch_capacity * dot_elements, stream);
+  rmm::device_uvector<uint32_t> tile_leader_ids(batch_capacity * padded_leaders, stream);
+
+  auto point_stride  = static_cast<int64_t>(point_elements);
+  auto leader_stride = static_cast<int64_t>(leader_elements);
+  auto dot_stride    = static_cast<int64_t>(dot_elements);
+  size_t selection_shared =
+    ROW_WARPS_PER_BLOCK * WARP_SIZE * params.fanout * sizeof(leader_candidate);
+
+  for (size_t tile_offset = 0; tile_offset < tiles.size(); tile_offset += batch_capacity) {
+    size_t batch_size = std::min(batch_capacity, tiles.size() - tile_offset);
+    raft::copy(device_tiles.data(), tiles.data() + tile_offset, batch_size, stream);
+
+    // Gather the batch's tile rows and leader vectors
+    int point_blocks = strided_grid_size(static_cast<int64_t>(batch_size * point_elements));
+    manyway_gather_tile_points_kernel<<<point_blocks, 256, 0, stream>>>(
+      dataset.data_handle(),
+      dim,
+      parents.memberships.data(),
+      device_tiles.data(),
+      static_cast<int>(batch_size),
+      tile_rows,
+      tile_points.data());
+    RAFT_CUDA_TRY(cudaGetLastError());
+
+    int leader_blocks = strided_grid_size(static_cast<int64_t>(batch_size * leader_elements));
+    manyway_gather_tile_leaders_kernel<<<leader_blocks, 256, 0, stream>>>(
+      dataset.data_handle(),
+      dim,
+      parents.memberships.data(),
+      device_tiles.data(),
+      static_cast<int>(batch_size),
+      padded_leaders,
+      tile_leaders.data(),
+      tile_leader_ids.data());
+    RAFT_CUDA_TRY(cudaGetLastError());
+
+    // point-leader dot products for the batch
+    batched_row_dot_products(res,
+                             tile_leaders.data(),
+                             padded_leaders,
+                             leader_stride,
+                             tile_points.data(),
+                             tile_rows,
+                             point_stride,
+                             tile_dots.data(),
+                             dot_stride,
+                             static_cast<int>(dim),
+                             static_cast<int>(batch_size));
+
+    // Keep each row's `fanout` nearest leaders and write child keys and memberships
+    int selection_blocks =
+      static_cast<int>((static_cast<int64_t>(batch_size) * tile_rows + ROW_WARPS_PER_BLOCK - 1) /
+                       ROW_WARPS_PER_BLOCK);
+    manyway_select_tiles_kernel<<<selection_blocks,
+                                  ROW_WARPS_PER_BLOCK * WARP_SIZE,
+                                  selection_shared,
+                                  stream>>>(tile_dots.data(),
+                                            static_cast<int>(batch_size),
+                                            tile_rows,
+                                            padded_leaders,
+                                            static_cast<int>(params.fanout),
+                                            static_cast<int>(params.occurrence_stride),
+                                            context.norms.data(),
+                                            tile_leader_ids.data(),
+                                            parents.memberships.data(),
+                                            device_tiles.data(),
+                                            keys.data(),
+                                            memberships.data());
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+}
+
+/**
+ * Split every oversized parent into overlapping nearest-leader children.
+ *
+ * All levels, including the root, traverse this boundary. `plan_split` decides on host how every
+ * parent maps into the next level, `carry_parents` copies completed parents unchanged, and
+ * `assign_bucket` assigns each bucket of split parents with tiled batched GEMMs. One input row of
+ * a split parent emits `fanout` memberships, and each membership's occurrence advances by
+ * `rank * occurrence_stride` so that across levels every copy of a row lands in a distinct
+ * scaffold slot.
+ *
+ * Child keys are dense and sequential across the whole output, and the final stable sort by key
+ * followed by a reduce-by-key compaction yields one contiguous range per child, which is the next
+ * level's partition set.
+ */
+template <typename T>
+auto split_manyway(raft::resources const& res,
+                   raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+                   partition_set const& parents,
+                   split_params const& params,
+                   split_context& context) -> partition_set
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+  auto plan   = plan_split(
+    parents.ranges, static_cast<int64_t>(parents.memberships.size()), params, context.seed);
+
+  rmm::device_uvector<uint32_t> keys(static_cast<size_t>(plan.output_rows), stream);
+  rmm::device_uvector<partition_membership> memberships(static_cast<size_t>(plan.output_rows),
+                                                        stream);
+
+  carry_parents(res, parents, plan, keys, memberships);
+  auto buckets = bucket_split_parents(plan, params);
+  for (size_t bucket_index = 0; bucket_index < buckets.size(); ++bucket_index) {
+    if (buckets[bucket_index].empty()) { continue; }
+    assign_bucket(res,
+                  dataset,
+                  parents,
+                  buckets[bucket_index],
+                  1 << bucket_index,
+                  params,
+                  context,
+                  keys,
+                  memberships);
+  }
+
+  thrust::stable_sort_by_key(thrust::cuda::par.on(stream),
+                             thrust::device_pointer_cast(keys.data()),
+                             thrust::device_pointer_cast(keys.data() + keys.size()),
+                             thrust::device_pointer_cast(memberships.data()));
+  auto ranges = collect_partition_ranges(res, keys.data(), plan.output_rows, plan.child_count);
+  return {std::move(memberships), std::move(ranges)};
+}
+
+// ----------------------------------------------------------------------------
+// Leaf processing: bounded leaf slicing and cross-input leaf KNN
+// ----------------------------------------------------------------------------
+
+/** Return true if one leaf of this dimension and leaf size goes into the GEMM workspace. */
+template <typename T>
+inline auto leaf_gemm_supported(int64_t dimension, uint32_t leaf_size) -> bool
+{
+  if (dimension <= 0 || dimension > std::numeric_limits<int>::max()) { return false; }
+
+  int64_t storage_dimension  = dimension;
+  size_t vector_element_size = sizeof(float);
+  size_t gram_element_size   = sizeof(float);
+  if constexpr (std::is_integral_v<T>) {
+    // The below check is a little paranoid
+    if (dimension > MAX_INTEGER_LEAF_DIMENSION) { return false; }
+    storage_dimension   = (dimension + 3) & ~int64_t{3};
+    vector_element_size = sizeof(int8_t);
+    gram_element_size   = sizeof(int32_t);
+  }
+
+  size_t vector_elements = static_cast<size_t>(leaf_size) * storage_dimension;
+  size_t gram_elements   = static_cast<size_t>(leaf_size) * leaf_size;
+  return vector_elements * vector_element_size + gram_elements * gram_element_size <=
+         GEMM_WORKSPACE_BYTES;
+}
+
+struct leaf_set {
+  partition_set const* partitions = nullptr;
+  std::vector<uint32_t> starts_host;
+  std::vector<uint32_t> ends_host;
+  rmm::device_uvector<uint32_t> starts;
+  rmm::device_uvector<uint32_t> ends;
+};
+
+/** Divide final grouped partitions into consecutive bounded leaves, without geometric resplitting.
+ *
+ * This is really just a fallback for when the tree isn't deep enough, and will produce obviously
+ * inferior leaves but at no additional cost.
+ */
+inline auto make_leaves(raft::resources const& res,
+                        partition_set const& partitions,
+                        uint32_t leaf_size) -> leaf_set
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+  std::vector<uint32_t> starts_host;
+  std::vector<uint32_t> ends_host;
+  starts_host.reserve((partitions.memberships.size() + leaf_size - 1) / leaf_size);
+  ends_host.reserve(starts_host.capacity());
+
+  int64_t covered = 0;
+  for (auto const& range : partitions.ranges) {
+    RAFT_EXPECTS(range.start == covered && range.end > range.start &&
+                   range.end <= static_cast<int64_t>(partitions.memberships.size()),
+                 "Fastener partition ranges must compactly cover all memberships");
+    for (int64_t start = range.start; start < range.end; start += leaf_size) {
+      starts_host.push_back(static_cast<uint32_t>(start));
+      ends_host.push_back(static_cast<uint32_t>(
+        std::min<int64_t>(range.end, start + static_cast<int64_t>(leaf_size))));
+    }
+    covered = range.end;
+  }
+  RAFT_EXPECTS(
+    covered == static_cast<int64_t>(partitions.memberships.size()) && !starts_host.empty(),
+    "Fastener partition ranges did not cover all memberships");
+
+  rmm::device_uvector<uint32_t> starts(starts_host.size(), stream);
+  rmm::device_uvector<uint32_t> ends(ends_host.size(), stream);
+  raft::copy(starts.data(), starts_host.data(), starts.size(), stream);
+  raft::copy(ends.data(), ends_host.data(), ends.size(), stream);
+  return {
+    &partitions, std::move(starts_host), std::move(ends_host), std::move(starts), std::move(ends)};
+}
+
+/** Fill every unwritten scaffold slot with its row ID; final prefix deduplication removes it. */
+static __global__ void initialize_self_scaffold_kernel(uint32_t* graph,
+                                                       int64_t rows,
+                                                       int graph_degree)
+{
+  int64_t linear = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  int64_t total  = rows * graph_degree;
+  int64_t stride = static_cast<int64_t>(blockDim.x) * gridDim.x;
+  for (; linear < total; linear += stride) {
+    graph[linear] = static_cast<uint32_t>(linear / graph_degree);
+  }
+}
+
+/** Copy the vectors of each leaf into a dense buffer of OutT, zero-padding rows past the leaf
+ *  end and dimensions past `input_dim`. Unsigned input values move to a center of zero. */
+template <typename T, typename OutT>
+__global__ void manyway_gather_leaf_vectors_kernel(T const* dataset,
+                                                   int64_t input_dim,
+                                                   int64_t output_dim,
+                                                   int leaf_size,
+                                                   partition_membership const* memberships,
+                                                   uint32_t const* leaf_starts,
+                                                   uint32_t const* leaf_ends,
+                                                   int64_t leaf_offset,
+                                                   int64_t leaf_count,
+                                                   OutT* leaf_vectors)
+{
+  int64_t linear = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  int64_t stride = static_cast<int64_t>(blockDim.x) * gridDim.x;
+  int64_t total  = leaf_count * leaf_size * output_dim;
+  for (; linear < total; linear += stride) {
+    int64_t d          = linear % output_dim;
+    int64_t local_row  = (linear / output_dim) % leaf_size;
+    int64_t local_leaf = linear / (output_dim * leaf_size);
+    int64_t leaf       = leaf_offset + local_leaf;
+    int64_t leaf_n     = static_cast<int64_t>(leaf_ends[leaf] - leaf_starts[leaf]);
+    OutT value         = 0;
+    if (local_row < leaf_n && d < input_dim) {
+      uint32_t point = memberships[leaf_starts[leaf] + local_row].id;
+      auto input     = dataset[static_cast<int64_t>(point) * input_dim + d];
+      if constexpr (std::is_same_v<T, uint8_t>) {
+        value = static_cast<OutT>(static_cast<int>(input) - 128);
+      } else {
+        value = static_cast<OutT>(input);
+      }
+    }
+    leaf_vectors[linear] = value;
+  }
+}
+
+/** Select the nearest cross-input neighbors of each point from the leaf Gram matrix. One block
+ *  does one leaf. One thread does one point. */
+template <typename GramT>
+static __global__ void manyway_leaf_gram_knn_kernel(GramT const* gram,
+                                                    partition_membership const* memberships,
+                                                    uint32_t const* origins,
+                                                    uint32_t const* leaf_starts,
+                                                    uint32_t const* leaf_ends,
+                                                    int64_t leaf_offset,
+                                                    int64_t leaf_count,
+                                                    int leaf_size,
+                                                    int leaf_degree,
+                                                    int union_degree,
+                                                    uint32_t* graph)
+{
+  int64_t local_leaf = blockIdx.x;
+  if (local_leaf >= leaf_count) { return; }
+  int64_t leaf   = leaf_offset + local_leaf;
+  uint32_t start = leaf_starts[leaf];
+  int leaf_n     = static_cast<int>(leaf_ends[leaf] - start);
+  if (leaf_n <= 1 || leaf_n > MAX_LEAF_SIZE) { return; }
+
+  // Stage the leaf's memberships and origin labels once per block.
+  __shared__ partition_membership records[MAX_LEAF_SIZE];
+  __shared__ uint32_t leaf_origins[MAX_LEAF_SIZE];
+  for (int i = threadIdx.x; i < leaf_n; i += blockDim.x) {
+    records[i]      = memberships[start + i];
+    leaf_origins[i] = origins[records[i].id];
+  }
+  __syncthreads();
+
+  int u = threadIdx.x;
+  if (u >= leaf_n) { return; }
+  double top_d[MAX_LEAF_DEGREE];
+  uint16_t top_v[MAX_LEAF_DEGREE];
+  for (int t = 0; t < leaf_degree; ++t) {
+    top_d[t] = std::numeric_limits<double>::max();
+    top_v[t] = std::numeric_limits<uint16_t>::max();
+  }
+
+  // Scan this point's Gram row, keeping the `leaf_degree` nearest cross-origin neighbors.
+  int64_t gram_base = local_leaf * leaf_size * leaf_size;
+  double norm_u     = static_cast<double>(gram[gram_base + u * leaf_size + u]);
+  for (int v = 0; v < leaf_n; ++v) {
+    if (u == v || leaf_origins[u] == leaf_origins[v] || records[u].id == records[v].id) {
+      continue;
+    }
+    double norm_v   = static_cast<double>(gram[gram_base + v * leaf_size + v]);
+    double dot      = static_cast<double>(gram[gram_base + v * leaf_size + u]);
+    double distance = norm_u + norm_v - 2.0 * dot;
+    if (isfinite(distance)) { distance = fmax(0.0, distance); }
+    int worst = 0;
+    for (int t = 1; t < leaf_degree; ++t) {
+      if (top_d[t] > top_d[worst] || (top_d[t] == top_d[worst] && top_v[t] > top_v[worst])) {
+        worst = t;
+      }
+    }
+    if (distance < top_d[worst] ||
+        (distance == top_d[worst] && records[v].id < records[top_v[worst]].id)) {
+      top_d[worst] = distance;
+      top_v[worst] = static_cast<uint16_t>(v);
+    }
+  }
+
+  // Pack the valid selections into this occurrence's slots of the scaffold graph.
+  int selected = 0;
+  for (int t = 0; t < leaf_degree; ++t) {
+    bool valid = top_v[t] != std::numeric_limits<uint16_t>::max() && isfinite(top_d[t]);
+    if (valid) {
+      int64_t output = static_cast<int64_t>(records[u].id) * union_degree +
+                       static_cast<int>(records[u].occurrence) * leaf_degree + selected;
+      graph[output] = records[top_v[t]].id;
+      ++selected;
+    }
+  }
+}
+
+/** Build directed cross-input nearest neighbors for every leaf occurrence. */
+template <typename T>
+auto build_leaf_neighbors(raft::resources const& res,
+                          raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+                          leaf_set const& leaves,
+                          uint32_t const* origins,
+                          int union_degree,
+                          build_params const& params) -> raft::device_matrix<uint32_t, int64_t>
+{
+  auto stream             = raft::resource::get_cuda_stream(res);
+  int64_t rows            = dataset.extent(0);
+  int leaf_size           = static_cast<int>(params.leaf_size);
+  int leaf_degree         = static_cast<int>(params.leaf_degree);
+  auto const& memberships = leaves.partitions->memberships;
+
+  // Prefill every slot with its own row id, leaf KNN overwrites the slots it fills
+  auto graph          = raft::make_device_matrix<uint32_t, int64_t>(res, rows, union_degree);
+  int scaffold_blocks = strided_grid_size(rows * union_degree);
+  initialize_self_scaffold_kernel<<<scaffold_blocks, 256, 0, stream>>>(
+    graph.data_handle(), rows, union_degree);
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half>) {
+    // Size the leaf batch so the vector and Gram buffers fit the GEMM workspace budget
+    int64_t input_dimension         = dataset.extent(1);
+    int dimension                   = static_cast<int>(input_dimension);
+    size_t vector_elements_per_leaf = static_cast<size_t>(leaf_size) * dimension;
+    size_t gram_elements_per_leaf   = static_cast<size_t>(leaf_size) * leaf_size;
+    size_t bytes_per_leaf =
+      vector_elements_per_leaf * sizeof(float) + gram_elements_per_leaf * sizeof(float);
+    size_t batch_capacity = std::max<size_t>(
+      1, std::min<size_t>(leaves.starts_host.size(), GEMM_WORKSPACE_BYTES / bytes_per_leaf));
+    rmm::device_uvector<float> leaf_vectors(batch_capacity * vector_elements_per_leaf, stream);
+    rmm::device_uvector<float> gram(batch_capacity * gram_elements_per_leaf, stream);
+    auto vector_stride = static_cast<int64_t>(vector_elements_per_leaf);
+    auto gram_stride   = static_cast<int64_t>(gram_elements_per_leaf);
+
+    for (size_t leaf_offset = 0; leaf_offset < leaves.starts_host.size();
+         leaf_offset += batch_capacity) {
+      size_t batch_size = std::min(batch_capacity, leaves.starts_host.size() - leaf_offset);
+      int gather_blocks =
+        strided_grid_size(static_cast<int64_t>(batch_size * vector_elements_per_leaf));
+      // Gather this batch's leaves into the dense vector buffer.
+      manyway_gather_leaf_vectors_kernel<<<gather_blocks, 256, 0, stream>>>(
+        dataset.data_handle(),
+        input_dimension,
+        input_dimension,
+        leaf_size,
+        memberships.data(),
+        leaves.starts.data(),
+        leaves.ends.data(),
+        static_cast<int64_t>(leaf_offset),
+        static_cast<int64_t>(batch_size),
+        leaf_vectors.data());
+      RAFT_CUDA_TRY(cudaGetLastError());
+      // the Gram matrix of every leaf in the batch
+      batched_row_dot_products(res,
+                               leaf_vectors.data(),
+                               leaf_size,
+                               vector_stride,
+                               leaf_vectors.data(),
+                               leaf_size,
+                               vector_stride,
+                               gram.data(),
+                               gram_stride,
+                               dimension,
+                               static_cast<int>(batch_size));
+      // select each point's nearest cross-input neighbors from the corresponding Gram matrix
+      manyway_leaf_gram_knn_kernel<float>
+        <<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(gram.data(),
+                                                                 memberships.data(),
+                                                                 origins,
+                                                                 leaves.starts.data(),
+                                                                 leaves.ends.data(),
+                                                                 static_cast<int64_t>(leaf_offset),
+                                                                 static_cast<int64_t>(batch_size),
+                                                                 leaf_size,
+                                                                 leaf_degree,
+                                                                 union_degree,
+                                                                 graph.data_handle());
+      RAFT_CUDA_TRY(cudaGetLastError());
+    }
+  } else if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>) {
+    // Same pipeline as the float path, with rows padded to a multiple of 4 and an INT32 Gram.
+    int64_t input_dimension         = dataset.extent(1);
+    int64_t padded_dimension        = (input_dimension + 3) & ~int64_t{3};
+    size_t vector_elements_per_leaf = static_cast<size_t>(leaf_size) * padded_dimension;
+    size_t gram_elements_per_leaf   = static_cast<size_t>(leaf_size) * leaf_size;
+    size_t bytes_per_leaf =
+      vector_elements_per_leaf * sizeof(int8_t) + gram_elements_per_leaf * sizeof(int32_t);
+    size_t batch_capacity = std::max<size_t>(
+      1, std::min<size_t>(leaves.starts_host.size(), GEMM_WORKSPACE_BYTES / bytes_per_leaf));
+    rmm::device_uvector<int8_t> leaf_vectors(batch_capacity * vector_elements_per_leaf, stream);
+    rmm::device_uvector<int32_t> gram(batch_capacity * gram_elements_per_leaf, stream);
+    int dimension      = static_cast<int>(padded_dimension);
+    auto vector_stride = static_cast<int64_t>(vector_elements_per_leaf);
+    auto gram_stride   = static_cast<int64_t>(gram_elements_per_leaf);
+
+    for (size_t leaf_offset = 0; leaf_offset < leaves.starts_host.size();
+         leaf_offset += batch_capacity) {
+      size_t batch_size = std::min(batch_capacity, leaves.starts_host.size() - leaf_offset);
+      int gather_blocks =
+        strided_grid_size(static_cast<int64_t>(batch_size * vector_elements_per_leaf));
+      manyway_gather_leaf_vectors_kernel<<<gather_blocks, 256, 0, stream>>>(
+        dataset.data_handle(),
+        input_dimension,
+        padded_dimension,
+        leaf_size,
+        memberships.data(),
+        leaves.starts.data(),
+        leaves.ends.data(),
+        static_cast<int64_t>(leaf_offset),
+        static_cast<int64_t>(batch_size),
+        leaf_vectors.data());
+      RAFT_CUDA_TRY(cudaGetLastError());
+      batched_row_dot_products(res,
+                               leaf_vectors.data(),
+                               leaf_size,
+                               vector_stride,
+                               leaf_vectors.data(),
+                               leaf_size,
+                               vector_stride,
+                               gram.data(),
+                               gram_stride,
+                               dimension,
+                               static_cast<int>(batch_size));
+      manyway_leaf_gram_knn_kernel<int32_t>
+        <<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(gram.data(),
+                                                                 memberships.data(),
+                                                                 origins,
+                                                                 leaves.starts.data(),
+                                                                 leaves.ends.data(),
+                                                                 static_cast<int64_t>(leaf_offset),
+                                                                 static_cast<int64_t>(batch_size),
+                                                                 leaf_size,
+                                                                 leaf_degree,
+                                                                 union_degree,
+                                                                 graph.data_handle());
+      RAFT_CUDA_TRY(cudaGetLastError());
+    }
+  }
+
+  return graph;
+}
+
+// ----------------------------------------------------------------------------
+// Scaffold build driver
+// ----------------------------------------------------------------------------
+
+/** Initialize the source-index label for every dataset row. */
+static __global__ void initialize_origins_kernel(uint32_t* origins,
+                                                 int64_t start,
+                                                 int64_t rows,
+                                                 uint32_t origin)
+{
+  int64_t local_row = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  if (local_row >= rows) { return; }
+  origins[start + local_row] = origin;
+}
+
+/** Calculate the squared L2 norm of each dataset row.
+ *
+ * This could be `raft::linalg::norm`, but half precision vectors might square to inf without a
+ * wider accumulator.
+ */
+template <typename T>
+__global__ void manyway_l2_norms_kernel(T const* dataset, int64_t rows, int64_t dim, float* norms)
+{
+  int lane    = threadIdx.x % WARP_SIZE;
+  int warp    = threadIdx.x / WARP_SIZE;
+  int64_t row = static_cast<int64_t>(blockIdx.x) * ROW_WARPS_PER_BLOCK + warp;
+  if (row >= rows) { return; }
+
+  // Each lane accumulates a strided slice of the row's squared values.
+  float sum      = 0.0f;
+  T const* point = dataset + row * dim;
+  for (int64_t d = lane; d < dim; d += WARP_SIZE) {
+    float value = static_cast<float>(point[d]);
+    sum         = fmaf(value, value, sum);
+  }
+  // Shuffle-reduce the partial sums; lane 0 holds the total.
+  for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
+    sum += __shfl_down_sync(0xffffffffu, sum, offset);
+  }
+  if (lane == 0) { norms[row] = sum; }
+}
+
+/** Build the many-way scaffold through splitting and leaf construction. */
+template <typename T>
+auto build(raft::resources const& res,
+           raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+           std::vector<int64_t> const& offsets,
+           build_params const& params = {}) -> raft::device_matrix<uint32_t, int64_t>
+{
+  auto stream  = raft::resource::get_cuda_stream(res);
+  int64_t rows = dataset.extent(0);
+
+  RAFT_EXPECTS(offsets.size() >= 3, "Fastener requires at least two input datasets");
+  RAFT_EXPECTS(rows > 0, "Fastener row count must be positive");
+  RAFT_EXPECTS(params.levels > 0, "Fastener levels must be positive");
+  RAFT_EXPECTS(params.root_fanout >= 1 && params.root_fanout <= MAX_FANOUT &&
+                 params.lower_fanout >= 1 && params.lower_fanout <= MAX_FANOUT,
+               "Fastener fanouts must be between 1 and 32");
+  RAFT_EXPECTS(params.leader_fraction > 0.0 && params.leader_fraction <= 1.0,
+               "Fastener leader fraction must be in (0, 1]");
+  RAFT_EXPECTS(params.max_leaders >= std::max(params.root_fanout, params.lower_fanout) &&
+                 params.max_leaders <= MAX_LEADERS,
+               "Fastener leader cap must cover both fanouts and not exceed 8192");
+  RAFT_EXPECTS(params.leaf_size >= 1 && params.leaf_size <= MAX_LEAF_SIZE,
+               "Fastener leaf size must be between 1 and %d",
+               MAX_LEAF_SIZE);
+  RAFT_EXPECTS(params.leaf_degree >= 1 && params.leaf_degree <= MAX_LEAF_DEGREE,
+               "Fastener leaf degree must be between 1 and %d",
+               MAX_LEAF_DEGREE);
+  RAFT_EXPECTS(leaf_gemm_supported<T>(dataset.extent(1), params.leaf_size),
+               "Fastener dataset dimension exceeds the leaf GEMM limits");
+
+  // the number of leaf partitions that a given point will end up in
+  uint64_t spill = params.root_fanout;
+  for (uint32_t level = 1; level < params.levels; ++level) {
+    RAFT_EXPECTS(spill <= std::numeric_limits<uint64_t>::max() / params.lower_fanout,
+                 "Fastener spill width overflow");
+    spill *= params.lower_fanout;
+  }
+  // this is because the degree of the candidate list is stored in uint8_t
+  RAFT_EXPECTS(spill * params.leaf_degree <= std::numeric_limits<uint8_t>::max(),
+               "Fastener candidate width must not exceed 255");
+  RAFT_EXPECTS(static_cast<uint64_t>(rows) <= std::numeric_limits<uint32_t>::max() / spill,
+               "Fastener total partition memberships (rows=%ld * spill=%lu) must fit in uint32_t",
+               static_cast<long>(rows),
+               spill);
+  int union_degree = static_cast<int>(spill * params.leaf_degree);
+
+  // Per-row index of the input partition this point came from, used to skip same-origin pairs
+  rmm::device_uvector<uint32_t> origins(static_cast<size_t>(rows), stream);
+
+  for (size_t part = 0; part + 1 < offsets.size(); ++part) {
+    int64_t part_rows = offsets[part + 1] - offsets[part];
+    int blocks        = static_cast<int>((part_rows + 255) / 256);
+    initialize_origins_kernel<<<blocks, 256, 0, stream>>>(
+      origins.data(), offsets[part], part_rows, static_cast<uint32_t>(part));
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+
+  split_context context(stream, rows);
+  int norm_blocks = static_cast<int>((rows + ROW_WARPS_PER_BLOCK - 1) / ROW_WARPS_PER_BLOCK);
+  manyway_l2_norms_kernel<<<norm_blocks, ROW_WARPS_PER_BLOCK * WARP_SIZE, 0, stream>>>(
+    dataset.data_handle(), rows, dataset.extent(1), context.norms.data());
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  // the actual splitting
+  auto partitions            = make_root_partition(res, rows);
+  uint32_t occurrence_stride = 1;
+  for (uint32_t level = 0; level < params.levels; ++level) {
+    uint32_t fanout = level == 0 ? params.root_fanout : params.lower_fanout;
+
+    partitions = split_manyway(res,
+                               dataset,
+                               partitions,
+                               split_params{.fanout            = fanout,
+                                            .leader_fraction   = params.leader_fraction,
+                                            .max_leaders       = params.max_leaders,
+                                            .leaf_size         = params.leaf_size,
+                                            .level             = level,
+                                            .occurrence_stride = occurrence_stride},
+                               context);
+    occurrence_stride *= fanout;
+  }
+
+  // Leaf construction: only consecutive range slicing occurs after configured geometric depth.
+  auto leaves   = make_leaves(res, partitions, params.leaf_size);
+  auto scaffold = build_leaf_neighbors(res, dataset, leaves, origins.data(), union_degree, params);
+  raft::resource::sync_stream(res);
+  return scaffold;
+}
+
+// ----------------------------------------------------------------------------
+// Candidate graph assembly: combine input graphs with the scaffold
+// ----------------------------------------------------------------------------
+
+/**
+ * Offset-copy one input partition graph and append its global scaffold neighbors.
+ */
+static __global__ void copy_partition_with_scaffold_kernel(uint32_t const* source,
+                                                           uint32_t const* scaffold,
+                                                           int64_t source_rows,
+                                                           int64_t source_degree,
+                                                           uint32_t* destination,
+                                                           int64_t destination_degree,
+                                                           int64_t base_degree,
+                                                           int64_t scaffold_degree,
+                                                           uint32_t offset)
+{
+  int64_t row = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  if (row >= source_rows) { return; }
+  int64_t source_base      = row * source_degree;
+  int64_t global_row       = row + offset;
+  int64_t destination_base = global_row * destination_degree;
+
+  // Rows from lower-degree input graphs are cyclically repeated to the common base width, which is
+  // not ideal (adds work to the sorting by distance) but practical applications for mismatched
+  // degrees seem hard to imagine.
+  for (int64_t j = 0; j < base_degree; ++j) {
+    destination[destination_base + j] = source[source_base + (j % source_degree)] + offset;
+  }
+  for (int64_t j = 0; j < scaffold_degree; ++j) {
+    destination[destination_base + base_degree + j] = scaffold[global_row * scaffold_degree + j];
+  }
+}
+
+/**
+ * @brief Form the pre-optimization candidate graph from input graphs and scaffold edges.
+ *
+ * The maximum input graph degree defines the base width, allowing partitions with mixed degrees.
+ */
+template <typename T, typename IdxT>
+auto append_to_input_graphs(
+  raft::resources const& res,
+  std::vector<cuvs::neighbors::cagra::index<T, IdxT>*> const& indices,
+  std::vector<int64_t> const& offsets,
+  raft::device_matrix_view<const uint32_t, int64_t, raft::row_major> scaffold)
+  -> raft::device_matrix<uint32_t, int64_t>
+{
+  auto stream         = raft::resource::get_cuda_stream(res);
+  int64_t base_degree = 0;
+  for (auto const* index : indices) {
+    base_degree = std::max<int64_t>(base_degree, index->graph_degree());
+  }
+  int64_t scaffold_degree = scaffold.extent(1);
+  int64_t graph_degree    = base_degree + scaffold_degree;
+  auto graph = raft::make_device_matrix<uint32_t, int64_t>(res, scaffold.extent(0), graph_degree);
+
+  for (size_t part = 0; part < indices.size(); ++part) {
+    auto source = indices[part]->graph();
+    RAFT_EXPECTS(source.extent(1) > 0, "Input CAGRA graphs must have nonzero degree");
+    int blocks = static_cast<int>((source.extent(0) + 255) / 256);
+    copy_partition_with_scaffold_kernel<<<blocks, 256, 0, stream>>>(
+      source.data_handle(),
+      scaffold.data_handle(),
+      source.extent(0),
+      source.extent(1),
+      graph.data_handle(),
+      graph_degree,
+      base_degree,
+      scaffold_degree,
+      static_cast<uint32_t>(offsets[part]));
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+  raft::resource::sync_stream(res);
+  return graph;
+}
+
+/**
+ * Remove invalid, self, and duplicate candidates from each sorted row, retain the nearest prefix,
+ * and cyclically pad short rows to the requested output width.
+ *
+ * Scaffold padding is measured to be negligible compared to the input graph edges.
+ */
+static __global__ void deduplicate_graph_prefix_kernel(uint32_t const* input,
+                                                       int64_t rows,
+                                                       int64_t input_degree,
+                                                       uint32_t* output,
+                                                       int64_t output_degree)
+{
+  constexpr int WARPS_PER_BLOCK = 256 / WARP_SIZE;
+  int lane                      = threadIdx.x % WARP_SIZE;
+  int warp                      = threadIdx.x / WARP_SIZE;
+  int64_t row                   = static_cast<int64_t>(blockIdx.x) * WARPS_PER_BLOCK + warp;
+  if (row >= rows) { return; }
+
+  int64_t input_base  = row * input_degree;
+  int64_t output_base = row * output_degree;
+  int selected        = 0;
+  for (int64_t tile = 0; tile < input_degree && selected < output_degree; tile += WARP_SIZE) {
+    int64_t column     = tile + lane;
+    bool first         = column < input_degree;
+    uint32_t candidate = first ? input[input_base + column] : uint32_t{0};
+    first              = first && candidate < rows && candidate != static_cast<uint32_t>(row);
+    for (int64_t prior = 0; prior < column && first; ++prior) {
+      if (input[input_base + prior] == candidate) { first = false; }
+    }
+
+    unsigned first_mask = __ballot_sync(0xffffffffu, first);
+    unsigned lower_mask = lane == 0 ? 0u : (0xffffffffu >> (WARP_SIZE - lane));
+    int output_column   = selected + __popc(first_mask & lower_mask);
+    if (first && output_column < output_degree) { output[output_base + output_column] = candidate; }
+    selected += __popc(first_mask);
+  }
+
+  if (selected == 0) {
+    if (lane == 0) { output[output_base] = static_cast<uint32_t>((row + 1) % rows); }
+    selected = 1;
+  }
+  if (selected > output_degree) { selected = static_cast<int>(output_degree); }
+  for (int64_t column = selected + lane; column < output_degree; column += WARP_SIZE) {
+    output[output_base + column] = output[output_base + (column % selected)];
+  }
+}
+
+/** Deduplicate a metric-sorted graph and retain a fixed-width nearest-candidate prefix. */
+inline auto cap_sorted_graph(
+  raft::resources const& res,
+  raft::device_matrix_view<const uint32_t, int64_t, raft::row_major> graph,
+  int64_t output_degree) -> raft::device_matrix<uint32_t, int64_t>
+{
+  RAFT_EXPECTS(output_degree > 0 && output_degree <= graph.extent(1),
+               "Pre-optimize graph degree cap must be within the sorted graph degree");
+  auto output = raft::make_device_matrix<uint32_t, int64_t>(res, graph.extent(0), output_degree);
+  constexpr int THREADS_PER_BLOCK = 256;
+  constexpr int WARPS_PER_BLOCK   = THREADS_PER_BLOCK / WARP_SIZE;
+  int blocks = static_cast<int>((graph.extent(0) + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);
+  deduplicate_graph_prefix_kernel<<<blocks,
+                                    THREADS_PER_BLOCK,
+                                    0,
+                                    raft::resource::get_cuda_stream(res)>>>(
+    graph.data_handle(), graph.extent(0), graph.extent(1), output.data_handle(), output_degree);
+  RAFT_CUDA_TRY(cudaGetLastError());
+  return output;
+}
+
+}  // namespace cuvs::neighbors::cagra::detail::merge_scaffold

From bc6ec7bbdc58cdc72f7b586581dd887c861b233a Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 00:50:10 +0000
Subject: [PATCH 03/41] remove VMM dataset consolidation from fastener merge

---
 .../src/cuvs/cuvs_ann_bench_param_parser.h    |  31 +
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |  26 +-
 cpp/include/cuvs/neighbors/cagra.hpp          | 113 ++-
 cpp/src/neighbors/cagra.cuh                   |  22 +-
 .../neighbors/detail/cagra/cagra_merge.cuh    | 286 +++++-
 cpp/tests/CMakeLists.txt                      |   7 +
 .../ann_cagra/test_merge_fastener.cu          | 900 ++++++++++++++++++
 .../config/algos/constraints/__init__.py      |  32 +-
 .../cuvs_bench/config/algos/cuvs_cagra.yaml   |  47 +
 9 files changed, 1405 insertions(+), 59 deletions(-)
 create mode 100644 cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu

diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 57b47d97db..3fee950087 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -423,6 +423,37 @@ void parse_build_param(const nlohmann::json& conf,
       throw std::runtime_error("invalid value for merge_type");
     }
   }
+  if (conf.contains("merge_algo")) {
+    std::string algo = conf.at("merge_algo");
+    if (algo == "AUTO") {
+      param.merge_params.algo = cuvs::neighbors::cagra::merge_algo::AUTO;
+    } else if (algo == "FASTENER") {
+      param.merge_params.algo = cuvs::neighbors::cagra::merge_algo::FASTENER;
+    } else if (algo == "REBUILD") {
+      param.merge_params.algo = cuvs::neighbors::cagra::merge_algo::REBUILD;
+    } else {
+      throw std::runtime_error("invalid value for merge_algo");
+    }
+  }
+  if (conf.contains("fastener_levels")) { param.merge_params.levels = conf.at("fastener_levels"); }
+  if (conf.contains("fastener_root_fanout")) {
+    param.merge_params.root_fanout = conf.at("fastener_root_fanout");
+  }
+  if (conf.contains("fastener_lower_fanout")) {
+    param.merge_params.lower_fanout = conf.at("fastener_lower_fanout");
+  }
+  if (conf.contains("fastener_leader_fraction")) {
+    param.merge_params.leader_fraction = conf.at("fastener_leader_fraction");
+  }
+  if (conf.contains("fastener_max_leaders")) {
+    param.merge_params.max_leaders = conf.at("fastener_max_leaders");
+  }
+  if (conf.contains("fastener_leaf_size")) {
+    param.merge_params.leaf_size = conf.at("fastener_leaf_size");
+  }
+  if (conf.contains("fastener_leaf_degree")) {
+    param.merge_params.leaf_degree = conf.at("fastener_leaf_degree");
+  }
   param.cagra_params = [conf](raft::matrix_extent<int64_t> extents,
                               cuvs::distance::DistanceType dist_type) {
     // Delayed parsing/initialization of cagra_params - it's called once the dataset shape is known
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index 87111e4761..7c4f4e6170 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 #pragma once
@@ -101,6 +101,7 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
     dataset_dependent_params cagra_params;
     size_t num_dataset_splits = 1;
     CagraMergeType merge_type = CagraMergeType::kPhysical;
+    cuvs::neighbors::cagra::merge_params merge_params;
   };
 
   cuvs_cagra(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
@@ -228,21 +229,10 @@ void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
       auto sub_dev =
         raft::make_device_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dim_);
 
-      auto sub_index = cuvs::neighbors::cagra::index<T, IdxT>(handle_, params.metric);
-      if (index_params_.merge_type == CagraMergeType::kPhysical) {
-        if (dataset_is_on_host) {
-          sub_index.update_dataset(handle_, sub_host);
-        } else {
-          sub_index.update_dataset(handle_, sub_dev);
-        }
-      }
-      if (index_params_.merge_type == CagraMergeType::kLogical) {
-        if (dataset_is_on_host) {
-          sub_index = cuvs::neighbors::cagra::build(handle_, params, sub_host);
-        } else {
-          sub_index = cuvs::neighbors::cagra::build(handle_, params, sub_dev);
-        }
-      }
+      // Build every partition before the merge so FASTENER and REBUILD consume identical prepared
+      // inputs. Partition construction remains outside cagra::merge's NVTX range.
+      auto sub_index = dataset_is_on_host ? cuvs::neighbors::cagra::build(handle_, params, sub_host)
+                                          : cuvs::neighbors::cagra::build(handle_, params, sub_dev);
       auto sub_index_shared =
         std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(std::move(sub_index));
       sub_indices_.push_back(std::move(sub_index_shared));
@@ -254,8 +244,8 @@ void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
         indices.push_back(ptr.get());
       }
 
-      index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(
-        std::move(cuvs::neighbors::cagra::merge(handle_, params, indices)));
+      index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(std::move(
+        cuvs::neighbors::cagra::merge(handle_, params, indices, index_params_.merge_params)));
     }
   }
 }
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index d1937cba27..fe46ca3132 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -697,6 +697,16 @@ struct CUVS_EXPORT index : cuvs::neighbors::index {
     graph_view_ = knn_graph;
   }
 
+  /**
+   * Replace the graph by taking ownership of an existing device matrix.
+   */
+  void update_graph(raft::resources const&,
+                    raft::device_matrix<graph_index_type, int64_t, raft::row_major>&& knn_graph)
+  {
+    graph_      = std::move(knn_graph);
+    graph_view_ = graph_.view();
+  }
+
   /**
    * Replace the graph with a new graph.
    *
@@ -2447,38 +2457,49 @@ void serialize_to_hnswlib(
  * @{
  */
 
-/** @brief Merge multiple CAGRA indices into a single index.
- *
- * This function merges multiple CAGRA indices into one, combining both the datasets and graph
- * structures.
- *
- * @note: When device memory is sufficient, the dataset attached to the returned index is allocated
- * in device memory by default; otherwise, host memory is used automatically.
- *
- * @note: This API only supports physical merge (`merge_strategy = MERGE_STRATEGY_PHYSICAL`), and
- * attempting a logical merge here will throw an error.
- *
- * Usage example:
- * @code{.cpp}
- *   using namespace cuvs::neighbors;
- *   auto dataset0 = raft::make_host_matrix<float, int64_t>(handle, size0, dim);
- *   auto dataset1 = raft::make_host_matrix<float, int64_t>(handle, size1, dim);
+enum class merge_algo {
+  /** Select Fastener when its complete preflight succeeds, otherwise preserve rebuild behavior. */
+  AUTO,
+  /** Require Fastener and reject unsupported configurations. */
+  FASTENER,
+  /** Concatenate datasets and rebuild the graph. */
+  REBUILD,
+};
+
+/** C++ controls for physical CAGRA index merge. */
+struct merge_params {
+  merge_algo algo        = merge_algo::AUTO;
+  uint32_t levels        = 2;
+  uint32_t root_fanout   = 2;
+  uint32_t lower_fanout  = 3;
+  double leader_fraction = 0.02;
+  uint32_t max_leaders   = 1000;
+  uint32_t leaf_size     = 256;
+  uint32_t leaf_degree   = 4;
+};
+
+/** @brief Merge multiple physical CAGRA indices into one.
  *
- *   auto index0 = cagra::build(res, index_params, dataset0);
- *   auto index1 = cagra::build(res, index_params, dataset1);
+ * The overload without `merge_params` uses `merge_algo::AUTO`. AUTO runs Fastener only after a
+ * non-mutating preflight validates every input and option; otherwise it calls the existing rebuild
+ * implementation. REBUILD always preserves every input dataset.
  *
- *   std::vector<cagra::index<float, uint32_t>*> indices{&index0, &index1};
+ * Fastener supports unfiltered, uncompressed `float`, `half`, `int8_t`, and `uint8_t`
+ * indices using L2Expanded and `uint32_t` graph IDs. Fastener uses `root_fanout` at the first
+ * split and `lower_fanout` at every later split. Fanouts from 1 through 32, leader fractions in
+ * (0, 1], leader caps through 8192, leaf sizes from 1 through 256, and leaf degrees from 1
+ * through 8 are supported. The configured spill width times `leaf_degree` must not exceed 255.
+ * `index_params::graph_degree` is the final output degree.
  *
- *   auto merged_index = cagra::merge(res, index_params, indices);
- * @endcode
+ * Fastener copies the input datasets into one contiguous device allocation and never mutates the
+ * input indices. When `attach_dataset_on_build` is true, the result owns that consolidated copy.
  *
- * @param[in] res RAFT resources used for the merge operation.
- * @param[in] params Parameters that control the merging process.
- * @param[in] indices A vector of pointers to the CAGRA indices to merge. All indices must:
- *                    - Have attached datasets with the same dimension.
- * @param[in] row_filter an optional device filter function object that greenlights rows
- *    to include in the merged index  (none_sample_filter for no filtering)
- * @return A new CAGRA index containing the merged indices, graph, and dataset.
+ * @param[in] res RAFT resources used for the merge.
+ * @param[in] params Parameters for the returned CAGRA index.
+ * @param[in] indices CAGRA indices to merge.
+ * @param[in] row_filter Optional row filter. Any filter selects rebuild in AUTO and is rejected by
+ * explicit FASTENER.
+ * @return The merged physical CAGRA index.
  */
 auto merge(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
@@ -2487,6 +2508,15 @@ auto merge(raft::resources const& res,
              cuvs::neighbors::filtering::none_sample_filter{})
   -> cuvs::neighbors::cagra::index<float, uint32_t>;
 
+/** @brief Merge with explicit C++ algorithm and Fastener controls. */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::index_params& params,
+           std::vector<cuvs::neighbors::cagra::index<float, uint32_t>*>& indices,
+           const cuvs::neighbors::cagra::merge_params& merge_params,
+           const cuvs::neighbors::filtering::base_filter& row_filter =
+             cuvs::neighbors::filtering::none_sample_filter{})
+  -> cuvs::neighbors::cagra::index<float, uint32_t>;
+
 /** @copydoc merge */
 auto merge(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
@@ -2495,10 +2525,28 @@ auto merge(raft::resources const& res,
              cuvs::neighbors::filtering::none_sample_filter{})
   -> cuvs::neighbors::cagra::index<half, uint32_t>;
 
+/** @copydoc merge */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::index_params& params,
+           std::vector<cuvs::neighbors::cagra::index<half, uint32_t>*>& indices,
+           const cuvs::neighbors::cagra::merge_params& merge_params,
+           const cuvs::neighbors::filtering::base_filter& row_filter =
+             cuvs::neighbors::filtering::none_sample_filter{})
+  -> cuvs::neighbors::cagra::index<half, uint32_t>;
+
+/** @copydoc merge */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::index_params& params,
+           std::vector<cuvs::neighbors::cagra::index<int8_t, uint32_t>*>& indices,
+           const cuvs::neighbors::filtering::base_filter& row_filter =
+             cuvs::neighbors::filtering::none_sample_filter{})
+  -> cuvs::neighbors::cagra::index<int8_t, uint32_t>;
+
 /** @copydoc merge */
 auto merge(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            std::vector<cuvs::neighbors::cagra::index<int8_t, uint32_t>*>& indices,
+           const cuvs::neighbors::cagra::merge_params& merge_params,
            const cuvs::neighbors::filtering::base_filter& row_filter =
              cuvs::neighbors::filtering::none_sample_filter{})
   -> cuvs::neighbors::cagra::index<int8_t, uint32_t>;
@@ -2510,6 +2558,15 @@ auto merge(raft::resources const& res,
            const cuvs::neighbors::filtering::base_filter& row_filter =
              cuvs::neighbors::filtering::none_sample_filter{})
   -> cuvs::neighbors::cagra::index<uint8_t, uint32_t>;
+
+/** @copydoc merge */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::index_params& params,
+           std::vector<cuvs::neighbors::cagra::index<uint8_t, uint32_t>*>& indices,
+           const cuvs::neighbors::cagra::merge_params& merge_params,
+           const cuvs::neighbors::filtering::base_filter& row_filter =
+             cuvs::neighbors::filtering::none_sample_filter{})
+  -> cuvs::neighbors::cagra::index<uint8_t, uint32_t>;
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index ee87c2c0ab..490c225367 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -427,6 +427,16 @@ index<T, IdxT> merge(raft::resources const& handle,
   return cagra::detail::merge<T, IdxT>(handle, params, indices, row_filter);
 }
 
+template <class T, class IdxT>
+index<T, IdxT> merge(raft::resources const& handle,
+                     const cagra::index_params& params,
+                     std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
+                     const cagra::merge_params& merge_params,
+                     const cuvs::neighbors::filtering::base_filter& row_filter)
+{
+  return cagra::detail::merge<T, IdxT>(handle, params, merge_params, indices, row_filter);
+}
+
 /** @} */  // end group cagra
 
 }  // namespace cuvs::neighbors::cagra
@@ -439,4 +449,14 @@ index<T, IdxT> merge(raft::resources const& handle,
     -> cuvs::neighbors::cagra::index<T, IdxT>                                           \
   {                                                                                     \
     return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices, row_filter); \
+  }                                                                                     \
+  auto merge(raft::resources const& handle,                                             \
+             const cuvs::neighbors::cagra::index_params& params,                        \
+             std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,             \
+             const cuvs::neighbors::cagra::merge_params& merge_params,                  \
+             const cuvs::neighbors::filtering::base_filter& row_filter)                 \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                                           \
+  {                                                                                     \
+    return cuvs::neighbors::cagra::merge<T, IdxT>(                                      \
+      handle, params, indices, merge_params, row_filter);                               \
   }
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 1dd4cbe075..147a59c522 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -1,9 +1,13 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 #pragma once
 
+#include "../../../core/nvtx.hpp"
+#include "cagra_merge_scaffold.cuh"
+#include "graph_core.cuh"
+
 #include <cuvs/neighbors/cagra.hpp>
 
 #include <raft/core/device_mdarray.hpp>
@@ -21,19 +25,44 @@
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/refine.hpp>
 
-#include <rmm/resource_ref.hpp>
-
-#include <chrono>
-#include <cstdio>
+#include <limits>
+#include <memory>
+#include <string>
+#include <type_traits>
 #include <vector>
 
 namespace cuvs::neighbors::cagra::detail {
 
+/** Copy every attached input dataset into its row range in one contiguous device matrix. */
+template <typename T, typename IdxT>
+void copy_input_datasets(raft::resources const& handle,
+                         std::vector<cuvs::neighbors::cagra::index<T, IdxT>*> const& indices,
+                         std::vector<int64_t> const& offsets,
+                         int64_t dim,
+                         T* destination)
+{
+  using cagra_index_t = cuvs::neighbors::cagra::index<T, IdxT>;
+  using ds_idx_type   = typename cagra_index_t::dataset_index_type;
+
+  for (std::size_t i = 0; i < indices.size(); ++i) {
+    auto const* source_dataset =
+      dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&indices[i]->data());
+    auto source = source_dataset->view();
+    raft::copy_matrix(destination + offsets[i] * dim,
+                      static_cast<std::size_t>(dim),
+                      source.data_handle(),
+                      static_cast<std::size_t>(source_dataset->stride()),
+                      static_cast<std::size_t>(dim),
+                      static_cast<std::size_t>(source.extent(0)),
+                      raft::resource::get_cuda_stream(handle));
+  }
+}
+
 template <class T, class IdxT>
-index<T, IdxT> merge(raft::resources const& handle,
-                     const cagra::index_params& params,
-                     std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
-                     const cuvs::neighbors::filtering::base_filter& row_filter)
+index<T, IdxT> merge_rebuild(raft::resources const& handle,
+                             const cagra::index_params& params,
+                             std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
+                             const cuvs::neighbors::filtering::base_filter& row_filter)
 {
   using cagra_index_t = cuvs::neighbors::cagra::index<T, IdxT>;
   using ds_idx_type   = typename cagra_index_t::dataset_index_type;
@@ -175,4 +204,243 @@ index<T, IdxT> merge(raft::resources const& handle,
   }
 }
 
+struct fastener_preflight_result {
+  bool eligible = false;
+  int64_t rows  = 0;
+  int64_t dim   = 0;
+  std::vector<int64_t> offsets;
+  std::string reason;
+};
+
+template <typename T, typename IdxT>
+auto preflight_fastener(cagra::index_params const& params,
+                        cagra::merge_params const& merge_params,
+                        std::vector<cuvs::neighbors::cagra::index<T, IdxT>*> const& indices,
+                        cuvs::neighbors::filtering::base_filter const& row_filter)
+  -> fastener_preflight_result
+{
+  fastener_preflight_result result;
+  auto reject = [&](char const* reason) {
+    result.reason = reason;
+    return result;
+  };
+
+  if constexpr (!(std::is_same_v<T, float> || std::is_same_v<T, half> ||
+                  std::is_same_v<T, int8_t> || std::is_same_v<T, uint8_t>) ||
+                !std::is_same_v<IdxT, uint32_t>) {
+    return reject("the scalar or graph index type is unsupported");
+  }
+  if (indices.size() < 2) { return reject("at least two input indices are required"); }
+  if (row_filter.get_filter_type() != cuvs::neighbors::filtering::FilterType::None) {
+    return reject("row filters are not supported");
+  }
+  if (params.compression.has_value()) { return reject("compressed output is not supported"); }
+  if (params.metric != cuvs::distance::DistanceType::L2Expanded) {
+    return reject("only L2Expanded is supported");
+  }
+  if (merge_params.levels == 0) { return reject("levels must be positive"); }
+  if (merge_params.root_fanout < 1 || merge_params.root_fanout > 32 ||
+      merge_params.lower_fanout < 1 || merge_params.lower_fanout > 32) {
+    return reject("root_fanout and lower_fanout must be between 1 and 32");
+  }
+  if (!(merge_params.leader_fraction > 0.0 && merge_params.leader_fraction <= 1.0)) {
+    return reject("leader_fraction must be in (0, 1]");
+  }
+  if (merge_params.max_leaders == 0 || merge_params.max_leaders > 8192) {
+    return reject("max_leaders must be between 1 and 8192");
+  }
+  if (merge_params.max_leaders < std::max(merge_params.root_fanout, merge_params.lower_fanout)) {
+    return reject("max_leaders must cover both configured fanouts");
+  }
+  if (merge_params.leaf_size == 0 || merge_params.leaf_size > merge_scaffold::MAX_LEAF_SIZE) {
+    return reject("leaf_size must be between 1 and 256");
+  }
+  if (merge_params.leaf_degree == 0 ||
+      merge_params.leaf_degree > static_cast<uint32_t>(merge_scaffold::MAX_LEAF_DEGREE)) {
+    return reject("leaf_degree must be between 1 and 8");
+  }
+
+  uint64_t const max_spill = std::numeric_limits<uint8_t>::max() / merge_params.leaf_degree;
+  uint64_t spill           = merge_params.root_fanout;
+  if (spill > max_spill) {
+    return reject("root_fanout * lower_fanout^(levels - 1) * leaf_degree must not exceed 255");
+  }
+  if (merge_params.lower_fanout > 1) {
+    for (uint32_t level = 1; level < merge_params.levels; ++level) {
+      if (spill > max_spill / merge_params.lower_fanout) {
+        return reject("root_fanout * lower_fanout^(levels - 1) * leaf_degree must not exceed 255");
+      }
+      spill *= merge_params.lower_fanout;
+    }
+  }
+  uint64_t const scaffold_degree = spill * merge_params.leaf_degree;
+
+  using index_type          = cuvs::neighbors::cagra::index<T, IdxT>;
+  using ds_idx_type         = typename index_type::dataset_index_type;
+  uint64_t rows             = 0;
+  uint64_t max_input_degree = 0;
+  result.offsets.reserve(indices.size() + 1);
+  result.offsets.push_back(0);
+
+  for (auto const* index : indices) {
+    if (index == nullptr) { return reject("all input index pointers must be non-null"); }
+    auto const* dataset =
+      dynamic_cast<cuvs::neighbors::strided_dataset<T, ds_idx_type> const*>(&index->data());
+    if (dataset == nullptr || dataset->n_rows() != static_cast<ds_idx_type>(index->size())) {
+      return reject("every input must have an attached, uncompressed dataset");
+    }
+    if (index->metric() != params.metric) {
+      return reject("every input metric must match index_params.metric");
+    }
+    if (result.offsets.size() == 1) {
+      result.dim = static_cast<int64_t>(index->dim());
+    } else if (result.dim != static_cast<int64_t>(index->dim())) {
+      return reject("all input dimensions must match");
+    }
+    auto graph = index->graph();
+    if (graph.extent(0) <= 0 || graph.extent(1) <= 0 ||
+        graph.extent(0) != static_cast<int64_t>(index->size())) {
+      return reject("every input must have a nonempty device graph");
+    }
+
+    auto const input_rows = static_cast<uint64_t>(index->size());
+    if (rows > std::numeric_limits<uint32_t>::max() - input_rows) {
+      return reject("the combined row count must fit in uint32_t");
+    }
+    rows += input_rows;
+    max_input_degree = std::max<uint64_t>(max_input_degree, static_cast<uint64_t>(graph.extent(1)));
+    result.offsets.push_back(static_cast<int64_t>(rows));
+  }
+
+  if (result.dim <= 0 || result.dim > std::numeric_limits<int>::max()) {
+    return reject("dataset dimension must be positive and fit cuBLAS int dimensions");
+  }
+  if (!merge_scaffold::leaf_gemm_supported<T>(result.dim, merge_params.leaf_size)) {
+    if constexpr (std::is_integral_v<T>) {
+      return reject("integer dataset dimension exceeds the INT32 leaf GEMM limit");
+    } else {
+      return reject("dataset dimension exceeds the leaf GEMM workspace limit");
+    }
+  }
+  if (rows > std::numeric_limits<uint32_t>::max() / spill) {
+    return reject("combined rows times the configured spill width must fit in uint32_t");
+  }
+  if (params.graph_degree == 0 || static_cast<uint64_t>(params.graph_degree) >= rows) {
+    return reject("graph_degree must be positive and smaller than the combined row count");
+  }
+  if (static_cast<uint64_t>(params.graph_degree) > max_input_degree + scaffold_degree) {
+    return reject("graph_degree exceeds the input graph plus scaffold capacity");
+  }
+
+  result.rows     = static_cast<int64_t>(rows);
+  result.eligible = true;
+  return result;
+}
+
+template <typename T, typename IdxT>
+auto merge_fastener(raft::resources const& handle,
+                    cagra::index_params const& params,
+                    cagra::merge_params const& merge_params,
+                    std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
+                    fastener_preflight_result const& preflight) -> index<T, IdxT>
+{
+  auto dataset = [&] {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/consolidate");
+    auto combined = raft::make_device_matrix<T, int64_t>(handle, preflight.rows, preflight.dim);
+    copy_input_datasets(handle, indices, preflight.offsets, preflight.dim, combined.data_handle());
+    return combined;
+  }();
+
+  merge_scaffold::build_params scaffold_params;
+  scaffold_params.levels          = merge_params.levels;
+  scaffold_params.root_fanout     = merge_params.root_fanout;
+  scaffold_params.lower_fanout    = merge_params.lower_fanout;
+  scaffold_params.leader_fraction = merge_params.leader_fraction;
+  scaffold_params.max_leaders     = merge_params.max_leaders;
+  scaffold_params.leaf_size       = merge_params.leaf_size;
+  scaffold_params.leaf_degree     = merge_params.leaf_degree;
+
+  // Graph combination: append shifted input graphs to the cross-input scaffold.
+  auto merged_graph = [&] {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/scaffold");
+    auto scaffold = merge_scaffold::build<T>(
+      handle, raft::make_const_mdspan(dataset.view()), preflight.offsets, scaffold_params);
+    return merge_scaffold::append_to_input_graphs<T, IdxT>(
+      handle, indices, preflight.offsets, raft::make_const_mdspan(scaffold.view()));
+  }();
+
+  {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/append/sort");
+    cagra::detail::graph::sort_knn_graph_device_inplace(
+      handle, params.metric, raft::make_const_mdspan(dataset.view()), merged_graph.view());
+    merged_graph = merge_scaffold::cap_sorted_graph(
+      handle, raft::make_const_mdspan(merged_graph.view()), params.graph_degree);
+  }
+
+  // Optimization: reduce the sorted candidate prefix to the requested final CAGRA graph.
+  auto optimized_graph =
+    raft::make_device_matrix<uint32_t, int64_t>(handle, preflight.rows, params.graph_degree);
+  {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/optimize");
+    cagra::detail::graph::optimize(
+      handle, merged_graph.view(), optimized_graph.view(), params.guarantee_connectivity);
+  }
+
+  index<T, IdxT> merged_index(handle, params.metric);
+  merged_index.update_graph(handle, std::move(optimized_graph));
+  if (params.attach_dataset_on_build) {
+    using matrix_t           = decltype(dataset);
+    using layout_t           = typename matrix_t::layout_type;
+    using container_policy_t = typename matrix_t::container_policy_type;
+    using owning_t           = owning_dataset<T, int64_t, layout_t, container_policy_t>;
+    auto out_layout          = raft::make_strided_layout(dataset.view().extents(),
+                                                cuda::std::array<int64_t, 2>{preflight.dim, 1});
+    merged_index.update_dataset(handle, owning_t{std::move(dataset), out_layout});
+  } else {
+    using ds_idx_type = typename index<T, IdxT>::dataset_index_type;
+    merged_index.update_dataset(
+      handle, std::make_unique<cuvs::neighbors::empty_dataset<ds_idx_type>>(preflight.dim));
+  }
+
+  return merged_index;
+}
+
+template <class T, class IdxT>
+index<T, IdxT> merge(raft::resources const& handle,
+                     cagra::index_params const& params,
+                     cagra::merge_params const& merge_params,
+                     std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
+                     cuvs::neighbors::filtering::base_filter const& row_filter)
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> merge_scope(
+    "cagra::merge(algo=%d,parts=%zu)", static_cast<int>(merge_params.algo), indices.size());
+
+  RAFT_EXPECTS(merge_params.algo == cagra::merge_algo::AUTO ||
+                 merge_params.algo == cagra::merge_algo::FASTENER ||
+                 merge_params.algo == cagra::merge_algo::REBUILD,
+               "Unknown cagra::merge algorithm");
+  if (merge_params.algo == cagra::merge_algo::REBUILD) {
+    return merge_rebuild(handle, params, indices, row_filter);
+  }
+
+  auto preflight = preflight_fastener<T, IdxT>(params, merge_params, indices, row_filter);
+  if (!preflight.eligible) {
+    if (merge_params.algo == cagra::merge_algo::AUTO) {
+      return merge_rebuild(handle, params, indices, row_filter);
+    }
+    RAFT_FAIL("FASTENER cagra::merge is unsupported: %s", preflight.reason.c_str());
+  }
+
+  return merge_fastener(handle, params, merge_params, indices, preflight);
+}
+
+template <class T, class IdxT>
+index<T, IdxT> merge(raft::resources const& handle,
+                     cagra::index_params const& params,
+                     std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
+                     cuvs::neighbors::filtering::base_filter const& row_filter)
+{
+  return merge(handle, params, cagra::merge_params{}, indices, row_filter);
+}
+
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index c0decb2243..c13af55e16 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -212,6 +212,13 @@ ConfigureTest(
   PERCENT 100
 )
 
+ConfigureTest(
+  NAME NEIGHBORS_ANN_CAGRA_MERGE_TEST
+  PATH neighbors/ann_cagra/test_merge_fastener.cu
+  GPUS 1
+  PERCENT 100
+)
+
 ConfigureTest(
   NAME NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
   PATH neighbors/ann_cagra/test_half_uint32_t.cu
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
new file mode 100644
index 0000000000..22a33e8238
--- /dev/null
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -0,0 +1,900 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "../../../src/neighbors/detail/cagra/cagra_merge.cuh"
+#include "../naive_knn.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <raft/core/copy.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuda_fp16.h>
+#include <gtest/gtest.h>
+
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+#include <vector>
+
+namespace cuvs::neighbors::cagra {
+namespace {
+
+template <typename T>
+auto make_value(int64_t row, int64_t column) -> T
+{
+  int value = static_cast<int>((row * 7 + column * 3) % 23) - 11;
+  if constexpr (std::is_same_v<T, half>) {
+    return __float2half(static_cast<float>(value) / 8.0f);
+  } else if constexpr (std::is_same_v<T, float>) {
+    return static_cast<float>(value) / 8.0f;
+  } else if constexpr (std::is_same_v<T, uint8_t>) {
+    return static_cast<uint8_t>(value + 32);
+  } else {
+    return static_cast<int8_t>(value);
+  }
+}
+
+template <typename T>
+auto make_dataset(raft::resources const& res, int64_t rows, int64_t dim, int64_t offset)
+{
+  auto dataset = raft::make_host_matrix<T, int64_t>(res, rows, dim);
+  for (int64_t i = 0; i < rows; ++i) {
+    for (int64_t j = 0; j < dim; ++j) {
+      dataset(i, j) = make_value<T>(i + offset, j);
+    }
+  }
+  return dataset;
+}
+
+auto make_ring_graph(raft::resources const& res, int64_t rows, int64_t degree)
+{
+  auto graph = raft::make_host_matrix<uint32_t, int64_t>(res, rows, degree);
+  for (int64_t i = 0; i < rows; ++i) {
+    for (int64_t j = 0; j < degree; ++j) {
+      graph(i, j) = static_cast<uint32_t>((i + j + 1) % rows);
+    }
+  }
+  return graph;
+}
+
+template <typename T>
+void expect_valid_graph(index<T, uint32_t> const& merged, int64_t rows, int64_t degree)
+{
+  ASSERT_EQ(merged.size(), rows);
+  ASSERT_EQ(merged.graph().extent(0), rows);
+  ASSERT_EQ(merged.graph().extent(1), degree);
+  auto host = raft::make_host_matrix<uint32_t, int64_t>(rows, degree);
+  raft::resources res;
+  raft::copy(host.data_handle(),
+             merged.graph().data_handle(),
+             host.size(),
+             raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+  for (int64_t row = 0; row < rows; ++row) {
+    for (int64_t column = 0; column < degree; ++column) {
+      EXPECT_LT(host(row, column), static_cast<uint32_t>(rows));
+      EXPECT_NE(host(row, column), static_cast<uint32_t>(row));
+    }
+  }
+}
+
+template <typename T>
+void expect_dataset_order(raft::resources const& res,
+                          index<T, uint32_t> const& merged,
+                          int64_t rows,
+                          int64_t dim)
+{
+  ASSERT_EQ(merged.dataset().extent(0), rows);
+  ASSERT_EQ(merged.dataset().extent(1), dim);
+  auto host = raft::make_host_matrix<T, int64_t>(res, rows, dim);
+  raft::copy(host.data_handle(),
+             merged.dataset().data_handle(),
+             host.size(),
+             raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+  for (int64_t row = 0; row < rows; ++row) {
+    for (int64_t column = 0; column < dim; ++column) {
+      EXPECT_EQ(static_cast<float>(host(row, column)),
+                static_cast<float>(make_value<T>(row, column)));
+    }
+  }
+}
+
+template <typename T>
+void run_explicit_fastener(cuvs::distance::DistanceType metric)
+{
+  raft::resources res;
+  constexpr int64_t rows   = 48;
+  constexpr int64_t dim    = 8;
+  constexpr int64_t degree = 4;
+  auto dataset0            = make_dataset<T>(res, rows, dim, 0);
+  auto dataset1            = make_dataset<T>(res, rows, dim, rows);
+  auto graph0              = make_ring_graph(res, rows, degree);
+  auto graph1              = make_ring_graph(res, rows, degree);
+  index<T, uint32_t> index0(
+    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
+  index<T, uint32_t> index1(
+    res, metric, raft::make_const_mdspan(dataset1.view()), raft::make_const_mdspan(graph1.view()));
+
+  index_params params;
+  params.metric                    = metric;
+  params.graph_degree              = degree;
+  params.intermediate_graph_degree = degree;
+  params.attach_dataset_on_build   = true;
+  params.guarantee_connectivity    = false;
+  merge_params fastener;
+  fastener.algo        = merge_algo::FASTENER;
+  fastener.leaf_size   = 64;
+  fastener.leaf_degree = 4;
+  std::vector<index<T, uint32_t>*> indices{&index0, &index1};
+
+  auto merged = merge(res, params, indices, fastener);
+  expect_valid_graph(merged, rows * 2, degree);
+  expect_dataset_order(res, merged, rows * 2, dim);
+  EXPECT_TRUE(merged.data().is_owning());
+  EXPECT_EQ(index0.dataset().extent(0), rows);
+  EXPECT_EQ(index1.dataset().extent(0), rows);
+  EXPECT_TRUE(index0.data().is_owning());
+  EXPECT_TRUE(index1.data().is_owning());
+  EXPECT_EQ(index0.size(), rows);
+  EXPECT_EQ(index1.size(), rows);
+  EXPECT_EQ(index0.dim(), dim);
+  EXPECT_EQ(index1.dim(), dim);
+}
+
+TEST(CagraMergeFastener, PlanSplitIsDeterministicDenseAndCompact)
+{
+  using namespace detail::merge_scaffold;
+  std::vector<partition_range> ranges{{0, 0, 40}, {1, 40, 300}, {2, 300, 1000}};
+  split_params params{.fanout            = 2,
+                      .leader_fraction   = 0.05,
+                      .max_leaders       = 16,
+                      .leaf_size         = 64,
+                      .level             = 1,
+                      .occurrence_stride = 2};
+
+  auto plan   = plan_split(ranges, 1000, params, DETERMINISTIC_SEED);
+  auto replay = plan_split(ranges, 1000, params, DETERMINISTIC_SEED);
+
+  ASSERT_EQ(plan.parents.size(), ranges.size());
+  EXPECT_TRUE(plan.parents[0].carried());
+  EXPECT_FALSE(plan.parents[1].carried());
+  EXPECT_FALSE(plan.parents[2].carried());
+
+  int64_t output_cursor = 0;
+  uint32_t key_cursor   = 0;
+  for (size_t i = 0; i < plan.parents.size(); ++i) {
+    auto const& entry = plan.parents[i];
+    EXPECT_EQ(entry.output_start, output_cursor);
+    EXPECT_EQ(entry.child_key_base, key_cursor);
+    output_cursor += entry.output_rows(params.fanout);
+    key_cursor += entry.child_count();
+    if (!entry.carried()) {
+      EXPECT_GE(entry.leader_count, static_cast<int32_t>(params.fanout));
+      EXPECT_LE(entry.leader_count, static_cast<int32_t>(params.max_leaders));
+      EXPECT_LT(entry.leader_offset, static_cast<uint32_t>(entry.size()));
+    }
+    EXPECT_EQ(entry.leader_offset, replay.parents[i].leader_offset);
+  }
+  EXPECT_EQ(plan.output_rows, output_cursor);
+  EXPECT_EQ(plan.child_count, key_cursor);
+  EXPECT_EQ(plan.output_rows, 40 + 260 * 2 + 700 * 2);
+
+  std::vector<partition_range> overlapping{{0, 0, 40}, {1, 30, 100}};
+  EXPECT_THROW(plan_split(overlapping, 100, params, DETERMINISTIC_SEED), raft::exception);
+}
+
+TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevels)
+{
+  using namespace detail::merge_scaffold;
+  raft::resources res;
+  auto stream            = raft::resource::get_cuda_stream(res);
+  constexpr int64_t rows = 96;
+  constexpr int64_t dim  = 4;
+  auto host_dataset      = make_dataset<float>(res, rows, dim, 0);
+  auto dataset           = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  raft::copy(dataset.data_handle(), host_dataset.data_handle(), dataset.size(), stream);
+
+  split_context context(stream, rows);
+  manyway_l2_norms_kernel<<<static_cast<int>((rows + 3) / 4), 128, 0, stream>>>(
+    dataset.data_handle(), rows, dim, context.norms.data());
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  auto root   = make_root_partition(res, rows);
+  auto first  = split_manyway(res,
+                             raft::make_const_mdspan(dataset.view()),
+                             root,
+                             split_params{.fanout            = 2,
+                                           .leader_fraction   = 0.01,
+                                           .max_leaders       = 2,
+                                           .leaf_size         = 64,
+                                           .level             = 0,
+                                           .occurrence_stride = 1},
+                             context);
+  auto second = split_manyway(res,
+                              raft::make_const_mdspan(dataset.view()),
+                              first,
+                              split_params{.fanout            = 2,
+                                           .leader_fraction   = 0.01,
+                                           .max_leaders       = 2,
+                                           .leaf_size         = 64,
+                                           .level             = 1,
+                                           .occurrence_stride = 2},
+                              context);
+  EXPECT_EQ(second.memberships.size(), static_cast<size_t>(rows * 4));
+
+  int64_t cursor = 0;
+  for (auto const& range : second.ranges) {
+    EXPECT_EQ(range.start, cursor);
+    EXPECT_GT(range.end, range.start);
+    EXPECT_LE(range.end, static_cast<int64_t>(second.memberships.size()));
+    cursor = range.end;
+  }
+  EXPECT_EQ(cursor, static_cast<int64_t>(second.memberships.size()));
+
+  std::vector<partition_membership> records(second.memberships.size());
+  raft::copy(records.data(), second.memberships.data(), records.size(), stream);
+  raft::resource::sync_stream(res);
+  for (auto const& record : records) {
+    EXPECT_LT(record.id, static_cast<uint32_t>(rows));
+    EXPECT_LT(record.occurrence, uint16_t{4});
+  }
+
+  constexpr int64_t small_rows = 32;
+  auto small_root              = make_root_partition(res, small_rows);
+  split_context small_context(stream, small_rows);
+  auto carried = split_manyway(res,
+                               raft::make_const_mdspan(dataset.view()),
+                               small_root,
+                               split_params{.fanout            = 3,
+                                            .leader_fraction   = 0.5,
+                                            .max_leaders       = 8,
+                                            .leaf_size         = 64,
+                                            .level             = 0,
+                                            .occurrence_stride = 1},
+                               small_context);
+  ASSERT_EQ(carried.ranges.size(), 1);
+  EXPECT_EQ(carried.memberships.size(), static_cast<size_t>(small_rows));
+  std::vector<partition_membership> carried_records(carried.memberships.size());
+  raft::copy(carried_records.data(), carried.memberships.data(), carried_records.size(), stream);
+  raft::resource::sync_stream(res);
+  for (int64_t row = 0; row < small_rows; ++row) {
+    EXPECT_EQ(carried_records[row].id, static_cast<uint32_t>(row));
+    EXPECT_EQ(carried_records[row].occurrence, uint16_t{0});
+  }
+}
+
+TEST(CagraMergeFastener, InitializesUnwrittenScaffoldSlotsWithSelf)
+{
+  using namespace detail::merge_scaffold;
+  raft::resources res;
+  auto stream              = raft::resource::get_cuda_stream(res);
+  constexpr int64_t rows   = 3;
+  constexpr int64_t degree = 4;
+  auto graph               = raft::make_device_matrix<uint32_t, int64_t>(res, rows, degree);
+
+  initialize_self_scaffold_kernel<<<1, 256, 0, stream>>>(graph.data_handle(), rows, degree);
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  std::vector<uint32_t> initialized(graph.size());
+  raft::copy(initialized.data(), graph.data_handle(), initialized.size(), stream);
+  raft::resource::sync_stream(res);
+  for (int64_t row = 0; row < rows; ++row) {
+    for (int64_t column = 0; column < degree; ++column) {
+      EXPECT_EQ(initialized[row * degree + column], static_cast<uint32_t>(row));
+    }
+  }
+}
+
+TEST(CagraMergeFastener, LeafGemmLimitsRejectOnlyUnrealisticIntegerDimensions)
+{
+  using namespace detail::merge_scaffold;
+  EXPECT_TRUE(leaf_gemm_supported<float>(4096, 256));
+  EXPECT_TRUE(leaf_gemm_supported<half>(4096, 256));
+  EXPECT_TRUE(leaf_gemm_supported<int8_t>(MAX_INTEGER_LEAF_DIMENSION, 256));
+  EXPECT_TRUE(leaf_gemm_supported<uint8_t>(MAX_INTEGER_LEAF_DIMENSION, 256));
+  EXPECT_FALSE(leaf_gemm_supported<int8_t>(MAX_INTEGER_LEAF_DIMENSION + 1, 256));
+  EXPECT_FALSE(leaf_gemm_supported<uint8_t>(MAX_INTEGER_LEAF_DIMENSION + 1, 256));
+
+  raft::resources res;
+  constexpr int64_t rows = 2;
+  constexpr int64_t dim  = MAX_INTEGER_LEAF_DIMENSION + 1;
+  auto dataset0          = make_dataset<int8_t>(res, rows, dim, 0);
+  auto dataset1          = make_dataset<int8_t>(res, rows, dim, rows);
+  auto graph0            = make_ring_graph(res, rows, 1);
+  auto graph1            = make_ring_graph(res, rows, 1);
+  auto metric            = cuvs::distance::DistanceType::L2Expanded;
+  index<int8_t, uint32_t> index0(
+    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
+  index<int8_t, uint32_t> index1(
+    res, metric, raft::make_const_mdspan(dataset1.view()), raft::make_const_mdspan(graph1.view()));
+
+  index_params params;
+  params.metric                    = metric;
+  params.graph_degree              = 1;
+  params.intermediate_graph_degree = 1;
+  merge_params fastener;
+  fastener.algo      = merge_algo::FASTENER;
+  fastener.leaf_size = 64;
+  std::vector<index<int8_t, uint32_t>*> indices{&index0, &index1};
+
+  auto result = detail::preflight_fastener(
+    params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
+  EXPECT_FALSE(result.eligible);
+  EXPECT_EQ(result.reason, "integer dataset dimension exceeds the INT32 leaf GEMM limit");
+  EXPECT_EQ(index0.dataset().extent(0), rows);
+  EXPECT_EQ(index1.dataset().extent(0), rows);
+}
+
+TEST(CagraMergeFastener, SupportsAllScalarTypes)
+{
+  auto metric = cuvs::distance::DistanceType::L2Expanded;
+  run_explicit_fastener<float>(metric);
+  run_explicit_fastener<half>(metric);
+  run_explicit_fastener<int8_t>(metric);
+  run_explicit_fastener<uint8_t>(metric);
+}
+
+TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
+{
+  raft::resources res;
+  constexpr int64_t rows   = 48;
+  constexpr int64_t dim    = 8;
+  constexpr int64_t degree = 4;
+  auto dataset0            = make_dataset<float>(res, rows, dim, 0);
+  auto dataset1            = make_dataset<float>(res, rows, dim, rows);
+  auto device_dataset1     = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  raft::copy(device_dataset1.data_handle(),
+             dataset1.data_handle(),
+             dataset1.size(),
+             raft::resource::get_cuda_stream(res));
+  auto graph0 = make_ring_graph(res, rows, degree);
+  auto graph1 = make_ring_graph(res, rows, degree);
+  auto metric = cuvs::distance::DistanceType::L2Expanded;
+  index<float, uint32_t> index0(
+    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
+  index<float, uint32_t> index1(res,
+                                metric,
+                                raft::make_const_mdspan(device_dataset1.view()),
+                                raft::make_const_mdspan(graph1.view()));
+  ASSERT_TRUE(index0.data().is_owning());
+  ASSERT_FALSE(index1.data().is_owning());
+
+  index_params params;
+  params.metric                    = metric;
+  params.graph_degree              = degree;
+  params.intermediate_graph_degree = degree;
+  params.attach_dataset_on_build   = true;
+  params.guarantee_connectivity    = false;
+  merge_params fastener;
+  fastener.algo        = merge_algo::FASTENER;
+  fastener.leaf_size   = 64;
+  fastener.leaf_degree = 4;
+  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+
+  auto merged = merge(res, params, indices, fastener);
+  expect_valid_graph(merged, rows * 2, degree);
+  expect_dataset_order(res, merged, rows * 2, dim);
+  EXPECT_TRUE(merged.data().is_owning());
+  EXPECT_EQ(index0.dataset().extent(0), rows);
+  EXPECT_EQ(index1.dataset().extent(0), rows);
+  EXPECT_TRUE(index0.data().is_owning());
+  EXPECT_FALSE(index1.data().is_owning());
+}
+
+TEST(CagraMergeFastener, DefaultFloatMergeSearchRecallAgainstBruteForce)
+{
+  raft::resources res;
+  auto stream                 = raft::resource::get_cuda_stream(res);
+  constexpr int64_t part_rows = 256;
+  constexpr int64_t rows      = 2 * part_rows;
+  constexpr int64_t dim       = 16;
+  constexpr int64_t queries   = 16;
+  constexpr int64_t k         = 12;
+
+  auto value = [](int64_t row, int64_t column) {
+    uint64_t bits = (static_cast<uint64_t>(row) + 1) * 0x9e3779b97f4a7c15ull;
+    bits ^= (static_cast<uint64_t>(column) + 1) * 0xbf58476d1ce4e5b9ull;
+    bits ^= bits >> 30;
+    bits *= 0x94d049bb133111ebull;
+    bits ^= bits >> 31;
+    return static_cast<float>(bits & 0xffffu) / 65535.0f;
+  };
+
+  auto dataset0 = raft::make_host_matrix<float, int64_t>(part_rows, dim);
+  auto dataset1 = raft::make_host_matrix<float, int64_t>(part_rows, dim);
+  for (int64_t row = 0; row < part_rows; ++row) {
+    for (int64_t column = 0; column < dim; ++column) {
+      dataset0(row, column) = value(row, column);
+      dataset1(row, column) = value(row + part_rows, column);
+    }
+  }
+
+  index_params partition_params;
+  partition_params.metric                    = cuvs::distance::DistanceType::L2Expanded;
+  partition_params.graph_degree              = 16;
+  partition_params.intermediate_graph_degree = 32;
+  partition_params.attach_dataset_on_build   = true;
+  partition_params.guarantee_connectivity    = false;
+  auto index0 = build(res, partition_params, raft::make_const_mdspan(dataset0.view()));
+  auto index1 = build(res, partition_params, raft::make_const_mdspan(dataset1.view()));
+  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+
+  merge_params fastener;
+  fastener.algo = merge_algo::FASTENER;
+  auto merged   = merge(res, partition_params, indices, fastener);
+  expect_valid_graph(merged, rows, partition_params.graph_degree);
+
+  auto query_host = raft::make_host_matrix<float, int64_t>(queries, dim);
+  for (int64_t query = 0; query < queries; ++query) {
+    int64_t source = query * 31;
+    for (int64_t column = 0; column < dim; ++column) {
+      query_host(query, column) = value(source, column);
+    }
+  }
+  auto query_device = raft::make_device_matrix<float, int64_t>(res, queries, dim);
+  raft::copy(query_device.data_handle(), query_host.data_handle(), query_host.size(), stream);
+  auto actual_indices   = raft::make_device_matrix<uint32_t, int64_t>(res, queries, k);
+  auto actual_distances = raft::make_device_matrix<float, int64_t>(res, queries, k);
+  search_params search_params;
+  search_params.itopk_size     = 32;
+  search_params.search_width   = 4;
+  search_params.max_iterations = 0;
+  search(res,
+         search_params,
+         merged,
+         raft::make_const_mdspan(query_device.view()),
+         actual_indices.view(),
+         actual_distances.view());
+
+  auto truth_indices   = raft::make_device_matrix<uint32_t, int64_t>(res, queries, k);
+  auto truth_distances = raft::make_device_matrix<float, int64_t>(res, queries, k);
+  cuvs::neighbors::naive_knn<float, float, uint32_t>(res,
+                                                     truth_distances.data_handle(),
+                                                     truth_indices.data_handle(),
+                                                     query_device.data_handle(),
+                                                     merged.dataset().data_handle(),
+                                                     static_cast<uint32_t>(queries),
+                                                     static_cast<uint32_t>(rows),
+                                                     static_cast<uint32_t>(dim),
+                                                     static_cast<uint32_t>(k),
+                                                     cuvs::distance::DistanceType::L2Expanded);
+
+  std::vector<uint32_t> actual(static_cast<size_t>(queries * k));
+  std::vector<uint32_t> truth(static_cast<size_t>(queries * k));
+  raft::copy(actual.data(), actual_indices.data_handle(), actual.size(), stream);
+  raft::copy(truth.data(), truth_indices.data_handle(), truth.size(), stream);
+  raft::resource::sync_stream(res);
+
+  size_t matches = 0;
+  for (int64_t query = 0; query < queries; ++query) {
+    for (int64_t candidate = 0; candidate < k; ++candidate) {
+      auto id = actual[query * k + candidate];
+      for (int64_t expected = 0; expected < k; ++expected) {
+        if (id == truth[query * k + expected]) {
+          ++matches;
+          break;
+        }
+      }
+    }
+  }
+  EXPECT_GE(static_cast<double>(matches) / static_cast<double>(queries * k), 0.60);
+}
+
+TEST(CagraMergeFastener, MixedDegreesAndThreeLevelUint8OptionsProduceExactDegree)
+{
+  raft::resources res;
+  constexpr int64_t rows = 64;
+  constexpr int64_t dim  = 16;
+  auto dataset0          = make_dataset<uint8_t>(res, rows, dim, 0);
+  auto dataset1          = make_dataset<uint8_t>(res, rows, dim, rows);
+  auto graph0            = make_ring_graph(res, rows, 6);
+  auto graph1            = make_ring_graph(res, rows, 8);
+  index<uint8_t, uint32_t> index0(res,
+                                  cuvs::distance::DistanceType::L2Expanded,
+                                  raft::make_const_mdspan(dataset0.view()),
+                                  raft::make_const_mdspan(graph0.view()));
+  index<uint8_t, uint32_t> index1(res,
+                                  cuvs::distance::DistanceType::L2Expanded,
+                                  raft::make_const_mdspan(dataset1.view()),
+                                  raft::make_const_mdspan(graph1.view()));
+
+  index_params params;
+  params.metric                  = cuvs::distance::DistanceType::L2Expanded;
+  params.graph_degree            = 8;
+  params.attach_dataset_on_build = true;
+  merge_params fastener;
+  fastener.algo            = merge_algo::FASTENER;
+  fastener.levels          = 3;
+  fastener.root_fanout     = 2;
+  fastener.lower_fanout    = 2;
+  fastener.leader_fraction = 0.25;
+  fastener.max_leaders     = 32;
+  fastener.leaf_size       = 64;
+  fastener.leaf_degree     = 8;
+  std::vector<index<uint8_t, uint32_t>*> indices{&index0, &index1};
+
+  auto merged = merge(res, params, indices, fastener);
+  expect_valid_graph(merged, rows * 2, 8);
+}
+
+TEST(CagraMergeFastener, AppendCyclicallyPadsMixedInputDegrees)
+{
+  raft::resources res;
+  auto dataset0 = make_dataset<float>(res, 2, 1, 0);
+  auto dataset1 = make_dataset<float>(res, 2, 1, 2);
+  auto graph0   = make_ring_graph(res, 2, 2);
+  auto graph1   = make_ring_graph(res, 2, 4);
+  index<float, uint32_t> index0(res,
+                                cuvs::distance::DistanceType::L2Expanded,
+                                raft::make_const_mdspan(dataset0.view()),
+                                raft::make_const_mdspan(graph0.view()));
+  index<float, uint32_t> index1(res,
+                                cuvs::distance::DistanceType::L2Expanded,
+                                raft::make_const_mdspan(dataset1.view()),
+                                raft::make_const_mdspan(graph1.view()));
+  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+  std::vector<int64_t> offsets{0, 2, 4};
+
+  auto scaffold_host        = raft::make_host_matrix<uint32_t, int64_t>(4, 1);
+  uint32_t scaffold_rows[4] = {2, 3, 0, 1};
+  for (int64_t row = 0; row < 4; ++row) {
+    scaffold_host(row, 0) = scaffold_rows[row];
+  }
+  auto scaffold = raft::make_device_matrix<uint32_t, int64_t>(res, 4, 1);
+  raft::copy(scaffold.data_handle(),
+             scaffold_host.data_handle(),
+             scaffold.size(),
+             raft::resource::get_cuda_stream(res));
+
+  auto appended = detail::merge_scaffold::append_to_input_graphs<float, uint32_t>(
+    res, indices, offsets, raft::make_const_mdspan(scaffold.view()));
+  ASSERT_EQ(appended.extent(0), 4);
+  ASSERT_EQ(appended.extent(1), 5);
+
+  auto host = raft::make_host_matrix<uint32_t, int64_t>(4, 5);
+  raft::copy(
+    host.data_handle(), appended.data_handle(), host.size(), raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+
+  // The degree-2 input repeats cyclically to the base width of 4; the degree-4 input copies
+  // through shifted by its offset; the scaffold column is already global.
+  uint32_t expected[4][5] = {{1, 0, 1, 0, 2}, {0, 1, 0, 1, 3}, {3, 2, 3, 2, 0}, {2, 3, 2, 3, 1}};
+  for (int64_t row = 0; row < 4; ++row) {
+    for (int64_t column = 0; column < 5; ++column) {
+      EXPECT_EQ(host(row, column), expected[row][column]) << row << "," << column;
+    }
+  }
+}
+
+TEST(CagraMergeFastener, MergeSupportsOutputDegreeBelowInputDegree)
+{
+  raft::resources res;
+  constexpr int64_t rows         = 48;
+  constexpr int64_t dim          = 8;
+  constexpr int64_t input_degree = 8;
+  auto dataset0                  = make_dataset<float>(res, rows, dim, 0);
+  auto dataset1                  = make_dataset<float>(res, rows, dim, rows);
+  auto graph0                    = make_ring_graph(res, rows, input_degree);
+  auto graph1                    = make_ring_graph(res, rows, input_degree);
+  index<float, uint32_t> index0(res,
+                                cuvs::distance::DistanceType::L2Expanded,
+                                raft::make_const_mdspan(dataset0.view()),
+                                raft::make_const_mdspan(graph0.view()));
+  index<float, uint32_t> index1(res,
+                                cuvs::distance::DistanceType::L2Expanded,
+                                raft::make_const_mdspan(dataset1.view()),
+                                raft::make_const_mdspan(graph1.view()));
+
+  index_params params;
+  params.metric                  = cuvs::distance::DistanceType::L2Expanded;
+  params.graph_degree            = 4;
+  params.attach_dataset_on_build = true;
+  merge_params fastener;
+  fastener.algo        = merge_algo::FASTENER;
+  fastener.leaf_size   = 64;
+  fastener.leaf_degree = 4;
+  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+
+  auto merged = merge(res, params, indices, fastener);
+  expect_valid_graph(merged, rows * 2, params.graph_degree);
+}
+
+auto make_float_indices(raft::resources const& res,
+                        cuvs::distance::DistanceType metric,
+                        raft::host_matrix<float, int64_t>& dataset0,
+                        raft::host_matrix<float, int64_t>& dataset1,
+                        raft::host_matrix<uint32_t, int64_t>& graph0,
+                        raft::host_matrix<uint32_t, int64_t>& graph1)
+{
+  std::vector<index<float, uint32_t>> output;
+  output.emplace_back(
+    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
+  output.emplace_back(
+    res, metric, raft::make_const_mdspan(dataset1.view()), raft::make_const_mdspan(graph1.view()));
+  return output;
+}
+
+TEST(CagraMergeFastener, InvalidManywayOptionsFailPreflightWithoutMutation)
+{
+  raft::resources res;
+  constexpr int64_t rows = 32;
+  constexpr int64_t dim  = 8;
+  auto dataset0          = make_dataset<float>(res, rows, dim, 0);
+  auto dataset1          = make_dataset<float>(res, rows, dim, rows);
+  auto graph0            = make_ring_graph(res, rows, 4);
+  auto graph1            = make_ring_graph(res, rows, 4);
+  index_params params;
+  params.metric                    = cuvs::distance::DistanceType::L2Expanded;
+  params.graph_degree              = 4;
+  params.intermediate_graph_degree = 8;
+  params.attach_dataset_on_build   = true;
+  auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
+  std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+
+  std::vector<merge_params> invalid;
+  auto add_invalid = [&](auto update) {
+    merge_params candidate;
+    candidate.algo = merge_algo::FASTENER;
+    update(candidate);
+    invalid.push_back(candidate);
+  };
+  add_invalid([](auto& value) { value.levels = 0; });
+  add_invalid([](auto& value) { value.root_fanout = 0; });
+  add_invalid([](auto& value) { value.root_fanout = 33; });
+  add_invalid([](auto& value) { value.lower_fanout = 0; });
+  add_invalid([](auto& value) { value.lower_fanout = 33; });
+  add_invalid([](auto& value) { value.leader_fraction = 0.0; });
+  add_invalid([](auto& value) { value.leader_fraction = 1.1; });
+  add_invalid(
+    [](auto& value) { value.leader_fraction = std::numeric_limits<double>::quiet_NaN(); });
+  add_invalid([](auto& value) { value.max_leaders = 0; });
+  add_invalid([](auto& value) { value.max_leaders = 8193; });
+  add_invalid([](auto& value) { value.max_leaders = 2; });
+  add_invalid([](auto& value) { value.leaf_size = 0; });
+  add_invalid([](auto& value) { value.leaf_size = 512; });
+  add_invalid([](auto& value) { value.leaf_degree = 0; });
+  add_invalid([](auto& value) { value.leaf_degree = 16; });
+  add_invalid([](auto& value) {
+    value.levels       = 3;
+    value.root_fanout  = 32;
+    value.lower_fanout = 32;
+  });
+
+  for (auto const& candidate : invalid) {
+    auto result = detail::preflight_fastener(
+      params, candidate, indices, cuvs::neighbors::filtering::none_sample_filter{});
+    EXPECT_FALSE(result.eligible) << result.reason;
+  }
+  EXPECT_EQ(owned[0].dataset().extent(0), rows);
+  EXPECT_EQ(owned[1].dataset().extent(0), rows);
+}
+
+TEST(CagraMergeFastener, DispatchRejectsOrFallsBackBeforeMutation)
+{
+  raft::resources res;
+  constexpr int64_t rows = 32;
+  constexpr int64_t dim  = 8;
+  auto dataset0          = make_dataset<float>(res, rows, dim, 0);
+  auto dataset1          = make_dataset<float>(res, rows, dim, rows);
+  auto graph0            = make_ring_graph(res, rows, 4);
+  auto graph1            = make_ring_graph(res, rows, 4);
+  index_params params;
+  params.metric                    = cuvs::distance::DistanceType::L2Expanded;
+  params.graph_degree              = 4;
+  params.intermediate_graph_degree = 8;
+  params.attach_dataset_on_build   = true;
+
+  {
+    auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
+    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    merge_params unsupported;
+    unsupported.algo      = merge_algo::FASTENER;
+    unsupported.leaf_size = 512;
+    EXPECT_ANY_THROW(merge(res, params, indices, unsupported));
+    EXPECT_EQ(owned[0].dataset().extent(0), rows);
+    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+  }
+  {
+    auto inner_product_params   = params;
+    inner_product_params.metric = cuvs::distance::DistanceType::InnerProduct;
+    auto owned =
+      make_float_indices(res, inner_product_params.metric, dataset0, dataset1, graph0, graph1);
+    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    merge_params fastener;
+    fastener.algo      = merge_algo::FASTENER;
+    fastener.leaf_size = 64;
+    EXPECT_ANY_THROW(merge(res, inner_product_params, indices, fastener));
+    EXPECT_EQ(owned[0].dataset().extent(0), rows);
+    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+  }
+  {
+    auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
+    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    merge_params automatic;
+    automatic.algo      = merge_algo::AUTO;
+    automatic.leaf_size = 512;
+    auto merged         = merge(res, params, indices, automatic);
+    EXPECT_EQ(merged.size(), rows * 2);
+    EXPECT_EQ(owned[0].dataset().extent(0), rows);
+    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+  }
+  {
+    auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
+    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    merge_params rebuild{merge_algo::REBUILD};
+    auto merged = merge(res, params, indices, rebuild);
+    EXPECT_EQ(merged.size(), rows * 2);
+    EXPECT_EQ(owned[0].dataset().extent(0), rows);
+    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+  }
+}
+
+TEST(CagraMergeFastener, InnerProductOrderingDiffersFromL2)
+{
+  raft::resources res;
+  auto dataset        = raft::make_host_matrix<float, int64_t>(3, 2);
+  dataset(0, 0)       = 1.0f;
+  dataset(0, 1)       = 0.0f;
+  dataset(1, 0)       = 2.0f;
+  dataset(1, 1)       = 0.0f;
+  dataset(2, 0)       = 100.0f;
+  dataset(2, 1)       = 100.0f;
+  auto device_dataset = raft::make_device_matrix<float, int64_t>(res, 3, 2);
+  raft::copy(device_dataset.data_handle(),
+             dataset.data_handle(),
+             dataset.size(),
+             raft::resource::get_cuda_stream(res));
+
+  auto source  = raft::make_host_matrix<uint32_t, int64_t>(3, 2);
+  source(0, 0) = 1;
+  source(0, 1) = 2;
+  source(1, 0) = 0;
+  source(1, 1) = 2;
+  source(2, 0) = 0;
+  source(2, 1) = 1;
+  auto l2      = raft::make_device_matrix<uint32_t, int64_t>(res, 3, 2);
+  auto ip      = raft::make_device_matrix<uint32_t, int64_t>(res, 3, 2);
+  raft::copy(
+    l2.data_handle(), source.data_handle(), source.size(), raft::resource::get_cuda_stream(res));
+  raft::copy(
+    ip.data_handle(), source.data_handle(), source.size(), raft::resource::get_cuda_stream(res));
+
+  detail::graph::sort_knn_graph_device_inplace(res,
+                                               cuvs::distance::DistanceType::L2Expanded,
+                                               raft::make_const_mdspan(device_dataset.view()),
+                                               l2.view());
+  detail::graph::sort_knn_graph_device_inplace(res,
+                                               cuvs::distance::DistanceType::InnerProduct,
+                                               raft::make_const_mdspan(device_dataset.view()),
+                                               ip.view());
+  auto l2_host = raft::make_host_matrix<uint32_t, int64_t>(3, 2);
+  auto ip_host = raft::make_host_matrix<uint32_t, int64_t>(3, 2);
+  raft::copy(
+    l2_host.data_handle(), l2.data_handle(), l2.size(), raft::resource::get_cuda_stream(res));
+  raft::copy(
+    ip_host.data_handle(), ip.data_handle(), ip.size(), raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+
+  EXPECT_EQ(l2_host(0, 0), 1);
+  EXPECT_EQ(ip_host(0, 0), 2);
+}
+
+TEST(CagraMergeFastener, Uint8InnerProductSortUsesExactWideAccumulation)
+{
+  raft::resources res;
+  constexpr int64_t dim = 1024;
+  auto dataset          = raft::make_host_matrix<uint8_t, int64_t>(3, dim);
+  for (int64_t d = 0; d < dim; ++d) {
+    dataset(0, d) = 255;
+    dataset(1, d) = 255;
+    dataset(2, d) = 255;
+  }
+  dataset(0, 0)       = 1;
+  dataset(1, 0)       = 1;
+  dataset(2, 0)       = 0;
+  auto device_dataset = raft::make_device_matrix<uint8_t, int64_t>(res, 3, dim);
+  raft::copy(device_dataset.data_handle(),
+             dataset.data_handle(),
+             dataset.size(),
+             raft::resource::get_cuda_stream(res));
+
+  auto graph_host  = raft::make_host_matrix<uint32_t, int64_t>(3, 2);
+  graph_host(0, 0) = 2;
+  graph_host(0, 1) = 1;
+  graph_host(1, 0) = 0;
+  graph_host(1, 1) = 2;
+  graph_host(2, 0) = 0;
+  graph_host(2, 1) = 1;
+  auto graph       = raft::make_device_matrix<uint32_t, int64_t>(res, 3, 2);
+  raft::copy(graph.data_handle(),
+             graph_host.data_handle(),
+             graph.size(),
+             raft::resource::get_cuda_stream(res));
+  detail::graph::sort_knn_graph_device_inplace(res,
+                                               cuvs::distance::DistanceType::InnerProduct,
+                                               raft::make_const_mdspan(device_dataset.view()),
+                                               graph.view());
+  raft::copy(graph_host.data_handle(),
+             graph.data_handle(),
+             graph.size(),
+             raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+  EXPECT_EQ(graph_host(0, 0), 1);
+}
+
+TEST(CagraMergeFastener, AppendSortAndDedupHandleOffsetsAndPadding)
+{
+  raft::resources res;
+  auto dataset0  = raft::make_host_matrix<float, int64_t>(2, 1);
+  auto dataset1  = raft::make_host_matrix<float, int64_t>(2, 1);
+  dataset0(0, 0) = 0.0f;
+  dataset0(1, 0) = 2.0f;
+  dataset1(0, 0) = 10.0f;
+  dataset1(1, 0) = 12.0f;
+  auto graph0    = make_ring_graph(res, 2, 1);
+  auto graph1    = make_ring_graph(res, 2, 1);
+  index<float, uint32_t> index0(res,
+                                cuvs::distance::DistanceType::L2Expanded,
+                                raft::make_const_mdspan(dataset0.view()),
+                                raft::make_const_mdspan(graph0.view()));
+  index<float, uint32_t> index1(res,
+                                cuvs::distance::DistanceType::L2Expanded,
+                                raft::make_const_mdspan(dataset1.view()),
+                                raft::make_const_mdspan(graph1.view()));
+  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+  std::vector<int64_t> offsets{0, 2, 4};
+
+  auto scaffold_host           = raft::make_host_matrix<uint32_t, int64_t>(4, 2);
+  uint32_t scaffold_rows[4][2] = {{1, 3}, {1, 2}, {2, 0}, {3, 1}};
+  for (int64_t row = 0; row < 4; ++row) {
+    for (int64_t column = 0; column < 2; ++column) {
+      scaffold_host(row, column) = scaffold_rows[row][column];
+    }
+  }
+  auto scaffold = raft::make_device_matrix<uint32_t, int64_t>(res, 4, 2);
+  raft::copy(scaffold.data_handle(),
+             scaffold_host.data_handle(),
+             scaffold.size(),
+             raft::resource::get_cuda_stream(res));
+  auto appended = detail::merge_scaffold::append_to_input_graphs<float, uint32_t>(
+    res, indices, offsets, raft::make_const_mdspan(scaffold.view()));
+
+  auto combined_host  = raft::make_host_matrix<float, int64_t>(4, 1);
+  combined_host(0, 0) = 0.0f;
+  combined_host(1, 0) = 2.0f;
+  combined_host(2, 0) = 10.0f;
+  combined_host(3, 0) = 12.0f;
+  auto combined       = raft::make_device_matrix<float, int64_t>(res, 4, 1);
+  raft::copy(combined.data_handle(),
+             combined_host.data_handle(),
+             combined.size(),
+             raft::resource::get_cuda_stream(res));
+  detail::graph::sort_knn_graph_device_inplace(res,
+                                               cuvs::distance::DistanceType::L2Expanded,
+                                               raft::make_const_mdspan(combined.view()),
+                                               appended.view());
+  auto output =
+    detail::merge_scaffold::cap_sorted_graph(res, raft::make_const_mdspan(appended.view()), 3);
+  auto host = raft::make_host_matrix<uint32_t, int64_t>(4, 3);
+  raft::copy(
+    host.data_handle(), output.data_handle(), host.size(), raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+
+  uint32_t expected[4][3] = {{1, 3, 1}, {0, 2, 0}, {3, 0, 3}, {2, 1, 2}};
+  for (int64_t row = 0; row < 4; ++row) {
+    for (int64_t column = 0; column < 3; ++column) {
+      EXPECT_EQ(host(row, column), expected[row][column]);
+    }
+  }
+}
+
+}  // namespace
+}  // namespace cuvs::neighbors::cagra
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
index f22852f0ae..bf3b1f8aea 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
+++ b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
@@ -1,5 +1,5 @@
 #
-# SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
 
@@ -20,9 +20,35 @@
 
 
 def cuvs_cagra_build(params, dims):
+    valid = True
     if "graph_degree" in params and "intermediate_graph_degree" in params:
-        return params["graph_degree"] <= params["intermediate_graph_degree"]
-    return True
+        valid = params["graph_degree"] <= params["intermediate_graph_degree"]
+    if params.get("merge_algo") == "FASTENER":
+        levels = params.get("fastener_levels", 2)
+        root_fanout = params.get("fastener_root_fanout", 2)
+        lower_fanout = params.get("fastener_lower_fanout", 3)
+        leader_fraction = params.get("fastener_leader_fraction", 0.02)
+        max_leaders = params.get("fastener_max_leaders", 1000)
+        leaf_size = params.get("fastener_leaf_size", 256)
+        leaf_degree = params.get("fastener_leaf_degree", 4)
+        valid = valid and levels > 0
+        valid = valid and 1 <= root_fanout <= 32
+        valid = valid and 1 <= lower_fanout <= 32
+        valid = valid and 0.0 < leader_fraction <= 1.0
+        valid = valid and max(root_fanout, lower_fanout) <= max_leaders <= 8192
+        valid = valid and 1 <= leaf_size <= 256
+        valid = valid and 1 <= leaf_degree <= 8
+        if valid:
+            max_spill = 255 // leaf_degree
+            spill = root_fanout
+            if lower_fanout > 1:
+                for _ in range(1, levels):
+                    if spill > max_spill // lower_fanout:
+                        valid = False
+                        break
+                    spill *= lower_fanout
+            valid = valid and spill <= max_spill
+    return valid
 
 
 def cuvs_ivf_pq_build(params, dims):
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
index cf59c3ce22..e36f606ad2 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
@@ -31,3 +31,50 @@ groups:
       itopk: [32, 64, 128, 256, 512]
       max_iterations: [0, 16]
       search_width: [1, 2, 4, 8]
+  merge_fastener_default:
+    build:
+      graph_degree: [64]
+      intermediate_graph_degree: [64]
+      graph_build_algo: ["NN_DESCENT"]
+      num_dataset_splits: [2, 8, 32]
+      merge_type: ["PHYSICAL"]
+      merge_algo: ["FASTENER"]
+      fastener_levels: [2]
+      fastener_root_fanout: [2]
+      fastener_lower_fanout: [3]
+      fastener_leader_fraction: [0.02]
+      fastener_max_leaders: [1000]
+      fastener_leaf_size: [256]
+      fastener_leaf_degree: [4]
+    search:
+      itopk: [64]
+      search_width: [1, 4, 16]
+  merge_fastener_high_quality:
+    build:
+      graph_degree: [64]
+      intermediate_graph_degree: [64]
+      graph_build_algo: ["NN_DESCENT"]
+      num_dataset_splits: [2, 8, 32]
+      merge_type: ["PHYSICAL"]
+      merge_algo: ["FASTENER"]
+      fastener_levels: [2]
+      fastener_root_fanout: [4]
+      fastener_lower_fanout: [2]
+      fastener_leader_fraction: [0.01]
+      fastener_max_leaders: [1000]
+      fastener_leaf_size: [256]
+      fastener_leaf_degree: [4]
+    search:
+      itopk: [64]
+      search_width: [1, 4, 16]
+  merge_rebuild:
+    build:
+      graph_degree: [64]
+      intermediate_graph_degree: [64]
+      graph_build_algo: ["NN_DESCENT"]
+      num_dataset_splits: [2, 8, 32]
+      merge_type: ["PHYSICAL"]
+      merge_algo: ["REBUILD"]
+    search:
+      itopk: [64]
+      search_width: [1, 4, 16]

From e2ce96f2104e9f35b68d75db543b36e2f0761d47 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 18:43:27 +0000
Subject: [PATCH 04/41] pedantic docstring change

---
 cpp/include/cuvs/neighbors/cagra.hpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index fe46ca3132..962041571b 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2508,7 +2508,9 @@ auto merge(raft::resources const& res,
              cuvs::neighbors::filtering::none_sample_filter{})
   -> cuvs::neighbors::cagra::index<float, uint32_t>;
 
-/** @brief Merge with explicit C++ algorithm and Fastener controls. */
+/** @copydoc merge 
+* @param[in] merge_params Parameters for the merge.
+*/
 auto merge(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            std::vector<cuvs::neighbors::cagra::index<float, uint32_t>*>& indices,

From a9149dbce8630b95d4d840f620eecb2f6a85e185 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 19:15:05 +0000
Subject: [PATCH 05/41] assert/validation changes

---
 .../neighbors/detail/cagra/cagra_merge.cuh    | 24 ++++++++++---------
 .../detail/cagra/cagra_merge_scaffold.cuh     |  6 +++--
 2 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 147a59c522..cbc50c02fe 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -220,8 +220,8 @@ auto preflight_fastener(cagra::index_params const& params,
   -> fastener_preflight_result
 {
   fastener_preflight_result result;
-  auto reject = [&](char const* reason) {
-    result.reason = reason;
+  auto reject = [&](std::string reason) {
+    result.reason = std::move(reason);
     return result;
   };
 
@@ -239,25 +239,29 @@ auto preflight_fastener(cagra::index_params const& params,
     return reject("only L2Expanded is supported");
   }
   if (merge_params.levels == 0) { return reject("levels must be positive"); }
-  if (merge_params.root_fanout < 1 || merge_params.root_fanout > 32 ||
-      merge_params.lower_fanout < 1 || merge_params.lower_fanout > 32) {
-    return reject("root_fanout and lower_fanout must be between 1 and 32");
+  if (merge_params.root_fanout < 1 || merge_params.root_fanout > merge_scaffold::MAX_FANOUT ||
+      merge_params.lower_fanout < 1 || merge_params.lower_fanout > merge_scaffold::MAX_FANOUT) {
+    return reject(&"root_fanout and lower_fanout must be between 1 and " [
+                  std::to_string(merge_scaffold::MAX_FANOUT)]);
   }
   if (!(merge_params.leader_fraction > 0.0 && merge_params.leader_fraction <= 1.0)) {
     return reject("leader_fraction must be in (0, 1]");
   }
-  if (merge_params.max_leaders == 0 || merge_params.max_leaders > 8192) {
-    return reject("max_leaders must be between 1 and 8192");
+  if (merge_params.max_leaders == 0 || merge_params.max_leaders > merge_scaffold::MAX_LEADERS) {
+    return reject(&"max_leaders must be between 1 and " [
+                  std::to_string(merge_scaffold::MAX_LEADERS)]);
   }
   if (merge_params.max_leaders < std::max(merge_params.root_fanout, merge_params.lower_fanout)) {
     return reject("max_leaders must cover both configured fanouts");
   }
   if (merge_params.leaf_size == 0 || merge_params.leaf_size > merge_scaffold::MAX_LEAF_SIZE) {
-    return reject("leaf_size must be between 1 and 256");
+    return reject(&"leaf_size must be between 1 and " [
+                  std::to_string(merge_scaffold::MAX_LEAF_SIZE)]);
   }
   if (merge_params.leaf_degree == 0 ||
       merge_params.leaf_degree > static_cast<uint32_t>(merge_scaffold::MAX_LEAF_DEGREE)) {
-    return reject("leaf_degree must be between 1 and 8");
+    return reject(&"leaf_degree must be between 1 and " [
+                  std::to_string(merge_scaffold::MAX_LEAF_DEGREE)]);
   }
 
   uint64_t const max_spill = std::numeric_limits<uint8_t>::max() / merge_params.leaf_degree;
@@ -360,7 +364,6 @@ auto merge_fastener(raft::resources const& handle,
   scaffold_params.leaf_size       = merge_params.leaf_size;
   scaffold_params.leaf_degree     = merge_params.leaf_degree;
 
-  // Graph combination: append shifted input graphs to the cross-input scaffold.
   auto merged_graph = [&] {
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/scaffold");
     auto scaffold = merge_scaffold::build<T>(
@@ -377,7 +380,6 @@ auto merge_fastener(raft::resources const& handle,
       handle, raft::make_const_mdspan(merged_graph.view()), params.graph_degree);
   }
 
-  // Optimization: reduce the sorted candidate prefix to the requested final CAGRA graph.
   auto optimized_graph =
     raft::make_device_matrix<uint32_t, int64_t>(handle, preflight.rows, params.graph_degree);
   {
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 3342d06530..cd228608a1 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -1175,12 +1175,14 @@ auto build(raft::resources const& res,
   RAFT_EXPECTS(params.levels > 0, "Fastener levels must be positive");
   RAFT_EXPECTS(params.root_fanout >= 1 && params.root_fanout <= MAX_FANOUT &&
                  params.lower_fanout >= 1 && params.lower_fanout <= MAX_FANOUT,
-               "Fastener fanouts must be between 1 and 32");
+               "Fastener fanouts must be between 1 and %u",
+               MAX_FANOUT);
   RAFT_EXPECTS(params.leader_fraction > 0.0 && params.leader_fraction <= 1.0,
                "Fastener leader fraction must be in (0, 1]");
   RAFT_EXPECTS(params.max_leaders >= std::max(params.root_fanout, params.lower_fanout) &&
                  params.max_leaders <= MAX_LEADERS,
-               "Fastener leader cap must cover both fanouts and not exceed 8192");
+               "Fastener leader cap must cover both fanouts and not exceed %u",
+               MAX_LEADERS);
   RAFT_EXPECTS(params.leaf_size >= 1 && params.leaf_size <= MAX_LEAF_SIZE,
                "Fastener leaf size must be between 1 and %d",
                MAX_LEAF_SIZE);

From cf78e323154a87973c3f5174898119ea03e35806 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 21:08:37 +0000
Subject: [PATCH 06/41] test improvements

---
 cpp/tests/neighbors/ann_cagra.cuh             |   7 +-
 .../ann_cagra/test_merge_fastener.cu          | 413 ++++++++++--------
 2 files changed, 232 insertions(+), 188 deletions(-)

diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 79bf339827..e5c0c66c46 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -277,6 +277,8 @@ struct AnnCagraInputs {
   cuvs::neighbors::MergeStrategy merge_strategy =
     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL;
   cuvs::neighbors::cagra::internal_dtype smem_dtype = cuvs::neighbors::cagra::internal_dtype::F16;
+  /** When set, physical merge uses this overload instead of the default-params merge. */
+  std::optional<cagra::merge_params> physical_merge_params = std::nullopt;
 };
 
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
@@ -1403,7 +1405,10 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
         std::vector<cagra::index<DataT, IdxT>*> indices_to_merge{&index0, &index1};
 
         if (ps.merge_strategy == cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL) {
-          auto merged = cagra::merge(handle_, index_params, indices_to_merge);
+          auto merged =
+            ps.physical_merge_params.has_value()
+              ? cagra::merge(handle_, index_params, indices_to_merge, *ps.physical_merge_params)
+              : cagra::merge(handle_, index_params, indices_to_merge);
           cagra::search(
             handle_, search_params, merged, search_queries_view, indices_out_view, dists_out_view);
         } else {
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 22a33e8238..e80becf20c 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -3,8 +3,13 @@
  * SPDX-License-Identifier: Apache-2.0
  */
 
+/**
+ * @file
+ * @brief Component and end-to-end coverage for CAGRA Fastener merges.
+ */
+
 #include "../../../src/neighbors/detail/cagra/cagra_merge.cuh"
-#include "../naive_knn.cuh"
+#include "../ann_cagra.cuh"
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
@@ -14,10 +19,11 @@
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/random/rng.cuh>
 
-#include <cuda_fp16.h>
 #include <gtest/gtest.h>
 
+#include <algorithm>
 #include <cstdint>
 #include <limits>
 #include <type_traits>
@@ -27,30 +33,38 @@ namespace cuvs::neighbors::cagra {
 namespace {
 
 template <typename T>
-auto make_value(int64_t row, int64_t column) -> T
+auto make_dataset(raft::resources const& res,
+                  int64_t rows,
+                  int64_t dim,
+                  uint64_t seed                       = 1234ULL,
+                  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
 {
-  int value = static_cast<int>((row * 7 + column * 3) % 23) - 11;
-  if constexpr (std::is_same_v<T, half>) {
-    return __float2half(static_cast<float>(value) / 8.0f);
-  } else if constexpr (std::is_same_v<T, float>) {
-    return static_cast<float>(value) / 8.0f;
-  } else if constexpr (std::is_same_v<T, uint8_t>) {
-    return static_cast<uint8_t>(value + 32);
-  } else {
-    return static_cast<int8_t>(value);
-  }
+  auto device = raft::make_device_matrix<T, int64_t>(res, rows, dim);
+  raft::random::RngState rng(seed);
+  InitDataset(res,
+              device.data_handle(),
+              static_cast<std::uint32_t>(rows),
+              static_cast<std::uint32_t>(dim),
+              metric,
+              rng);
+  auto host   = raft::make_host_matrix<T, int64_t>(res, rows, dim);
+  auto stream = raft::resource::get_cuda_stream(res);
+  raft::copy(host.data_handle(), device.data_handle(), host.size(), stream);
+  raft::resource::sync_stream(res);
+  return host;
 }
 
 template <typename T>
-auto make_dataset(raft::resources const& res, int64_t rows, int64_t dim, int64_t offset)
+auto concat_host_datasets(raft::resources const& res,
+                          raft::host_matrix_view<const T, int64_t> first,
+                          raft::host_matrix_view<const T, int64_t> second)
 {
-  auto dataset = raft::make_host_matrix<T, int64_t>(res, rows, dim);
-  for (int64_t i = 0; i < rows; ++i) {
-    for (int64_t j = 0; j < dim; ++j) {
-      dataset(i, j) = make_value<T>(i + offset, j);
-    }
-  }
-  return dataset;
+  EXPECT_EQ(first.extent(1), second.extent(1));
+  auto out = raft::make_host_matrix<T, int64_t>(
+    res, first.extent(0) + second.extent(0), first.extent(1));
+  std::copy_n(first.data_handle(), first.size(), out.data_handle());
+  std::copy_n(second.data_handle(), second.size(), out.data_handle() + first.size());
+  return out;
 }
 
 auto make_ring_graph(raft::resources const& res, int64_t rows, int64_t degree)
@@ -88,21 +102,20 @@ void expect_valid_graph(index<T, uint32_t> const& merged, int64_t rows, int64_t
 template <typename T>
 void expect_dataset_order(raft::resources const& res,
                           index<T, uint32_t> const& merged,
-                          int64_t rows,
-                          int64_t dim)
+                          raft::host_matrix_view<const T, int64_t> expected)
 {
-  ASSERT_EQ(merged.dataset().extent(0), rows);
-  ASSERT_EQ(merged.dataset().extent(1), dim);
-  auto host = raft::make_host_matrix<T, int64_t>(res, rows, dim);
+  ASSERT_EQ(merged.dataset().extent(0), expected.extent(0));
+  ASSERT_EQ(merged.dataset().extent(1), expected.extent(1));
+  auto host = raft::make_host_matrix<T, int64_t>(res, expected.extent(0), expected.extent(1));
   raft::copy(host.data_handle(),
              merged.dataset().data_handle(),
              host.size(),
              raft::resource::get_cuda_stream(res));
   raft::resource::sync_stream(res);
-  for (int64_t row = 0; row < rows; ++row) {
-    for (int64_t column = 0; column < dim; ++column) {
+  for (int64_t row = 0; row < expected.extent(0); ++row) {
+    for (int64_t column = 0; column < expected.extent(1); ++column) {
       EXPECT_EQ(static_cast<float>(host(row, column)),
-                static_cast<float>(make_value<T>(row, column)));
+                static_cast<float>(expected(row, column)));
     }
   }
 }
@@ -114,10 +127,12 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
   constexpr int64_t rows   = 48;
   constexpr int64_t dim    = 8;
   constexpr int64_t degree = 4;
-  auto dataset0            = make_dataset<T>(res, rows, dim, 0);
-  auto dataset1            = make_dataset<T>(res, rows, dim, rows);
-  auto graph0              = make_ring_graph(res, rows, degree);
-  auto graph1              = make_ring_graph(res, rows, degree);
+  auto dataset0            = make_dataset<T>(res, rows, dim, 1234ULL, metric);
+  auto dataset1            = make_dataset<T>(res, rows, dim, 5678ULL, metric);
+  auto expected = concat_host_datasets<T>(
+    res, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(dataset1.view()));
+  auto graph0 = make_ring_graph(res, rows, degree);
+  auto graph1 = make_ring_graph(res, rows, degree);
   index<T, uint32_t> index0(
     res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
   index<T, uint32_t> index1(
@@ -137,7 +152,7 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
 
   auto merged = merge(res, params, indices, fastener);
   expect_valid_graph(merged, rows * 2, degree);
-  expect_dataset_order(res, merged, rows * 2, dim);
+  expect_dataset_order(res, merged, raft::make_const_mdspan(expected.view()));
   EXPECT_TRUE(merged.data().is_owning());
   EXPECT_EQ(index0.dataset().extent(0), rows);
   EXPECT_EQ(index1.dataset().extent(0), rows);
@@ -149,6 +164,10 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
   EXPECT_EQ(index1.dim(), dim);
 }
 
+/**
+ * Verifies that split planning is deterministic, lays out child keys and output ranges densely,
+ * carries completed parents forward, and rejects overlapping parent ranges.
+ */
 TEST(CagraMergeFastener, PlanSplitIsDeterministicDenseAndCompact)
 {
   using namespace detail::merge_scaffold;
@@ -191,6 +210,10 @@ TEST(CagraMergeFastener, PlanSplitIsDeterministicDenseAndCompact)
   EXPECT_THROW(plan_split(overlapping, 100, params, DETERMINISTIC_SEED), raft::exception);
 }
 
+/**
+ * Verifies that repeated levels use the same many-way split operation, emit valid memberships and
+ * contiguous ranges, and preserve every membership of parents already within the leaf-size limit.
+ */
 TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevels)
 {
   using namespace detail::merge_scaffold;
@@ -198,7 +221,7 @@ TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevel
   auto stream            = raft::resource::get_cuda_stream(res);
   constexpr int64_t rows = 96;
   constexpr int64_t dim  = 4;
-  auto host_dataset      = make_dataset<float>(res, rows, dim, 0);
+  auto host_dataset      = make_dataset<float>(res, rows, dim);
   auto dataset           = raft::make_device_matrix<float, int64_t>(res, rows, dim);
   raft::copy(dataset.data_handle(), host_dataset.data_handle(), dataset.size(), stream);
 
@@ -271,6 +294,10 @@ TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevel
   }
 }
 
+/**
+ * Verifies that scaffold slots not populated by a leaf neighbor are initialized with the source
+ * row, providing a valid sentinel for later graph combination and compaction.
+ */
 TEST(CagraMergeFastener, InitializesUnwrittenScaffoldSlotsWithSelf)
 {
   using namespace detail::merge_scaffold;
@@ -293,6 +320,74 @@ TEST(CagraMergeFastener, InitializesUnwrittenScaffoldSlotsWithSelf)
   }
 }
 
+/**
+ * Builds a many-way scaffold over two input ranges that share one exact duplicate vector and
+ * checks that partitioning plus intra-leaf KNN place each duplicate in the other's scaffold
+ * neighbors. Identical points select the same leaders, so they co-occur in the spilled children;
+ * zero distance then makes them mutual cross-origin nearest neighbors inside those leaves.
+ */
+TEST(CagraMergeFastener, DuplicateAcrossInputsAppearInEachOthersScaffoldNeighbors)
+{
+  using namespace detail::merge_scaffold;
+  raft::resources res;
+  auto stream                    = raft::resource::get_cuda_stream(res);
+  constexpr int64_t part0_rows   = 8;
+  constexpr int64_t part1_rows   = 8;
+  constexpr int64_t rows         = part0_rows + part1_rows;
+  constexpr int64_t dim          = 8;
+  constexpr uint32_t duplicate0  = 3;
+  constexpr uint32_t duplicate1  = static_cast<uint32_t>(part0_rows + 5);
+  constexpr uint32_t leaf_degree = 2;
+
+  auto host = raft::make_host_matrix<float, int64_t>(res, rows, dim);
+  for (int64_t row = 0; row < rows; ++row) {
+    for (int64_t column = 0; column < dim; ++column) {
+      host(row, column) = static_cast<float>(row * 17 + column);
+    }
+  }
+  for (int64_t column = 0; column < dim; ++column) {
+    host(duplicate1, column) = host(duplicate0, column);
+  }
+
+  auto dataset = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  raft::copy(dataset.data_handle(), host.data_handle(), dataset.size(), stream);
+
+  // Force a real root split (rows > leaf_size). Dense leader sampling keeps each spilled child
+  // small enough that make_leaves does not separate the co-assigned duplicates.
+  build_params params;
+  params.levels          = 1;
+  params.root_fanout     = 2;
+  params.lower_fanout    = 2;
+  params.leader_fraction = 1.0;
+  params.max_leaders     = 16;
+  params.leaf_size       = 8;
+  params.leaf_degree     = leaf_degree;
+
+  std::vector<int64_t> offsets{0, part0_rows, rows};
+  auto scaffold = build(res, raft::make_const_mdspan(dataset.view()), offsets, params);
+  ASSERT_EQ(scaffold.extent(0), rows);
+  ASSERT_EQ(scaffold.extent(1), params.root_fanout * leaf_degree);
+
+  auto scaffold_host =
+    raft::make_host_matrix<uint32_t, int64_t>(res, scaffold.extent(0), scaffold.extent(1));
+  raft::copy(scaffold_host.data_handle(), scaffold.data_handle(), scaffold.size(), stream);
+  raft::resource::sync_stream(res);
+
+  auto row_contains = [&](uint32_t row, uint32_t neighbor) {
+    for (int64_t column = 0; column < scaffold_host.extent(1); ++column) {
+      if (scaffold_host(row, column) == neighbor) { return true; }
+    }
+    return false;
+  };
+
+  EXPECT_TRUE(row_contains(duplicate0, duplicate1));
+  EXPECT_TRUE(row_contains(duplicate1, duplicate0));
+}
+
+/**
+ * Verifies the leaf-GEMM eligibility boundary for every scalar type and confirms that an integer
+ * dimension beyond the safe accumulation limit is rejected before either input index is mutated.
+ */
 TEST(CagraMergeFastener, LeafGemmLimitsRejectOnlyUnrealisticIntegerDimensions)
 {
   using namespace detail::merge_scaffold;
@@ -306,8 +401,8 @@ TEST(CagraMergeFastener, LeafGemmLimitsRejectOnlyUnrealisticIntegerDimensions)
   raft::resources res;
   constexpr int64_t rows = 2;
   constexpr int64_t dim  = MAX_INTEGER_LEAF_DIMENSION + 1;
-  auto dataset0          = make_dataset<int8_t>(res, rows, dim, 0);
-  auto dataset1          = make_dataset<int8_t>(res, rows, dim, rows);
+  auto dataset0          = make_dataset<int8_t>(res, rows, dim, 1234ULL);
+  auto dataset1          = make_dataset<int8_t>(res, rows, dim, 5678ULL);
   auto graph0            = make_ring_graph(res, rows, 1);
   auto graph1            = make_ring_graph(res, rows, 1);
   auto metric            = cuvs::distance::DistanceType::L2Expanded;
@@ -333,6 +428,10 @@ TEST(CagraMergeFastener, LeafGemmLimitsRejectOnlyUnrealisticIntegerDimensions)
   EXPECT_EQ(index1.dataset().extent(0), rows);
 }
 
+/**
+ * Exercises an explicit Fastener merge for float, half, int8, and uint8 data, including graph
+ * validity, concatenated dataset order, output ownership, and preservation of both input indices.
+ */
 TEST(CagraMergeFastener, SupportsAllScalarTypes)
 {
   auto metric = cuvs::distance::DistanceType::L2Expanded;
@@ -342,14 +441,20 @@ TEST(CagraMergeFastener, SupportsAllScalarTypes)
   run_explicit_fastener<uint8_t>(metric);
 }
 
+/**
+ * Verifies that merging one owning and one non-owning input preserves both inputs and their
+ * ownership modes while producing an owning output with the expected dataset order.
+ */
 TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
 {
   raft::resources res;
   constexpr int64_t rows   = 48;
   constexpr int64_t dim    = 8;
   constexpr int64_t degree = 4;
-  auto dataset0            = make_dataset<float>(res, rows, dim, 0);
-  auto dataset1            = make_dataset<float>(res, rows, dim, rows);
+  auto dataset0            = make_dataset<float>(res, rows, dim, 1234ULL);
+  auto dataset1            = make_dataset<float>(res, rows, dim, 5678ULL);
+  auto expected = concat_host_datasets<float>(
+    res, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(dataset1.view()));
   auto device_dataset1     = raft::make_device_matrix<float, int64_t>(res, rows, dim);
   raft::copy(device_dataset1.data_handle(),
              dataset1.data_handle(),
@@ -381,7 +486,7 @@ TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
 
   auto merged = merge(res, params, indices, fastener);
   expect_valid_graph(merged, rows * 2, degree);
-  expect_dataset_order(res, merged, rows * 2, dim);
+  expect_dataset_order(res, merged, raft::make_const_mdspan(expected.view()));
   EXPECT_TRUE(merged.data().is_owning());
   EXPECT_EQ(index0.dataset().extent(0), rows);
   EXPECT_EQ(index1.dataset().extent(0), rows);
@@ -389,112 +494,65 @@ TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
   EXPECT_FALSE(index1.data().is_owning());
 }
 
-TEST(CagraMergeFastener, DefaultFloatMergeSearchRecallAgainstBruteForce)
+/**
+ * Reuses AnnCagraIndexMergeTest on the primary float L2 shapes from generate_inputs(): build two
+ * CAGRA graphs over halves of the same InitDataset vectors, merge with default Fastener knobs, and
+ * require at least 95% recall against brute force.
+ */
+namespace {
+std::vector<AnnCagraInputs> generate_fastener_merge_recall_inputs()
 {
-  raft::resources res;
-  auto stream                 = raft::resource::get_cuda_stream(res);
-  constexpr int64_t part_rows = 256;
-  constexpr int64_t rows      = 2 * part_rows;
-  constexpr int64_t dim       = 16;
-  constexpr int64_t queries   = 16;
-  constexpr int64_t k         = 12;
-
-  auto value = [](int64_t row, int64_t column) {
-    uint64_t bits = (static_cast<uint64_t>(row) + 1) * 0x9e3779b97f4a7c15ull;
-    bits ^= (static_cast<uint64_t>(column) + 1) * 0xbf58476d1ce4e5b9ull;
-    bits ^= bits >> 30;
-    bits *= 0x94d049bb133111ebull;
-    bits ^= bits >> 31;
-    return static_cast<float>(bits & 0xffffu) / 65535.0f;
-  };
-
-  auto dataset0 = raft::make_host_matrix<float, int64_t>(part_rows, dim);
-  auto dataset1 = raft::make_host_matrix<float, int64_t>(part_rows, dim);
-  for (int64_t row = 0; row < part_rows; ++row) {
-    for (int64_t column = 0; column < dim; ++column) {
-      dataset0(row, column) = value(row, column);
-      dataset1(row, column) = value(row + part_rows, column);
-    }
-  }
-
-  index_params partition_params;
-  partition_params.metric                    = cuvs::distance::DistanceType::L2Expanded;
-  partition_params.graph_degree              = 16;
-  partition_params.intermediate_graph_degree = 32;
-  partition_params.attach_dataset_on_build   = true;
-  partition_params.guarantee_connectivity    = false;
-  auto index0 = build(res, partition_params, raft::make_const_mdspan(dataset0.view()));
-  auto index1 = build(res, partition_params, raft::make_const_mdspan(dataset1.view()));
-  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
-
-  merge_params fastener;
-  fastener.algo = merge_algo::FASTENER;
-  auto merged   = merge(res, partition_params, indices, fastener);
-  expect_valid_graph(merged, rows, partition_params.graph_degree);
-
-  auto query_host = raft::make_host_matrix<float, int64_t>(queries, dim);
-  for (int64_t query = 0; query < queries; ++query) {
-    int64_t source = query * 31;
-    for (int64_t column = 0; column < dim; ++column) {
-      query_host(query, column) = value(source, column);
-    }
+  // Same core shape as the leading AnnCagra float L2 cases: 1000x16 database, 100 queries, k=16.
+  auto inputs = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {1000},
+    {16},
+    {16},
+    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
+    {search_algo::AUTO},
+    {10},
+    {0},
+    {256},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {true},
+    {false},
+    {0.95},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL});
+  for (auto& input : inputs) {
+    // Default Fastener controls; only the algorithm is forced so AUTO cannot fall back to rebuild.
+    merge_params fastener;
+    fastener.algo               = merge_algo::FASTENER;
+    input.physical_merge_params = fastener;
   }
-  auto query_device = raft::make_device_matrix<float, int64_t>(res, queries, dim);
-  raft::copy(query_device.data_handle(), query_host.data_handle(), query_host.size(), stream);
-  auto actual_indices   = raft::make_device_matrix<uint32_t, int64_t>(res, queries, k);
-  auto actual_distances = raft::make_device_matrix<float, int64_t>(res, queries, k);
-  search_params search_params;
-  search_params.itopk_size     = 32;
-  search_params.search_width   = 4;
-  search_params.max_iterations = 0;
-  search(res,
-         search_params,
-         merged,
-         raft::make_const_mdspan(query_device.view()),
-         actual_indices.view(),
-         actual_distances.view());
-
-  auto truth_indices   = raft::make_device_matrix<uint32_t, int64_t>(res, queries, k);
-  auto truth_distances = raft::make_device_matrix<float, int64_t>(res, queries, k);
-  cuvs::neighbors::naive_knn<float, float, uint32_t>(res,
-                                                     truth_distances.data_handle(),
-                                                     truth_indices.data_handle(),
-                                                     query_device.data_handle(),
-                                                     merged.dataset().data_handle(),
-                                                     static_cast<uint32_t>(queries),
-                                                     static_cast<uint32_t>(rows),
-                                                     static_cast<uint32_t>(dim),
-                                                     static_cast<uint32_t>(k),
-                                                     cuvs::distance::DistanceType::L2Expanded);
-
-  std::vector<uint32_t> actual(static_cast<size_t>(queries * k));
-  std::vector<uint32_t> truth(static_cast<size_t>(queries * k));
-  raft::copy(actual.data(), actual_indices.data_handle(), actual.size(), stream);
-  raft::copy(truth.data(), truth_indices.data_handle(), truth.size(), stream);
-  raft::resource::sync_stream(res);
+  return inputs;
+}
+}  // namespace
 
-  size_t matches = 0;
-  for (int64_t query = 0; query < queries; ++query) {
-    for (int64_t candidate = 0; candidate < k; ++candidate) {
-      auto id = actual[query * k + candidate];
-      for (int64_t expected = 0; expected < k; ++expected) {
-        if (id == truth[query * k + expected]) {
-          ++matches;
-          break;
-        }
-      }
-    }
-  }
-  EXPECT_GE(static_cast<double>(matches) / static_cast<double>(queries * k), 0.60);
+typedef AnnCagraIndexMergeTest<float, float, std::uint32_t> AnnCagraFastenerMergeRecallTest;
+TEST_P(AnnCagraFastenerMergeRecallTest, DefaultFloatMergeSearchRecallAgainstBruteForce)
+{
+  this->testCagra<uint32_t>();
 }
+INSTANTIATE_TEST_CASE_P(CagraMergeFastener,
+                        AnnCagraFastenerMergeRecallTest,
+                        ::testing::ValuesIn(generate_fastener_merge_recall_inputs()));
 
+/**
+ * Exercises a three-level, non-default uint8 merge with different input graph degrees and verifies
+ * that every output row has the requested degree and contains only valid neighbor identifiers.
+ */
 TEST(CagraMergeFastener, MixedDegreesAndThreeLevelUint8OptionsProduceExactDegree)
 {
   raft::resources res;
   constexpr int64_t rows = 64;
   constexpr int64_t dim  = 16;
-  auto dataset0          = make_dataset<uint8_t>(res, rows, dim, 0);
-  auto dataset1          = make_dataset<uint8_t>(res, rows, dim, rows);
+  auto dataset0          = make_dataset<uint8_t>(res, rows, dim, 1234ULL);
+  auto dataset1          = make_dataset<uint8_t>(res, rows, dim, 5678ULL);
   auto graph0            = make_ring_graph(res, rows, 6);
   auto graph1            = make_ring_graph(res, rows, 8);
   index<uint8_t, uint32_t> index0(res,
@@ -525,11 +583,15 @@ TEST(CagraMergeFastener, MixedDegreesAndThreeLevelUint8OptionsProduceExactDegree
   expect_valid_graph(merged, rows * 2, 8);
 }
 
+/**
+ * Verifies that input graphs with different degrees are shifted to global identifiers, cyclically
+ * padded to a common width, and appended to the already-global scaffold columns in row order.
+ */
 TEST(CagraMergeFastener, AppendCyclicallyPadsMixedInputDegrees)
 {
   raft::resources res;
-  auto dataset0 = make_dataset<float>(res, 2, 1, 0);
-  auto dataset1 = make_dataset<float>(res, 2, 1, 2);
+  auto dataset0 = make_dataset<float>(res, 2, 1, 1234ULL);
+  auto dataset1 = make_dataset<float>(res, 2, 1, 5678ULL);
   auto graph0   = make_ring_graph(res, 2, 2);
   auto graph1   = make_ring_graph(res, 2, 4);
   index<float, uint32_t> index0(res,
@@ -574,14 +636,18 @@ TEST(CagraMergeFastener, AppendCyclicallyPadsMixedInputDegrees)
   }
 }
 
+/**
+ * Verifies that Fastener can merge higher-degree input graphs into a valid graph whose requested
+ * output degree is smaller than either input degree.
+ */
 TEST(CagraMergeFastener, MergeSupportsOutputDegreeBelowInputDegree)
 {
   raft::resources res;
   constexpr int64_t rows         = 48;
   constexpr int64_t dim          = 8;
   constexpr int64_t input_degree = 8;
-  auto dataset0                  = make_dataset<float>(res, rows, dim, 0);
-  auto dataset1                  = make_dataset<float>(res, rows, dim, rows);
+  auto dataset0                  = make_dataset<float>(res, rows, dim, 1234ULL);
+  auto dataset1                  = make_dataset<float>(res, rows, dim, 5678ULL);
   auto graph0                    = make_ring_graph(res, rows, input_degree);
   auto graph1                    = make_ring_graph(res, rows, input_degree);
   index<float, uint32_t> index0(res,
@@ -622,13 +688,17 @@ auto make_float_indices(raft::resources const& res,
   return output;
 }
 
+/**
+ * Exercises invalid levels, fanouts, leader settings, leaf settings, and spill width, and verifies
+ * that preflight rejects each configuration without changing either input dataset.
+ */
 TEST(CagraMergeFastener, InvalidManywayOptionsFailPreflightWithoutMutation)
 {
   raft::resources res;
   constexpr int64_t rows = 32;
   constexpr int64_t dim  = 8;
-  auto dataset0          = make_dataset<float>(res, rows, dim, 0);
-  auto dataset1          = make_dataset<float>(res, rows, dim, rows);
+  auto dataset0          = make_dataset<float>(res, rows, dim, 1234ULL);
+  auto dataset1          = make_dataset<float>(res, rows, dim, 5678ULL);
   auto graph0            = make_ring_graph(res, rows, 4);
   auto graph1            = make_ring_graph(res, rows, 4);
   index_params params;
@@ -677,13 +747,17 @@ TEST(CagraMergeFastener, InvalidManywayOptionsFailPreflightWithoutMutation)
   EXPECT_EQ(owned[1].dataset().extent(0), rows);
 }
 
+/**
+ * Verifies dispatch semantics: explicit Fastener rejects unsupported settings and metrics, AUTO
+ * falls back successfully, REBUILD remains available, and every path preserves its input indices.
+ */
 TEST(CagraMergeFastener, DispatchRejectsOrFallsBackBeforeMutation)
 {
   raft::resources res;
   constexpr int64_t rows = 32;
   constexpr int64_t dim  = 8;
-  auto dataset0          = make_dataset<float>(res, rows, dim, 0);
-  auto dataset1          = make_dataset<float>(res, rows, dim, rows);
+  auto dataset0          = make_dataset<float>(res, rows, dim, 1234ULL);
+  auto dataset1          = make_dataset<float>(res, rows, dim, 5678ULL);
   auto graph0            = make_ring_graph(res, rows, 4);
   auto graph1            = make_ring_graph(res, rows, 4);
   index_params params;
@@ -737,6 +811,10 @@ TEST(CagraMergeFastener, DispatchRejectsOrFallsBackBeforeMutation)
   }
 }
 
+/**
+ * Verifies that the shared graph sorter applies metric-specific ordering by producing different
+ * first neighbors for L2 distance and inner-product similarity on the same candidate graph.
+ */
 TEST(CagraMergeFastener, InnerProductOrderingDiffersFromL2)
 {
   raft::resources res;
@@ -787,49 +865,10 @@ TEST(CagraMergeFastener, InnerProductOrderingDiffersFromL2)
   EXPECT_EQ(ip_host(0, 0), 2);
 }
 
-TEST(CagraMergeFastener, Uint8InnerProductSortUsesExactWideAccumulation)
-{
-  raft::resources res;
-  constexpr int64_t dim = 1024;
-  auto dataset          = raft::make_host_matrix<uint8_t, int64_t>(3, dim);
-  for (int64_t d = 0; d < dim; ++d) {
-    dataset(0, d) = 255;
-    dataset(1, d) = 255;
-    dataset(2, d) = 255;
-  }
-  dataset(0, 0)       = 1;
-  dataset(1, 0)       = 1;
-  dataset(2, 0)       = 0;
-  auto device_dataset = raft::make_device_matrix<uint8_t, int64_t>(res, 3, dim);
-  raft::copy(device_dataset.data_handle(),
-             dataset.data_handle(),
-             dataset.size(),
-             raft::resource::get_cuda_stream(res));
-
-  auto graph_host  = raft::make_host_matrix<uint32_t, int64_t>(3, 2);
-  graph_host(0, 0) = 2;
-  graph_host(0, 1) = 1;
-  graph_host(1, 0) = 0;
-  graph_host(1, 1) = 2;
-  graph_host(2, 0) = 0;
-  graph_host(2, 1) = 1;
-  auto graph       = raft::make_device_matrix<uint32_t, int64_t>(res, 3, 2);
-  raft::copy(graph.data_handle(),
-             graph_host.data_handle(),
-             graph.size(),
-             raft::resource::get_cuda_stream(res));
-  detail::graph::sort_knn_graph_device_inplace(res,
-                                               cuvs::distance::DistanceType::InnerProduct,
-                                               raft::make_const_mdspan(device_dataset.view()),
-                                               graph.view());
-  raft::copy(graph_host.data_handle(),
-             graph.data_handle(),
-             graph.size(),
-             raft::resource::get_cuda_stream(res));
-  raft::resource::sync_stream(res);
-  EXPECT_EQ(graph_host(0, 0), 1);
-}
-
+/**
+ * Verifies the graph-combination pipeline end to end: local identifiers are offset, scaffold edges
+ * are appended, rows are metric-sorted, duplicates are removed, and short rows are padded.
+ */
 TEST(CagraMergeFastener, AppendSortAndDedupHandleOffsetsAndPadding)
 {
   raft::resources res;

From 209485c2947d7cff2484ac23954136babd7ac401 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 21:09:19 +0000
Subject: [PATCH 07/41] assert/message changes & aligned signatures

---
 cpp/src/neighbors/cagra.cuh                   |  2 +-
 .../neighbors/detail/cagra/cagra_merge.cuh    | 20 +++++++++----------
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 490c225367..484e2b599d 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -434,7 +434,7 @@ index<T, IdxT> merge(raft::resources const& handle,
                      const cagra::merge_params& merge_params,
                      const cuvs::neighbors::filtering::base_filter& row_filter)
 {
-  return cagra::detail::merge<T, IdxT>(handle, params, merge_params, indices, row_filter);
+  return cagra::detail::merge<T, IdxT>(handle, params, indices, merge_params, row_filter);
 }
 
 /** @} */  // end group cagra
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index cbc50c02fe..e9c967640f 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -241,27 +241,27 @@ auto preflight_fastener(cagra::index_params const& params,
   if (merge_params.levels == 0) { return reject("levels must be positive"); }
   if (merge_params.root_fanout < 1 || merge_params.root_fanout > merge_scaffold::MAX_FANOUT ||
       merge_params.lower_fanout < 1 || merge_params.lower_fanout > merge_scaffold::MAX_FANOUT) {
-    return reject(&"root_fanout and lower_fanout must be between 1 and " [
-                  std::to_string(merge_scaffold::MAX_FANOUT)]);
+    return reject("root_fanout and lower_fanout must be between 1 and " +
+                  std::to_string(merge_scaffold::MAX_FANOUT));
   }
   if (!(merge_params.leader_fraction > 0.0 && merge_params.leader_fraction <= 1.0)) {
     return reject("leader_fraction must be in (0, 1]");
   }
   if (merge_params.max_leaders == 0 || merge_params.max_leaders > merge_scaffold::MAX_LEADERS) {
-    return reject(&"max_leaders must be between 1 and " [
-                  std::to_string(merge_scaffold::MAX_LEADERS)]);
+    return reject("max_leaders must be between 1 and " +
+                  std::to_string(merge_scaffold::MAX_LEADERS));
   }
   if (merge_params.max_leaders < std::max(merge_params.root_fanout, merge_params.lower_fanout)) {
     return reject("max_leaders must cover both configured fanouts");
   }
   if (merge_params.leaf_size == 0 || merge_params.leaf_size > merge_scaffold::MAX_LEAF_SIZE) {
-    return reject(&"leaf_size must be between 1 and " [
-                  std::to_string(merge_scaffold::MAX_LEAF_SIZE)]);
+    return reject("leaf_size must be between 1 and " +
+                  std::to_string(merge_scaffold::MAX_LEAF_SIZE));
   }
   if (merge_params.leaf_degree == 0 ||
       merge_params.leaf_degree > static_cast<uint32_t>(merge_scaffold::MAX_LEAF_DEGREE)) {
-    return reject(&"leaf_degree must be between 1 and " [
-                  std::to_string(merge_scaffold::MAX_LEAF_DEGREE)]);
+    return reject("leaf_degree must be between 1 and " +
+                  std::to_string(merge_scaffold::MAX_LEAF_DEGREE));
   }
 
   uint64_t const max_spill = std::numeric_limits<uint8_t>::max() / merge_params.leaf_degree;
@@ -410,8 +410,8 @@ auto merge_fastener(raft::resources const& handle,
 template <class T, class IdxT>
 index<T, IdxT> merge(raft::resources const& handle,
                      cagra::index_params const& params,
-                     cagra::merge_params const& merge_params,
                      std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
+                     cagra::merge_params const& merge_params,
                      cuvs::neighbors::filtering::base_filter const& row_filter)
 {
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> merge_scope(
@@ -442,7 +442,7 @@ index<T, IdxT> merge(raft::resources const& handle,
                      std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
                      cuvs::neighbors::filtering::base_filter const& row_filter)
 {
-  return merge(handle, params, cagra::merge_params{}, indices, row_filter);
+  return merge(handle, params, indices, cagra::merge_params{}, row_filter);
 }
 
 }  // namespace cuvs::neighbors::cagra::detail

From da96f2620bb381bd018b12f9fa109211a018fbec Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 21:26:02 +0000
Subject: [PATCH 08/41] pre-commit hooks

---
 .../ann/src/cuvs/cuvs_ann_bench_param_parser.h    |  2 +-
 cpp/include/cuvs/neighbors/cagra.hpp              |  6 +++---
 .../detail/cagra/cagra_merge_scaffold.cuh         |  2 +-
 .../neighbors/ann_cagra/test_merge_fastener.cu    | 15 +++++++--------
 4 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 3fee950087..dd8ebc0abb 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 962041571b..e1bc1ac851 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2508,9 +2508,9 @@ auto merge(raft::resources const& res,
              cuvs::neighbors::filtering::none_sample_filter{})
   -> cuvs::neighbors::cagra::index<float, uint32_t>;
 
-/** @copydoc merge 
-* @param[in] merge_params Parameters for the merge.
-*/
+/** @copydoc merge
+ * @param[in] merge_params Parameters for the merge.
+ */
 auto merge(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            std::vector<cuvs::neighbors::cagra::index<float, uint32_t>*>& indices,
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index cd228608a1..2e0d2769c6 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 #pragma once
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index e80becf20c..64dcfc90de 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -60,8 +60,8 @@ auto concat_host_datasets(raft::resources const& res,
                           raft::host_matrix_view<const T, int64_t> second)
 {
   EXPECT_EQ(first.extent(1), second.extent(1));
-  auto out = raft::make_host_matrix<T, int64_t>(
-    res, first.extent(0) + second.extent(0), first.extent(1));
+  auto out =
+    raft::make_host_matrix<T, int64_t>(res, first.extent(0) + second.extent(0), first.extent(1));
   std::copy_n(first.data_handle(), first.size(), out.data_handle());
   std::copy_n(second.data_handle(), second.size(), out.data_handle() + first.size());
   return out;
@@ -114,8 +114,7 @@ void expect_dataset_order(raft::resources const& res,
   raft::resource::sync_stream(res);
   for (int64_t row = 0; row < expected.extent(0); ++row) {
     for (int64_t column = 0; column < expected.extent(1); ++column) {
-      EXPECT_EQ(static_cast<float>(host(row, column)),
-                static_cast<float>(expected(row, column)));
+      EXPECT_EQ(static_cast<float>(host(row, column)), static_cast<float>(expected(row, column)));
     }
   }
 }
@@ -129,7 +128,7 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
   constexpr int64_t degree = 4;
   auto dataset0            = make_dataset<T>(res, rows, dim, 1234ULL, metric);
   auto dataset1            = make_dataset<T>(res, rows, dim, 5678ULL, metric);
-  auto expected = concat_host_datasets<T>(
+  auto expected            = concat_host_datasets<T>(
     res, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(dataset1.view()));
   auto graph0 = make_ring_graph(res, rows, degree);
   auto graph1 = make_ring_graph(res, rows, degree);
@@ -453,9 +452,9 @@ TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
   constexpr int64_t degree = 4;
   auto dataset0            = make_dataset<float>(res, rows, dim, 1234ULL);
   auto dataset1            = make_dataset<float>(res, rows, dim, 5678ULL);
-  auto expected = concat_host_datasets<float>(
+  auto expected            = concat_host_datasets<float>(
     res, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(dataset1.view()));
-  auto device_dataset1     = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  auto device_dataset1 = raft::make_device_matrix<float, int64_t>(res, rows, dim);
   raft::copy(device_dataset1.data_handle(),
              dataset1.data_handle(),
              dataset1.size(),

From 9a3cabf52d46543fd1c2970df9edc9a90ab72e99 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 22:16:39 +0000
Subject: [PATCH 09/41] clearer check rationale

---
 .../neighbors/detail/cagra/cagra_merge.cuh    | 11 ++-
 .../detail/cagra/cagra_merge_scaffold.cuh     | 85 ++++++++++---------
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 25 ++++--
 3 files changed, 67 insertions(+), 54 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index e9c967640f..d6e40adb79 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -266,14 +266,13 @@ auto preflight_fastener(cagra::index_params const& params,
 
   uint64_t const max_spill = std::numeric_limits<uint8_t>::max() / merge_params.leaf_degree;
   uint64_t spill           = merge_params.root_fanout;
-  if (spill > max_spill) {
-    return reject("root_fanout * lower_fanout^(levels - 1) * leaf_degree must not exceed 255");
-  }
+  auto const candidate_width_limit =
+    "root_fanout * lower_fanout^(levels - 1) * leaf_degree must not exceed " +
+    std::to_string(std::numeric_limits<uint8_t>::max());
+  if (spill > max_spill) { return reject(candidate_width_limit); }
   if (merge_params.lower_fanout > 1) {
     for (uint32_t level = 1; level < merge_params.levels; ++level) {
-      if (spill > max_spill / merge_params.lower_fanout) {
-        return reject("root_fanout * lower_fanout^(levels - 1) * leaf_degree must not exceed 255");
-      }
+      if (spill > max_spill / merge_params.lower_fanout) { return reject(candidate_width_limit); }
       spill *= merge_params.lower_fanout;
     }
   }
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 2e0d2769c6..245b2620b6 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -15,6 +15,7 @@
 #include <raft/core/resource/cublas_handle.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/util/cuda_rt_essentials.hpp>
 #include <raft/util/cudart_utils.hpp>
 
 #include <rmm/device_uvector.hpp>
@@ -48,7 +49,8 @@ inline constexpr int MAX_LEAF_DEGREE         = 8;
 inline constexpr int ASSIGNMENT_TILE_ROWS    = 2048;
 inline constexpr uint64_t DETERMINISTIC_SEED = 0x4c616e6472756d;
 inline constexpr size_t GEMM_WORKSPACE_BYTES = size_t{2} * 1024 * 1024 * 1024;
-inline constexpr int WARP_SIZE               = 32;
+inline constexpr int THREADS_PER_BLOCK       = 256;
+inline constexpr int MAX_STRIDED_GRID_BLOCKS = 1 << 20;
 /** Warps per block in the one-row-per-warp kernels; their launch geometry derives from this. */
 inline constexpr int ROW_WARPS_PER_BLOCK = 4;
 // Centered INT8 dot products accumulate into INT32. Overflow requires more than 131,071
@@ -56,11 +58,12 @@ inline constexpr int ROW_WARPS_PER_BLOCK = 4;
 inline constexpr int64_t MAX_INTEGER_LEAF_DIMENSION =
   std::numeric_limits<int32_t>::max() / (128 * 128);
 
-/** Grid size for the grid-stride kernels: blocks of 256 threads covering `items`, capped so
+/** Grid size for the grid-stride kernels: blocks of THREADS_PER_BLOCK covering `items`, capped so
  * oversized workloads loop within resident threads instead. */
 inline auto strided_grid_size(int64_t items) -> int
 {
-  return static_cast<int>(std::min<int64_t>((items + 255) / 256, 1048576));
+  return static_cast<int>(std::min<int64_t>((items + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK,
+                                            MAX_STRIDED_GRID_BLOCKS));
 }
 
 // ----------------------------------------------------------------------------
@@ -74,7 +77,7 @@ struct build_params {
   uint32_t lower_fanout  = 3;
   double leader_fraction = 0.02;
   uint32_t max_leaders   = 1000;
-  uint32_t leaf_size     = 256;
+  uint32_t leaf_size     = MAX_LEAF_SIZE;
   uint32_t leaf_degree   = 4;
 };
 
@@ -83,7 +86,7 @@ struct split_params {
   uint32_t fanout            = 1;
   double leader_fraction     = 0.02;
   uint32_t max_leaders       = 1000;
-  uint32_t leaf_size         = 256;
+  uint32_t leaf_size         = MAX_LEAF_SIZE;
   uint32_t level             = 0;
   uint32_t occurrence_stride = 1;
 };
@@ -337,8 +340,9 @@ inline auto make_root_partition(raft::resources const& res, int64_t rows) -> par
   auto stream = raft::resource::get_cuda_stream(res);
   partition_set root{rmm::device_uvector<partition_membership>(static_cast<size_t>(rows), stream),
                      {{uint32_t{0}, int64_t{0}, rows}}};
-  int blocks = static_cast<int>((rows + 255) / 256);
-  initialize_root_memberships_kernel<<<blocks, 256, 0, stream>>>(root.memberships.data(), rows);
+  int blocks = static_cast<int>((rows + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK);
+  initialize_root_memberships_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
+    root.memberships.data(), rows);
   RAFT_CUDA_TRY(cudaGetLastError());
   return root;
 }
@@ -430,8 +434,8 @@ static __global__ void manyway_select_tiles_kernel(float const* dots,
                                                    uint32_t* output_keys,
                                                    partition_membership* output_memberships)
 {
-  int lane           = threadIdx.x % WARP_SIZE;
-  int warp           = threadIdx.x / WARP_SIZE;
+  int lane           = threadIdx.x % raft::WarpSize;
+  int warp           = threadIdx.x / raft::WarpSize;
   int64_t linear_row = static_cast<int64_t>(blockIdx.x) * ROW_WARPS_PER_BLOCK + warp;
   int64_t total_rows = static_cast<int64_t>(batch_size) * tile_rows;
   if (linear_row >= total_rows) { return; }
@@ -444,7 +448,7 @@ static __global__ void manyway_select_tiles_kernel(float const* dots,
   auto membership     = input_memberships[input_index];
   leader_candidate local_best[MAX_FANOUT];
 
-  for (int leader = lane; leader < tile.leader_count; leader += WARP_SIZE) {
+  for (int leader = lane; leader < tile.leader_count; leader += raft::WarpSize) {
     uint32_t leader_id = leader_ids[static_cast<int64_t>(batch) * padded_leaders + leader];
     float dot = dots[(static_cast<int64_t>(batch) * tile_rows + row) * padded_leaders + leader];
     float distance = fmaxf(0.0f, norms[membership.id] + norms[leader_id] - 2.0f * dot);
@@ -454,7 +458,7 @@ static __global__ void manyway_select_tiles_kernel(float const* dots,
 
   extern __shared__ unsigned char shared_bytes[];
   auto* shared_candidates = reinterpret_cast<leader_candidate*>(shared_bytes);
-  int shared_base         = (warp * WARP_SIZE + lane) * fanout;
+  int shared_base         = (warp * raft::WarpSize + lane) * fanout;
   for (int j = 0; j < fanout; ++j) {
     shared_candidates[shared_base + j] = local_best[j];
   }
@@ -462,8 +466,8 @@ static __global__ void manyway_select_tiles_kernel(float const* dots,
 
   if (lane == 0) {
     leader_candidate selected[MAX_FANOUT];
-    int warp_base = warp * WARP_SIZE * fanout;
-    for (int candidate = 0; candidate < WARP_SIZE * fanout; ++candidate) {
+    int warp_base = warp * raft::WarpSize * fanout;
+    for (int candidate = 0; candidate < raft::WarpSize * fanout; ++candidate) {
       if (shared_candidates[warp_base + candidate].valid()) {
         manyway_insert_local(shared_candidates[warp_base + candidate], fanout, selected);
       }
@@ -539,7 +543,10 @@ inline void carry_parents(raft::resources const& res,
   auto stream = raft::resource::get_cuda_stream(res);
   rmm::device_uvector<carry_span> device_carries(carries.size(), stream);
   raft::copy(device_carries.data(), carries.data(), carries.size(), stream);
-  carry_completed_parents_kernel<<<static_cast<int>(carries.size()), 256, 0, stream>>>(
+  carry_completed_parents_kernel<<<static_cast<int>(carries.size()),
+                                   THREADS_PER_BLOCK,
+                                   0,
+                                   stream>>>(
     parents.memberships.data(), device_carries.data(), memberships.data(), keys.data());
   RAFT_CUDA_TRY(cudaGetLastError());
 }
@@ -654,7 +661,7 @@ void assign_bucket(raft::resources const& res,
   auto leader_stride = static_cast<int64_t>(leader_elements);
   auto dot_stride    = static_cast<int64_t>(dot_elements);
   size_t selection_shared =
-    ROW_WARPS_PER_BLOCK * WARP_SIZE * params.fanout * sizeof(leader_candidate);
+    ROW_WARPS_PER_BLOCK * raft::WarpSize * params.fanout * sizeof(leader_candidate);
 
   for (size_t tile_offset = 0; tile_offset < tiles.size(); tile_offset += batch_capacity) {
     size_t batch_size = std::min(batch_capacity, tiles.size() - tile_offset);
@@ -662,7 +669,7 @@ void assign_bucket(raft::resources const& res,
 
     // Gather the batch's tile rows and leader vectors
     int point_blocks = strided_grid_size(static_cast<int64_t>(batch_size * point_elements));
-    manyway_gather_tile_points_kernel<<<point_blocks, 256, 0, stream>>>(
+    manyway_gather_tile_points_kernel<<<point_blocks, THREADS_PER_BLOCK, 0, stream>>>(
       dataset.data_handle(),
       dim,
       parents.memberships.data(),
@@ -673,7 +680,7 @@ void assign_bucket(raft::resources const& res,
     RAFT_CUDA_TRY(cudaGetLastError());
 
     int leader_blocks = strided_grid_size(static_cast<int64_t>(batch_size * leader_elements));
-    manyway_gather_tile_leaders_kernel<<<leader_blocks, 256, 0, stream>>>(
+    manyway_gather_tile_leaders_kernel<<<leader_blocks, THREADS_PER_BLOCK, 0, stream>>>(
       dataset.data_handle(),
       dim,
       parents.memberships.data(),
@@ -702,7 +709,7 @@ void assign_bucket(raft::resources const& res,
       static_cast<int>((static_cast<int64_t>(batch_size) * tile_rows + ROW_WARPS_PER_BLOCK - 1) /
                        ROW_WARPS_PER_BLOCK);
     manyway_select_tiles_kernel<<<selection_blocks,
-                                  ROW_WARPS_PER_BLOCK * WARP_SIZE,
+                                  ROW_WARPS_PER_BLOCK * raft::WarpSize,
                                   selection_shared,
                                   stream>>>(tile_dots.data(),
                                             static_cast<int>(batch_size),
@@ -991,7 +998,7 @@ auto build_leaf_neighbors(raft::resources const& res,
   // Prefill every slot with its own row id, leaf KNN overwrites the slots it fills
   auto graph          = raft::make_device_matrix<uint32_t, int64_t>(res, rows, union_degree);
   int scaffold_blocks = strided_grid_size(rows * union_degree);
-  initialize_self_scaffold_kernel<<<scaffold_blocks, 256, 0, stream>>>(
+  initialize_self_scaffold_kernel<<<scaffold_blocks, THREADS_PER_BLOCK, 0, stream>>>(
     graph.data_handle(), rows, union_degree);
   RAFT_CUDA_TRY(cudaGetLastError());
 
@@ -1016,7 +1023,7 @@ auto build_leaf_neighbors(raft::resources const& res,
       int gather_blocks =
         strided_grid_size(static_cast<int64_t>(batch_size * vector_elements_per_leaf));
       // Gather this batch's leaves into the dense vector buffer.
-      manyway_gather_leaf_vectors_kernel<<<gather_blocks, 256, 0, stream>>>(
+      manyway_gather_leaf_vectors_kernel<<<gather_blocks, THREADS_PER_BLOCK, 0, stream>>>(
         dataset.data_handle(),
         input_dimension,
         input_dimension,
@@ -1076,7 +1083,7 @@ auto build_leaf_neighbors(raft::resources const& res,
       size_t batch_size = std::min(batch_capacity, leaves.starts_host.size() - leaf_offset);
       int gather_blocks =
         strided_grid_size(static_cast<int64_t>(batch_size * vector_elements_per_leaf));
-      manyway_gather_leaf_vectors_kernel<<<gather_blocks, 256, 0, stream>>>(
+      manyway_gather_leaf_vectors_kernel<<<gather_blocks, THREADS_PER_BLOCK, 0, stream>>>(
         dataset.data_handle(),
         input_dimension,
         padded_dimension,
@@ -1141,20 +1148,20 @@ static __global__ void initialize_origins_kernel(uint32_t* origins,
 template <typename T>
 __global__ void manyway_l2_norms_kernel(T const* dataset, int64_t rows, int64_t dim, float* norms)
 {
-  int lane    = threadIdx.x % WARP_SIZE;
-  int warp    = threadIdx.x / WARP_SIZE;
+  int lane    = threadIdx.x % raft::WarpSize;
+  int warp    = threadIdx.x / raft::WarpSize;
   int64_t row = static_cast<int64_t>(blockIdx.x) * ROW_WARPS_PER_BLOCK + warp;
   if (row >= rows) { return; }
 
   // Each lane accumulates a strided slice of the row's squared values.
   float sum      = 0.0f;
   T const* point = dataset + row * dim;
-  for (int64_t d = lane; d < dim; d += WARP_SIZE) {
+  for (int64_t d = lane; d < dim; d += raft::WarpSize) {
     float value = static_cast<float>(point[d]);
     sum         = fmaf(value, value, sum);
   }
   // Shuffle-reduce the partial sums; lane 0 holds the total.
-  for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
+  for (int offset = raft::WarpSize / 2; offset > 0; offset /= 2) {
     sum += __shfl_down_sync(0xffffffffu, sum, offset);
   }
   if (lane == 0) { norms[row] = sum; }
@@ -1201,7 +1208,8 @@ auto build(raft::resources const& res,
   }
   // this is because the degree of the candidate list is stored in uint8_t
   RAFT_EXPECTS(spill * params.leaf_degree <= std::numeric_limits<uint8_t>::max(),
-               "Fastener candidate width must not exceed 255");
+               "Fastener candidate width must not exceed %u",
+               static_cast<unsigned>(std::numeric_limits<uint8_t>::max()));
   RAFT_EXPECTS(static_cast<uint64_t>(rows) <= std::numeric_limits<uint32_t>::max() / spill,
                "Fastener total partition memberships (rows=%ld * spill=%lu) must fit in uint32_t",
                static_cast<long>(rows),
@@ -1213,15 +1221,15 @@ auto build(raft::resources const& res,
 
   for (size_t part = 0; part + 1 < offsets.size(); ++part) {
     int64_t part_rows = offsets[part + 1] - offsets[part];
-    int blocks        = static_cast<int>((part_rows + 255) / 256);
-    initialize_origins_kernel<<<blocks, 256, 0, stream>>>(
+    int blocks        = static_cast<int>((part_rows + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK);
+    initialize_origins_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
       origins.data(), offsets[part], part_rows, static_cast<uint32_t>(part));
     RAFT_CUDA_TRY(cudaGetLastError());
   }
 
   split_context context(stream, rows);
   int norm_blocks = static_cast<int>((rows + ROW_WARPS_PER_BLOCK - 1) / ROW_WARPS_PER_BLOCK);
-  manyway_l2_norms_kernel<<<norm_blocks, ROW_WARPS_PER_BLOCK * WARP_SIZE, 0, stream>>>(
+  manyway_l2_norms_kernel<<<norm_blocks, ROW_WARPS_PER_BLOCK * raft::WarpSize, 0, stream>>>(
     dataset.data_handle(), rows, dataset.extent(1), context.norms.data());
   RAFT_CUDA_TRY(cudaGetLastError());
 
@@ -1310,8 +1318,8 @@ auto append_to_input_graphs(
   for (size_t part = 0; part < indices.size(); ++part) {
     auto source = indices[part]->graph();
     RAFT_EXPECTS(source.extent(1) > 0, "Input CAGRA graphs must have nonzero degree");
-    int blocks = static_cast<int>((source.extent(0) + 255) / 256);
-    copy_partition_with_scaffold_kernel<<<blocks, 256, 0, stream>>>(
+    int blocks = static_cast<int>((source.extent(0) + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK);
+    copy_partition_with_scaffold_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
       source.data_handle(),
       scaffold.data_handle(),
       source.extent(0),
@@ -1339,16 +1347,16 @@ static __global__ void deduplicate_graph_prefix_kernel(uint32_t const* input,
                                                        uint32_t* output,
                                                        int64_t output_degree)
 {
-  constexpr int WARPS_PER_BLOCK = 256 / WARP_SIZE;
-  int lane                      = threadIdx.x % WARP_SIZE;
-  int warp                      = threadIdx.x / WARP_SIZE;
+  constexpr int WARPS_PER_BLOCK = THREADS_PER_BLOCK / raft::WarpSize;
+  int lane                      = threadIdx.x % raft::WarpSize;
+  int warp                      = threadIdx.x / raft::WarpSize;
   int64_t row                   = static_cast<int64_t>(blockIdx.x) * WARPS_PER_BLOCK + warp;
   if (row >= rows) { return; }
 
   int64_t input_base  = row * input_degree;
   int64_t output_base = row * output_degree;
   int selected        = 0;
-  for (int64_t tile = 0; tile < input_degree && selected < output_degree; tile += WARP_SIZE) {
+  for (int64_t tile = 0; tile < input_degree && selected < output_degree; tile += raft::WarpSize) {
     int64_t column     = tile + lane;
     bool first         = column < input_degree;
     uint32_t candidate = first ? input[input_base + column] : uint32_t{0};
@@ -1358,7 +1366,7 @@ static __global__ void deduplicate_graph_prefix_kernel(uint32_t const* input,
     }
 
     unsigned first_mask = __ballot_sync(0xffffffffu, first);
-    unsigned lower_mask = lane == 0 ? 0u : (0xffffffffu >> (WARP_SIZE - lane));
+    unsigned lower_mask = lane == 0 ? 0u : (0xffffffffu >> (raft::WarpSize - lane));
     int output_column   = selected + __popc(first_mask & lower_mask);
     if (first && output_column < output_degree) { output[output_base + output_column] = candidate; }
     selected += __popc(first_mask);
@@ -1369,7 +1377,7 @@ static __global__ void deduplicate_graph_prefix_kernel(uint32_t const* input,
     selected = 1;
   }
   if (selected > output_degree) { selected = static_cast<int>(output_degree); }
-  for (int64_t column = selected + lane; column < output_degree; column += WARP_SIZE) {
+  for (int64_t column = selected + lane; column < output_degree; column += raft::WarpSize) {
     output[output_base + column] = output[output_base + (column % selected)];
   }
 }
@@ -1383,8 +1391,7 @@ inline auto cap_sorted_graph(
   RAFT_EXPECTS(output_degree > 0 && output_degree <= graph.extent(1),
                "Pre-optimize graph degree cap must be within the sorted graph degree");
   auto output = raft::make_device_matrix<uint32_t, int64_t>(res, graph.extent(0), output_degree);
-  constexpr int THREADS_PER_BLOCK = 256;
-  constexpr int WARPS_PER_BLOCK   = THREADS_PER_BLOCK / WARP_SIZE;
+  constexpr int WARPS_PER_BLOCK = THREADS_PER_BLOCK / raft::WarpSize;
   int blocks = static_cast<int>((graph.extent(0) + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);
   deduplicate_graph_prefix_kernel<<<blocks,
                                     THREADS_PER_BLOCK,
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 00fb0de36b..5f6c1c499a 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -175,6 +175,11 @@ __global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size,
   }
 }
 
+// kern_sort capacity is WarpSize * numElementsPerThread; largest specialization uses 32.
+constexpr int kMaxSortElementsPerThread = 32;
+constexpr uint64_t kMaxSortDegree =
+  static_cast<uint64_t>(raft::WarpSize) * kMaxSortElementsPerThread;
+
 template <typename DataT, typename IdxT>
 using sort_kernel_type =
   void (*)(DataT const*, IdxT, uint32_t, IdxT*, uint32_t, uint32_t, cuvs::distance::DistanceType);
@@ -182,15 +187,16 @@ using sort_kernel_type =
 template <typename DataT, typename IdxT>
 auto select_sort_kernel(uint64_t degree) -> sort_kernel_type<DataT, IdxT>
 {
-  if (degree <= 32) { return kern_sort<DataT, IdxT, 1>; }
-  if (degree <= 64) { return kern_sort<DataT, IdxT, 2>; }
-  if (degree <= 128) { return kern_sort<DataT, IdxT, 4>; }
-  if (degree <= 256) { return kern_sort<DataT, IdxT, 8>; }
-  if (degree <= 512) { return kern_sort<DataT, IdxT, 16>; }
-  if (degree <= 1024) { return kern_sort<DataT, IdxT, 32>; }
+  if (degree <= raft::WarpSize * 1) { return kern_sort<DataT, IdxT, 1>; }
+  if (degree <= raft::WarpSize * 2) { return kern_sort<DataT, IdxT, 2>; }
+  if (degree <= raft::WarpSize * 4) { return kern_sort<DataT, IdxT, 4>; }
+  if (degree <= raft::WarpSize * 8) { return kern_sort<DataT, IdxT, 8>; }
+  if (degree <= raft::WarpSize * 16) { return kern_sort<DataT, IdxT, 16>; }
+  if (degree <= kMaxSortDegree) { return kern_sort<DataT, IdxT, kMaxSortElementsPerThread>; }
   RAFT_FAIL(
-    "The degree of input knn graph is too large (%lu). It must be equal to or smaller than 1024.",
-    degree);
+    "The degree of input knn graph is too large (%lu). It must be equal to or smaller than %lu.",
+    degree,
+    kMaxSortDegree);
 }
 
 template <typename IdxT, typename OutputMatrixView>
@@ -1017,7 +1023,8 @@ void sort_knn_graph_device_inplace(
   RAFT_EXPECTS(rows <= static_cast<uint64_t>(std::numeric_limits<uint32_t>::max()) &&
                  dim <= static_cast<uint64_t>(std::numeric_limits<uint32_t>::max()),
                "Dataset extents must fit in uint32_t");
-  RAFT_EXPECTS(degree > 0 && degree <= 1024, "Graph degree must be in [1, 1024]");
+  RAFT_EXPECTS(
+    degree > 0 && degree <= kMaxSortDegree, "Graph degree must be in [1, %lu]", kMaxSortDegree);
 
   auto kernel = select_sort_kernel<DataT, IdxT>(degree);
 

From af25e86d5da863623eb0984413484f4bb3b6f463 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 22 Jul 2026 22:18:54 +0000
Subject: [PATCH 10/41] test arg mismatch

---
 cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 64dcfc90de..9bcdd46779 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -506,7 +506,8 @@ std::vector<AnnCagraInputs> generate_fastener_merge_recall_inputs()
     {100},
     {1000},
     {16},
-    {16},
+    {16},  // k
+    {32},  // graph_degree
     {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
     {search_algo::AUTO},
     {10},

From 2eb64fafef22c6274559db43b06d676bcc65bbd4 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 23 Jul 2026 21:05:43 +0000
Subject: [PATCH 11/41] refactor to use existing select_k kernel

---
 .../detail/cagra/cagra_merge_scaffold.cuh     | 239 ++++++++----------
 1 file changed, 109 insertions(+), 130 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 245b2620b6..18dd2415c6 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -7,6 +7,7 @@
 #include "../neighbors_device_intrinsics.cuh"
 
 #include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/selection/select_k.hpp>
 
 #include <raft/core/copy.hpp>
 #include <raft/core/device_mdarray.hpp>
@@ -281,51 +282,6 @@ inline auto bucket_split_parents(split_plan const& plan, split_params const& par
   return buckets;
 }
 
-/** One candidate assignment of a row to a leader. Defaults to the invalid sentinel, which sorts
- * after every real candidate. */
-struct leader_candidate {
-  float distance  = std::numeric_limits<float>::max();
-  uint32_t leader = std::numeric_limits<uint32_t>::max();
-
-  __device__ auto valid() const -> bool { return leader != std::numeric_limits<uint32_t>::max(); }
-};
-
-/** Nearer candidates order first. Equal distances order by the lower leader index, keeping the
- * selection deterministic. */
-__device__ inline auto operator<(leader_candidate a, leader_candidate b) -> bool
-{
-  return a.distance < b.distance || (a.distance == b.distance && a.leader < b.leader);
-}
-
-/** Replace the worst entry in the best list if the new candidate is better. */
-__device__ inline void manyway_insert_local(leader_candidate candidate,
-                                            int fanout,
-                                            leader_candidate* best)
-{
-  int worst = 0;
-  for (int j = 1; j < fanout; ++j) {
-    if (best[worst] < best[j]) { worst = j; }
-  }
-  if (candidate < best[worst]) { best[worst] = candidate; }
-}
-
-/** Sort the selected candidates in place from the nearest to the farthest.
- *
- * This is hand-rolled because thrust::sort has an allocation and I can't get a cuda::std::sort to
- * compile. */
-__device__ inline void manyway_sort_selected(int fanout, leader_candidate* selected)
-{
-  for (int i = 1; i < fanout; ++i) {
-    leader_candidate candidate = selected[i];
-    int j                      = i;
-    while (j > 0 && candidate < selected[j - 1]) {
-      selected[j] = selected[j - 1];
-      --j;
-    }
-    selected[j] = candidate;
-  }
-}
-
 /** Write the identity membership for each dataset row. */
 static __global__ void initialize_root_memberships_kernel(partition_membership* memberships,
                                                           int64_t rows)
@@ -419,64 +375,69 @@ __global__ void manyway_gather_tile_leaders_kernel(T const* dataset,
   }
 }
 
-/** Select the nearest leaders for each tile row from the dot products. Write the child keys and
- *  memberships. One warp does one row. */
-static __global__ void manyway_select_tiles_kernel(float const* dots,
-                                                   int batch_size,
-                                                   int tile_rows,
-                                                   int padded_leaders,
-                                                   int fanout,
-                                                   int occurrence_stride,
-                                                   float const* norms,
-                                                   uint32_t const* leader_ids,
-                                                   partition_membership const* input_memberships,
-                                                   assignment_tile const* tiles,
-                                                   uint32_t* output_keys,
-                                                   partition_membership* output_memberships)
+/** Convert the batched point-leader dot products into squared L2 distances in place. Invalid
+ * padded rows and leaders become infinity so the generic selection primitive ignores them. */
+static __global__ void manyway_materialize_tile_distances_kernel(
+  float* dots,
+  int batch_size,
+  int tile_rows,
+  int padded_leaders,
+  float const* norms,
+  uint32_t const* leader_ids,
+  partition_membership const* input_memberships,
+  assignment_tile const* tiles)
 {
-  int lane           = threadIdx.x % raft::WarpSize;
-  int warp           = threadIdx.x / raft::WarpSize;
-  int64_t linear_row = static_cast<int64_t>(blockIdx.x) * ROW_WARPS_PER_BLOCK + warp;
-  int64_t total_rows = static_cast<int64_t>(batch_size) * tile_rows;
-  if (linear_row >= total_rows) { return; }
-  int batch = static_cast<int>(linear_row / tile_rows);
-  int row   = static_cast<int>(linear_row % tile_rows);
-  auto tile = tiles[batch];
-  if (row >= tile.rows) { return; }
-
-  int64_t input_index = tile.input_start + row;
-  auto membership     = input_memberships[input_index];
-  leader_candidate local_best[MAX_FANOUT];
-
-  for (int leader = lane; leader < tile.leader_count; leader += raft::WarpSize) {
-    uint32_t leader_id = leader_ids[static_cast<int64_t>(batch) * padded_leaders + leader];
-    float dot = dots[(static_cast<int64_t>(batch) * tile_rows + row) * padded_leaders + leader];
-    float distance = fmaxf(0.0f, norms[membership.id] + norms[leader_id] - 2.0f * dot);
-    manyway_insert_local(
-      {.distance = distance, .leader = static_cast<uint32_t>(leader)}, fanout, local_best);
+  int64_t linear = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  int64_t stride = static_cast<int64_t>(blockDim.x) * gridDim.x;
+  int64_t total  = static_cast<int64_t>(batch_size) * tile_rows * padded_leaders;
+  for (; linear < total; linear += stride) {
+    int leader     = static_cast<int>(linear % padded_leaders);
+    int row        = static_cast<int>((linear / padded_leaders) % tile_rows);
+    int batch      = static_cast<int>(linear / (static_cast<int64_t>(padded_leaders) * tile_rows));
+    auto tile      = tiles[batch];
+    float distance = std::numeric_limits<float>::infinity();
+    if (row < tile.rows && leader < tile.leader_count) {
+      auto membership = input_memberships[tile.input_start + row];
+      auto leader_id =
+        leader_ids[static_cast<int64_t>(batch) * padded_leaders + static_cast<int64_t>(leader)];
+      // This could omit the point norm because it is constant across every leader in a row and
+      // select_k only depends on their relative ordering. Doing so would also require removing the
+      // nonnegative clamp because the resulting ranking scores may legitimately be negative.
+      // I'm not doing this for the sake of simplicity and because it would be unlikely to have a
+      // performance impact; we still need those norms for sorting the edges by distance.
+      distance = fmaxf(0.0f, norms[membership.id] + norms[leader_id] - 2.0f * dots[linear]);
+    }
+    dots[linear] = distance;
   }
+}
 
-  extern __shared__ unsigned char shared_bytes[];
-  auto* shared_candidates = reinterpret_cast<leader_candidate*>(shared_bytes);
-  int shared_base         = (warp * raft::WarpSize + lane) * fanout;
-  for (int j = 0; j < fanout; ++j) {
-    shared_candidates[shared_base + j] = local_best[j];
-  }
-  __syncwarp();
-
-  if (lane == 0) {
-    leader_candidate selected[MAX_FANOUT];
-    int warp_base = warp * raft::WarpSize * fanout;
-    for (int candidate = 0; candidate < raft::WarpSize * fanout; ++candidate) {
-      if (shared_candidates[warp_base + candidate].valid()) {
-        manyway_insert_local(shared_candidates[warp_base + candidate], fanout, selected);
-      }
-    }
-    manyway_sort_selected(fanout, selected);
-    int64_t output_base =
-      tile.output_start + (input_index - tile.input_start) * static_cast<int64_t>(fanout);
+/** Write selected leader offsets as child keys and copy their point memberships. */
+static __global__ void manyway_emit_tile_assignments_kernel(
+  int const* selected_leaders,
+  int batch_size,
+  int tile_rows,
+  int fanout,
+  int occurrence_stride,
+  partition_membership const* input_memberships,
+  assignment_tile const* tiles,
+  uint32_t* output_keys,
+  partition_membership* output_memberships)
+{
+  int64_t linear = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  int64_t stride = static_cast<int64_t>(blockDim.x) * gridDim.x;
+  int64_t total  = static_cast<int64_t>(batch_size) * tile_rows;
+  for (; linear < total; linear += stride) {
+    int row   = static_cast<int>(linear % tile_rows);
+    int batch = static_cast<int>(linear / tile_rows);
+    auto tile = tiles[batch];
+    if (row >= tile.rows) { continue; }
+
+    auto membership       = input_memberships[tile.input_start + row];
+    int64_t output_base   = tile.output_start + static_cast<int64_t>(row) * fanout;
+    int64_t selected_base = linear * fanout;
     for (int rank = 0; rank < fanout; ++rank) {
-      output_keys[output_base + rank]        = tile.child_key_base + selected[rank].leader;
+      auto leader = static_cast<uint32_t>(selected_leaders[selected_base + rank]);
+      output_keys[output_base + rank]        = tile.child_key_base + leader;
       output_memberships[output_base + rank] = {
         membership.id,
         static_cast<uint16_t>(membership.occurrence + rank * occurrence_stride),
@@ -611,10 +572,9 @@ void batched_row_dot_products(raft::resources const& res,
  * Parents are cut into tiles of `assignment_tile_rows` rows; every tile in a bucket shares the
  * same padded leader count, so one strided batched GEMM per batch produces all point-leader dot
  * products. Tiles are processed in batches sized to the GEMM workspace: each batch gathers its
- * tile vectors and its parents' leader vectors into dense float buffers, and the selection kernel
- * converts dots to distances with the precomputed row norms (|x|^2 + |l|^2 - 2 x.l), keeps the
- * `fanout` nearest leaders per row (ties broken by lower leader index), and writes the child keys
- * and memberships.
+ * tile vectors and its parents' leader vectors into dense float buffers, converts dots to
+ * distances with the precomputed row norms (|x|^2 + |l|^2 - 2 x.l), uses `select_k` to keep the
+ * `fanout` nearest leaders per row, and writes the child keys and memberships.
  */
 template <typename T>
 void assign_bucket(raft::resources const& res,
@@ -640,12 +600,14 @@ void assign_bucket(raft::resources const& res,
   }
 
   // Size the batch so all per-tile buffers fit in the GEMM workspace budget
-  size_t point_elements  = static_cast<size_t>(tile_rows) * dim;
-  size_t leader_elements = static_cast<size_t>(padded_leaders) * dim;
-  size_t dot_elements    = static_cast<size_t>(tile_rows) * padded_leaders;
-  size_t bytes_per_batch = (point_elements + leader_elements + dot_elements) * sizeof(float) +
-                           static_cast<size_t>(padded_leaders) * sizeof(uint32_t) +
-                           sizeof(assignment_tile);
+  size_t point_elements    = static_cast<size_t>(tile_rows) * dim;
+  size_t leader_elements   = static_cast<size_t>(padded_leaders) * dim;
+  size_t dot_elements      = static_cast<size_t>(tile_rows) * padded_leaders;
+  size_t selected_elements = static_cast<size_t>(tile_rows) * params.fanout;
+  size_t bytes_per_batch =
+    (point_elements + leader_elements + dot_elements + selected_elements) * sizeof(float) +
+    static_cast<size_t>(padded_leaders) * sizeof(uint32_t) + selected_elements * sizeof(int) +
+    sizeof(assignment_tile);
   RAFT_EXPECTS(bytes_per_batch <= context.gemm_workspace_bytes,
                "Fastener assignment workspace is too small for one tile");
   size_t batch_capacity = std::max<size_t>(
@@ -656,12 +618,12 @@ void assign_bucket(raft::resources const& res,
   rmm::device_uvector<float> tile_leaders(batch_capacity * leader_elements, stream);
   rmm::device_uvector<float> tile_dots(batch_capacity * dot_elements, stream);
   rmm::device_uvector<uint32_t> tile_leader_ids(batch_capacity * padded_leaders, stream);
+  rmm::device_uvector<float> selected_distances(batch_capacity * selected_elements, stream);
+  rmm::device_uvector<int> selected_leaders(batch_capacity * selected_elements, stream);
 
   auto point_stride  = static_cast<int64_t>(point_elements);
   auto leader_stride = static_cast<int64_t>(leader_elements);
   auto dot_stride    = static_cast<int64_t>(dot_elements);
-  size_t selection_shared =
-    ROW_WARPS_PER_BLOCK * raft::WarpSize * params.fanout * sizeof(leader_candidate);
 
   for (size_t tile_offset = 0; tile_offset < tiles.size(); tile_offset += batch_capacity) {
     size_t batch_size = std::min(batch_capacity, tiles.size() - tile_offset);
@@ -704,25 +666,42 @@ void assign_bucket(raft::resources const& res,
                              static_cast<int>(dim),
                              static_cast<int>(batch_size));
 
-    // Keep each row's `fanout` nearest leaders and write child keys and memberships
-    int selection_blocks =
-      static_cast<int>((static_cast<int64_t>(batch_size) * tile_rows + ROW_WARPS_PER_BLOCK - 1) /
-                       ROW_WARPS_PER_BLOCK);
-    manyway_select_tiles_kernel<<<selection_blocks,
-                                  ROW_WARPS_PER_BLOCK * raft::WarpSize,
-                                  selection_shared,
-                                  stream>>>(tile_dots.data(),
-                                            static_cast<int>(batch_size),
-                                            tile_rows,
-                                            padded_leaders,
-                                            static_cast<int>(params.fanout),
-                                            static_cast<int>(params.occurrence_stride),
-                                            context.norms.data(),
-                                            tile_leader_ids.data(),
-                                            parents.memberships.data(),
-                                            device_tiles.data(),
-                                            keys.data(),
-                                            memberships.data());
+    // Materialize distances, keep each row's nearest leaders, and emit their memberships
+    int64_t selection_rows = static_cast<int64_t>(batch_size) * tile_rows;
+    int distance_blocks    = strided_grid_size(selection_rows * padded_leaders);
+    manyway_materialize_tile_distances_kernel<<<distance_blocks, THREADS_PER_BLOCK, 0, stream>>>(
+      tile_dots.data(),
+      static_cast<int>(batch_size),
+      tile_rows,
+      padded_leaders,
+      context.norms.data(),
+      tile_leader_ids.data(),
+      parents.memberships.data(),
+      device_tiles.data());
+    RAFT_CUDA_TRY(cudaGetLastError());
+
+    cuvs::selection::select_k(res,
+                              raft::make_device_matrix_view<const float, int64_t>(
+                                tile_dots.data(), selection_rows, padded_leaders),
+                              std::nullopt,
+                              raft::make_device_matrix_view<float, int64_t>(
+                                selected_distances.data(), selection_rows, params.fanout),
+                              raft::make_device_matrix_view<int, int64_t>(
+                                selected_leaders.data(), selection_rows, params.fanout),
+                              true,
+                              true);
+
+    int assignment_blocks = strided_grid_size(selection_rows);
+    manyway_emit_tile_assignments_kernel<<<assignment_blocks, THREADS_PER_BLOCK, 0, stream>>>(
+      selected_leaders.data(),
+      static_cast<int>(batch_size),
+      tile_rows,
+      static_cast<int>(params.fanout),
+      static_cast<int>(params.occurrence_stride),
+      parents.memberships.data(),
+      device_tiles.data(),
+      keys.data(),
+      memberships.data());
     RAFT_CUDA_TRY(cudaGetLastError());
   }
 }

From 6f65f878edad0b086417a1a61e135cd534d19dcd Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Fri, 24 Jul 2026 00:05:26 +0000
Subject: [PATCH 12/41] fixed attached dataset alignment issue

---
 cpp/include/cuvs/neighbors/cagra.hpp           |  3 ++-
 cpp/src/neighbors/detail/cagra/cagra_merge.cuh | 10 +++-------
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index e1bc1ac851..0d1867b00f 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2492,7 +2492,8 @@ struct merge_params {
  * `index_params::graph_degree` is the final output degree.
  *
  * Fastener copies the input datasets into one contiguous device allocation and never mutates the
- * input indices. When `attach_dataset_on_build` is true, the result owns that consolidated copy.
+ * input indices. When `attach_dataset_on_build` is true, the result owns a 16-byte-row-aligned
+ * copy of that consolidated dataset (matching `cagra::build` / `update_dataset`).
  *
  * @param[in] res RAFT resources used for the merge.
  * @param[in] params Parameters for the returned CAGRA index.
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index d6e40adb79..88db6218bb 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -390,13 +390,9 @@ auto merge_fastener(raft::resources const& handle,
   index<T, IdxT> merged_index(handle, params.metric);
   merged_index.update_graph(handle, std::move(optimized_graph));
   if (params.attach_dataset_on_build) {
-    using matrix_t           = decltype(dataset);
-    using layout_t           = typename matrix_t::layout_type;
-    using container_policy_t = typename matrix_t::container_policy_type;
-    using owning_t           = owning_dataset<T, int64_t, layout_t, container_policy_t>;
-    auto out_layout          = raft::make_strided_layout(dataset.view().extents(),
-                                                cuda::std::array<int64_t, 2>{preflight.dim, 1});
-    merged_index.update_dataset(handle, owning_t{std::move(dataset), out_layout});
+    // CAGRA search assumes 16-byte row alignment (vectorized loads). Consolidate remains dense for
+    // the scaffold; attach through make_aligned_dataset so unaligned dims are padded.
+    merged_index.update_dataset(handle, make_aligned_dataset(handle, std::move(dataset), 16));
   } else {
     using ds_idx_type = typename index<T, IdxT>::dataset_index_type;
     merged_index.update_dataset(

From c8fd5407e86fcb5ec44cd0b1cdc2a56795558dbb Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Fri, 24 Jul 2026 19:08:35 +0000
Subject: [PATCH 13/41] CI fixes

---
 .../neighbors/detail/cagra/cagra_merge.cuh    |   6 +-
 .../detail/cagra/cagra_merge_scaffold.cuh     | 251 ++++++------------
 .../ann_cagra/test_merge_fastener.cu          |  49 ++--
 3 files changed, 116 insertions(+), 190 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 88db6218bb..82a9fe280e 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -319,11 +319,7 @@ auto preflight_fastener(cagra::index_params const& params,
     return reject("dataset dimension must be positive and fit cuBLAS int dimensions");
   }
   if (!merge_scaffold::leaf_gemm_supported<T>(result.dim, merge_params.leaf_size)) {
-    if constexpr (std::is_integral_v<T>) {
-      return reject("integer dataset dimension exceeds the INT32 leaf GEMM limit");
-    } else {
-      return reject("dataset dimension exceeds the leaf GEMM workspace limit");
-    }
+    return reject("dataset dimension exceeds the leaf GEMM workspace limit");
   }
   if (rows > std::numeric_limits<uint32_t>::max() / spill) {
     return reject("combined rows times the configured spill width must fit in uint32_t");
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 18dd2415c6..e75db0ca33 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -54,10 +54,6 @@ inline constexpr int THREADS_PER_BLOCK       = 256;
 inline constexpr int MAX_STRIDED_GRID_BLOCKS = 1 << 20;
 /** Warps per block in the one-row-per-warp kernels; their launch geometry derives from this. */
 inline constexpr int ROW_WARPS_PER_BLOCK = 4;
-// Centered INT8 dot products accumulate into INT32. Overflow requires more than 131,071
-// dimensions even at the worst-case magnitude of 128, which is not realistic.
-inline constexpr int64_t MAX_INTEGER_LEAF_DIMENSION =
-  std::numeric_limits<int32_t>::max() / (128 * 128);
 
 /** Grid size for the grid-stride kernels: blocks of THREADS_PER_BLOCK covering `items`, capped so
  * oversized workloads loop within resident threads instead. */
@@ -517,28 +513,23 @@ inline void carry_parents(raft::resources const& res,
  * out_i[b * a_rows + a] = A_i[a] . B_i[b].
  *
  * Pretty much a wrapper around `cublasGemmStridedBatchedEx`. */
-template <typename DataT, typename AccT>
-void batched_row_dot_products(raft::resources const& res,
-                              DataT const* a,
-                              int a_rows,
-                              long long a_stride,
-                              DataT const* b,
-                              int b_rows,
-                              long long b_stride,
-                              AccT* out,
-                              long long out_stride,
-                              int row_width,
-                              int batch_count)
+/** Float strided-batched GEMM for row-wise dots. Integer datasets are gathered to float first;
+ * native INT8 cuBLAS paths are not portable across architectures (e.g. Ada returns
+ * CUBLAS_STATUS_NOT_SUPPORTED for CUDA_R_8I / CUBLAS_COMPUTE_32I strided-batched GEMM). */
+inline void batched_row_dot_products(raft::resources const& res,
+                                     float const* a,
+                                     int a_rows,
+                                     long long a_stride,
+                                     float const* b,
+                                     int b_rows,
+                                     long long b_stride,
+                                     float* out,
+                                     long long out_stride,
+                                     int row_width,
+                                     int batch_count)
 {
-  static_assert((std::is_same_v<DataT, float> && std::is_same_v<AccT, float>) ||
-                  (std::is_same_v<DataT, int8_t> && std::is_same_v<AccT, int32_t>),
-                "Unsupported batched dot product type combination");
-  constexpr auto data_type = std::is_same_v<DataT, float> ? CUDA_R_32F : CUDA_R_8I;
-  constexpr auto out_type  = std::is_same_v<AccT, float> ? CUDA_R_32F : CUDA_R_32I;
-  constexpr auto compute_type =
-    std::is_same_v<AccT, float> ? CUBLAS_COMPUTE_32F : CUBLAS_COMPUTE_32I;
-  AccT alpha         = 1;
-  AccT beta          = 0;
+  float alpha        = 1.0f;
+  float beta         = 0.0f;
   auto cublas_handle = raft::resource::get_cublas_handle(res);
   RAFT_CUBLAS_TRY(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_HOST));
   RAFT_CUBLAS_TRY(cublasGemmStridedBatchedEx(cublas_handle,
@@ -549,20 +540,20 @@ void batched_row_dot_products(raft::resources const& res,
                                              row_width,
                                              &alpha,
                                              a,
-                                             data_type,
+                                             CUDA_R_32F,
                                              row_width,
                                              a_stride,
                                              b,
-                                             data_type,
+                                             CUDA_R_32F,
                                              row_width,
                                              b_stride,
                                              &beta,
                                              out,
-                                             out_type,
+                                             CUDA_R_32F,
                                              a_rows,
                                              out_stride,
                                              batch_count,
-                                             compute_type,
+                                             CUBLAS_COMPUTE_32F,
                                              CUBLAS_GEMM_DEFAULT));
 }
 
@@ -762,27 +753,17 @@ auto split_manyway(raft::resources const& res,
 // Leaf processing: bounded leaf slicing and cross-input leaf KNN
 // ----------------------------------------------------------------------------
 
-/** Return true if one leaf of this dimension and leaf size goes into the GEMM workspace. */
+/** Return true if one leaf of this dimension and leaf size fits the float GEMM workspace. */
 template <typename T>
 inline auto leaf_gemm_supported(int64_t dimension, uint32_t leaf_size) -> bool
 {
+  // All scalar types (including integers) gather into float leaf buffers before the GEMM.
+  (void)sizeof(T);
   if (dimension <= 0 || dimension > std::numeric_limits<int>::max()) { return false; }
 
-  int64_t storage_dimension  = dimension;
-  size_t vector_element_size = sizeof(float);
-  size_t gram_element_size   = sizeof(float);
-  if constexpr (std::is_integral_v<T>) {
-    // The below check is a little paranoid
-    if (dimension > MAX_INTEGER_LEAF_DIMENSION) { return false; }
-    storage_dimension   = (dimension + 3) & ~int64_t{3};
-    vector_element_size = sizeof(int8_t);
-    gram_element_size   = sizeof(int32_t);
-  }
-
-  size_t vector_elements = static_cast<size_t>(leaf_size) * storage_dimension;
+  size_t vector_elements = static_cast<size_t>(leaf_size) * static_cast<size_t>(dimension);
   size_t gram_elements   = static_cast<size_t>(leaf_size) * leaf_size;
-  return vector_elements * vector_element_size + gram_elements * gram_element_size <=
-         GEMM_WORKSPACE_BYTES;
+  return vector_elements * sizeof(float) + gram_elements * sizeof(float) <= GEMM_WORKSPACE_BYTES;
 }
 
 struct leaf_set {
@@ -846,7 +827,7 @@ static __global__ void initialize_self_scaffold_kernel(uint32_t* graph,
 }
 
 /** Copy the vectors of each leaf into a dense buffer of OutT, zero-padding rows past the leaf
- *  end and dimensions past `input_dim`. Unsigned input values move to a center of zero. */
+ *  end and dimensions past `input_dim`. Every scalar type is promoted to OutT (float) as-is. */
 template <typename T, typename OutT>
 __global__ void manyway_gather_leaf_vectors_kernel(T const* dataset,
                                                    int64_t input_dim,
@@ -872,11 +853,7 @@ __global__ void manyway_gather_leaf_vectors_kernel(T const* dataset,
     if (local_row < leaf_n && d < input_dim) {
       uint32_t point = memberships[leaf_starts[leaf] + local_row].id;
       auto input     = dataset[static_cast<int64_t>(point) * input_dim + d];
-      if constexpr (std::is_same_v<T, uint8_t>) {
-        value = static_cast<OutT>(static_cast<int>(input) - 128);
-      } else {
-        value = static_cast<OutT>(input);
-      }
+      value          = static_cast<OutT>(input);
     }
     leaf_vectors[linear] = value;
   }
@@ -981,124 +958,62 @@ auto build_leaf_neighbors(raft::resources const& res,
     graph.data_handle(), rows, union_degree);
   RAFT_CUDA_TRY(cudaGetLastError());
 
-  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half>) {
-    // Size the leaf batch so the vector and Gram buffers fit the GEMM workspace budget
-    int64_t input_dimension         = dataset.extent(1);
-    int dimension                   = static_cast<int>(input_dimension);
-    size_t vector_elements_per_leaf = static_cast<size_t>(leaf_size) * dimension;
-    size_t gram_elements_per_leaf   = static_cast<size_t>(leaf_size) * leaf_size;
-    size_t bytes_per_leaf =
-      vector_elements_per_leaf * sizeof(float) + gram_elements_per_leaf * sizeof(float);
-    size_t batch_capacity = std::max<size_t>(
-      1, std::min<size_t>(leaves.starts_host.size(), GEMM_WORKSPACE_BYTES / bytes_per_leaf));
-    rmm::device_uvector<float> leaf_vectors(batch_capacity * vector_elements_per_leaf, stream);
-    rmm::device_uvector<float> gram(batch_capacity * gram_elements_per_leaf, stream);
-    auto vector_stride = static_cast<int64_t>(vector_elements_per_leaf);
-    auto gram_stride   = static_cast<int64_t>(gram_elements_per_leaf);
-
-    for (size_t leaf_offset = 0; leaf_offset < leaves.starts_host.size();
-         leaf_offset += batch_capacity) {
-      size_t batch_size = std::min(batch_capacity, leaves.starts_host.size() - leaf_offset);
-      int gather_blocks =
-        strided_grid_size(static_cast<int64_t>(batch_size * vector_elements_per_leaf));
-      // Gather this batch's leaves into the dense vector buffer.
-      manyway_gather_leaf_vectors_kernel<<<gather_blocks, THREADS_PER_BLOCK, 0, stream>>>(
-        dataset.data_handle(),
-        input_dimension,
-        input_dimension,
-        leaf_size,
-        memberships.data(),
-        leaves.starts.data(),
-        leaves.ends.data(),
-        static_cast<int64_t>(leaf_offset),
-        static_cast<int64_t>(batch_size),
-        leaf_vectors.data());
-      RAFT_CUDA_TRY(cudaGetLastError());
-      // the Gram matrix of every leaf in the batch
-      batched_row_dot_products(res,
-                               leaf_vectors.data(),
-                               leaf_size,
-                               vector_stride,
-                               leaf_vectors.data(),
-                               leaf_size,
-                               vector_stride,
-                               gram.data(),
-                               gram_stride,
-                               dimension,
-                               static_cast<int>(batch_size));
-      // select each point's nearest cross-input neighbors from the corresponding Gram matrix
-      manyway_leaf_gram_knn_kernel<float>
-        <<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(gram.data(),
-                                                                 memberships.data(),
-                                                                 origins,
-                                                                 leaves.starts.data(),
-                                                                 leaves.ends.data(),
-                                                                 static_cast<int64_t>(leaf_offset),
-                                                                 static_cast<int64_t>(batch_size),
-                                                                 leaf_size,
-                                                                 leaf_degree,
-                                                                 union_degree,
-                                                                 graph.data_handle());
-      RAFT_CUDA_TRY(cudaGetLastError());
-    }
-  } else if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>) {
-    // Same pipeline as the float path, with rows padded to a multiple of 4 and an INT32 Gram.
-    int64_t input_dimension         = dataset.extent(1);
-    int64_t padded_dimension        = (input_dimension + 3) & ~int64_t{3};
-    size_t vector_elements_per_leaf = static_cast<size_t>(leaf_size) * padded_dimension;
-    size_t gram_elements_per_leaf   = static_cast<size_t>(leaf_size) * leaf_size;
-    size_t bytes_per_leaf =
-      vector_elements_per_leaf * sizeof(int8_t) + gram_elements_per_leaf * sizeof(int32_t);
-    size_t batch_capacity = std::max<size_t>(
-      1, std::min<size_t>(leaves.starts_host.size(), GEMM_WORKSPACE_BYTES / bytes_per_leaf));
-    rmm::device_uvector<int8_t> leaf_vectors(batch_capacity * vector_elements_per_leaf, stream);
-    rmm::device_uvector<int32_t> gram(batch_capacity * gram_elements_per_leaf, stream);
-    int dimension      = static_cast<int>(padded_dimension);
-    auto vector_stride = static_cast<int64_t>(vector_elements_per_leaf);
-    auto gram_stride   = static_cast<int64_t>(gram_elements_per_leaf);
-
-    for (size_t leaf_offset = 0; leaf_offset < leaves.starts_host.size();
-         leaf_offset += batch_capacity) {
-      size_t batch_size = std::min(batch_capacity, leaves.starts_host.size() - leaf_offset);
-      int gather_blocks =
-        strided_grid_size(static_cast<int64_t>(batch_size * vector_elements_per_leaf));
-      manyway_gather_leaf_vectors_kernel<<<gather_blocks, THREADS_PER_BLOCK, 0, stream>>>(
-        dataset.data_handle(),
-        input_dimension,
-        padded_dimension,
-        leaf_size,
-        memberships.data(),
-        leaves.starts.data(),
-        leaves.ends.data(),
-        static_cast<int64_t>(leaf_offset),
-        static_cast<int64_t>(batch_size),
-        leaf_vectors.data());
-      RAFT_CUDA_TRY(cudaGetLastError());
-      batched_row_dot_products(res,
-                               leaf_vectors.data(),
-                               leaf_size,
-                               vector_stride,
-                               leaf_vectors.data(),
-                               leaf_size,
-                               vector_stride,
-                               gram.data(),
-                               gram_stride,
-                               dimension,
-                               static_cast<int>(batch_size));
-      manyway_leaf_gram_knn_kernel<int32_t>
-        <<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(gram.data(),
-                                                                 memberships.data(),
-                                                                 origins,
-                                                                 leaves.starts.data(),
-                                                                 leaves.ends.data(),
-                                                                 static_cast<int64_t>(leaf_offset),
-                                                                 static_cast<int64_t>(batch_size),
-                                                                 leaf_size,
-                                                                 leaf_degree,
-                                                                 union_degree,
-                                                                 graph.data_handle());
-      RAFT_CUDA_TRY(cudaGetLastError());
-    }
+  // Gather every scalar type into float leaf buffers. Native INT8 cuBLAS is not portable across
+  // architectures (Ada returns CUBLAS_STATUS_NOT_SUPPORTED for the strided-batched INT8 path).
+  int64_t input_dimension         = dataset.extent(1);
+  int dimension                   = static_cast<int>(input_dimension);
+  size_t vector_elements_per_leaf = static_cast<size_t>(leaf_size) * dimension;
+  size_t gram_elements_per_leaf   = static_cast<size_t>(leaf_size) * leaf_size;
+  size_t bytes_per_leaf =
+    vector_elements_per_leaf * sizeof(float) + gram_elements_per_leaf * sizeof(float);
+  size_t batch_capacity = std::max<size_t>(
+    1, std::min<size_t>(leaves.starts_host.size(), GEMM_WORKSPACE_BYTES / bytes_per_leaf));
+  rmm::device_uvector<float> leaf_vectors(batch_capacity * vector_elements_per_leaf, stream);
+  rmm::device_uvector<float> gram(batch_capacity * gram_elements_per_leaf, stream);
+  auto vector_stride = static_cast<int64_t>(vector_elements_per_leaf);
+  auto gram_stride   = static_cast<int64_t>(gram_elements_per_leaf);
+
+  for (size_t leaf_offset = 0; leaf_offset < leaves.starts_host.size();
+       leaf_offset += batch_capacity) {
+    size_t batch_size = std::min(batch_capacity, leaves.starts_host.size() - leaf_offset);
+    int gather_blocks =
+      strided_grid_size(static_cast<int64_t>(batch_size * vector_elements_per_leaf));
+    manyway_gather_leaf_vectors_kernel<<<gather_blocks, THREADS_PER_BLOCK, 0, stream>>>(
+      dataset.data_handle(),
+      input_dimension,
+      input_dimension,
+      leaf_size,
+      memberships.data(),
+      leaves.starts.data(),
+      leaves.ends.data(),
+      static_cast<int64_t>(leaf_offset),
+      static_cast<int64_t>(batch_size),
+      leaf_vectors.data());
+    RAFT_CUDA_TRY(cudaGetLastError());
+    batched_row_dot_products(res,
+                             leaf_vectors.data(),
+                             leaf_size,
+                             vector_stride,
+                             leaf_vectors.data(),
+                             leaf_size,
+                             vector_stride,
+                             gram.data(),
+                             gram_stride,
+                             dimension,
+                             static_cast<int>(batch_size));
+    manyway_leaf_gram_knn_kernel<float>
+      <<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(gram.data(),
+                                                               memberships.data(),
+                                                               origins,
+                                                               leaves.starts.data(),
+                                                               leaves.ends.data(),
+                                                               static_cast<int64_t>(leaf_offset),
+                                                               static_cast<int64_t>(batch_size),
+                                                               leaf_size,
+                                                               leaf_degree,
+                                                               union_degree,
+                                                               graph.data_handle());
+    RAFT_CUDA_TRY(cudaGetLastError());
   }
 
   return graph;
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 9bcdd46779..50c44f95d1 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -104,13 +104,22 @@ void expect_dataset_order(raft::resources const& res,
                           index<T, uint32_t> const& merged,
                           raft::host_matrix_view<const T, int64_t> expected)
 {
-  ASSERT_EQ(merged.dataset().extent(0), expected.extent(0));
-  ASSERT_EQ(merged.dataset().extent(1), expected.extent(1));
-  auto host = raft::make_host_matrix<T, int64_t>(res, expected.extent(0), expected.extent(1));
-  raft::copy(host.data_handle(),
-             merged.dataset().data_handle(),
-             host.size(),
-             raft::resource::get_cuda_stream(res));
+  auto view = merged.dataset();
+  ASSERT_EQ(view.extent(0), expected.extent(0));
+  ASSERT_EQ(view.extent(1), expected.extent(1));
+  // The merged owning dataset is attached through make_aligned_dataset, which pads each row to a
+  // 16-byte stride. For int8/uint8 (and any dim whose byte width is not a multiple of 16) the
+  // stride exceeds the logical dimension, so the rows must be copied honoring that stride rather
+  // than as one contiguous block.
+  auto host          = raft::make_host_matrix<T, int64_t>(res, expected.extent(0), expected.extent(1));
+  auto stream        = raft::resource::get_cuda_stream(res);
+  int64_t row_stride = view.stride(0);
+  for (int64_t row = 0; row < view.extent(0); ++row) {
+    raft::copy(host.data_handle() + row * view.extent(1),
+               view.data_handle() + row * row_stride,
+               view.extent(1),
+               stream);
+  }
   raft::resource::sync_stream(res);
   for (int64_t row = 0; row < expected.extent(0); ++row) {
     for (int64_t column = 0; column < expected.extent(1); ++column) {
@@ -384,22 +393,28 @@ TEST(CagraMergeFastener, DuplicateAcrossInputsAppearInEachOthersScaffoldNeighbor
 }
 
 /**
- * Verifies the leaf-GEMM eligibility boundary for every scalar type and confirms that an integer
- * dimension beyond the safe accumulation limit is rejected before either input index is mutated.
+ * Verifies the leaf-GEMM eligibility boundary for every scalar type and confirms that a dimension
+ * that cannot fit one float leaf in the GEMM workspace is rejected before either input is mutated.
  */
-TEST(CagraMergeFastener, LeafGemmLimitsRejectOnlyUnrealisticIntegerDimensions)
+TEST(CagraMergeFastener, LeafGemmLimitsRejectOversizedWorkspaceDimensions)
 {
   using namespace detail::merge_scaffold;
   EXPECT_TRUE(leaf_gemm_supported<float>(4096, 256));
   EXPECT_TRUE(leaf_gemm_supported<half>(4096, 256));
-  EXPECT_TRUE(leaf_gemm_supported<int8_t>(MAX_INTEGER_LEAF_DIMENSION, 256));
-  EXPECT_TRUE(leaf_gemm_supported<uint8_t>(MAX_INTEGER_LEAF_DIMENSION, 256));
-  EXPECT_FALSE(leaf_gemm_supported<int8_t>(MAX_INTEGER_LEAF_DIMENSION + 1, 256));
-  EXPECT_FALSE(leaf_gemm_supported<uint8_t>(MAX_INTEGER_LEAF_DIMENSION + 1, 256));
+  EXPECT_TRUE(leaf_gemm_supported<int8_t>(4096, 256));
+  EXPECT_TRUE(leaf_gemm_supported<uint8_t>(4096, 256));
+
+  // One float leaf of this width exceeds GEMM_WORKSPACE_BYTES (~2 GiB).
+  constexpr uint32_t leaf_size = 256;
+  constexpr int64_t oversized_dim =
+    static_cast<int64_t>(GEMM_WORKSPACE_BYTES / sizeof(float) / leaf_size) + 1;
+  EXPECT_FALSE(leaf_gemm_supported<float>(oversized_dim, leaf_size));
+  EXPECT_FALSE(leaf_gemm_supported<int8_t>(oversized_dim, leaf_size));
+  EXPECT_FALSE(leaf_gemm_supported<uint8_t>(oversized_dim, leaf_size));
 
   raft::resources res;
   constexpr int64_t rows = 2;
-  constexpr int64_t dim  = MAX_INTEGER_LEAF_DIMENSION + 1;
+  constexpr int64_t dim  = oversized_dim;
   auto dataset0          = make_dataset<int8_t>(res, rows, dim, 1234ULL);
   auto dataset1          = make_dataset<int8_t>(res, rows, dim, 5678ULL);
   auto graph0            = make_ring_graph(res, rows, 1);
@@ -416,13 +431,13 @@ TEST(CagraMergeFastener, LeafGemmLimitsRejectOnlyUnrealisticIntegerDimensions)
   params.intermediate_graph_degree = 1;
   merge_params fastener;
   fastener.algo      = merge_algo::FASTENER;
-  fastener.leaf_size = 64;
+  fastener.leaf_size = leaf_size;
   std::vector<index<int8_t, uint32_t>*> indices{&index0, &index1};
 
   auto result = detail::preflight_fastener(
     params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
   EXPECT_FALSE(result.eligible);
-  EXPECT_EQ(result.reason, "integer dataset dimension exceeds the INT32 leaf GEMM limit");
+  EXPECT_EQ(result.reason, "dataset dimension exceeds the leaf GEMM workspace limit");
   EXPECT_EQ(index0.dataset().extent(0), rows);
   EXPECT_EQ(index1.dataset().extent(0), rows);
 }

From 951b0e29c3baab73c4bf88db4fa858e61700da5f Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Mon, 27 Jul 2026 16:00:05 +0000
Subject: [PATCH 14/41] aligned test with earlier ci fix

---
 cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 50c44f95d1..f4d19b7b8d 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -111,8 +111,8 @@ void expect_dataset_order(raft::resources const& res,
   // 16-byte stride. For int8/uint8 (and any dim whose byte width is not a multiple of 16) the
   // stride exceeds the logical dimension, so the rows must be copied honoring that stride rather
   // than as one contiguous block.
-  auto host          = raft::make_host_matrix<T, int64_t>(res, expected.extent(0), expected.extent(1));
-  auto stream        = raft::resource::get_cuda_stream(res);
+  auto host   = raft::make_host_matrix<T, int64_t>(res, expected.extent(0), expected.extent(1));
+  auto stream = raft::resource::get_cuda_stream(res);
   int64_t row_stride = view.stride(0);
   for (int64_t row = 0; row < view.extent(0); ++row) {
     raft::copy(host.data_handle() + row * view.extent(1),

From 731bb3035d9956ec2a352f3dbd0332f8625e956d Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Mon, 27 Jul 2026 19:35:07 +0000
Subject: [PATCH 15/41] adjusting test with excessive assumptions

---
 .../neighbors/ann_cagra/test_merge_fastener.cu    | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index f4d19b7b8d..edb5d61c9f 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -145,6 +145,8 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
     res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
   index<T, uint32_t> index1(
     res, metric, raft::make_const_mdspan(dataset1.view()), raft::make_const_mdspan(graph1.view()));
+  auto index0_owns_data = index0.data().is_owning();
+  auto index1_owns_data = index1.data().is_owning();
 
   index_params params;
   params.metric                    = metric;
@@ -164,8 +166,8 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
   EXPECT_TRUE(merged.data().is_owning());
   EXPECT_EQ(index0.dataset().extent(0), rows);
   EXPECT_EQ(index1.dataset().extent(0), rows);
-  EXPECT_TRUE(index0.data().is_owning());
-  EXPECT_TRUE(index1.data().is_owning());
+  EXPECT_EQ(index0.data().is_owning(), index0_owns_data);
+  EXPECT_EQ(index1.data().is_owning(), index1_owns_data);
   EXPECT_EQ(index0.size(), rows);
   EXPECT_EQ(index1.size(), rows);
   EXPECT_EQ(index0.dim(), dim);
@@ -469,7 +471,12 @@ TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
   auto dataset1            = make_dataset<float>(res, rows, dim, 5678ULL);
   auto expected            = concat_host_datasets<float>(
     res, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(dataset1.view()));
+  auto device_dataset0 = raft::make_device_matrix<float, int64_t>(res, rows, dim);
   auto device_dataset1 = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  raft::copy(device_dataset0.data_handle(),
+             dataset0.data_handle(),
+             dataset0.size(),
+             raft::resource::get_cuda_stream(res));
   raft::copy(device_dataset1.data_handle(),
              dataset1.data_handle(),
              dataset1.size(),
@@ -483,6 +490,10 @@ TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
                                 metric,
                                 raft::make_const_mdspan(device_dataset1.view()),
                                 raft::make_const_mdspan(graph1.view()));
+  // Explicitly transfer the first allocation so this test remains mixed-ownership even when
+  // mapped host allocations are device-accessible and would otherwise be wrapped non-owningly.
+  index0.update_dataset(
+    res, make_aligned_dataset(res, std::move(device_dataset0), /* align_bytes = */ 16));
   ASSERT_TRUE(index0.data().is_owning());
   ASSERT_FALSE(index1.data().is_owning());
 

From 3b83294edb3175d26153c39af9eab59d5ab90dea Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 29 Jul 2026 14:03:04 +0000
Subject: [PATCH 16/41] switch to TF32 matmul and remove obsolete template

---
 .../neighbors/detail/cagra/cagra_merge.cuh    |  2 +-
 .../detail/cagra/cagra_merge_scaffold.cuh     | 43 ++++++++++++++-----
 .../ann_cagra/test_merge_fastener.cu          | 25 ++++++-----
 3 files changed, 48 insertions(+), 22 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 82a9fe280e..73e43e6c44 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -318,7 +318,7 @@ auto preflight_fastener(cagra::index_params const& params,
   if (result.dim <= 0 || result.dim > std::numeric_limits<int>::max()) {
     return reject("dataset dimension must be positive and fit cuBLAS int dimensions");
   }
-  if (!merge_scaffold::leaf_gemm_supported<T>(result.dim, merge_params.leaf_size)) {
+  if (!merge_scaffold::leaf_gemm_supported(result.dim, merge_params.leaf_size)) {
     return reject("dataset dimension exceeds the leaf GEMM workspace limit");
   }
   if (rows > std::numeric_limits<uint32_t>::max() / spill) {
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index e75db0ca33..a031b634a9 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -508,14 +508,35 @@ inline void carry_parents(raft::resources const& res,
   RAFT_CUDA_TRY(cudaGetLastError());
 }
 
-/** Compute every pairwise dot product between the rows of A_i and the rows of B_i for a strided
+/**
+ * Compute type for both scaffold GEMMs.
+ *
+ * TF32 inputs cost nothing for three of the four supported dataset types: int8_t, uint8_t and half
+ * values are all exactly representable in TF32's 11-bit significand, and the accumulator stays
+ * float, so only float datasets lose input precision. Both consumers form
+ * |u|^2 + |v|^2 - 2 u.v, which cancels for near-duplicate points, but they only rank merge
+ * candidates; every surviving edge is re-scored at full precision by sort_knn_graph_device_inplace
+ * before the graph is optimized.
+ *
+ * Wider tensor-core modes are not worth their precision. These shapes are bandwidth-bound once off
+ * the FP32 path, so CUBLAS_COMPUTE_32F_FAST_16BF measures no faster than TF32 despite roughly
+ * double the arithmetic peak.
+ */
+inline constexpr cublasComputeType_t GEMM_COMPUTE_TYPE = CUBLAS_COMPUTE_32F_FAST_TF32;
+
+/**
+ * Compute every pairwise dot product between the rows of A_i and the rows of B_i for a strided
  * batch of row-major matrices with rows of length `row_width`:
  * out_i[b * a_rows + a] = A_i[a] . B_i[b].
  *
- * Pretty much a wrapper around `cublasGemmStridedBatchedEx`. */
-/** Float strided-batched GEMM for row-wise dots. Integer datasets are gathered to float first;
- * native INT8 cuBLAS paths are not portable across architectures (e.g. Ada returns
- * CUBLAS_STATUS_NOT_SUPPORTED for CUDA_R_8I / CUBLAS_COMPUTE_32I strided-batched GEMM). */
+ * Pretty much a wrapper around `cublasGemmStridedBatchedEx`.
+ *
+ * Every dataset scalar type is gathered to float before this call. Native INT8 cuBLAS paths are
+ * not portable across architectures (e.g. Ada returns CUBLAS_STATUS_NOT_SUPPORTED for
+ * CUDA_R_8I / CUBLAS_COMPUTE_32I strided-batched GEMM), and would gain little here in any case:
+ * these shapes run at the memory roofline, where shrinking the operands without shrinking the
+ * float output recovers well under the type's arithmetic advantage.
+ */
 inline void batched_row_dot_products(raft::resources const& res,
                                      float const* a,
                                      int a_rows,
@@ -553,7 +574,7 @@ inline void batched_row_dot_products(raft::resources const& res,
                                              a_rows,
                                              out_stride,
                                              batch_count,
-                                             CUBLAS_COMPUTE_32F,
+                                             GEMM_COMPUTE_TYPE,
                                              CUBLAS_GEMM_DEFAULT));
 }
 
@@ -753,12 +774,12 @@ auto split_manyway(raft::resources const& res,
 // Leaf processing: bounded leaf slicing and cross-input leaf KNN
 // ----------------------------------------------------------------------------
 
-/** Return true if one leaf of this dimension and leaf size fits the float GEMM workspace. */
-template <typename T>
+/** Return true if one leaf of this dimension and leaf size fits the float GEMM workspace.
+ *
+ * The limit does not depend on the dataset scalar type: every type, integers included, is gathered
+ * into float leaf vectors and produces a float Gram matrix. */
 inline auto leaf_gemm_supported(int64_t dimension, uint32_t leaf_size) -> bool
 {
-  // All scalar types (including integers) gather into float leaf buffers before the GEMM.
-  (void)sizeof(T);
   if (dimension <= 0 || dimension > std::numeric_limits<int>::max()) { return false; }
 
   size_t vector_elements = static_cast<size_t>(leaf_size) * static_cast<size_t>(dimension);
@@ -1090,7 +1111,7 @@ auto build(raft::resources const& res,
   RAFT_EXPECTS(params.leaf_degree >= 1 && params.leaf_degree <= MAX_LEAF_DEGREE,
                "Fastener leaf degree must be between 1 and %d",
                MAX_LEAF_DEGREE);
-  RAFT_EXPECTS(leaf_gemm_supported<T>(dataset.extent(1), params.leaf_size),
+  RAFT_EXPECTS(leaf_gemm_supported(dataset.extent(1), params.leaf_size),
                "Fastener dataset dimension exceeds the leaf GEMM limits");
 
   // the number of leaf partitions that a given point will end up in
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index edb5d61c9f..aed9eb425d 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -395,24 +395,29 @@ TEST(CagraMergeFastener, DuplicateAcrossInputsAppearInEachOthersScaffoldNeighbor
 }
 
 /**
- * Verifies the leaf-GEMM eligibility boundary for every scalar type and confirms that a dimension
- * that cannot fit one float leaf in the GEMM workspace is rejected before either input is mutated.
+ * Verifies the leaf-GEMM eligibility boundary, which is the same for every scalar type because
+ * every type gathers into float leaf vectors, and confirms that a dimension that cannot fit one
+ * float leaf in the GEMM workspace is rejected before either input is mutated.
  */
 TEST(CagraMergeFastener, LeafGemmLimitsRejectOversizedWorkspaceDimensions)
 {
   using namespace detail::merge_scaffold;
-  EXPECT_TRUE(leaf_gemm_supported<float>(4096, 256));
-  EXPECT_TRUE(leaf_gemm_supported<half>(4096, 256));
-  EXPECT_TRUE(leaf_gemm_supported<int8_t>(4096, 256));
-  EXPECT_TRUE(leaf_gemm_supported<uint8_t>(4096, 256));
+  EXPECT_TRUE(leaf_gemm_supported(4096, 256));
+  EXPECT_FALSE(leaf_gemm_supported(0, 256));
+  EXPECT_FALSE(leaf_gemm_supported(-1, 256));
 
-  // One float leaf of this width exceeds GEMM_WORKSPACE_BYTES (~2 GiB).
+  // The widest leaf whose float vectors and float Gram matrix together fit GEMM_WORKSPACE_BYTES.
   constexpr uint32_t leaf_size = 256;
+  constexpr int64_t widest_dim =
+    static_cast<int64_t>((GEMM_WORKSPACE_BYTES - size_t{leaf_size} * leaf_size * sizeof(float)) /
+                         sizeof(float) / leaf_size);
+  EXPECT_TRUE(leaf_gemm_supported(widest_dim, leaf_size));
+  EXPECT_FALSE(leaf_gemm_supported(widest_dim + 1, leaf_size));
+
+  // Comfortably past the workspace, used below to check pre-mutation rejection.
   constexpr int64_t oversized_dim =
     static_cast<int64_t>(GEMM_WORKSPACE_BYTES / sizeof(float) / leaf_size) + 1;
-  EXPECT_FALSE(leaf_gemm_supported<float>(oversized_dim, leaf_size));
-  EXPECT_FALSE(leaf_gemm_supported<int8_t>(oversized_dim, leaf_size));
-  EXPECT_FALSE(leaf_gemm_supported<uint8_t>(oversized_dim, leaf_size));
+  EXPECT_FALSE(leaf_gemm_supported(oversized_dim, leaf_size));
 
   raft::resources res;
   constexpr int64_t rows = 2;

From 5e3f436d18337f79f5e8e0d1d38a1532d66285ba Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 29 Jul 2026 15:13:37 +0000
Subject: [PATCH 17/41] updating C API

---
 c/include/cuvs/neighbors/cagra.h | 53 ++++++++++++++++++++--
 c/src/neighbors/cagra.cpp        | 77 +++++++++++++++++++++++++-------
 c/tests/neighbors/ann_cagra_c.cu | 11 ++++-
 3 files changed, 120 insertions(+), 21 deletions(-)

diff --git a/c/include/cuvs/neighbors/cagra.h b/c/include/cuvs/neighbors/cagra.h
index 22809da37e..69aec60f76 100644
--- a/c/include/cuvs/neighbors/cagra.h
+++ b/c/include/cuvs/neighbors/cagra.h
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -228,6 +228,27 @@ struct cuvsCagraIndexParams {
 
 typedef struct cuvsCagraIndexParams* cuvsCagraIndexParams_t;
 
+/** Algorithm used to merge physical CAGRA indices. */
+enum cuvsCagraMergeAlgo {
+  CUVS_CAGRA_MERGE_AUTO     = 0,
+  CUVS_CAGRA_MERGE_FASTENER = 1,
+  CUVS_CAGRA_MERGE_REBUILD  = 2
+};
+
+/** Parameters controlling how physical CAGRA indices are merged. */
+struct cuvsCagraMergeParams {
+  enum cuvsCagraMergeAlgo algo;
+  uint32_t levels;
+  uint32_t root_fanout;
+  uint32_t lower_fanout;
+  double leader_fraction;
+  uint32_t max_leaders;
+  uint32_t leaf_size;
+  uint32_t leaf_degree;
+};
+
+typedef struct cuvsCagraMergeParams* cuvsCagraMergeParams_t;
+
 /**
  * @brief Allocate CAGRA Index params, and populate with default values
  *
@@ -244,6 +265,12 @@ CUVS_EXPORT cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* param
  */
 CUVS_EXPORT cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params);
 
+/** Allocate CAGRA merge params and populate them with AUTO defaults. */
+CUVS_EXPORT cuvsError_t cuvsCagraMergeParamsCreate(cuvsCagraMergeParams_t* params);
+
+/** De-allocate CAGRA merge params. */
+CUVS_EXPORT cuvsError_t cuvsCagraMergeParamsDestroy(cuvsCagraMergeParams_t params);
+
 /**
  * @brief Allocate CAGRA Compression params, and populate with default values
  *
@@ -843,7 +870,7 @@ CUVS_EXPORT cuvsError_t cuvsCagraIndexFromArgs(cuvsResources_t res,
  *
  * All input indices must have been built with the same data type (`index.dtype`) and
  * have the same dimensionality (`index.dims`). The merged index uses the output
- * parameters specified in `cuvsCagraIndexParams`.
+ * parameters specified in `cuvsCagraIndexParams`. The merge algorithm is selected automatically.
  *
  * Input indices must have:
  *  - `index.dtype.code` and `index.dtype.bits` matching across all indices.
@@ -883,7 +910,7 @@ CUVS_EXPORT cuvsError_t cuvsCagraIndexFromArgs(cuvsResources_t res,
  * @endcode
  *
  * @param[in] res cuvsResources_t opaque C handle
- * @param[in] params cuvsCagraIndexParams_t parameters controlling merge behavior
+ * @param[in] params cuvsCagraIndexParams_t parameters for the output index
  * @param[in] indices Array of input cuvsCagraIndex_t handles to merge
  * @param[in] num_indices Number of input indices
  * @param[in] filter Filter that can be used to filter out vectors from the merged index
@@ -897,6 +924,26 @@ CUVS_EXPORT cuvsError_t cuvsCagraMerge(cuvsResources_t res,
                            cuvsFilter filter,
                            cuvsCagraIndex_t output_index);
 
+/**
+ * @brief Merge multiple CAGRA indices with explicit merge parameters.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsCagraIndexParams_t parameters for the output index
+ * @param[in] merge_params cuvsCagraMergeParams_t parameters controlling the merge algorithm, or
+ *                         NULL to use AUTO defaults
+ * @param[in] indices Array of input cuvsCagraIndex_t handles to merge
+ * @param[in] num_indices Number of input indices
+ * @param[in] filter Filter that can be used to filter out vectors from the merged index
+ * @param[out] output_index Output handle initialized with `cuvsCagraIndexCreate`
+ */
+CUVS_EXPORT cuvsError_t cuvsCagraMergeWithParams(cuvsResources_t res,
+                                                 cuvsCagraIndexParams_t params,
+                                                 cuvsCagraMergeParams_t merge_params,
+                                                 cuvsCagraIndex_t* indices,
+                                                 size_t num_indices,
+                                                 cuvsFilter filter,
+                                                 cuvsCagraIndex_t output_index);
+
 /**
  * @}
  */
diff --git a/c/src/neighbors/cagra.cpp b/c/src/neighbors/cagra.cpp
index 004b810c78..0d968d9d38 100644
--- a/c/src/neighbors/cagra.cpp
+++ b/c/src/neighbors/cagra.cpp
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -294,6 +294,7 @@ void* _deserialize(cuvsResources_t res, const char* filename)
 template <typename T>
 void* _merge(cuvsResources_t res,
              cuvsCagraIndexParams params,
+             const cuvs::neighbors::cagra::merge_params& merge_params,
              cuvsCagraIndex_t* indices,
              size_t num_indices,
              cuvsFilter filter)
@@ -335,7 +336,7 @@ void* _merge(cuvsResources_t res,
 
   if (filter.type == NO_FILTER) {
     return new cuvs::neighbors::cagra::index<T, uint32_t>(
-      cuvs::neighbors::cagra::merge(*res_ptr, params_cpp, index_ptrs));
+      cuvs::neighbors::cagra::merge(*res_ptr, params_cpp, index_ptrs, merge_params));
   } else if (filter.type == BITSET) {
     using filter_mdspan_type    = raft::device_vector_view<std::uint32_t, int64_t, raft::row_major>;
     auto removed_indices_tensor = reinterpret_cast<DLManagedTensor*>(filter.addr);
@@ -343,8 +344,8 @@ void* _merge(cuvsResources_t res,
     cuvs::core::bitset_view<std::uint32_t, int64_t> removed_indices_bitset(
       removed_indices, total_size);
     auto bitset_filter_obj = cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset);
-    return new cuvs::neighbors::cagra::index<T, uint32_t>(
-      cuvs::neighbors::cagra::merge(*res_ptr, params_cpp, index_ptrs, bitset_filter_obj));
+    return new cuvs::neighbors::cagra::index<T, uint32_t>(cuvs::neighbors::cagra::merge(
+      *res_ptr, params_cpp, index_ptrs, merge_params, bitset_filter_obj));
   } else {
     RAFT_FAIL("Unsupported filter type: BITMAP");
   }
@@ -696,13 +697,38 @@ extern "C" cuvsError_t cuvsCagraMerge(cuvsResources_t res,
                                       size_t num_indices,
                                       cuvsFilter filter,
                                       cuvsCagraIndex_t output_index)
+{
+  return cuvsCagraMergeWithParams(
+    res, params, nullptr, indices, num_indices, filter, output_index);
+}
+
+extern "C" cuvsError_t cuvsCagraMergeWithParams(cuvsResources_t res,
+                                                cuvsCagraIndexParams_t params,
+                                                cuvsCagraMergeParams_t merge_params,
+                                                cuvsCagraIndex_t* indices,
+                                                size_t num_indices,
+                                                cuvsFilter filter,
+                                                cuvsCagraIndex_t output_index)
 {
   return cuvs::core::translate_exceptions([=] {
-    // Basic checks on inputs
     RAFT_EXPECTS(indices != nullptr && num_indices > 0, "indices array cannot be null or empty");
     RAFT_EXPECTS(params != nullptr, "params cannot be null");
 
-    // Use first index dtype as reference
+    auto merge_params_cpp = cuvs::neighbors::cagra::merge_params{};
+    if (merge_params != nullptr) {
+      RAFT_EXPECTS(merge_params->algo >= CUVS_CAGRA_MERGE_AUTO &&
+                     merge_params->algo <= CUVS_CAGRA_MERGE_REBUILD,
+                   "Unsupported CAGRA merge algorithm");
+      merge_params_cpp = {
+        .algo = static_cast<cuvs::neighbors::cagra::merge_algo>(merge_params->algo),
+        .levels = merge_params->levels,
+        .root_fanout = merge_params->root_fanout,
+        .lower_fanout = merge_params->lower_fanout,
+        .leader_fraction = merge_params->leader_fraction,
+        .max_leaders = merge_params->max_leaders,
+        .leaf_size = merge_params->leaf_size,
+        .leaf_degree = merge_params->leaf_degree};
+    }
     auto dtype = (*indices[0]).dtype;
     for (size_t i = 1; i < num_indices; ++i) {
       RAFT_EXPECTS((*indices[i]).dtype.code == dtype.code && (*indices[i]).dtype.bits == dtype.bits,
@@ -710,20 +736,19 @@ extern "C" cuvsError_t cuvsCagraMerge(cuvsResources_t res,
       RAFT_EXPECTS((*indices[i]).addr != 0, "All input indices must be built (non-empty)");
     }
     RAFT_EXPECTS(output_index != nullptr, "Output index pointer must not be null");
-    output_index->dtype = dtype;  // output index type matches inputs
-    // Dispatch based on data type
+    output_index->dtype = dtype;
     if (dtype.code == kDLFloat && dtype.bits == 32) {
-      output_index->addr =
-        reinterpret_cast<uintptr_t>(_merge<float>(res, *params, indices, num_indices, filter));
+      output_index->addr = reinterpret_cast<uintptr_t>(
+        _merge<float>(res, *params, merge_params_cpp, indices, num_indices, filter));
     } else if (dtype.code == kDLFloat && dtype.bits == 16) {
-      output_index->addr =
-        reinterpret_cast<uintptr_t>(_merge<half>(res, *params, indices, num_indices, filter));
+      output_index->addr = reinterpret_cast<uintptr_t>(
+        _merge<half>(res, *params, merge_params_cpp, indices, num_indices, filter));
     } else if (dtype.code == kDLInt && dtype.bits == 8) {
-      output_index->addr =
-        reinterpret_cast<uintptr_t>(_merge<int8_t>(res, *params, indices, num_indices, filter));
+      output_index->addr = reinterpret_cast<uintptr_t>(
+        _merge<int8_t>(res, *params, merge_params_cpp, indices, num_indices, filter));
     } else if (dtype.code == kDLUInt && dtype.bits == 8) {
-      output_index->addr =
-        reinterpret_cast<uintptr_t>(_merge<uint8_t>(res, *params, indices, num_indices, filter));
+      output_index->addr = reinterpret_cast<uintptr_t>(
+        _merge<uint8_t>(res, *params, merge_params_cpp, indices, num_indices, filter));
     } else {
       RAFT_FAIL("Unsupported index data type: code=%d, bits=%d", dtype.code, dtype.bits);
     }
@@ -769,6 +794,26 @@ extern "C" cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params
   });
 }
 
+extern "C" cuvsError_t cuvsCagraMergeParamsCreate(cuvsCagraMergeParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto defaults = cuvs::neighbors::cagra::merge_params{};
+    *params       = new cuvsCagraMergeParams{.algo            = CUVS_CAGRA_MERGE_AUTO,
+                                             .levels          = defaults.levels,
+                                             .root_fanout     = defaults.root_fanout,
+                                             .lower_fanout    = defaults.lower_fanout,
+                                             .leader_fraction = defaults.leader_fraction,
+                                             .max_leaders     = defaults.max_leaders,
+                                             .leaf_size       = defaults.leaf_size,
+                                             .leaf_degree     = defaults.leaf_degree};
+  });
+}
+
+extern "C" cuvsError_t cuvsCagraMergeParamsDestroy(cuvsCagraMergeParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
 extern "C" cuvsError_t cuvsCagraCompressionParamsCreate(cuvsCagraCompressionParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
diff --git a/c/tests/neighbors/ann_cagra_c.cu b/c/tests/neighbors/ann_cagra_c.cu
index 9c14bbea7d..963820d1b5 100644
--- a/c/tests/neighbors/ann_cagra_c.cu
+++ b/c/tests/neighbors/ann_cagra_c.cu
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -511,7 +511,13 @@ TEST(CagraC, BuildMergeSearch)
   filter.addr = 0;
 
   cuvsCagraIndex_t index_array[2] = {index_main, index_add};
-  ASSERT_EQ(cuvsCagraMerge(res, build_params, index_array, 2, filter, index_merged), CUVS_SUCCESS);
+  cuvsCagraMergeParams_t merge_params;
+  ASSERT_EQ(cuvsCagraMergeParamsCreate(&merge_params), CUVS_SUCCESS);
+  EXPECT_EQ(merge_params->algo, CUVS_CAGRA_MERGE_AUTO);
+  merge_params->algo = CUVS_CAGRA_MERGE_REBUILD;
+  ASSERT_EQ(
+    cuvsCagraMergeWithParams(res, build_params, merge_params, index_array, 2, filter, index_merged),
+    CUVS_SUCCESS);
 
   int64_t merged_dim = -1;
   ASSERT_EQ(cuvsCagraIndexGetDims(index_merged, &merged_dim), CUVS_SUCCESS);
@@ -562,6 +568,7 @@ TEST(CagraC, BuildMergeSearch)
   EXPECT_NEAR(distance_host, 0.0f, 1e-6);
 
   cuvsCagraSearchParamsDestroy(search_params);
+  cuvsCagraMergeParamsDestroy(merge_params);
   cuvsCagraIndexParamsDestroy(build_params);
   cuvsCagraIndexDestroy(index_merged);
   cuvsCagraIndexDestroy(index_add);

From bf2196336f3fc88d012025451745cf405e61a0cc Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 29 Jul 2026 20:17:18 +0000
Subject: [PATCH 18/41] round-robin leaf splitting + more tests

---
 .../detail/cagra/cagra_merge_scaffold.cuh     |  84 ++--
 .../ann_cagra/test_merge_fastener.cu          | 368 ++++++++++++++++++
 2 files changed, 426 insertions(+), 26 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index a031b634a9..9073cb34b3 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -787,18 +787,30 @@ inline auto leaf_gemm_supported(int64_t dimension, uint32_t leaf_size) -> bool
   return vector_elements * sizeof(float) + gram_elements * sizeof(float) <= GEMM_WORKSPACE_BYTES;
 }
 
+/** Leaves as strided views into `partitions->memberships`: leaf `i` holds the `counts[i]` records
+ * at `starts[i] + k * strides[i]`. A unit stride is a plain contiguous slice. */
 struct leaf_set {
   partition_set const* partitions = nullptr;
   std::vector<uint32_t> starts_host;
-  std::vector<uint32_t> ends_host;
+  std::vector<uint32_t> counts_host;
+  std::vector<uint32_t> strides_host;
   rmm::device_uvector<uint32_t> starts;
-  rmm::device_uvector<uint32_t> ends;
+  rmm::device_uvector<uint32_t> counts;
+  rmm::device_uvector<uint32_t> strides;
 };
 
-/** Divide final grouped partitions into consecutive bounded leaves, without geometric resplitting.
+/** Divide final grouped partitions into bounded leaves, without geometric resplitting.
  *
  * This is really just a fallback for when the tree isn't deep enough, and will produce obviously
  * inferior leaves but at no additional cost.
+ *
+ * A partition's memberships are ascending in consolidated row id (the root is the identity and
+ * every regroup is a stable sort), and origins are contiguous row-id blocks, so a partition is
+ * always origin-sorted. Slicing it into consecutive chunks can therefore hand the leaf kernel a
+ * single-origin leaf, and that kernel skips every same-origin pair -- such a leaf contributes no
+ * cross-input edges at all. Dealing the members round-robin across the same number of leaves
+ * spreads every origin block of length >= the leaf count over every leaf instead. A range that
+ * already fits in one leaf yields a unit stride, i.e. exactly the consecutive slice.
  */
 inline auto make_leaves(raft::resources const& res,
                         partition_set const& partitions,
@@ -806,19 +818,26 @@ inline auto make_leaves(raft::resources const& res,
 {
   auto stream = raft::resource::get_cuda_stream(res);
   std::vector<uint32_t> starts_host;
-  std::vector<uint32_t> ends_host;
+  std::vector<uint32_t> counts_host;
+  std::vector<uint32_t> strides_host;
   starts_host.reserve((partitions.memberships.size() + leaf_size - 1) / leaf_size);
-  ends_host.reserve(starts_host.capacity());
+  counts_host.reserve(starts_host.capacity());
+  strides_host.reserve(starts_host.capacity());
 
   int64_t covered = 0;
   for (auto const& range : partitions.ranges) {
     RAFT_EXPECTS(range.start == covered && range.end > range.start &&
                    range.end <= static_cast<int64_t>(partitions.memberships.size()),
                  "Fastener partition ranges must compactly cover all memberships");
-    for (int64_t start = range.start; start < range.end; start += leaf_size) {
-      starts_host.push_back(static_cast<uint32_t>(start));
-      ends_host.push_back(static_cast<uint32_t>(
-        std::min<int64_t>(range.end, start + static_cast<int64_t>(leaf_size))));
+    // Deal round-robin: leaf j takes local positions j, j + leaves, j + 2 * leaves, ... Leaf sizes
+    // differ by at most one and none exceeds leaf_size, so this also avoids the tiny trailing leaf
+    // that consecutive slicing leaves behind (a one-row leaf contributes nothing at all).
+    int64_t const size   = range.end - range.start;
+    int64_t const leaves = (size + static_cast<int64_t>(leaf_size) - 1) / leaf_size;
+    for (int64_t leaf = 0; leaf < leaves; ++leaf) {
+      starts_host.push_back(static_cast<uint32_t>(range.start + leaf));
+      counts_host.push_back(static_cast<uint32_t>((size - leaf + leaves - 1) / leaves));
+      strides_host.push_back(static_cast<uint32_t>(leaves));
     }
     covered = range.end;
   }
@@ -827,11 +846,18 @@ inline auto make_leaves(raft::resources const& res,
     "Fastener partition ranges did not cover all memberships");
 
   rmm::device_uvector<uint32_t> starts(starts_host.size(), stream);
-  rmm::device_uvector<uint32_t> ends(ends_host.size(), stream);
+  rmm::device_uvector<uint32_t> counts(counts_host.size(), stream);
+  rmm::device_uvector<uint32_t> strides(strides_host.size(), stream);
   raft::copy(starts.data(), starts_host.data(), starts.size(), stream);
-  raft::copy(ends.data(), ends_host.data(), ends.size(), stream);
-  return {
-    &partitions, std::move(starts_host), std::move(ends_host), std::move(starts), std::move(ends)};
+  raft::copy(counts.data(), counts_host.data(), counts.size(), stream);
+  raft::copy(strides.data(), strides_host.data(), strides.size(), stream);
+  return {&partitions,
+          std::move(starts_host),
+          std::move(counts_host),
+          std::move(strides_host),
+          std::move(starts),
+          std::move(counts),
+          std::move(strides)};
 }
 
 /** Fill every unwritten scaffold slot with its row ID; final prefix deduplication removes it. */
@@ -856,7 +882,8 @@ __global__ void manyway_gather_leaf_vectors_kernel(T const* dataset,
                                                    int leaf_size,
                                                    partition_membership const* memberships,
                                                    uint32_t const* leaf_starts,
-                                                   uint32_t const* leaf_ends,
+                                                   uint32_t const* leaf_counts,
+                                                   uint32_t const* leaf_strides,
                                                    int64_t leaf_offset,
                                                    int64_t leaf_count,
                                                    OutT* leaf_vectors)
@@ -869,12 +896,13 @@ __global__ void manyway_gather_leaf_vectors_kernel(T const* dataset,
     int64_t local_row  = (linear / output_dim) % leaf_size;
     int64_t local_leaf = linear / (output_dim * leaf_size);
     int64_t leaf       = leaf_offset + local_leaf;
-    int64_t leaf_n     = static_cast<int64_t>(leaf_ends[leaf] - leaf_starts[leaf]);
+    int64_t leaf_n     = static_cast<int64_t>(leaf_counts[leaf]);
     OutT value         = 0;
     if (local_row < leaf_n && d < input_dim) {
-      uint32_t point = memberships[leaf_starts[leaf] + local_row].id;
-      auto input     = dataset[static_cast<int64_t>(point) * input_dim + d];
-      value          = static_cast<OutT>(input);
+      uint32_t point =
+        memberships[leaf_starts[leaf] + local_row * static_cast<int64_t>(leaf_strides[leaf])].id;
+      auto input = dataset[static_cast<int64_t>(point) * input_dim + d];
+      value      = static_cast<OutT>(input);
     }
     leaf_vectors[linear] = value;
   }
@@ -887,7 +915,8 @@ static __global__ void manyway_leaf_gram_knn_kernel(GramT const* gram,
                                                     partition_membership const* memberships,
                                                     uint32_t const* origins,
                                                     uint32_t const* leaf_starts,
-                                                    uint32_t const* leaf_ends,
+                                                    uint32_t const* leaf_counts,
+                                                    uint32_t const* leaf_strides,
                                                     int64_t leaf_offset,
                                                     int64_t leaf_count,
                                                     int leaf_size,
@@ -897,16 +926,17 @@ static __global__ void manyway_leaf_gram_knn_kernel(GramT const* gram,
 {
   int64_t local_leaf = blockIdx.x;
   if (local_leaf >= leaf_count) { return; }
-  int64_t leaf   = leaf_offset + local_leaf;
-  uint32_t start = leaf_starts[leaf];
-  int leaf_n     = static_cast<int>(leaf_ends[leaf] - start);
+  int64_t leaf         = leaf_offset + local_leaf;
+  uint32_t start       = leaf_starts[leaf];
+  uint32_t leaf_stride = leaf_strides[leaf];
+  int leaf_n           = static_cast<int>(leaf_counts[leaf]);
   if (leaf_n <= 1 || leaf_n > MAX_LEAF_SIZE) { return; }
 
   // Stage the leaf's memberships and origin labels once per block.
   __shared__ partition_membership records[MAX_LEAF_SIZE];
   __shared__ uint32_t leaf_origins[MAX_LEAF_SIZE];
   for (int i = threadIdx.x; i < leaf_n; i += blockDim.x) {
-    records[i]      = memberships[start + i];
+    records[i]      = memberships[start + static_cast<uint32_t>(i) * leaf_stride];
     leaf_origins[i] = origins[records[i].id];
   }
   __syncthreads();
@@ -1006,7 +1036,8 @@ auto build_leaf_neighbors(raft::resources const& res,
       leaf_size,
       memberships.data(),
       leaves.starts.data(),
-      leaves.ends.data(),
+      leaves.counts.data(),
+      leaves.strides.data(),
       static_cast<int64_t>(leaf_offset),
       static_cast<int64_t>(batch_size),
       leaf_vectors.data());
@@ -1027,7 +1058,8 @@ auto build_leaf_neighbors(raft::resources const& res,
                                                                memberships.data(),
                                                                origins,
                                                                leaves.starts.data(),
-                                                               leaves.ends.data(),
+                                                               leaves.counts.data(),
+                                                               leaves.strides.data(),
                                                                static_cast<int64_t>(leaf_offset),
                                                                static_cast<int64_t>(batch_size),
                                                                leaf_size,
@@ -1167,7 +1199,7 @@ auto build(raft::resources const& res,
     occurrence_stride *= fanout;
   }
 
-  // Leaf construction: only consecutive range slicing occurs after configured geometric depth.
+  // Leaf construction: only range slicing occurs after configured geometric depth.
   auto leaves   = make_leaves(res, partitions, params.leaf_size);
   auto scaffold = build_leaf_neighbors(res, dataset, leaves, origins.data(), union_degree, params);
   raft::resource::sync_stream(res);
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index aed9eb425d..62ccf87122 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -26,6 +26,7 @@
 #include <algorithm>
 #include <cstdint>
 #include <limits>
+#include <numeric>
 #include <type_traits>
 #include <vector>
 
@@ -99,6 +100,45 @@ void expect_valid_graph(index<T, uint32_t> const& merged, int64_t rows, int64_t
   }
 }
 
+/** Weakly-connected component count of the merged graph, treating every edge as undirected.
+ * `expect_valid_graph` only checks that entries are in range and not self-loops, so it cannot see a
+ * merge that left the input graphs as separate components. */
+template <typename T>
+auto count_connected_components(index<T, uint32_t> const& merged) -> int64_t
+{
+  raft::resources res;
+  int64_t rows   = merged.graph().extent(0);
+  int64_t degree = merged.graph().extent(1);
+  auto host      = raft::make_host_matrix<uint32_t, int64_t>(rows, degree);
+  raft::copy(host.data_handle(),
+             merged.graph().data_handle(),
+             host.size(),
+             raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+
+  std::vector<int64_t> parent(static_cast<size_t>(rows));
+  std::iota(parent.begin(), parent.end(), int64_t{0});
+  auto find = [&parent](int64_t node) {
+    while (parent[node] != node) {
+      parent[node] = parent[parent[node]];
+      node         = parent[node];
+    }
+    return node;
+  };
+  for (int64_t row = 0; row < rows; ++row) {
+    for (int64_t column = 0; column < degree; ++column) {
+      int64_t left  = find(row);
+      int64_t right = find(static_cast<int64_t>(host(row, column)));
+      if (left != right) { parent[left] = right; }
+    }
+  }
+  int64_t components = 0;
+  for (int64_t row = 0; row < rows; ++row) {
+    if (find(row) == row) { ++components; }
+  }
+  return components;
+}
+
 template <typename T>
 void expect_dataset_order(raft::resources const& res,
                           index<T, uint32_t> const& merged,
@@ -394,6 +434,163 @@ TEST(CagraMergeFastener, DuplicateAcrossInputsAppearInEachOthersScaffoldNeighbor
   EXPECT_TRUE(row_contains(duplicate1, duplicate0));
 }
 
+/**
+ * A partition's memberships are ascending in consolidated row id: the root is the identity, tiles
+ * emit in parent input order, and every regroup is a stable sort. `make_leaves` relies on this to
+ * know that consecutive slicing groups a leaf by origin, so pin the invariant down explicitly --
+ * a future non-stable sort should trip a test rather than silently weaken the guarantee.
+ */
+TEST(CagraMergeFastener, MembershipsRemainAscendingWithinEachPartition)
+{
+  using namespace detail::merge_scaffold;
+  raft::resources res;
+  auto stream            = raft::resource::get_cuda_stream(res);
+  constexpr int64_t rows = 96;
+  constexpr int64_t dim  = 4;
+  auto host_dataset      = make_dataset<float>(res, rows, dim);
+  auto dataset           = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  raft::copy(dataset.data_handle(), host_dataset.data_handle(), dataset.size(), stream);
+
+  split_context context(stream, rows);
+  manyway_l2_norms_kernel<<<static_cast<int>((rows + 3) / 4), 128, 0, stream>>>(
+    dataset.data_handle(), rows, dim, context.norms.data());
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  auto partitions            = make_root_partition(res, rows);
+  uint32_t occurrence_stride = 1;
+  for (uint32_t level = 0; level < 2; ++level) {
+    partitions = split_manyway(res,
+                               raft::make_const_mdspan(dataset.view()),
+                               partitions,
+                               split_params{.fanout            = 2,
+                                            .leader_fraction   = 0.05,
+                                            .max_leaders       = 4,
+                                            .leaf_size         = 16,
+                                            .level             = level,
+                                            .occurrence_stride = occurrence_stride},
+                               context);
+    occurrence_stride *= 2;
+  }
+
+  std::vector<partition_membership> records(partitions.memberships.size());
+  raft::copy(records.data(), partitions.memberships.data(), records.size(), stream);
+  raft::resource::sync_stream(res);
+
+  for (auto const& range : partitions.ranges) {
+    for (int64_t slot = range.start + 1; slot < range.end; ++slot) {
+      EXPECT_LT(records[slot - 1].id, records[slot].id)
+        << "partition [" << range.start << ", " << range.end << ") is not ascending at " << slot;
+    }
+  }
+}
+
+/**
+ * Regression for the single-origin leaf case: because memberships are ascending in row id and
+ * origins are contiguous row-id blocks, every partition is origin-sorted. Slicing an oversized one
+ * into consecutive chunks can therefore hand the leaf kernel a leaf drawn from a single input, and
+ * that kernel skips every same-origin pair -- such a leaf emits no cross-input edges at all.
+ *
+ * Two identical inputs whose row counts are multiples of `leaf_size` align every consecutive chunk
+ * to one origin. `levels=1` with `root_fanout=1` and a leader fraction below one row clamps to a
+ * single leader, so all rows land in one oversized partition and the fallback is guaranteed to run.
+ */
+TEST(CagraMergeFastener, IdenticalInputsAlignedToLeafSizeGetCrossOriginScaffoldEdges)
+{
+  using namespace detail::merge_scaffold;
+  raft::resources res;
+  auto stream                    = raft::resource::get_cuda_stream(res);
+  constexpr int64_t part_rows    = 128;  // two whole leaves per input
+  constexpr int64_t rows         = part_rows * 2;
+  constexpr int64_t dim          = 8;
+  constexpr uint32_t leaf_degree = 4;
+
+  // Both halves identical, so each row's exact duplicate sits in the other input at distance zero.
+  auto host = raft::make_host_matrix<float, int64_t>(res, rows, dim);
+  for (int64_t row = 0; row < part_rows; ++row) {
+    for (int64_t column = 0; column < dim; ++column) {
+      auto value                    = static_cast<float>(row * 17 + column);
+      host(row, column)             = value;
+      host(part_rows + row, column) = value;
+    }
+  }
+  auto dataset = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  raft::copy(dataset.data_handle(), host.data_handle(), dataset.size(), stream);
+
+  build_params params;
+  params.levels          = 1;
+  params.root_fanout     = 1;
+  params.lower_fanout    = 1;
+  params.leader_fraction = 1.0 / static_cast<double>(rows * 2);
+  params.max_leaders     = 16;
+  params.leaf_size       = 64;
+  params.leaf_degree     = leaf_degree;
+
+  std::vector<int64_t> offsets{0, part_rows, rows};
+  auto scaffold = build(res, raft::make_const_mdspan(dataset.view()), offsets, params);
+  ASSERT_EQ(scaffold.extent(0), rows);
+  ASSERT_EQ(scaffold.extent(1), static_cast<int64_t>(leaf_degree));
+
+  auto scaffold_host = raft::make_host_matrix<uint32_t, int64_t>(res, rows, scaffold.extent(1));
+  raft::copy(scaffold_host.data_handle(), scaffold.data_handle(), scaffold.size(), stream);
+  raft::resource::sync_stream(res);
+
+  // Slots a leaf never fills keep their self-ID prefill, so a row with no cross-origin entry is
+  // exactly a row that a single-origin leaf failed to serve.
+  for (int64_t row = 0; row < rows; ++row) {
+    bool crosses = false;
+    for (int64_t column = 0; column < scaffold_host.extent(1); ++column) {
+      auto neighbor = static_cast<int64_t>(scaffold_host(row, column));
+      crosses       = crosses || ((neighbor < part_rows) != (row < part_rows));
+    }
+    EXPECT_TRUE(crosses) << "row " << row << " has no cross-input scaffold neighbor";
+  }
+}
+
+/**
+ * End-to-end form of the same case: with no cross-input edge the merge returns the two input ring
+ * graphs side by side, i.e. two disconnected components rather than one merged index.
+ */
+TEST(CagraMergeFastener, IdenticalInputsAlignedToLeafSizeStayConnected)
+{
+  raft::resources res;
+  constexpr int64_t part_rows = 128;  // two whole leaves per input
+  constexpr int64_t rows      = part_rows * 2;
+  constexpr int64_t dim       = 8;
+  constexpr int64_t degree    = 4;
+
+  auto data   = make_dataset<float>(res, part_rows, dim, 1234ULL);
+  auto graph0 = make_ring_graph(res, part_rows, degree);
+  auto graph1 = make_ring_graph(res, part_rows, degree);
+  auto metric = cuvs::distance::DistanceType::L2Expanded;
+  index<float, uint32_t> index0(
+    res, metric, raft::make_const_mdspan(data.view()), raft::make_const_mdspan(graph0.view()));
+  index<float, uint32_t> index1(
+    res, metric, raft::make_const_mdspan(data.view()), raft::make_const_mdspan(graph1.view()));
+
+  index_params params;
+  params.metric                    = metric;
+  params.graph_degree              = degree;
+  params.intermediate_graph_degree = degree;
+  params.attach_dataset_on_build   = true;
+  params.guarantee_connectivity    = false;
+
+  merge_params fastener;
+  fastener.algo            = merge_algo::FASTENER;
+  fastener.levels          = 1;
+  fastener.root_fanout     = 1;
+  fastener.lower_fanout    = 1;
+  fastener.leader_fraction = 1.0 / static_cast<double>(rows * 2);
+  fastener.max_leaders     = 16;
+  fastener.leaf_size       = 64;
+  fastener.leaf_degree     = 4;
+
+  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+  auto merged = merge(res, params, indices, fastener);
+  expect_valid_graph(merged, rows, degree);
+  EXPECT_EQ(count_connected_components(merged), 1)
+    << "the inputs were merged without any edge connecting them";
+}
+
 /**
  * Verifies the leaf-GEMM eligibility boundary, which is the same for every scalar type because
  * every type gathers into float leaf vectors, and confirms that a dimension that cannot fit one
@@ -449,6 +646,61 @@ TEST(CagraMergeFastener, LeafGemmLimitsRejectOversizedWorkspaceDimensions)
   EXPECT_EQ(index1.dataset().extent(0), rows);
 }
 
+/**
+ * The appended candidate graph is sorted by sort_knn_graph_device_inplace, whose kernel capacity is
+ * kMaxSortDegree. Preflight must reject a combined candidate width above that limit, because the
+ * sorter would otherwise fail only after consolidation and scaffold construction had already run --
+ * and under AUTO, past the point where the rebuild fallback could still be taken.
+ */
+TEST(CagraMergeFastener, PreflightRejectsCandidateDegreeAboveSortLimit)
+{
+  raft::resources res;
+  constexpr int64_t rows = 600;
+  constexpr int64_t dim  = 8;
+  auto metric            = cuvs::distance::DistanceType::L2Expanded;
+
+  // Defaults give spill = root_fanout * lower_fanout = 6 and leaf_degree 4, i.e.
+  // scaffold_degree 24.
+  constexpr uint64_t scaffold_degree = 24;
+  constexpr auto sort_limit          = detail::graph::kMaxSortDegree;
+
+  auto dataset0 = make_dataset<float>(res, rows, dim, 1234ULL);
+  auto dataset1 = make_dataset<float>(res, rows, dim, 5678ULL);
+
+  auto check = [&](int64_t input_degree) {
+    auto graph0 = make_ring_graph(res, rows, input_degree);
+    auto graph1 = make_ring_graph(res, rows, input_degree);
+    index<float, uint32_t> index0(res,
+                                  metric,
+                                  raft::make_const_mdspan(dataset0.view()),
+                                  raft::make_const_mdspan(graph0.view()));
+    index<float, uint32_t> index1(res,
+                                  metric,
+                                  raft::make_const_mdspan(dataset1.view()),
+                                  raft::make_const_mdspan(graph1.view()));
+
+    index_params params;
+    params.metric                    = metric;
+    params.graph_degree              = 64;
+    params.intermediate_graph_degree = 64;
+    merge_params fastener;
+    fastener.algo = merge_algo::FASTENER;
+    std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+    return detail::preflight_fastener(
+      params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
+  };
+
+  // Exactly at the limit stays eligible; one degree past it is rejected for that specific reason.
+  auto at_limit = check(static_cast<int64_t>(sort_limit - scaffold_degree));
+  EXPECT_TRUE(at_limit.eligible) << at_limit.reason;
+
+  auto past_limit = check(static_cast<int64_t>(sort_limit - scaffold_degree) + 1);
+  EXPECT_FALSE(past_limit.eligible);
+  EXPECT_EQ(past_limit.reason,
+            "the widest input graph degree plus the scaffold degree must not exceed " +
+              std::to_string(sort_limit));
+}
+
 /**
  * Exercises an explicit Fastener merge for float, half, int8, and uint8 data, including graph
  * validity, concatenated dataset order, output ownership, and preservation of both input indices.
@@ -564,6 +816,122 @@ std::vector<AnnCagraInputs> generate_fastener_merge_recall_inputs()
 }
 }  // namespace
 
+/**
+ * Search recall for a Fastener merge of `n_inputs` real CAGRA indices, against exact brute-force
+ * ground truth.
+ *
+ * The shared AnnCagraIndexMergeTest fixture always splits its database into exactly two parts and
+ * is instantiated for float only, so it cannot cover a non-float dtype or a fan-in above two. This
+ * builds the parts directly instead. Slicing the database into consecutive ranges and merging in
+ * that order reproduces the original row order in the merged index, so ground-truth indices
+ * computed over the whole database apply to the merged result without remapping.
+ */
+template <typename DataT>
+void run_fastener_merge_recall(size_t n_inputs, double min_recall)
+{
+  raft::resources res;
+  auto stream               = raft::resource::get_cuda_stream(res);
+  constexpr int64_t rows    = 2000;
+  constexpr int64_t dim     = 16;
+  constexpr int64_t n_query = 100;
+  constexpr uint32_t k      = 16;
+  constexpr uint32_t degree = 32;
+  auto metric               = cuvs::distance::DistanceType::L2Expanded;
+
+  auto host_data    = make_dataset<DataT>(res, rows, dim, 1234ULL, metric);
+  auto host_queries = make_dataset<DataT>(res, n_query, dim, 9876ULL, metric);
+  auto data         = raft::make_device_matrix<DataT, int64_t>(res, rows, dim);
+  auto queries      = raft::make_device_matrix<DataT, int64_t>(res, n_query, dim);
+  raft::copy(data.data_handle(), host_data.data_handle(), host_data.size(), stream);
+  raft::copy(queries.data_handle(), host_queries.data_handle(), host_queries.size(), stream);
+  raft::resource::sync_stream(res);
+
+  index_params build_params;
+  build_params.metric                    = metric;
+  build_params.graph_degree              = degree;
+  build_params.intermediate_graph_degree = degree * 2;
+  build_params.attach_dataset_on_build   = true;
+
+  // Consecutive, near-equal slices so the merged row order matches the original database.
+  std::vector<index<DataT, uint32_t>> parts;
+  parts.reserve(n_inputs);
+  int64_t rows_per_part = raft::ceildiv<int64_t>(rows, static_cast<int64_t>(n_inputs));
+  for (size_t part = 0; part < n_inputs; ++part) {
+    int64_t start = static_cast<int64_t>(part) * rows_per_part;
+    int64_t count = std::min<int64_t>(rows_per_part, rows - start);
+    ASSERT_GT(count, 0);
+    auto slice = raft::make_device_matrix_view<const DataT, int64_t>(
+      data.data_handle() + start * dim, count, dim);
+    parts.emplace_back(cagra::build(res, build_params, slice));
+  }
+
+  std::vector<index<DataT, uint32_t>*> inputs;
+  for (auto& part : parts) {
+    inputs.push_back(&part);
+  }
+  merge_params fastener;
+  fastener.algo = merge_algo::FASTENER;
+  auto merged   = merge(res, build_params, inputs, fastener);
+  ASSERT_EQ(merged.size(), rows);
+
+  auto found_indices   = raft::make_device_matrix<uint32_t, int64_t>(res, n_query, k);
+  auto found_distances = raft::make_device_matrix<float, int64_t>(res, n_query, k);
+  search_params search;
+  search.itopk_size = 256;
+  cagra::search(res,
+                search,
+                merged,
+                raft::make_const_mdspan(queries.view()),
+                found_indices.view(),
+                found_distances.view());
+
+  auto exact_indices   = raft::make_device_matrix<uint32_t, int64_t>(res, n_query, k);
+  auto exact_distances = raft::make_device_matrix<float, int64_t>(res, n_query, k);
+  cuvs::neighbors::naive_knn<float, DataT, uint32_t>(res,
+                                                     exact_distances.data_handle(),
+                                                     exact_indices.data_handle(),
+                                                     queries.data_handle(),
+                                                     data.data_handle(),
+                                                     n_query,
+                                                     rows,
+                                                     dim,
+                                                     k,
+                                                     metric);
+
+  size_t total = static_cast<size_t>(n_query) * k;
+  std::vector<uint32_t> found_idx(total), exact_idx(total);
+  std::vector<float> found_dist(total), exact_dist(total);
+  raft::copy(found_idx.data(), found_indices.data_handle(), total, stream);
+  raft::copy(exact_idx.data(), exact_indices.data_handle(), total, stream);
+  raft::copy(found_dist.data(), found_distances.data_handle(), total, stream);
+  raft::copy(exact_dist.data(), exact_distances.data_handle(), total, stream);
+  raft::resource::sync_stream(res);
+
+  EXPECT_TRUE(cuvs::neighbors::eval_neighbours(
+    exact_idx, found_idx, exact_dist, found_dist, n_query, k, 0.001, min_recall));
+}
+
+/**
+ * Non-float recall coverage. The dtype tests elsewhere in this file only assert that the merged
+ * graph is structurally valid; this checks that a uint8 merge is actually searchable.
+ */
+TEST(CagraMergeFastener, Uint8TwoWayMergeSearchRecall)
+{
+  run_fastener_merge_recall<uint8_t>(2, 0.85);
+}
+
+/** Recall coverage for a fan-in above two, which the shared fixture cannot express. */
+TEST(CagraMergeFastener, FloatFourWayMergeSearchRecall)
+{
+  run_fastener_merge_recall<float>(4, 0.85);
+}
+
+/** Both dimensions at once: non-float data merged four ways. */
+TEST(CagraMergeFastener, Uint8FourWayMergeSearchRecall)
+{
+  run_fastener_merge_recall<uint8_t>(4, 0.85);
+}
+
 typedef AnnCagraIndexMergeTest<float, float, std::uint32_t> AnnCagraFastenerMergeRecallTest;
 TEST_P(AnnCagraFastenerMergeRecallTest, DefaultFloatMergeSearchRecallAgainstBruteForce)
 {

From bf331a2f642914d1ec55fa29a570c84a053093de Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Wed, 29 Jul 2026 20:27:28 +0000
Subject: [PATCH 19/41] Device OOM fallback for AUTO

---
 .../neighbors/detail/cagra/cagra_merge.cuh    | 26 +++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 73e43e6c44..7344ece924 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -27,6 +27,7 @@
 
 #include <limits>
 #include <memory>
+#include <new>
 #include <string>
 #include <type_traits>
 #include <vector>
@@ -330,6 +331,14 @@ auto preflight_fastener(cagra::index_params const& params,
   if (static_cast<uint64_t>(params.graph_degree) > max_input_degree + scaffold_degree) {
     return reject("graph_degree exceeds the input graph plus scaffold capacity");
   }
+  // The appended candidate graph is sorted by sort_knn_graph_device_inplace, whose kernel capacity
+  // is kMaxSortDegree. Without this check an input degree at the limit plus any scaffold passes
+  // preflight and then fails inside the sorter, after the merge has already started mutating -- and
+  // in AUTO that also loses the rebuild fallback.
+  if (max_input_degree + scaffold_degree > cagra::detail::graph::kMaxSortDegree) {
+    return reject("the widest input graph degree plus the scaffold degree must not exceed " +
+                  std::to_string(cagra::detail::graph::kMaxSortDegree));
+  }
 
   result.rows     = static_cast<int64_t>(rows);
   result.eligible = true;
@@ -424,6 +433,23 @@ index<T, IdxT> merge(raft::resources const& handle,
     RAFT_FAIL("FASTENER cagra::merge is unsupported: %s", preflight.reason.c_str());
   }
 
+  if (merge_params.algo == cagra::merge_algo::AUTO) {
+    // Preflight validates shapes and configured limits, but it cannot know whether the consolidated
+    // dataset, the temporary GEMM workspaces and the candidate graph will all fit at run time. The
+    // rebuild path can still fall back to host memory, so without this retry an AUTO merge that
+    // succeeded before this algorithm existed could start failing. Fastener never mutates its
+    // inputs -- it only reads them and allocates its own output -- so the rebuild is free to run on
+    // the same indices after a failed attempt has unwound. Explicit FASTENER still surfaces the
+    // failure rather than silently doing something much slower.
+    try {
+      return merge_fastener(handle, params, merge_params, indices, preflight);
+    } catch (std::bad_alloc const& failure) {
+      RAFT_LOG_WARN("Fastener cagra::merge could not allocate (%s); falling back to rebuild",
+                    failure.what());
+      return merge_rebuild(handle, params, indices, row_filter);
+    }
+  }
+
   return merge_fastener(handle, params, merge_params, indices, preflight);
 }
 

From 488ab76e4524bf6d410a7f4656d450817e1dc5f2 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 30 Jul 2026 14:37:25 +0000
Subject: [PATCH 20/41] inferred tile_rows

---
 .../detail/cagra/cagra_merge_scaffold.cuh     | 22 ++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 9073cb34b3..39875146a3 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -602,8 +602,28 @@ void assign_bucket(raft::resources const& res,
   auto stream = raft::resource::get_cuda_stream(res);
   auto dim    = dataset.extent(1);
 
+  // Derive the tile height from this bucket's shapes rather than assuming the configured default
+  // fits. The assignment workspace holds the gathered leader matrix once, plus per-row point,
+  // dot-product and selection storage, so a large dimension or a wide padded leader count can
+  // overflow the budget at the default height -- preflight only validates the leaf GEMM shape, so
+  // such a configuration would otherwise pass preflight and then trip the assertion below.
+  //
+  // This only ever shrinks the tile, so every configuration that fits today is unaffected.
+  size_t leader_bytes =
+    static_cast<size_t>(padded_leaders) * static_cast<size_t>(dim) * sizeof(float) +
+    static_cast<size_t>(padded_leaders) * sizeof(uint32_t) + sizeof(assignment_tile);
+  size_t row_bytes = (static_cast<size_t>(dim) + static_cast<size_t>(padded_leaders) +
+     static_cast<size_t>(params.fanout)) *
+      sizeof(float) +
+    static_cast<size_t>(params.fanout) * sizeof(int);
+  RAFT_EXPECTS(leader_bytes + row_bytes <= context.gemm_workspace_bytes,
+               "Fastener assignment workspace cannot fit the leader matrix and a single point row");
+  int tile_rows = static_cast<int>(
+    std::max<size_t>(1,
+                     std::min<size_t>(static_cast<size_t>(context.assignment_tile_rows),
+                                      (context.gemm_workspace_bytes - leader_bytes) / row_bytes)));
+
   // Cut every parent in the bucket into fixed-height tiles
-  int tile_rows = context.assignment_tile_rows;
   std::vector<assignment_tile> tiles;
   for (auto const* entry : bucket) {
     for (int64_t start = entry->parent.start; start < entry->parent.end; start += tile_rows) {

From a44ca4254923ee281e7dcb2258a6adf7ed270663 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 30 Jul 2026 15:09:34 +0000
Subject: [PATCH 21/41] removed needless sync

---
 cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 39875146a3..fa4ae35950 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -613,9 +613,9 @@ void assign_bucket(raft::resources const& res,
     static_cast<size_t>(padded_leaders) * static_cast<size_t>(dim) * sizeof(float) +
     static_cast<size_t>(padded_leaders) * sizeof(uint32_t) + sizeof(assignment_tile);
   size_t row_bytes = (static_cast<size_t>(dim) + static_cast<size_t>(padded_leaders) +
-     static_cast<size_t>(params.fanout)) *
-      sizeof(float) +
-    static_cast<size_t>(params.fanout) * sizeof(int);
+                      static_cast<size_t>(params.fanout)) *
+                       sizeof(float) +
+                     static_cast<size_t>(params.fanout) * sizeof(int);
   RAFT_EXPECTS(leader_bytes + row_bytes <= context.gemm_workspace_bytes,
                "Fastener assignment workspace cannot fit the leader matrix and a single point row");
   int tile_rows = static_cast<int>(
@@ -1298,7 +1298,7 @@ auto append_to_input_graphs(
       static_cast<uint32_t>(offsets[part]));
     RAFT_CUDA_TRY(cudaGetLastError());
   }
-  raft::resource::sync_stream(res);
+
   return graph;
 }
 

From b9b7da401a813724324734897448d6c7aeb6cf69 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 30 Jul 2026 16:48:53 +0000
Subject: [PATCH 22/41] bumping default max leaders to power of 2

---
 cpp/include/cuvs/neighbors/cagra.hpp                    | 6 +++---
 cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 6c88009439..6d36ce855f 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2839,9 +2839,9 @@ struct merge_params {
   uint32_t root_fanout   = 2;
   uint32_t lower_fanout  = 3;
   double leader_fraction = 0.02;
-  uint32_t max_leaders   = 1000;
-  uint32_t leaf_size     = 256;
-  uint32_t leaf_degree   = 4;
+  uint32_t max_leaders = 1024;
+  uint32_t leaf_size   = 256;
+  uint32_t leaf_degree = 4;
 };
 
 /** @brief Merge multiple physical CAGRA indices into one.
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index fa4ae35950..8faf6a3a5b 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -73,7 +73,7 @@ struct build_params {
   uint32_t root_fanout   = 2;
   uint32_t lower_fanout  = 3;
   double leader_fraction = 0.02;
-  uint32_t max_leaders   = 1000;
+  uint32_t max_leaders   = 1024;
   uint32_t leaf_size     = MAX_LEAF_SIZE;
   uint32_t leaf_degree   = 4;
 };
@@ -82,7 +82,7 @@ struct build_params {
 struct split_params {
   uint32_t fanout            = 1;
   double leader_fraction     = 0.02;
-  uint32_t max_leaders       = 1000;
+  uint32_t max_leaders       = 1024;
   uint32_t leaf_size         = MAX_LEAF_SIZE;
   uint32_t level             = 0;
   uint32_t occurrence_stride = 1;

From ef0cf5e9bc59d8cea487419422d2b2d6955f18da Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 30 Jul 2026 17:20:03 +0000
Subject: [PATCH 23/41] determinism test

---
 .../ann_cagra/test_merge_fastener.cu          | 58 +++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 62ccf87122..7e49309070 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -434,6 +434,64 @@ TEST(CagraMergeFastener, DuplicateAcrossInputsAppearInEachOthersScaffoldNeighbor
   EXPECT_TRUE(row_contains(duplicate1, duplicate0));
 }
 
+/**
+ * Scaffold construction should be deterministic, and if future changes break that determinism,
+ * they should do so deliberately.
+ *
+ * Everything the merge contributes ahead of graph::optimize is deterministic by design: the leader
+ * sample is a fixed seed and a strided offset, assignment is a batched GEMM plus a deterministic
+ * select_k, and every regroup is a stable sort. graph::optimize is not deterministic because its
+ * reverse graph is built with an atomic bump allocator, so the adjacency order, and therefore the
+ * pruning outcome, varies between runs. Making that deterministic is out of scope, but we still
+ * want to be checking we're not accidentally introducing non-determinism.
+ *
+ * Sized to exercise the paths that could plausibly introduce non-determinism: enough rows for
+ * several assignment tiles per parent, two configured levels, and both leader buckets.
+ */
+TEST(CagraMergeFastener, ScaffoldConstructionIsBitReproducible)
+{
+  using namespace detail::merge_scaffold;
+  raft::resources res;
+  auto stream                 = raft::resource::get_cuda_stream(res);
+  constexpr int64_t part_rows = 4000;  // > ASSIGNMENT_TILE_ROWS, so parents span multiple tiles
+  constexpr int64_t rows      = part_rows * 2;
+  constexpr int64_t dim       = 32;
+
+  auto host = make_dataset<float>(res, rows, dim, 4242ULL);
+  auto data = raft::make_device_matrix<float, int64_t>(res, rows, dim);
+  raft::copy(data.data_handle(), host.data_handle(), host.size(), stream);
+  raft::resource::sync_stream(res);
+
+  std::vector<int64_t> offsets{0, part_rows, rows};
+  build_params params;  // default two-level controls
+
+  auto scaffold_bytes = [&] {
+    auto scaffold = build<float>(res, raft::make_const_mdspan(data.view()), offsets, params);
+    std::vector<uint32_t> host_scaffold(
+      static_cast<size_t>(scaffold.extent(0) * scaffold.extent(1)));
+    raft::copy(host_scaffold.data(), scaffold.data_handle(), host_scaffold.size(), stream);
+    raft::resource::sync_stream(res);
+    return host_scaffold;
+  };
+
+  auto first  = scaffold_bytes();
+  auto second = scaffold_bytes();
+
+  ASSERT_EQ(first.size(), second.size());
+  ASSERT_GT(first.size(), 0u);
+  size_t differing        = 0;
+  size_t first_difference = first.size();
+  for (size_t i = 0; i < first.size(); ++i) {
+    if (first[i] != second[i]) {
+      ++differing;
+      first_difference = std::min(first_difference, i);
+    }
+  }
+  EXPECT_EQ(differing, 0u) << differing << " of " << first.size()
+                           << " scaffold entries differ between identical runs, first at index "
+                           << first_difference;
+}
+
 /**
  * A partition's memberships are ascending in consolidated row id: the root is the identity, tiles
  * emit in parent input order, and every regroup is a stable sort. `make_leaves` relies on this to

From 298bfcf641007ed6c6bf8e640dfd9c00f1ac007d Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 30 Jul 2026 17:21:35 +0000
Subject: [PATCH 24/41] missed files on default increase

---
 .../cuvs_bench/config/algos/constraints/__init__.py           | 2 +-
 python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
index bf3b1f8aea..9f2e41bf66 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
+++ b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
@@ -28,7 +28,7 @@ def cuvs_cagra_build(params, dims):
         root_fanout = params.get("fastener_root_fanout", 2)
         lower_fanout = params.get("fastener_lower_fanout", 3)
         leader_fraction = params.get("fastener_leader_fraction", 0.02)
-        max_leaders = params.get("fastener_max_leaders", 1000)
+        max_leaders = params.get("fastener_max_leaders", 1024)
         leaf_size = params.get("fastener_leaf_size", 256)
         leaf_degree = params.get("fastener_leaf_degree", 4)
         valid = valid and levels > 0
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
index e36f606ad2..b2c2c22eda 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
@@ -43,7 +43,7 @@ groups:
       fastener_root_fanout: [2]
       fastener_lower_fanout: [3]
       fastener_leader_fraction: [0.02]
-      fastener_max_leaders: [1000]
+      fastener_max_leaders: [1024]
       fastener_leaf_size: [256]
       fastener_leaf_degree: [4]
     search:
@@ -61,7 +61,7 @@ groups:
       fastener_root_fanout: [4]
       fastener_lower_fanout: [2]
       fastener_leader_fraction: [0.01]
-      fastener_max_leaders: [1000]
+      fastener_max_leaders: [1024]
       fastener_leaf_size: [256]
       fastener_leaf_degree: [4]
     search:

From 8891aaebb217d912a9d92a74f470454f6de886be Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 30 Jul 2026 20:14:38 +0000
Subject: [PATCH 25/41] removed obsolete template

---
 .../detail/cagra/cagra_merge_scaffold.cuh     | 29 +++++++++----------
 1 file changed, 14 insertions(+), 15 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 8faf6a3a5b..74189f7d3a 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -930,8 +930,7 @@ __global__ void manyway_gather_leaf_vectors_kernel(T const* dataset,
 
 /** Select the nearest cross-input neighbors of each point from the leaf Gram matrix. One block
  *  does one leaf. One thread does one point. */
-template <typename GramT>
-static __global__ void manyway_leaf_gram_knn_kernel(GramT const* gram,
+static __global__ void manyway_leaf_gram_knn_kernel(float const* gram,
                                                     partition_membership const* memberships,
                                                     uint32_t const* origins,
                                                     uint32_t const* leaf_starts,
@@ -1073,19 +1072,19 @@ auto build_leaf_neighbors(raft::resources const& res,
                              gram_stride,
                              dimension,
                              static_cast<int>(batch_size));
-    manyway_leaf_gram_knn_kernel<float>
-      <<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(gram.data(),
-                                                               memberships.data(),
-                                                               origins,
-                                                               leaves.starts.data(),
-                                                               leaves.counts.data(),
-                                                               leaves.strides.data(),
-                                                               static_cast<int64_t>(leaf_offset),
-                                                               static_cast<int64_t>(batch_size),
-                                                               leaf_size,
-                                                               leaf_degree,
-                                                               union_degree,
-                                                               graph.data_handle());
+    manyway_leaf_gram_knn_kernel<<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(
+      gram.data(),
+      memberships.data(),
+      origins,
+      leaves.starts.data(),
+      leaves.counts.data(),
+      leaves.strides.data(),
+      static_cast<int64_t>(leaf_offset),
+      static_cast<int64_t>(batch_size),
+      leaf_size,
+      leaf_degree,
+      union_degree,
+      graph.data_handle());
     RAFT_CUDA_TRY(cudaGetLastError());
   }
 

From 38c59f75e037fe278de487c9792f22210cd4567d Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 30 Jul 2026 20:35:48 +0000
Subject: [PATCH 26/41] style miss

---
 cpp/include/cuvs/neighbors/cagra.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 6d36ce855f..297fe121be 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2839,9 +2839,9 @@ struct merge_params {
   uint32_t root_fanout   = 2;
   uint32_t lower_fanout  = 3;
   double leader_fraction = 0.02;
-  uint32_t max_leaders = 1024;
-  uint32_t leaf_size   = 256;
-  uint32_t leaf_degree = 4;
+  uint32_t max_leaders   = 1024;
+  uint32_t leaf_size     = 256;
+  uint32_t leaf_degree   = 4;
 };
 
 /** @brief Merge multiple physical CAGRA indices into one.

From 9dfa693e2567603e842b5694def6ce29184203ec Mon Sep 17 00:00:00 2001
From: Ben Landrum <36489943+landrumb@users.noreply.github.com>
Date: Mon, 3 Aug 2026 10:23:07 -0700
Subject: [PATCH 27/41] artem's suggestions

Co-authored-by: Artem M. Chirkin <9253178+achirkin@users.noreply.github.com>
---
 cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 74189f7d3a..ac9c49ef54 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -59,7 +59,7 @@ inline constexpr int ROW_WARPS_PER_BLOCK = 4;
  * oversized workloads loop within resident threads instead. */
 inline auto strided_grid_size(int64_t items) -> int
 {
-  return static_cast<int>(std::min<int64_t>((items + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK,
+  return static_cast<int>(std::min<int64_t>(raft::div_rounding_up_safe(items, THREADS_PER_BLOCK),
                                             MAX_STRIDED_GRID_BLOCKS));
 }
 
@@ -114,10 +114,8 @@ struct partition_range {
   int64_t end   = 0;
 };
 
-struct partition_set {
-  rmm::device_uvector<partition_membership> memberships;
-  std::vector<partition_range> ranges;
-};
+  raft::device_vector<partition_membership> memberships;
+  raft::host_vector<partition_range> ranges;
 
 // ----------------------------------------------------------------------------
 // Many-way partitioning

From 5a74ddb2479b12238e70677de9951f857beacff1 Mon Sep 17 00:00:00 2001
From: Ben Landrum <36489943+landrumb@users.noreply.github.com>
Date: Mon, 3 Aug 2026 10:23:55 -0700
Subject: [PATCH 28/41] additional suggestion

Co-authored-by: Artem M. Chirkin <9253178+achirkin@users.noreply.github.com>
---
 cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index ac9c49ef54..2daf05a158 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -290,7 +290,7 @@ inline auto make_root_partition(raft::resources const& res, int64_t rows) -> par
   auto stream = raft::resource::get_cuda_stream(res);
   partition_set root{rmm::device_uvector<partition_membership>(static_cast<size_t>(rows), stream),
                      {{uint32_t{0}, int64_t{0}, rows}}};
-  int blocks = static_cast<int>((rows + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK);
+  int blocks = raft::div_rounding_up_safe<int>(rows, THREADS_PER_BLOCK);
   initialize_root_memberships_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
     root.memberships.data(), rows);
   RAFT_CUDA_TRY(cudaGetLastError());

From 87fafd3911566998fe59b69d2bc05a3fab3a2943 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Mon, 3 Aug 2026 23:21:00 +0000
Subject: [PATCH 29/41] replacing rmm + non-constant workspace

---
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |  13 +-
 cpp/src/neighbors/cagra.cuh                   |  19 +-
 cpp/src/neighbors/cagra_merge_inst.cu.in      |  35 +-
 .../neighbors/detail/cagra/cagra_merge.cuh    |  32 +-
 .../detail/cagra/cagra_merge_scaffold.cuh     | 455 +++++++++------
 .../ann_cagra/test_merge_fastener.cu          | 527 +++++++++++-------
 6 files changed, 690 insertions(+), 391 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index 0700582fe6..993fd69d36 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -402,12 +402,13 @@ void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
       *dataset_                = raft::make_device_matrix<T, int64_t>(handle_, merged_rows, stride);
       auto merged_dataset_view = cuvs::neighbors::device_padded_dataset_view<T, int64_t>(
         raft::make_const_mdspan(dataset_->view()), static_cast<uint32_t>(dim_));
-      index_ = std::make_shared<index_type>(cuvs::neighbors::cagra::merge(handle_,
-                                                                         params,
-                                                                         indices,
-                                                                         merged_dataset_view,
-                                                                         index_params_.merge_params,
-                                                                         merge_row_filter));
+      index_ =
+        std::make_shared<index_type>(cuvs::neighbors::cagra::merge(handle_,
+                                                                   params,
+                                                                   indices,
+                                                                   merged_dataset_view,
+                                                                   index_params_.merge_params,
+                                                                   merge_row_filter));
     }
   }
 }
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index d545729046..20f2ec0c45 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -523,14 +523,17 @@ cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT> merge(
     handle, params, indices, merged_dataset, row_filter);
 }
 
-template <class T, class IdxT>
-index<T, IdxT> merge(raft::resources const& handle,
-                     const cagra::index_params& params,
-                     std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices,
-                     const cagra::merge_params& merge_params,
-                     const cuvs::neighbors::filtering::base_filter& row_filter)
+template <class T, class IdxT, cuvs::neighbors::ann_dataset_view DatasetViewT>
+cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT> merge(
+  raft::resources const& handle,
+  const cagra::index_params& params,
+  std::vector<cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT>*>& indices,
+  DatasetViewT merged_dataset,
+  const cagra::merge_params& merge_params,
+  const cuvs::neighbors::filtering::base_filter& row_filter)
 {
-  return cagra::detail::merge<T, IdxT>(handle, params, indices, merge_params, row_filter);
+  return cagra::detail::merge<T, IdxT, DatasetViewT>(
+    handle, params, indices, merged_dataset, merge_params, row_filter);
 }
 
 template <typename T, typename IdxT = uint32_t, typename OutputIdxT = uint32_t>
@@ -613,7 +616,7 @@ void search(
     const cuvs::neighbors::cagra::index_params& params,                                \
     std::vector<cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT>*>& indices,       \
     DatasetViewT merged_dataset,                                                       \
-    cuvs::neighbors::filtering::base_filter const& row_filter);                       \
+    cuvs::neighbors::filtering::base_filter const& row_filter);                        \
   template CUVS_EXPORT cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT>            \
   cuvs::neighbors::cagra::merge<T, IdxT, DatasetViewT>(                                \
     raft::resources const& handle,                                                     \
diff --git a/cpp/src/neighbors/cagra_merge_inst.cu.in b/cpp/src/neighbors/cagra_merge_inst.cu.in
index 93591a77df..da95e279b6 100644
--- a/cpp/src/neighbors/cagra_merge_inst.cu.in
+++ b/cpp/src/neighbors/cagra_merge_inst.cu.in
@@ -33,8 +33,39 @@ CUVS_INST_CAGRA_MERGE(data_t, index_t, inst_device_standard_view_t);
 template <typename AnchorT = data_t>
 CUVS_EXPORT void merge_instantiation_keepalive()
 {
-  (void)&cuvs::neighbors::cagra::merge<data_t, index_t, inst_device_padded_view_t>;
-  (void)&cuvs::neighbors::cagra::merge<data_t, index_t, inst_device_standard_view_t>;
+  // Two merge overloads exist (with and without merge_params), so &merge<...> is ambiguous;
+  // select each explicitly by its function type.
+  using padded_index_t = cuvs::neighbors::cagra::index<data_t, index_t, inst_device_padded_view_t>;
+  using standard_index_t =
+    cuvs::neighbors::cagra::index<data_t, index_t, inst_device_standard_view_t>;
+  using filter_t = cuvs::neighbors::filtering::base_filter const&;
+
+  (void)static_cast<padded_index_t (*)(raft::resources const&,
+                                       cuvs::neighbors::cagra::index_params const&,
+                                       std::vector<padded_index_t*>&,
+                                       inst_device_padded_view_t,
+                                       filter_t)>(
+    &cuvs::neighbors::cagra::merge<data_t, index_t, inst_device_padded_view_t>);
+  (void)static_cast<padded_index_t (*)(raft::resources const&,
+                                       cuvs::neighbors::cagra::index_params const&,
+                                       std::vector<padded_index_t*>&,
+                                       inst_device_padded_view_t,
+                                       cuvs::neighbors::cagra::merge_params const&,
+                                       filter_t)>(
+    &cuvs::neighbors::cagra::merge<data_t, index_t, inst_device_padded_view_t>);
+  (void)static_cast<standard_index_t (*)(raft::resources const&,
+                                         cuvs::neighbors::cagra::index_params const&,
+                                         std::vector<standard_index_t*>&,
+                                         inst_device_standard_view_t,
+                                         filter_t)>(
+    &cuvs::neighbors::cagra::merge<data_t, index_t, inst_device_standard_view_t>);
+  (void)static_cast<standard_index_t (*)(raft::resources const&,
+                                         cuvs::neighbors::cagra::index_params const&,
+                                         std::vector<standard_index_t*>&,
+                                         inst_device_standard_view_t,
+                                         cuvs::neighbors::cagra::merge_params const&,
+                                         filter_t)>(
+    &cuvs::neighbors::cagra::merge<data_t, index_t, inst_device_standard_view_t>);
 }
 template CUVS_EXPORT void merge_instantiation_keepalive<data_t>();
 
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 78bed80112..5a087939a8 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -199,11 +199,10 @@ cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT> merge_rebuild(
   return index;
 }
 
-
 struct fastener_preflight_result {
-  bool eligible = false;
-  int64_t rows  = 0;
-  int64_t dim   = 0;
+  bool eligible  = false;
+  int64_t rows   = 0;
+  int64_t dim    = 0;
   int64_t stride = 0;
   std::vector<int64_t> offsets;
   std::string reason;
@@ -212,6 +211,7 @@ struct fastener_preflight_result {
 /** Validate every input and option without mutating anything. */
 template <typename T, typename IdxT, cuvs::neighbors::ann_dataset_view DatasetViewT>
 auto preflight_fastener(
+  raft::resources const& handle,
   cagra::index_params const& params,
   cagra::merge_params const& merge_params,
   std::vector<cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT>*> const& indices,
@@ -237,7 +237,6 @@ auto preflight_fastener(
   if (row_filter.get_filter_type() != cuvs::neighbors::filtering::FilterType::None) {
     return reject("row filters are not supported");
   }
-  if (params.compression.has_value()) { return reject("compressed output is not supported"); }
   if (params.metric != cuvs::distance::DistanceType::L2Expanded) {
     return reject("only L2Expanded is supported");
   }
@@ -326,7 +325,8 @@ auto preflight_fastener(
   if (result.dim <= 0 || result.dim > std::numeric_limits<int>::max()) {
     return reject("dataset dimension must be positive and fit cuBLAS int dimensions");
   }
-  if (!merge_scaffold::leaf_gemm_supported(result.dim, merge_params.leaf_size)) {
+  if (!merge_scaffold::leaf_gemm_supported(
+        result.dim, merge_params.leaf_size, raft::resource::get_workspace_free_bytes(handle))) {
     return reject("dataset dimension exceeds the leaf GEMM workspace limit");
   }
   if (rows > std::numeric_limits<uint32_t>::max() / spill) {
@@ -401,11 +401,11 @@ auto merge_fastener(raft::resources const& handle,
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/consolidate");
     // Zero first so the pad columns are defined: the sorter treats the padded width as the
     // dimension, which is exact for L2 only because the padding contributes nothing.
-    RAFT_CUDA_TRY(cudaMemsetAsync(destination,
-                                  0,
-                                  static_cast<std::size_t>(preflight.rows) *
-                                    static_cast<std::size_t>(stride) * sizeof(T),
-                                  raft::resource::get_cuda_stream(handle)));
+    RAFT_CUDA_TRY(cudaMemsetAsync(
+      destination,
+      0,
+      static_cast<std::size_t>(preflight.rows) * static_cast<std::size_t>(stride) * sizeof(T),
+      raft::resource::get_cuda_stream(handle)));
     copy_input_datasets<T, IdxT, DatasetViewT>(
       handle, indices, preflight.offsets, preflight.dim, stride, destination);
   }
@@ -450,7 +450,9 @@ auto merge_fastener(raft::resources const& handle,
 
   // The caller owns merged_dataset; the returned index holds only a view of it.
   cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT> merged_index(handle, params.metric);
-  merged_index.update_graph(handle, raft::make_const_mdspan(optimized_graph.view()));
+  // Must move: the device_matrix_view overload only stores a view, which would dangle once
+  // optimized_graph goes out of scope.
+  merged_index.update_graph(handle, std::move(optimized_graph));
   merged_index.update_device_dataset_same_layout(handle, merged_dataset);
   return merged_index;
 }
@@ -477,7 +479,7 @@ auto merge(raft::resources const& handle,
   }
 
   auto preflight =
-    preflight_fastener<T, IdxT, DatasetViewT>(params, merge_params, indices, row_filter);
+    preflight_fastener<T, IdxT, DatasetViewT>(handle, params, merge_params, indices, row_filter);
   if (!preflight.eligible) {
     if (merge_params.algo == cagra::merge_algo::AUTO) {
       return merge_rebuild<T, IdxT, DatasetViewT>(
@@ -516,7 +518,9 @@ auto merge(raft::resources const& handle,
            cuvs::neighbors::filtering::base_filter const& row_filter)
   -> cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT>
 {
-  return merge<T, IdxT, DatasetViewT>(
+  // Fully qualified: an unqualified call also finds cuvs::neighbors::cagra::merge via ADL on the
+  // index arguments, which is ambiguous with this overload.
+  return cuvs::neighbors::cagra::detail::merge<T, IdxT, DatasetViewT>(
     handle, params, indices, merged_dataset, cagra::merge_params{}, row_filter);
 }
 
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 38eb683610..12a5b2c489 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -15,11 +15,13 @@
 #include <raft/core/error.hpp>
 #include <raft/core/resource/cublas_handle.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/util/cuda_rt_essentials.hpp>
 #include <raft/util/cudart_utils.hpp>
 
-#include <rmm/device_uvector.hpp>
+#include <rmm/aligned.hpp>
+#include <rmm/exec_policy.hpp>
 
 #include <thrust/device_ptr.h>
 #include <thrust/execution_policy.h>
@@ -49,7 +51,6 @@ inline constexpr uint32_t MAX_LEAF_SIZE      = 256;
 inline constexpr int MAX_LEAF_DEGREE         = 8;
 inline constexpr int ASSIGNMENT_TILE_ROWS    = 2048;
 inline constexpr uint64_t DETERMINISTIC_SEED = 0x4c616e6472756d;
-inline constexpr size_t GEMM_WORKSPACE_BYTES = size_t{2} * 1024 * 1024 * 1024;
 inline constexpr int THREADS_PER_BLOCK       = 256;
 inline constexpr int MAX_STRIDED_GRID_BLOCKS = 1 << 20;
 /** Warps per block in the one-row-per-warp kernels; their launch geometry derives from this. */
@@ -59,8 +60,8 @@ inline constexpr int ROW_WARPS_PER_BLOCK = 4;
  * oversized workloads loop within resident threads instead. */
 inline auto strided_grid_size(int64_t items) -> int
 {
-  return static_cast<int>(std::min<int64_t>(raft::div_rounding_up_safe(items, THREADS_PER_BLOCK),
-                                            MAX_STRIDED_GRID_BLOCKS));
+  return static_cast<int>(std::min<int64_t>(
+    raft::div_rounding_up_safe<int64_t>(items, THREADS_PER_BLOCK), MAX_STRIDED_GRID_BLOCKS));
 }
 
 // ----------------------------------------------------------------------------
@@ -91,18 +92,23 @@ struct split_params {
 /** Device state and tuning knobs shared by every split level: the precomputed row norms, the
  * tiling and workspace capacities, and the seed feeding the deterministic leader samples. */
 struct split_context {
-  split_context(rmm::cuda_stream_view stream, int64_t rows)
-    : norms(static_cast<size_t>(rows), stream)
+  /** Norms are dataset-sized, so they come from the large workspace resource rather than the
+   *  bounded one. Taking `raft::resources` lets the caller control where this memory lives. */
+  split_context(raft::resources const& res, int64_t rows, int64_t dim)
+    : norms(raft::make_device_mdarray<float, int64_t>(
+        res,
+        raft::resource::get_large_workspace_resource_ref(res),
+        raft::make_extents<int64_t>(rows))),
+      logical_dim(dim)
   {
   }
 
-  rmm::device_uvector<float> norms;
+  raft::device_vector<float, int64_t> norms;
   /** Logical dimension of the dataset. The dataset view's extent(1) is its row pitch, which
    *  may exceed this when the consolidated dataset is padded for CAGRA row alignment. */
-  int64_t logical_dim         = 0;
-  int assignment_tile_rows    = ASSIGNMENT_TILE_ROWS;
-  size_t gemm_workspace_bytes = GEMM_WORKSPACE_BYTES;
-  uint64_t seed               = DETERMINISTIC_SEED;
+  int64_t logical_dim;
+  int assignment_tile_rows = ASSIGNMENT_TILE_ROWS;
+  uint64_t seed            = DETERMINISTIC_SEED;
 };
 
 struct partition_membership {
@@ -117,8 +123,13 @@ struct partition_range {
   int64_t end   = 0;
 };
 
-  raft::device_vector<partition_membership> memberships;
-  raft::host_vector<partition_range> ranges;
+/** Memberships live on the device; their grouping into contiguous ranges is host-side metadata.
+ *  Both are fixed-size once built, so raft mdarrays are a good fit -- but note they are neither
+ *  default-constructible nor resizable, so every producer sizes them exactly at construction. */
+struct partition_set {
+  raft::device_vector<partition_membership, int64_t> memberships;
+  raft::host_vector<partition_range, int64_t> ranges;
+};
 
 // ----------------------------------------------------------------------------
 // Many-way partitioning
@@ -198,7 +209,7 @@ struct split_plan {
  * starting from an offset hashed from the seed, level, and parent identity, so reruns select the
  * same leaders. Child keys and output rows are dense in parent order.
  */
-inline auto plan_split(std::vector<partition_range> const& ranges,
+inline auto plan_split(raft::host_vector<partition_range, int64_t> const& ranges,
                        int64_t membership_count,
                        split_params const& params,
                        uint64_t seed) -> split_plan
@@ -213,13 +224,13 @@ inline auto plan_split(std::vector<partition_range> const& ranges,
                MAX_LEADERS);
 
   split_plan plan;
-  plan.parents.reserve(ranges.size());
+  plan.parents.reserve(static_cast<size_t>(ranges.size()));
   int64_t covered     = 0;
   int64_t output_rows = 0;
   int64_t child_keys  = 0;
 
-  for (size_t parent_index = 0; parent_index < ranges.size(); ++parent_index) {
-    auto const& parent = ranges[parent_index];
+  for (size_t parent_index = 0; parent_index < static_cast<size_t>(ranges.size()); ++parent_index) {
+    auto const& parent = ranges(static_cast<int64_t>(parent_index));
     RAFT_EXPECTS(parent.start == covered && parent.end > parent.start,
                  "Fastener parent ranges must compactly cover all memberships");
     covered = parent.end;
@@ -291,11 +302,16 @@ static __global__ void initialize_root_memberships_kernel(partition_membership*
 inline auto make_root_partition(raft::resources const& res, int64_t rows) -> partition_set
 {
   auto stream = raft::resource::get_cuda_stream(res);
-  partition_set root{rmm::device_uvector<partition_membership>(static_cast<size_t>(rows), stream),
-                     {{uint32_t{0}, int64_t{0}, rows}}};
-  int blocks = raft::div_rounding_up_safe<int>(rows, THREADS_PER_BLOCK);
+  // Memberships are dataset-sized, so they come from the large workspace rather than the bounded
+  // one; the single root range is host metadata.
+  auto memberships = raft::make_device_mdarray<partition_membership, int64_t>(
+    res, raft::resource::get_large_workspace_resource_ref(res), raft::make_extents<int64_t>(rows));
+  auto ranges = raft::make_host_vector<partition_range, int64_t>(res, 1);
+  ranges(0)   = partition_range{uint32_t{0}, int64_t{0}, rows};
+  partition_set root{std::move(memberships), std::move(ranges)};
+  int blocks = raft::div_rounding_up_safe<int>(static_cast<int>(rows), THREADS_PER_BLOCK);
   initialize_root_memberships_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
-    root.memberships.data(), rows);
+    root.memberships.data_handle(), rows);
   RAFT_CUDA_TRY(cudaGetLastError());
   return root;
 }
@@ -449,34 +465,39 @@ static __global__ void manyway_emit_tile_assignments_kernel(
 inline auto collect_partition_ranges(raft::resources const& res,
                                      uint32_t const* keys,
                                      int64_t count,
-                                     uint32_t key_count) -> std::vector<partition_range>
+                                     uint32_t key_count)
+  -> raft::host_vector<partition_range, int64_t>
 {
-  auto stream          = raft::resource::get_cuda_stream(res);
-  auto output_capacity = static_cast<size_t>(std::min<int64_t>(count, key_count));
-  rmm::device_uvector<uint32_t> device_unique_keys(output_capacity, stream);
-  rmm::device_uvector<uint32_t> device_counts(output_capacity, stream);
-  auto reduced_end = thrust::reduce_by_key(thrust::cuda::par.on(stream),
-                                           thrust::device_pointer_cast(keys),
-                                           thrust::device_pointer_cast(keys + count),
-                                           thrust::make_constant_iterator(uint32_t{1}),
-                                           thrust::device_pointer_cast(device_unique_keys.data()),
-                                           thrust::device_pointer_cast(device_counts.data()));
-  auto group_count =
-    static_cast<size_t>(reduced_end.first - thrust::device_pointer_cast(device_unique_keys.data()));
-
-  std::vector<uint32_t> unique_keys(group_count);
-  std::vector<uint32_t> counts(group_count);
-  raft::copy(unique_keys.data(), device_unique_keys.data(), group_count, stream);
-  raft::copy(counts.data(), device_counts.data(), group_count, stream);
+  auto stream             = raft::resource::get_cuda_stream(res);
+  auto output_capacity    = std::min<int64_t>(count, key_count);
+  auto large_mr           = raft::resource::get_large_workspace_resource_ref(res);
+  auto device_unique_keys = raft::make_device_mdarray<uint32_t, int64_t>(
+    res, large_mr, raft::make_extents<int64_t>(output_capacity));
+  auto device_counts = raft::make_device_mdarray<uint32_t, int64_t>(
+    res, large_mr, raft::make_extents<int64_t>(output_capacity));
+  rmm::exec_policy_nosync thrust_policy{stream, large_mr};
+  auto reduced_end =
+    thrust::reduce_by_key(thrust_policy,
+                          thrust::device_pointer_cast(keys),
+                          thrust::device_pointer_cast(keys + count),
+                          thrust::make_constant_iterator(uint32_t{1}),
+                          thrust::device_pointer_cast(device_unique_keys.data_handle()),
+                          thrust::device_pointer_cast(device_counts.data_handle()));
+  auto group_count = static_cast<int64_t>(
+    reduced_end.first - thrust::device_pointer_cast(device_unique_keys.data_handle()));
+
+  auto unique_keys = raft::make_host_vector<uint32_t, int64_t>(res, group_count);
+  auto counts      = raft::make_host_vector<uint32_t, int64_t>(res, group_count);
+  raft::copy(unique_keys.data_handle(), device_unique_keys.data_handle(), group_count, stream);
+  raft::copy(counts.data_handle(), device_counts.data_handle(), group_count, stream);
   raft::resource::sync_stream(res);
 
-  std::vector<partition_range> groups;
-  groups.reserve(group_count);
+  auto groups    = raft::make_host_vector<partition_range, int64_t>(res, group_count);
   int64_t cursor = 0;
-  for (size_t index = 0; index < group_count; ++index) {
-    RAFT_EXPECTS(unique_keys[index] < key_count, "Many-way group key is out of range");
-    groups.push_back({unique_keys[index], cursor, cursor + counts[index]});
-    cursor += counts[index];
+  for (int64_t index = 0; index < group_count; ++index) {
+    RAFT_EXPECTS(unique_keys(index) < key_count, "Many-way group key is out of range");
+    groups(index) = {unique_keys(index), cursor, cursor + counts(index)};
+    cursor += counts(index);
   }
   RAFT_EXPECTS(cursor == count, "Many-way partition membership histogram lost entries");
   return groups;
@@ -486,8 +507,8 @@ inline auto collect_partition_ranges(raft::resources const& res,
 inline void carry_parents(raft::resources const& res,
                           partition_set const& parents,
                           split_plan const& plan,
-                          rmm::device_uvector<uint32_t>& keys,
-                          rmm::device_uvector<partition_membership>& memberships)
+                          raft::device_vector<uint32_t, int64_t>& keys,
+                          raft::device_vector<partition_membership, int64_t>& memberships)
 {
   std::vector<carry_span> carries;
   for (auto const& entry : plan.parents) {
@@ -500,14 +521,19 @@ inline void carry_parents(raft::resources const& res,
   }
   if (carries.empty()) { return; }
 
-  auto stream = raft::resource::get_cuda_stream(res);
-  rmm::device_uvector<carry_span> device_carries(carries.size(), stream);
-  raft::copy(device_carries.data(), carries.data(), carries.size(), stream);
+  auto stream         = raft::resource::get_cuda_stream(res);
+  auto device_carries = raft::make_device_mdarray<carry_span, int64_t>(
+    res,
+    raft::resource::get_large_workspace_resource_ref(res),
+    raft::make_extents<int64_t>(static_cast<int64_t>(carries.size())));
+  raft::copy(device_carries.data_handle(), carries.data(), carries.size(), stream);
   carry_completed_parents_kernel<<<static_cast<int>(carries.size()),
                                    THREADS_PER_BLOCK,
                                    0,
-                                   stream>>>(
-    parents.memberships.data(), device_carries.data(), memberships.data(), keys.data());
+                                   stream>>>(parents.memberships.data_handle(),
+                                             device_carries.data_handle(),
+                                             memberships.data_handle(),
+                                             keys.data_handle());
   RAFT_CUDA_TRY(cudaGetLastError());
 }
 
@@ -599,33 +625,52 @@ void assign_bucket(raft::resources const& res,
                    int padded_leaders,
                    split_params const& params,
                    split_context& context,
-                   rmm::device_uvector<uint32_t>& keys,
-                   rmm::device_uvector<partition_membership>& memberships)
+                   raft::device_vector<uint32_t, int64_t>& keys,
+                   raft::device_vector<partition_membership, int64_t>& memberships)
 {
-  auto stream          = raft::resource::get_cuda_stream(res);
+  auto stream           = raft::resource::get_cuda_stream(res);
   auto const row_stride = dataset.extent(1);
-  auto dim             = context.logical_dim;
-
-  // Derive the tile height from this bucket's shapes rather than assuming the configured default
-  // fits. The assignment workspace holds the gathered leader matrix once, plus per-row point,
-  // dot-product and selection storage, so a large dimension or a wide padded leader count can
-  // overflow the budget at the default height -- preflight only validates the leaf GEMM shape, so
-  // such a configuration would otherwise pass preflight and then trip the assertion below.
-  //
-  // This only ever shrinks the tile, so every configuration that fits today is unaffected.
-  size_t leader_bytes =
-    static_cast<size_t>(padded_leaders) * static_cast<size_t>(dim) * sizeof(float) +
-    static_cast<size_t>(padded_leaders) * sizeof(uint32_t) + sizeof(assignment_tile);
-  size_t row_bytes = (static_cast<size_t>(dim) + static_cast<size_t>(padded_leaders) +
-                      static_cast<size_t>(params.fanout)) *
-                       sizeof(float) +
-                     static_cast<size_t>(params.fanout) * sizeof(int);
-  RAFT_EXPECTS(leader_bytes + row_bytes <= context.gemm_workspace_bytes,
+  auto dim              = context.logical_dim;
+  auto workspace_mr     = raft::resource::get_workspace_resource_ref(res);
+  auto workspace_bytes  = raft::resource::get_workspace_free_bytes(res);
+
+  // RMM's limiting workspace accounts for every allocation rounded to the CUDA allocation
+  // alignment. Model the seven explicit buffers independently so a raw-byte sum cannot overrun a
+  // small caller-configured workspace through alignment alone. RAFT select-k also allocates value
+  // and index scratch; each is no larger than its input matrix, so reserve two aligned dot-matrix
+  // buffers as a conservative upper bound.
+  auto assignment_workspace_bytes = [&](size_t capacity, size_t rows_per_tile) {
+    auto aligned = [](size_t bytes) {
+      return rmm::align_up(bytes, rmm::CUDA_ALLOCATION_ALIGNMENT);
+    };
+    size_t point_elements    = rows_per_tile * static_cast<size_t>(dim);
+    size_t leader_elements   = static_cast<size_t>(padded_leaders) * static_cast<size_t>(dim);
+    size_t dot_elements      = rows_per_tile * static_cast<size_t>(padded_leaders);
+    size_t selected_elements = rows_per_tile * static_cast<size_t>(params.fanout);
+    return aligned(capacity * sizeof(assignment_tile)) +
+           aligned(capacity * point_elements * sizeof(float)) +
+           aligned(capacity * leader_elements * sizeof(float)) +
+           aligned(capacity * dot_elements * sizeof(float)) +
+           aligned(capacity * static_cast<size_t>(padded_leaders) * sizeof(uint32_t)) +
+           aligned(capacity * selected_elements * sizeof(float)) +
+           aligned(capacity * selected_elements * sizeof(int)) +
+           aligned(capacity * dot_elements * sizeof(float)) +
+           aligned(capacity * dot_elements * sizeof(int));
+  };
+
+  RAFT_EXPECTS(assignment_workspace_bytes(1, 1) <= workspace_bytes,
                "Fastener assignment workspace cannot fit the leader matrix and a single point row");
-  int tile_rows = static_cast<int>(
-    std::max<size_t>(1,
-                     std::min<size_t>(static_cast<size_t>(context.assignment_tile_rows),
-                                      (context.gemm_workspace_bytes - leader_bytes) / row_bytes)));
+  size_t min_tile_rows = 1;
+  size_t max_tile_rows = static_cast<size_t>(context.assignment_tile_rows);
+  while (min_tile_rows < max_tile_rows) {
+    size_t candidate = min_tile_rows + (max_tile_rows - min_tile_rows + 1) / 2;
+    if (assignment_workspace_bytes(1, candidate) <= workspace_bytes) {
+      min_tile_rows = candidate;
+    } else {
+      max_tile_rows = candidate - 1;
+    }
+  }
+  int tile_rows = static_cast<int>(min_tile_rows);
 
   // Cut every parent in the bucket into fixed-height tiles
   std::vector<assignment_tile> tiles;
@@ -636,26 +681,48 @@ void assign_bucket(raft::resources const& res,
   }
 
   // Size the batch so all per-tile buffers fit in the GEMM workspace budget
-  size_t point_elements    = static_cast<size_t>(tile_rows) * dim;
-  size_t leader_elements   = static_cast<size_t>(padded_leaders) * dim;
-  size_t dot_elements      = static_cast<size_t>(tile_rows) * padded_leaders;
-  size_t selected_elements = static_cast<size_t>(tile_rows) * params.fanout;
-  size_t bytes_per_batch =
-    (point_elements + leader_elements + dot_elements + selected_elements) * sizeof(float) +
-    static_cast<size_t>(padded_leaders) * sizeof(uint32_t) + selected_elements * sizeof(int) +
-    sizeof(assignment_tile);
-  RAFT_EXPECTS(bytes_per_batch <= context.gemm_workspace_bytes,
-               "Fastener assignment workspace is too small for one tile");
-  size_t batch_capacity = std::max<size_t>(
-    1, std::min<size_t>(tiles.size(), context.gemm_workspace_bytes / bytes_per_batch));
-
-  rmm::device_uvector<assignment_tile> device_tiles(batch_capacity, stream);
-  rmm::device_uvector<float> tile_points(batch_capacity * point_elements, stream);
-  rmm::device_uvector<float> tile_leaders(batch_capacity * leader_elements, stream);
-  rmm::device_uvector<float> tile_dots(batch_capacity * dot_elements, stream);
-  rmm::device_uvector<uint32_t> tile_leader_ids(batch_capacity * padded_leaders, stream);
-  rmm::device_uvector<float> selected_distances(batch_capacity * selected_elements, stream);
-  rmm::device_uvector<int> selected_leaders(batch_capacity * selected_elements, stream);
+  size_t point_elements     = static_cast<size_t>(tile_rows) * dim;
+  size_t leader_elements    = static_cast<size_t>(padded_leaders) * dim;
+  size_t dot_elements       = static_cast<size_t>(tile_rows) * padded_leaders;
+  size_t selected_elements  = static_cast<size_t>(tile_rows) * params.fanout;
+  size_t min_batch_capacity = 1;
+  size_t max_batch_capacity = tiles.size();
+  while (min_batch_capacity < max_batch_capacity) {
+    size_t candidate = min_batch_capacity + (max_batch_capacity - min_batch_capacity + 1) / 2;
+    if (assignment_workspace_bytes(candidate, static_cast<size_t>(tile_rows)) <= workspace_bytes) {
+      min_batch_capacity = candidate;
+    } else {
+      max_batch_capacity = candidate - 1;
+    }
+  }
+  size_t batch_capacity = min_batch_capacity;
+
+  auto device_tiles = raft::make_device_mdarray<assignment_tile, int64_t>(
+    res, workspace_mr, raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity)));
+  auto tile_points = raft::make_device_mdarray<float, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity * point_elements)));
+  auto tile_leaders = raft::make_device_mdarray<float, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity * leader_elements)));
+  auto tile_dots = raft::make_device_mdarray<float, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity * dot_elements)));
+  auto tile_leader_ids = raft::make_device_mdarray<uint32_t, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity) * padded_leaders));
+  auto selected_distances = raft::make_device_mdarray<float, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity * selected_elements)));
+  auto selected_leaders = raft::make_device_mdarray<int, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity * selected_elements)));
 
   auto point_stride  = static_cast<int64_t>(point_elements);
   auto leader_stride = static_cast<int64_t>(leader_elements);
@@ -663,7 +730,7 @@ void assign_bucket(raft::resources const& res,
 
   for (size_t tile_offset = 0; tile_offset < tiles.size(); tile_offset += batch_capacity) {
     size_t batch_size = std::min(batch_capacity, tiles.size() - tile_offset);
-    raft::copy(device_tiles.data(), tiles.data() + tile_offset, batch_size, stream);
+    raft::copy(device_tiles.data_handle(), tiles.data() + tile_offset, batch_size, stream);
 
     // Gather the batch's tile rows and leader vectors
     int point_blocks = strided_grid_size(static_cast<int64_t>(batch_size * point_elements));
@@ -671,11 +738,11 @@ void assign_bucket(raft::resources const& res,
       dataset.data_handle(),
       dim,
       row_stride,
-      parents.memberships.data(),
-      device_tiles.data(),
+      parents.memberships.data_handle(),
+      device_tiles.data_handle(),
       static_cast<int>(batch_size),
       tile_rows,
-      tile_points.data());
+      tile_points.data_handle());
     RAFT_CUDA_TRY(cudaGetLastError());
 
     int leader_blocks = strided_grid_size(static_cast<int64_t>(batch_size * leader_elements));
@@ -683,23 +750,23 @@ void assign_bucket(raft::resources const& res,
       dataset.data_handle(),
       dim,
       row_stride,
-      parents.memberships.data(),
-      device_tiles.data(),
+      parents.memberships.data_handle(),
+      device_tiles.data_handle(),
       static_cast<int>(batch_size),
       padded_leaders,
-      tile_leaders.data(),
-      tile_leader_ids.data());
+      tile_leaders.data_handle(),
+      tile_leader_ids.data_handle());
     RAFT_CUDA_TRY(cudaGetLastError());
 
     // point-leader dot products for the batch
     batched_row_dot_products(res,
-                             tile_leaders.data(),
+                             tile_leaders.data_handle(),
                              padded_leaders,
                              leader_stride,
-                             tile_points.data(),
+                             tile_points.data_handle(),
                              tile_rows,
                              point_stride,
-                             tile_dots.data(),
+                             tile_dots.data_handle(),
                              dot_stride,
                              static_cast<int>(dim),
                              static_cast<int>(batch_size));
@@ -708,38 +775,38 @@ void assign_bucket(raft::resources const& res,
     int64_t selection_rows = static_cast<int64_t>(batch_size) * tile_rows;
     int distance_blocks    = strided_grid_size(selection_rows * padded_leaders);
     manyway_materialize_tile_distances_kernel<<<distance_blocks, THREADS_PER_BLOCK, 0, stream>>>(
-      tile_dots.data(),
+      tile_dots.data_handle(),
       static_cast<int>(batch_size),
       tile_rows,
       padded_leaders,
-      context.norms.data(),
-      tile_leader_ids.data(),
-      parents.memberships.data(),
-      device_tiles.data());
+      context.norms.data_handle(),
+      tile_leader_ids.data_handle(),
+      parents.memberships.data_handle(),
+      device_tiles.data_handle());
     RAFT_CUDA_TRY(cudaGetLastError());
 
     cuvs::selection::select_k(res,
                               raft::make_device_matrix_view<const float, int64_t>(
-                                tile_dots.data(), selection_rows, padded_leaders),
+                                tile_dots.data_handle(), selection_rows, padded_leaders),
                               std::nullopt,
                               raft::make_device_matrix_view<float, int64_t>(
-                                selected_distances.data(), selection_rows, params.fanout),
+                                selected_distances.data_handle(), selection_rows, params.fanout),
                               raft::make_device_matrix_view<int, int64_t>(
-                                selected_leaders.data(), selection_rows, params.fanout),
+                                selected_leaders.data_handle(), selection_rows, params.fanout),
                               true,
                               true);
 
     int assignment_blocks = strided_grid_size(selection_rows);
     manyway_emit_tile_assignments_kernel<<<assignment_blocks, THREADS_PER_BLOCK, 0, stream>>>(
-      selected_leaders.data(),
+      selected_leaders.data_handle(),
       static_cast<int>(batch_size),
       tile_rows,
       static_cast<int>(params.fanout),
       static_cast<int>(params.occurrence_stride),
-      parents.memberships.data(),
-      device_tiles.data(),
-      keys.data(),
-      memberships.data());
+      parents.memberships.data_handle(),
+      device_tiles.data_handle(),
+      keys.data_handle(),
+      memberships.data_handle());
     RAFT_CUDA_TRY(cudaGetLastError());
   }
 }
@@ -769,9 +836,12 @@ auto split_manyway(raft::resources const& res,
   auto plan   = plan_split(
     parents.ranges, static_cast<int64_t>(parents.memberships.size()), params, context.seed);
 
-  rmm::device_uvector<uint32_t> keys(static_cast<size_t>(plan.output_rows), stream);
-  rmm::device_uvector<partition_membership> memberships(static_cast<size_t>(plan.output_rows),
-                                                        stream);
+  // Both scale with the dataset, so they come from the large workspace.
+  auto const large_mr = raft::resource::get_large_workspace_resource_ref(res);
+  auto keys           = raft::make_device_mdarray<uint32_t, int64_t>(
+    res, large_mr, raft::make_extents<int64_t>(plan.output_rows));
+  auto memberships = raft::make_device_mdarray<partition_membership, int64_t>(
+    res, large_mr, raft::make_extents<int64_t>(plan.output_rows));
 
   carry_parents(res, parents, plan, keys, memberships);
   auto buckets = bucket_split_parents(plan, params);
@@ -788,11 +858,13 @@ auto split_manyway(raft::resources const& res,
                   memberships);
   }
 
-  thrust::stable_sort_by_key(thrust::cuda::par.on(stream),
-                             thrust::device_pointer_cast(keys.data()),
-                             thrust::device_pointer_cast(keys.data() + keys.size()),
-                             thrust::device_pointer_cast(memberships.data()));
-  auto ranges = collect_partition_ranges(res, keys.data(), plan.output_rows, plan.child_count);
+  rmm::exec_policy_nosync thrust_policy{stream, large_mr};
+  thrust::stable_sort_by_key(thrust_policy,
+                             thrust::device_pointer_cast(keys.data_handle()),
+                             thrust::device_pointer_cast(keys.data_handle() + keys.size()),
+                             thrust::device_pointer_cast(memberships.data_handle()));
+  auto ranges =
+    collect_partition_ranges(res, keys.data_handle(), plan.output_rows, plan.child_count);
   return {std::move(memberships), std::move(ranges)};
 }
 
@@ -804,13 +876,16 @@ auto split_manyway(raft::resources const& res,
  *
  * The limit does not depend on the dataset scalar type: every type, integers included, is gathered
  * into float leaf vectors and produces a float Gram matrix. */
-inline auto leaf_gemm_supported(int64_t dimension, uint32_t leaf_size) -> bool
+inline auto leaf_gemm_supported(int64_t dimension, uint32_t leaf_size, size_t workspace_bytes)
+  -> bool
 {
   if (dimension <= 0 || dimension > std::numeric_limits<int>::max()) { return false; }
 
   size_t vector_elements = static_cast<size_t>(leaf_size) * static_cast<size_t>(dimension);
   size_t gram_elements   = static_cast<size_t>(leaf_size) * leaf_size;
-  return vector_elements * sizeof(float) + gram_elements * sizeof(float) <= GEMM_WORKSPACE_BYTES;
+  return rmm::align_up(vector_elements * sizeof(float), rmm::CUDA_ALLOCATION_ALIGNMENT) +
+           rmm::align_up(gram_elements * sizeof(float), rmm::CUDA_ALLOCATION_ALIGNMENT) <=
+         workspace_bytes;
 }
 
 /** Leaves as strided views into `partitions->memberships`: leaf `i` holds the `counts[i]` records
@@ -820,9 +895,9 @@ struct leaf_set {
   std::vector<uint32_t> starts_host;
   std::vector<uint32_t> counts_host;
   std::vector<uint32_t> strides_host;
-  rmm::device_uvector<uint32_t> starts;
-  rmm::device_uvector<uint32_t> counts;
-  rmm::device_uvector<uint32_t> strides;
+  raft::device_vector<uint32_t, int64_t> starts;
+  raft::device_vector<uint32_t, int64_t> counts;
+  raft::device_vector<uint32_t, int64_t> strides;
 };
 
 /** Divide final grouped partitions into bounded leaves, without geometric resplitting.
@@ -851,7 +926,8 @@ inline auto make_leaves(raft::resources const& res,
   strides_host.reserve(starts_host.capacity());
 
   int64_t covered = 0;
-  for (auto const& range : partitions.ranges) {
+  for (int64_t range_index = 0; range_index < partitions.ranges.extent(0); ++range_index) {
+    auto const& range = partitions.ranges(range_index);
     RAFT_EXPECTS(range.start == covered && range.end > range.start &&
                    range.end <= static_cast<int64_t>(partitions.memberships.size()),
                  "Fastener partition ranges must compactly cover all memberships");
@@ -871,12 +947,16 @@ inline auto make_leaves(raft::resources const& res,
     covered == static_cast<int64_t>(partitions.memberships.size()) && !starts_host.empty(),
     "Fastener partition ranges did not cover all memberships");
 
-  rmm::device_uvector<uint32_t> starts(starts_host.size(), stream);
-  rmm::device_uvector<uint32_t> counts(counts_host.size(), stream);
-  rmm::device_uvector<uint32_t> strides(strides_host.size(), stream);
-  raft::copy(starts.data(), starts_host.data(), starts.size(), stream);
-  raft::copy(counts.data(), counts_host.data(), counts.size(), stream);
-  raft::copy(strides.data(), strides_host.data(), strides.size(), stream);
+  auto large_mr = raft::resource::get_large_workspace_resource_ref(res);
+  auto starts   = raft::make_device_mdarray<uint32_t, int64_t>(
+    res, large_mr, raft::make_extents<int64_t>(static_cast<int64_t>(starts_host.size())));
+  auto counts = raft::make_device_mdarray<uint32_t, int64_t>(
+    res, large_mr, raft::make_extents<int64_t>(static_cast<int64_t>(counts_host.size())));
+  auto strides = raft::make_device_mdarray<uint32_t, int64_t>(
+    res, large_mr, raft::make_extents<int64_t>(static_cast<int64_t>(strides_host.size())));
+  raft::copy(starts.data_handle(), starts_host.data(), starts.size(), stream);
+  raft::copy(counts.data_handle(), counts_host.data(), counts.size(), stream);
+  raft::copy(strides.data_handle(), strides_host.data(), strides.size(), stream);
   return {&partitions,
           std::move(starts_host),
           std::move(counts_host),
@@ -1030,7 +1110,10 @@ auto build_leaf_neighbors(raft::resources const& res,
   auto const& memberships = leaves.partitions->memberships;
 
   // Prefill every slot with its own row id, leaf KNN overwrites the slots it fills
-  auto graph          = raft::make_device_matrix<uint32_t, int64_t>(res, rows, union_degree);
+  auto graph = raft::make_device_mdarray<uint32_t, int64_t>(
+    res,
+    raft::resource::get_large_workspace_resource_ref(res),
+    raft::make_extents<int64_t>(rows, union_degree));
   int scaffold_blocks = strided_grid_size(rows * union_degree);
   initialize_self_scaffold_kernel<<<scaffold_blocks, THREADS_PER_BLOCK, 0, stream>>>(
     graph.data_handle(), rows, union_degree);
@@ -1043,12 +1126,35 @@ auto build_leaf_neighbors(raft::resources const& res,
   int dimension                   = static_cast<int>(input_dimension);
   size_t vector_elements_per_leaf = static_cast<size_t>(leaf_size) * dimension;
   size_t gram_elements_per_leaf   = static_cast<size_t>(leaf_size) * leaf_size;
-  size_t bytes_per_leaf =
-    vector_elements_per_leaf * sizeof(float) + gram_elements_per_leaf * sizeof(float);
-  size_t batch_capacity = std::max<size_t>(
-    1, std::min<size_t>(leaves.starts_host.size(), GEMM_WORKSPACE_BYTES / bytes_per_leaf));
-  rmm::device_uvector<float> leaf_vectors(batch_capacity * vector_elements_per_leaf, stream);
-  rmm::device_uvector<float> gram(batch_capacity * gram_elements_per_leaf, stream);
+  auto workspace_mr               = raft::resource::get_workspace_resource_ref(res);
+  auto workspace_bytes            = raft::resource::get_workspace_free_bytes(res);
+  auto leaf_workspace_bytes       = [&](size_t capacity) {
+    return rmm::align_up(capacity * vector_elements_per_leaf * sizeof(float),
+                         rmm::CUDA_ALLOCATION_ALIGNMENT) +
+           rmm::align_up(capacity * gram_elements_per_leaf * sizeof(float),
+                         rmm::CUDA_ALLOCATION_ALIGNMENT);
+  };
+  RAFT_EXPECTS(leaf_workspace_bytes(1) <= workspace_bytes,
+               "Fastener leaf workspace cannot fit one leaf");
+  size_t min_batch_capacity = 1;
+  size_t max_batch_capacity = leaves.starts_host.size();
+  while (min_batch_capacity < max_batch_capacity) {
+    size_t candidate = min_batch_capacity + (max_batch_capacity - min_batch_capacity + 1) / 2;
+    if (leaf_workspace_bytes(candidate) <= workspace_bytes) {
+      min_batch_capacity = candidate;
+    } else {
+      max_batch_capacity = candidate - 1;
+    }
+  }
+  size_t batch_capacity = min_batch_capacity;
+  auto leaf_vectors     = raft::make_device_mdarray<float, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity * vector_elements_per_leaf)));
+  auto gram = raft::make_device_mdarray<float, int64_t>(
+    res,
+    workspace_mr,
+    raft::make_extents<int64_t>(static_cast<int64_t>(batch_capacity * gram_elements_per_leaf)));
   auto vector_stride = static_cast<int64_t>(vector_elements_per_leaf);
   auto gram_stride   = static_cast<int64_t>(gram_elements_per_leaf);
 
@@ -1063,32 +1169,32 @@ auto build_leaf_neighbors(raft::resources const& res,
       row_stride,
       input_dimension,
       leaf_size,
-      memberships.data(),
-      leaves.starts.data(),
-      leaves.counts.data(),
-      leaves.strides.data(),
+      memberships.data_handle(),
+      leaves.starts.data_handle(),
+      leaves.counts.data_handle(),
+      leaves.strides.data_handle(),
       static_cast<int64_t>(leaf_offset),
       static_cast<int64_t>(batch_size),
-      leaf_vectors.data());
+      leaf_vectors.data_handle());
     RAFT_CUDA_TRY(cudaGetLastError());
     batched_row_dot_products(res,
-                             leaf_vectors.data(),
+                             leaf_vectors.data_handle(),
                              leaf_size,
                              vector_stride,
-                             leaf_vectors.data(),
+                             leaf_vectors.data_handle(),
                              leaf_size,
                              vector_stride,
-                             gram.data(),
+                             gram.data_handle(),
                              gram_stride,
                              dimension,
                              static_cast<int>(batch_size));
     manyway_leaf_gram_knn_kernel<<<static_cast<int>(batch_size), leaf_size, 0, stream>>>(
-      gram.data(),
-      memberships.data(),
+      gram.data_handle(),
+      memberships.data_handle(),
       origins,
-      leaves.starts.data(),
-      leaves.counts.data(),
-      leaves.strides.data(),
+      leaves.starts.data_handle(),
+      leaves.counts.data_handle(),
+      leaves.strides.data_handle(),
       static_cast<int64_t>(leaf_offset),
       static_cast<int64_t>(batch_size),
       leaf_size,
@@ -1174,8 +1280,9 @@ auto build(raft::resources const& res,
   RAFT_EXPECTS(params.leaf_degree >= 1 && params.leaf_degree <= MAX_LEAF_DEGREE,
                "Fastener leaf degree must be between 1 and %d",
                MAX_LEAF_DEGREE);
-  RAFT_EXPECTS(leaf_gemm_supported(dim, params.leaf_size),
-               "Fastener dataset dimension exceeds the leaf GEMM limits");
+  RAFT_EXPECTS(
+    leaf_gemm_supported(dim, params.leaf_size, raft::resource::get_workspace_free_bytes(res)),
+    "Fastener dataset dimension exceeds the leaf GEMM limits");
 
   // the number of leaf partitions that a given point will end up in
   uint64_t spill = params.root_fanout;
@@ -1195,21 +1302,21 @@ auto build(raft::resources const& res,
   int union_degree = static_cast<int>(spill * params.leaf_degree);
 
   // Per-row index of the input partition this point came from, used to skip same-origin pairs
-  rmm::device_uvector<uint32_t> origins(static_cast<size_t>(rows), stream);
+  auto origins = raft::make_device_mdarray<uint32_t, int64_t>(
+    res, raft::resource::get_large_workspace_resource_ref(res), raft::make_extents<int64_t>(rows));
 
   for (size_t part = 0; part + 1 < offsets.size(); ++part) {
     int64_t part_rows = offsets[part + 1] - offsets[part];
     int blocks        = static_cast<int>((part_rows + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK);
     initialize_origins_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
-      origins.data(), offsets[part], part_rows, static_cast<uint32_t>(part));
+      origins.data_handle(), offsets[part], part_rows, static_cast<uint32_t>(part));
     RAFT_CUDA_TRY(cudaGetLastError());
   }
 
-  split_context context(stream, rows);
-  context.logical_dim = dim;
+  split_context context(res, rows, dim);
   int norm_blocks = static_cast<int>((rows + ROW_WARPS_PER_BLOCK - 1) / ROW_WARPS_PER_BLOCK);
   manyway_l2_norms_kernel<<<norm_blocks, ROW_WARPS_PER_BLOCK * raft::WarpSize, 0, stream>>>(
-    dataset.data_handle(), rows, dim, dataset.extent(1), context.norms.data());
+    dataset.data_handle(), rows, dim, dataset.extent(1), context.norms.data_handle());
   RAFT_CUDA_TRY(cudaGetLastError());
 
   // the actual splitting
@@ -1232,9 +1339,9 @@ auto build(raft::resources const& res,
   }
 
   // Leaf construction: only range slicing occurs after configured geometric depth.
-  auto leaves   = make_leaves(res, partitions, params.leaf_size);
+  auto leaves = make_leaves(res, partitions, params.leaf_size);
   auto scaffold =
-    build_leaf_neighbors(res, dataset, dim, leaves, origins.data(), union_degree, params);
+    build_leaf_neighbors(res, dataset, dim, leaves, origins.data_handle(), union_degree, params);
   raft::resource::sync_stream(res);
   return scaffold;
 }
@@ -1293,7 +1400,10 @@ auto append_to_input_graphs(
   }
   int64_t scaffold_degree = scaffold.extent(1);
   int64_t graph_degree    = base_degree + scaffold_degree;
-  auto graph = raft::make_device_matrix<uint32_t, int64_t>(res, scaffold.extent(0), graph_degree);
+  auto graph              = raft::make_device_mdarray<uint32_t, int64_t>(
+    res,
+    raft::resource::get_large_workspace_resource_ref(res),
+    raft::make_extents<int64_t>(scaffold.extent(0), graph_degree));
 
   for (size_t part = 0; part < indices.size(); ++part) {
     auto source = indices[part]->graph();
@@ -1370,7 +1480,10 @@ inline auto cap_sorted_graph(
 {
   RAFT_EXPECTS(output_degree > 0 && output_degree <= graph.extent(1),
                "Pre-optimize graph degree cap must be within the sorted graph degree");
-  auto output = raft::make_device_matrix<uint32_t, int64_t>(res, graph.extent(0), output_degree);
+  auto output = raft::make_device_mdarray<uint32_t, int64_t>(
+    res,
+    raft::resource::get_large_workspace_resource_ref(res),
+    raft::make_extents<int64_t>(graph.extent(0), output_degree));
   constexpr int WARPS_PER_BLOCK = THREADS_PER_BLOCK / raft::WarpSize;
   int blocks = static_cast<int>((graph.extent(0) + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);
   deduplicate_graph_prefix_kernel<<<blocks,
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 7e49309070..c317c8668d 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -10,6 +10,7 @@
 
 #include "../../../src/neighbors/detail/cagra/cagra_merge.cuh"
 #include "../ann_cagra.cuh"
+#include "../cagra_padded_build_helpers.cuh"
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
@@ -33,6 +34,67 @@
 namespace cuvs::neighbors::cagra {
 namespace {
 
+/** Padded device storage plus a view of it.
+ *
+ * The new dataset API makes an index hold only a *view*, so the storage must outlive every index
+ * built from it. Keeping both together in one object makes that lifetime explicit. Rows are padded
+ * to the CAGRA-required width and zero-filled, matching what cagra::merge produces.
+ */
+/** plan_split now takes a raft::host_vector; tests still express ranges as literals. */
+inline auto make_ranges(raft::resources const& res,
+                        std::initializer_list<detail::merge_scaffold::partition_range> items)
+  -> raft::host_vector<detail::merge_scaffold::partition_range, int64_t>
+{
+  auto out = raft::make_host_vector<detail::merge_scaffold::partition_range, int64_t>(
+    res, static_cast<int64_t>(items.size()));
+  int64_t i = 0;
+  for (auto const& item : items) {
+    out(i++) = item;
+  }
+  return out;
+}
+
+template <typename T>
+struct padded_storage {
+  raft::device_matrix<T, int64_t> matrix;
+  cuvs::neighbors::device_padded_dataset_view<T, int64_t> view;
+};
+
+template <typename T>
+auto make_padded(raft::resources const& res, raft::host_matrix_view<const T, int64_t> src)
+  -> padded_storage<T>
+{
+  auto stream       = raft::resource::get_cuda_stream(res);
+  auto const dim    = static_cast<uint32_t>(src.extent(1));
+  auto const stride = cuvs::neighbors::cagra_required_row_width<T>(dim, 16);
+  auto matrix =
+    raft::make_device_matrix<T, int64_t>(res, src.extent(0), static_cast<int64_t>(stride));
+  RAFT_CUDA_TRY(cudaMemsetAsync(
+    matrix.data_handle(), 0, static_cast<size_t>(matrix.size()) * sizeof(T), stream));
+  raft::copy_matrix(matrix.data_handle(),
+                    static_cast<size_t>(stride),
+                    src.data_handle(),
+                    static_cast<size_t>(dim),
+                    static_cast<size_t>(dim),
+                    static_cast<size_t>(src.extent(0)),
+                    stream);
+  raft::resource::sync_stream(res);
+  cuvs::neighbors::device_padded_dataset_view<T, int64_t> view(
+    raft::make_const_mdspan(matrix.view()), dim);
+  return padded_storage<T>{std::move(matrix), view};
+}
+
+/** Allocate merged-dataset storage matching what cagra::merge expects for `rows` of `dim`. */
+template <typename T>
+auto make_merged_storage(raft::resources const& res, int64_t rows, int64_t dim) -> padded_storage<T>
+{
+  auto const stride = cuvs::neighbors::cagra_required_row_width<T>(static_cast<uint32_t>(dim), 16);
+  auto matrix       = raft::make_device_matrix<T, int64_t>(res, rows, static_cast<int64_t>(stride));
+  cuvs::neighbors::device_padded_dataset_view<T, int64_t> view(
+    raft::make_const_mdspan(matrix.view()), static_cast<uint32_t>(dim));
+  return padded_storage<T>{std::move(matrix), view};
+}
+
 template <typename T>
 auto make_dataset(raft::resources const& res,
                   int64_t rows,
@@ -80,7 +142,9 @@ auto make_ring_graph(raft::resources const& res, int64_t rows, int64_t degree)
 }
 
 template <typename T>
-void expect_valid_graph(index<T, uint32_t> const& merged, int64_t rows, int64_t degree)
+void expect_valid_graph(cagra::device_padded_index<T, uint32_t> const& merged,
+                        int64_t rows,
+                        int64_t degree)
 {
   ASSERT_EQ(merged.size(), rows);
   ASSERT_EQ(merged.graph().extent(0), rows);
@@ -104,7 +168,7 @@ void expect_valid_graph(index<T, uint32_t> const& merged, int64_t rows, int64_t
  * `expect_valid_graph` only checks that entries are in range and not self-loops, so it cannot see a
  * merge that left the input graphs as separate components. */
 template <typename T>
-auto count_connected_components(index<T, uint32_t> const& merged) -> int64_t
+auto count_connected_components(cagra::device_padded_index<T, uint32_t> const& merged) -> int64_t
 {
   raft::resources res;
   int64_t rows   = merged.graph().extent(0);
@@ -141,24 +205,22 @@ auto count_connected_components(index<T, uint32_t> const& merged) -> int64_t
 
 template <typename T>
 void expect_dataset_order(raft::resources const& res,
-                          index<T, uint32_t> const& merged,
+                          cagra::device_padded_index<T, uint32_t> const& merged,
                           raft::host_matrix_view<const T, int64_t> expected)
 {
   auto view = merged.dataset();
-  ASSERT_EQ(view.extent(0), expected.extent(0));
-  ASSERT_EQ(view.extent(1), expected.extent(1));
-  // The merged owning dataset is attached through make_aligned_dataset, which pads each row to a
-  // 16-byte stride. For int8/uint8 (and any dim whose byte width is not a multiple of 16) the
-  // stride exceeds the logical dimension, so the rows must be copied honoring that stride rather
-  // than as one contiguous block.
+  ASSERT_EQ(view.n_rows(), expected.extent(0));
+  ASSERT_EQ(static_cast<int64_t>(view.dim()), expected.extent(1));
+  // The merged dataset is padded to a 16-byte row stride. For int8/uint8 (and any dim whose byte
+  // width is not a multiple of 16) the stride exceeds the logical dimension, so rows must be copied
+  // honouring that stride rather than as one contiguous block.
   auto host   = raft::make_host_matrix<T, int64_t>(res, expected.extent(0), expected.extent(1));
   auto stream = raft::resource::get_cuda_stream(res);
-  int64_t row_stride = view.stride(0);
-  for (int64_t row = 0; row < view.extent(0); ++row) {
-    raft::copy(host.data_handle() + row * view.extent(1),
-               view.data_handle() + row * row_stride,
-               view.extent(1),
-               stream);
+  int64_t const row_stride = static_cast<int64_t>(view.stride());
+  int64_t const dim        = static_cast<int64_t>(view.dim());
+  for (int64_t row = 0; row < view.n_rows(); ++row) {
+    raft::copy(
+      host.data_handle() + row * dim, view.view().data_handle() + row * row_stride, dim, stream);
   }
   raft::resource::sync_stream(res);
   for (int64_t row = 0; row < expected.extent(0); ++row) {
@@ -179,14 +241,17 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
   auto dataset1            = make_dataset<T>(res, rows, dim, 5678ULL, metric);
   auto expected            = concat_host_datasets<T>(
     res, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(dataset1.view()));
-  auto graph0 = make_ring_graph(res, rows, degree);
-  auto graph1 = make_ring_graph(res, rows, degree);
-  index<T, uint32_t> index0(
-    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
-  index<T, uint32_t> index1(
-    res, metric, raft::make_const_mdspan(dataset1.view()), raft::make_const_mdspan(graph1.view()));
-  auto index0_owns_data = index0.data().is_owning();
-  auto index1_owns_data = index1.data().is_owning();
+  auto graph0         = make_ring_graph(res, rows, degree);
+  auto graph1         = make_ring_graph(res, rows, degree);
+  auto index0_storage = make_padded<T>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<T, uint32_t> index0(
+    res, metric, index0_storage.view, raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<T>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<T, uint32_t> index1(
+    res, metric, index1_storage.view, raft::make_const_mdspan(graph1.view()));
+  // Ownership is expressed by the view type now, so record the observable shape instead.
+  auto const index0_rows = index0.dataset().n_rows();
+  auto const index1_rows = index1.dataset().n_rows();
 
   index_params params;
   params.metric                    = metric;
@@ -198,16 +263,16 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
   fastener.algo        = merge_algo::FASTENER;
   fastener.leaf_size   = 64;
   fastener.leaf_degree = 4;
-  std::vector<index<T, uint32_t>*> indices{&index0, &index1};
+  std::vector<cagra::device_padded_index<T, uint32_t>*> indices{&index0, &index1};
 
-  auto merged = merge(res, params, indices, fastener);
+  auto merged_storage = make_merged_storage<T>(res, rows * 2, dim);
+  auto merged         = merge(res, params, indices, merged_storage.view, fastener);
   expect_valid_graph(merged, rows * 2, degree);
   expect_dataset_order(res, merged, raft::make_const_mdspan(expected.view()));
-  EXPECT_TRUE(merged.data().is_owning());
-  EXPECT_EQ(index0.dataset().extent(0), rows);
-  EXPECT_EQ(index1.dataset().extent(0), rows);
-  EXPECT_EQ(index0.data().is_owning(), index0_owns_data);
-  EXPECT_EQ(index1.data().is_owning(), index1_owns_data);
+  EXPECT_EQ(index0.dataset().n_rows(), rows);
+  EXPECT_EQ(index1.dataset().n_rows(), rows);
+  EXPECT_EQ(index0.dataset().n_rows(), index0_rows);
+  EXPECT_EQ(index1.dataset().n_rows(), index1_rows);
   EXPECT_EQ(index0.size(), rows);
   EXPECT_EQ(index1.size(), rows);
   EXPECT_EQ(index0.dim(), dim);
@@ -221,7 +286,8 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
 TEST(CagraMergeFastener, PlanSplitIsDeterministicDenseAndCompact)
 {
   using namespace detail::merge_scaffold;
-  std::vector<partition_range> ranges{{0, 0, 40}, {1, 40, 300}, {2, 300, 1000}};
+  raft::resources res;
+  auto ranges = make_ranges(res, {{0, 0, 40}, {1, 40, 300}, {2, 300, 1000}});
   split_params params{.fanout            = 2,
                       .leader_fraction   = 0.05,
                       .max_leaders       = 16,
@@ -256,7 +322,7 @@ TEST(CagraMergeFastener, PlanSplitIsDeterministicDenseAndCompact)
   EXPECT_EQ(plan.child_count, key_cursor);
   EXPECT_EQ(plan.output_rows, 40 + 260 * 2 + 700 * 2);
 
-  std::vector<partition_range> overlapping{{0, 0, 40}, {1, 30, 100}};
+  auto overlapping = make_ranges(res, {{0, 0, 40}, {1, 30, 100}});
   EXPECT_THROW(plan_split(overlapping, 100, params, DETERMINISTIC_SEED), raft::exception);
 }
 
@@ -275,9 +341,9 @@ TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevel
   auto dataset           = raft::make_device_matrix<float, int64_t>(res, rows, dim);
   raft::copy(dataset.data_handle(), host_dataset.data_handle(), dataset.size(), stream);
 
-  split_context context(stream, rows);
+  split_context context(res, rows, dim);
   manyway_l2_norms_kernel<<<static_cast<int>((rows + 3) / 4), 128, 0, stream>>>(
-    dataset.data_handle(), rows, dim, context.norms.data());
+    dataset.data_handle(), rows, dim, dim, context.norms.data_handle());
   RAFT_CUDA_TRY(cudaGetLastError());
 
   auto root   = make_root_partition(res, rows);
@@ -304,7 +370,8 @@ TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevel
   EXPECT_EQ(second.memberships.size(), static_cast<size_t>(rows * 4));
 
   int64_t cursor = 0;
-  for (auto const& range : second.ranges) {
+  for (int64_t range_index = 0; range_index < second.ranges.extent(0); ++range_index) {
+    auto const& range = second.ranges(range_index);
     EXPECT_EQ(range.start, cursor);
     EXPECT_GT(range.end, range.start);
     EXPECT_LE(range.end, static_cast<int64_t>(second.memberships.size()));
@@ -313,7 +380,7 @@ TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevel
   EXPECT_EQ(cursor, static_cast<int64_t>(second.memberships.size()));
 
   std::vector<partition_membership> records(second.memberships.size());
-  raft::copy(records.data(), second.memberships.data(), records.size(), stream);
+  raft::copy(records.data(), second.memberships.data_handle(), records.size(), stream);
   raft::resource::sync_stream(res);
   for (auto const& record : records) {
     EXPECT_LT(record.id, static_cast<uint32_t>(rows));
@@ -322,7 +389,7 @@ TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevel
 
   constexpr int64_t small_rows = 32;
   auto small_root              = make_root_partition(res, small_rows);
-  split_context small_context(stream, small_rows);
+  split_context small_context(res, small_rows, dim);
   auto carried = split_manyway(res,
                                raft::make_const_mdspan(dataset.view()),
                                small_root,
@@ -336,7 +403,8 @@ TEST(CagraMergeFastener, SplitManywayCarriesSmallParentsAndSupportsRepeatedLevel
   ASSERT_EQ(carried.ranges.size(), 1);
   EXPECT_EQ(carried.memberships.size(), static_cast<size_t>(small_rows));
   std::vector<partition_membership> carried_records(carried.memberships.size());
-  raft::copy(carried_records.data(), carried.memberships.data(), carried_records.size(), stream);
+  raft::copy(
+    carried_records.data(), carried.memberships.data_handle(), carried_records.size(), stream);
   raft::resource::sync_stream(res);
   for (int64_t row = 0; row < small_rows; ++row) {
     EXPECT_EQ(carried_records[row].id, static_cast<uint32_t>(row));
@@ -414,7 +482,7 @@ TEST(CagraMergeFastener, DuplicateAcrossInputsAppearInEachOthersScaffoldNeighbor
   params.leaf_degree     = leaf_degree;
 
   std::vector<int64_t> offsets{0, part0_rows, rows};
-  auto scaffold = build(res, raft::make_const_mdspan(dataset.view()), offsets, params);
+  auto scaffold = build(res, raft::make_const_mdspan(dataset.view()), dim, offsets, params);
   ASSERT_EQ(scaffold.extent(0), rows);
   ASSERT_EQ(scaffold.extent(1), params.root_fanout * leaf_degree);
 
@@ -466,7 +534,7 @@ TEST(CagraMergeFastener, ScaffoldConstructionIsBitReproducible)
   build_params params;  // default two-level controls
 
   auto scaffold_bytes = [&] {
-    auto scaffold = build<float>(res, raft::make_const_mdspan(data.view()), offsets, params);
+    auto scaffold = build<float>(res, raft::make_const_mdspan(data.view()), dim, offsets, params);
     std::vector<uint32_t> host_scaffold(
       static_cast<size_t>(scaffold.extent(0) * scaffold.extent(1)));
     raft::copy(host_scaffold.data(), scaffold.data_handle(), host_scaffold.size(), stream);
@@ -509,9 +577,9 @@ TEST(CagraMergeFastener, MembershipsRemainAscendingWithinEachPartition)
   auto dataset           = raft::make_device_matrix<float, int64_t>(res, rows, dim);
   raft::copy(dataset.data_handle(), host_dataset.data_handle(), dataset.size(), stream);
 
-  split_context context(stream, rows);
+  split_context context(res, rows, dim);
   manyway_l2_norms_kernel<<<static_cast<int>((rows + 3) / 4), 128, 0, stream>>>(
-    dataset.data_handle(), rows, dim, context.norms.data());
+    dataset.data_handle(), rows, dim, dim, context.norms.data_handle());
   RAFT_CUDA_TRY(cudaGetLastError());
 
   auto partitions            = make_root_partition(res, rows);
@@ -531,10 +599,11 @@ TEST(CagraMergeFastener, MembershipsRemainAscendingWithinEachPartition)
   }
 
   std::vector<partition_membership> records(partitions.memberships.size());
-  raft::copy(records.data(), partitions.memberships.data(), records.size(), stream);
+  raft::copy(records.data(), partitions.memberships.data_handle(), records.size(), stream);
   raft::resource::sync_stream(res);
 
-  for (auto const& range : partitions.ranges) {
+  for (int64_t range_index = 0; range_index < partitions.ranges.extent(0); ++range_index) {
+    auto const& range = partitions.ranges(range_index);
     for (int64_t slot = range.start + 1; slot < range.end; ++slot) {
       EXPECT_LT(records[slot - 1].id, records[slot].id)
         << "partition [" << range.start << ", " << range.end << ") is not ascending at " << slot;
@@ -584,7 +653,7 @@ TEST(CagraMergeFastener, IdenticalInputsAlignedToLeafSizeGetCrossOriginScaffoldE
   params.leaf_degree     = leaf_degree;
 
   std::vector<int64_t> offsets{0, part_rows, rows};
-  auto scaffold = build(res, raft::make_const_mdspan(dataset.view()), offsets, params);
+  auto scaffold = build(res, raft::make_const_mdspan(dataset.view()), dim, offsets, params);
   ASSERT_EQ(scaffold.extent(0), rows);
   ASSERT_EQ(scaffold.extent(1), static_cast<int64_t>(leaf_degree));
 
@@ -616,14 +685,16 @@ TEST(CagraMergeFastener, IdenticalInputsAlignedToLeafSizeStayConnected)
   constexpr int64_t dim       = 8;
   constexpr int64_t degree    = 4;
 
-  auto data   = make_dataset<float>(res, part_rows, dim, 1234ULL);
-  auto graph0 = make_ring_graph(res, part_rows, degree);
-  auto graph1 = make_ring_graph(res, part_rows, degree);
-  auto metric = cuvs::distance::DistanceType::L2Expanded;
-  index<float, uint32_t> index0(
-    res, metric, raft::make_const_mdspan(data.view()), raft::make_const_mdspan(graph0.view()));
-  index<float, uint32_t> index1(
-    res, metric, raft::make_const_mdspan(data.view()), raft::make_const_mdspan(graph1.view()));
+  auto data           = make_dataset<float>(res, part_rows, dim, 1234ULL);
+  auto graph0         = make_ring_graph(res, part_rows, degree);
+  auto graph1         = make_ring_graph(res, part_rows, degree);
+  auto metric         = cuvs::distance::DistanceType::L2Expanded;
+  auto index0_storage = make_padded<float>(res, raft::make_const_mdspan(data.view()));
+  cagra::index<float, uint32_t> index0(
+    res, metric, index0_storage.view, raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<float>(res, raft::make_const_mdspan(data.view()));
+  cagra::index<float, uint32_t> index1(
+    res, metric, index1_storage.view, raft::make_const_mdspan(graph1.view()));
 
   index_params params;
   params.metric                    = metric;
@@ -642,8 +713,9 @@ TEST(CagraMergeFastener, IdenticalInputsAlignedToLeafSizeStayConnected)
   fastener.leaf_size       = 64;
   fastener.leaf_degree     = 4;
 
-  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
-  auto merged = merge(res, params, indices, fastener);
+  std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&index0, &index1};
+  auto merged_storage = make_merged_storage<float>(res, rows, dim);
+  auto merged         = merge(res, params, indices, merged_storage.view, fastener);
   expect_valid_graph(merged, rows, degree);
   EXPECT_EQ(count_connected_components(merged), 1)
     << "the inputs were merged without any edge connecting them";
@@ -657,24 +729,26 @@ TEST(CagraMergeFastener, IdenticalInputsAlignedToLeafSizeStayConnected)
 TEST(CagraMergeFastener, LeafGemmLimitsRejectOversizedWorkspaceDimensions)
 {
   using namespace detail::merge_scaffold;
-  EXPECT_TRUE(leaf_gemm_supported(4096, 256));
-  EXPECT_FALSE(leaf_gemm_supported(0, 256));
-  EXPECT_FALSE(leaf_gemm_supported(-1, 256));
+  constexpr size_t workspace_bytes = size_t{64} * 1024 * 1024;
+  EXPECT_TRUE(leaf_gemm_supported(4096, 256, workspace_bytes));
+  EXPECT_FALSE(leaf_gemm_supported(0, 256, workspace_bytes));
+  EXPECT_FALSE(leaf_gemm_supported(-1, 256, workspace_bytes));
 
-  // The widest leaf whose float vectors and float Gram matrix together fit GEMM_WORKSPACE_BYTES.
+  // The widest leaf whose float vectors and float Gram matrix together fit the configured
+  // workspace.
   constexpr uint32_t leaf_size = 256;
-  constexpr int64_t widest_dim =
-    static_cast<int64_t>((GEMM_WORKSPACE_BYTES - size_t{leaf_size} * leaf_size * sizeof(float)) /
-                         sizeof(float) / leaf_size);
-  EXPECT_TRUE(leaf_gemm_supported(widest_dim, leaf_size));
-  EXPECT_FALSE(leaf_gemm_supported(widest_dim + 1, leaf_size));
+  constexpr int64_t widest_dim = static_cast<int64_t>(
+    (workspace_bytes - size_t{leaf_size} * leaf_size * sizeof(float)) / sizeof(float) / leaf_size);
+  EXPECT_TRUE(leaf_gemm_supported(widest_dim, leaf_size, workspace_bytes));
+  EXPECT_FALSE(leaf_gemm_supported(widest_dim + 1, leaf_size, workspace_bytes));
 
   // Comfortably past the workspace, used below to check pre-mutation rejection.
   constexpr int64_t oversized_dim =
-    static_cast<int64_t>(GEMM_WORKSPACE_BYTES / sizeof(float) / leaf_size) + 1;
-  EXPECT_FALSE(leaf_gemm_supported(oversized_dim, leaf_size));
+    static_cast<int64_t>(workspace_bytes / sizeof(float) / leaf_size) + 1;
+  EXPECT_FALSE(leaf_gemm_supported(oversized_dim, leaf_size, workspace_bytes));
 
   raft::resources res;
+  raft::resource::set_workspace_to_global_resource(res, workspace_bytes);
   constexpr int64_t rows = 2;
   constexpr int64_t dim  = oversized_dim;
   auto dataset0          = make_dataset<int8_t>(res, rows, dim, 1234ULL);
@@ -682,10 +756,12 @@ TEST(CagraMergeFastener, LeafGemmLimitsRejectOversizedWorkspaceDimensions)
   auto graph0            = make_ring_graph(res, rows, 1);
   auto graph1            = make_ring_graph(res, rows, 1);
   auto metric            = cuvs::distance::DistanceType::L2Expanded;
-  index<int8_t, uint32_t> index0(
-    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
-  index<int8_t, uint32_t> index1(
-    res, metric, raft::make_const_mdspan(dataset1.view()), raft::make_const_mdspan(graph1.view()));
+  auto index0_storage    = make_padded<int8_t>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<int8_t, uint32_t> index0(
+    res, metric, index0_storage.view, raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<int8_t>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<int8_t, uint32_t> index1(
+    res, metric, index1_storage.view, raft::make_const_mdspan(graph1.view()));
 
   index_params params;
   params.metric                    = metric;
@@ -697,11 +773,11 @@ TEST(CagraMergeFastener, LeafGemmLimitsRejectOversizedWorkspaceDimensions)
   std::vector<index<int8_t, uint32_t>*> indices{&index0, &index1};
 
   auto result = detail::preflight_fastener(
-    params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
+    res, params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
   EXPECT_FALSE(result.eligible);
   EXPECT_EQ(result.reason, "dataset dimension exceeds the leaf GEMM workspace limit");
-  EXPECT_EQ(index0.dataset().extent(0), rows);
-  EXPECT_EQ(index1.dataset().extent(0), rows);
+  EXPECT_EQ(index0.dataset().n_rows(), rows);
+  EXPECT_EQ(index1.dataset().n_rows(), rows);
 }
 
 /**
@@ -726,16 +802,14 @@ TEST(CagraMergeFastener, PreflightRejectsCandidateDegreeAboveSortLimit)
   auto dataset1 = make_dataset<float>(res, rows, dim, 5678ULL);
 
   auto check = [&](int64_t input_degree) {
-    auto graph0 = make_ring_graph(res, rows, input_degree);
-    auto graph1 = make_ring_graph(res, rows, input_degree);
-    index<float, uint32_t> index0(res,
-                                  metric,
-                                  raft::make_const_mdspan(dataset0.view()),
-                                  raft::make_const_mdspan(graph0.view()));
-    index<float, uint32_t> index1(res,
-                                  metric,
-                                  raft::make_const_mdspan(dataset1.view()),
-                                  raft::make_const_mdspan(graph1.view()));
+    auto graph0         = make_ring_graph(res, rows, input_degree);
+    auto graph1         = make_ring_graph(res, rows, input_degree);
+    auto index0_storage = make_padded<float>(res, raft::make_const_mdspan(dataset0.view()));
+    cagra::index<float, uint32_t> index0(
+      res, metric, index0_storage.view, raft::make_const_mdspan(graph0.view()));
+    auto index1_storage = make_padded<float>(res, raft::make_const_mdspan(dataset1.view()));
+    cagra::index<float, uint32_t> index1(
+      res, metric, index1_storage.view, raft::make_const_mdspan(graph1.view()));
 
     index_params params;
     params.metric                    = metric;
@@ -743,9 +817,9 @@ TEST(CagraMergeFastener, PreflightRejectsCandidateDegreeAboveSortLimit)
     params.intermediate_graph_degree = 64;
     merge_params fastener;
     fastener.algo = merge_algo::FASTENER;
-    std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+    std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&index0, &index1};
     return detail::preflight_fastener(
-      params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
+      res, params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
   };
 
   // Exactly at the limit stays eligible; one degree past it is rejected for that specific reason.
@@ -772,6 +846,45 @@ TEST(CagraMergeFastener, SupportsAllScalarTypes)
   run_explicit_fastener<uint8_t>(metric);
 }
 
+/** The assignment and leaf buffers must shrink to the caller's bounded workspace rather than
+ * allocating against a fixed compile-time budget. */
+TEST(CagraMergeFastener, BatchesWithinConfiguredWorkspace)
+{
+  raft::resources res;
+  constexpr size_t workspace_bytes = size_t{64} * 1024;
+  raft::resource::set_workspace_to_global_resource(res, workspace_bytes);
+
+  constexpr int64_t rows   = 512;
+  constexpr int64_t dim    = 16;
+  constexpr int64_t degree = 8;
+  auto dataset0            = make_dataset<float>(res, rows, dim, 1234ULL);
+  auto dataset1            = make_dataset<float>(res, rows, dim, 5678ULL);
+  auto graph0              = make_ring_graph(res, rows, degree);
+  auto graph1              = make_ring_graph(res, rows, degree);
+  auto metric              = cuvs::distance::DistanceType::L2Expanded;
+  auto index0_storage      = make_padded<float>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<float, uint32_t> index0(
+    res, metric, index0_storage.view, raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<float>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<float, uint32_t> index1(
+    res, metric, index1_storage.view, raft::make_const_mdspan(graph1.view()));
+
+  index_params params;
+  params.metric                    = metric;
+  params.graph_degree              = degree;
+  params.intermediate_graph_degree = degree;
+  params.guarantee_connectivity    = false;
+  merge_params fastener;
+  fastener.algo      = merge_algo::FASTENER;
+  fastener.leaf_size = 32;
+  std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&index0, &index1};
+
+  auto merged_storage = make_merged_storage<float>(res, rows * 2, dim);
+  auto merged         = merge(res, params, indices, merged_storage.view, fastener);
+  expect_valid_graph(merged, rows * 2, degree);
+  EXPECT_EQ(raft::resource::get_workspace_used_bytes(res), 0);
+}
+
 /**
  * Verifies that merging one owning and one non-owning input preserves both inputs and their
  * ownership modes while producing an owning output with the expected dataset order.
@@ -796,21 +909,20 @@ TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
              dataset1.data_handle(),
              dataset1.size(),
              raft::resource::get_cuda_stream(res));
-  auto graph0 = make_ring_graph(res, rows, degree);
-  auto graph1 = make_ring_graph(res, rows, degree);
-  auto metric = cuvs::distance::DistanceType::L2Expanded;
-  index<float, uint32_t> index0(
-    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
-  index<float, uint32_t> index1(res,
-                                metric,
-                                raft::make_const_mdspan(device_dataset1.view()),
-                                raft::make_const_mdspan(graph1.view()));
-  // Explicitly transfer the first allocation so this test remains mixed-ownership even when
-  // mapped host allocations are device-accessible and would otherwise be wrapped non-owningly.
-  index0.update_dataset(
-    res, make_aligned_dataset(res, std::move(device_dataset0), /* align_bytes = */ 16));
-  ASSERT_TRUE(index0.data().is_owning());
-  ASSERT_FALSE(index1.data().is_owning());
+  auto graph0         = make_ring_graph(res, rows, degree);
+  auto graph1         = make_ring_graph(res, rows, degree);
+  auto metric         = cuvs::distance::DistanceType::L2Expanded;
+  auto index0_storage = make_padded<float>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<float, uint32_t> index0(
+    res, metric, index0_storage.view, raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<float>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<float, uint32_t> index1(
+    res, metric, index1_storage.view, raft::make_const_mdspan(graph1.view()));
+  // The old mixed-ownership assertions are gone: under the dataset-view API an index always holds
+  // a non-owning view and ownership lives in the type, so there is no runtime ownership to probe.
+  // What still matters is that merging leaves both inputs intact.
+  ASSERT_EQ(index0.dataset().n_rows(), rows);
+  ASSERT_EQ(index1.dataset().n_rows(), rows);
 
   index_params params;
   params.metric                    = metric;
@@ -822,16 +934,17 @@ TEST(CagraMergeFastener, MixedDatasetOwnershipPreservesInputs)
   fastener.algo        = merge_algo::FASTENER;
   fastener.leaf_size   = 64;
   fastener.leaf_degree = 4;
-  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+  std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&index0, &index1};
 
-  auto merged = merge(res, params, indices, fastener);
+  auto merged_storage = make_merged_storage<float>(res, rows * 2, dim);
+  auto merged         = merge(res, params, indices, merged_storage.view, fastener);
   expect_valid_graph(merged, rows * 2, degree);
   expect_dataset_order(res, merged, raft::make_const_mdspan(expected.view()));
-  EXPECT_TRUE(merged.data().is_owning());
-  EXPECT_EQ(index0.dataset().extent(0), rows);
-  EXPECT_EQ(index1.dataset().extent(0), rows);
-  EXPECT_TRUE(index0.data().is_owning());
-  EXPECT_FALSE(index1.data().is_owning());
+  EXPECT_EQ(merged.dataset().n_rows(), rows * 2);
+  EXPECT_EQ(index0.dataset().n_rows(), rows);
+  EXPECT_EQ(index1.dataset().n_rows(), rows);
+  EXPECT_EQ(index0.dataset().dim(), static_cast<uint32_t>(dim));
+  EXPECT_EQ(index1.dataset().dim(), static_cast<uint32_t>(dim));
 }
 
 /**
@@ -861,7 +974,7 @@ std::vector<AnnCagraInputs> generate_fastener_merge_recall_inputs()
     {false},
     {0.95},
     {std::optional<float>{std::nullopt}},
-    {std::optional<vpq_params>{std::nullopt}},
+    // AnnCagraInputs::compression was removed upstream along with index_params::compression.
     {std::optional<bool>{std::nullopt}},
     {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL});
   for (auto& input : inputs) {
@@ -911,7 +1024,9 @@ void run_fastener_merge_recall(size_t n_inputs, double min_recall)
   build_params.attach_dataset_on_build   = true;
 
   // Consecutive, near-equal slices so the merged row order matches the original database.
-  std::vector<index<DataT, uint32_t>> parts;
+  std::vector<cuvs::neighbors::test::padded_device_matrix_for_cagra<DataT>> part_storage;
+  part_storage.reserve(n_inputs);
+  std::vector<cagra::device_padded_index<DataT, uint32_t>> parts;
   parts.reserve(n_inputs);
   int64_t rows_per_part = raft::ceildiv<int64_t>(rows, static_cast<int64_t>(n_inputs));
   for (size_t part = 0; part < n_inputs; ++part) {
@@ -920,16 +1035,21 @@ void run_fastener_merge_recall(size_t n_inputs, double min_recall)
     ASSERT_GT(count, 0);
     auto slice = raft::make_device_matrix_view<const DataT, int64_t>(
       data.data_handle() + start * dim, count, dim);
-    parts.emplace_back(cagra::build(res, build_params, slice));
+    // build() takes a dataset view now, and the index keeps only a view, so the padded storage
+    // must outlive every part.
+    part_storage.emplace_back(res, slice);
+    parts.emplace_back(cagra::build(res, build_params, part_storage.back().view));
+    parts.back().update_device_dataset_same_layout(res, part_storage.back().view);
   }
 
-  std::vector<index<DataT, uint32_t>*> inputs;
+  std::vector<cagra::device_padded_index<DataT, uint32_t>*> inputs;
   for (auto& part : parts) {
     inputs.push_back(&part);
   }
   merge_params fastener;
-  fastener.algo = merge_algo::FASTENER;
-  auto merged   = merge(res, build_params, inputs, fastener);
+  fastener.algo       = merge_algo::FASTENER;
+  auto merged_storage = make_merged_storage<DataT>(res, rows, dim);
+  auto merged         = merge(res, build_params, inputs, merged_storage.view, fastener);
   ASSERT_EQ(merged.size(), rows);
 
   auto found_indices   = raft::make_device_matrix<uint32_t, int64_t>(res, n_query, k);
@@ -1012,14 +1132,16 @@ TEST(CagraMergeFastener, MixedDegreesAndThreeLevelUint8OptionsProduceExactDegree
   auto dataset1          = make_dataset<uint8_t>(res, rows, dim, 5678ULL);
   auto graph0            = make_ring_graph(res, rows, 6);
   auto graph1            = make_ring_graph(res, rows, 8);
-  index<uint8_t, uint32_t> index0(res,
-                                  cuvs::distance::DistanceType::L2Expanded,
-                                  raft::make_const_mdspan(dataset0.view()),
-                                  raft::make_const_mdspan(graph0.view()));
-  index<uint8_t, uint32_t> index1(res,
-                                  cuvs::distance::DistanceType::L2Expanded,
-                                  raft::make_const_mdspan(dataset1.view()),
-                                  raft::make_const_mdspan(graph1.view()));
+  auto index0_storage    = make_padded<uint8_t>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<uint8_t, uint32_t> index0(res,
+                                         cuvs::distance::DistanceType::L2Expanded,
+                                         index0_storage.view,
+                                         raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<uint8_t>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<uint8_t, uint32_t> index1(res,
+                                         cuvs::distance::DistanceType::L2Expanded,
+                                         index1_storage.view,
+                                         raft::make_const_mdspan(graph1.view()));
 
   index_params params;
   params.metric                  = cuvs::distance::DistanceType::L2Expanded;
@@ -1034,9 +1156,10 @@ TEST(CagraMergeFastener, MixedDegreesAndThreeLevelUint8OptionsProduceExactDegree
   fastener.max_leaders     = 32;
   fastener.leaf_size       = 64;
   fastener.leaf_degree     = 8;
-  std::vector<index<uint8_t, uint32_t>*> indices{&index0, &index1};
+  std::vector<cagra::device_padded_index<uint8_t, uint32_t>*> indices{&index0, &index1};
 
-  auto merged = merge(res, params, indices, fastener);
+  auto merged_storage = make_merged_storage<uint8_t>(res, rows * 2, dim);
+  auto merged         = merge(res, params, indices, merged_storage.view, fastener);
   expect_valid_graph(merged, rows * 2, 8);
 }
 
@@ -1047,19 +1170,21 @@ TEST(CagraMergeFastener, MixedDegreesAndThreeLevelUint8OptionsProduceExactDegree
 TEST(CagraMergeFastener, AppendCyclicallyPadsMixedInputDegrees)
 {
   raft::resources res;
-  auto dataset0 = make_dataset<float>(res, 2, 1, 1234ULL);
-  auto dataset1 = make_dataset<float>(res, 2, 1, 5678ULL);
-  auto graph0   = make_ring_graph(res, 2, 2);
-  auto graph1   = make_ring_graph(res, 2, 4);
-  index<float, uint32_t> index0(res,
-                                cuvs::distance::DistanceType::L2Expanded,
-                                raft::make_const_mdspan(dataset0.view()),
-                                raft::make_const_mdspan(graph0.view()));
-  index<float, uint32_t> index1(res,
-                                cuvs::distance::DistanceType::L2Expanded,
-                                raft::make_const_mdspan(dataset1.view()),
-                                raft::make_const_mdspan(graph1.view()));
-  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+  auto dataset0       = make_dataset<float>(res, 2, 1, 1234ULL);
+  auto dataset1       = make_dataset<float>(res, 2, 1, 5678ULL);
+  auto graph0         = make_ring_graph(res, 2, 2);
+  auto graph1         = make_ring_graph(res, 2, 4);
+  auto index0_storage = make_padded<float>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<float, uint32_t> index0(res,
+                                       cuvs::distance::DistanceType::L2Expanded,
+                                       index0_storage.view,
+                                       raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<float>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<float, uint32_t> index1(res,
+                                       cuvs::distance::DistanceType::L2Expanded,
+                                       index1_storage.view,
+                                       raft::make_const_mdspan(graph1.view()));
+  std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&index0, &index1};
   std::vector<int64_t> offsets{0, 2, 4};
 
   auto scaffold_host        = raft::make_host_matrix<uint32_t, int64_t>(4, 1);
@@ -1107,14 +1232,16 @@ TEST(CagraMergeFastener, MergeSupportsOutputDegreeBelowInputDegree)
   auto dataset1                  = make_dataset<float>(res, rows, dim, 5678ULL);
   auto graph0                    = make_ring_graph(res, rows, input_degree);
   auto graph1                    = make_ring_graph(res, rows, input_degree);
-  index<float, uint32_t> index0(res,
-                                cuvs::distance::DistanceType::L2Expanded,
-                                raft::make_const_mdspan(dataset0.view()),
-                                raft::make_const_mdspan(graph0.view()));
-  index<float, uint32_t> index1(res,
-                                cuvs::distance::DistanceType::L2Expanded,
-                                raft::make_const_mdspan(dataset1.view()),
-                                raft::make_const_mdspan(graph1.view()));
+  auto index0_storage = make_padded<float>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<float, uint32_t> index0(res,
+                                       cuvs::distance::DistanceType::L2Expanded,
+                                       index0_storage.view,
+                                       raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<float>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<float, uint32_t> index1(res,
+                                       cuvs::distance::DistanceType::L2Expanded,
+                                       index1_storage.view,
+                                       raft::make_const_mdspan(graph1.view()));
 
   index_params params;
   params.metric                  = cuvs::distance::DistanceType::L2Expanded;
@@ -1124,25 +1251,33 @@ TEST(CagraMergeFastener, MergeSupportsOutputDegreeBelowInputDegree)
   fastener.algo        = merge_algo::FASTENER;
   fastener.leaf_size   = 64;
   fastener.leaf_degree = 4;
-  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+  std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&index0, &index1};
 
-  auto merged = merge(res, params, indices, fastener);
+  auto merged_storage = make_merged_storage<float>(res, rows * 2, dim);
+  auto merged         = merge(res, params, indices, merged_storage.view, fastener);
   expect_valid_graph(merged, rows * 2, params.graph_degree);
 }
 
+/** Indices hold only views, so the padded storage travels with them. */
+struct float_index_pair {
+  padded_storage<float> storage0;
+  padded_storage<float> storage1;
+  std::vector<cagra::device_padded_index<float, uint32_t>> indices;
+};
+
 auto make_float_indices(raft::resources const& res,
                         cuvs::distance::DistanceType metric,
                         raft::host_matrix<float, int64_t>& dataset0,
                         raft::host_matrix<float, int64_t>& dataset1,
                         raft::host_matrix<uint32_t, int64_t>& graph0,
-                        raft::host_matrix<uint32_t, int64_t>& graph1)
+                        raft::host_matrix<uint32_t, int64_t>& graph1) -> float_index_pair
 {
-  std::vector<index<float, uint32_t>> output;
-  output.emplace_back(
-    res, metric, raft::make_const_mdspan(dataset0.view()), raft::make_const_mdspan(graph0.view()));
-  output.emplace_back(
-    res, metric, raft::make_const_mdspan(dataset1.view()), raft::make_const_mdspan(graph1.view()));
-  return output;
+  auto storage0 = make_padded<float>(res, raft::make_const_mdspan(dataset0.view()));
+  auto storage1 = make_padded<float>(res, raft::make_const_mdspan(dataset1.view()));
+  std::vector<cagra::device_padded_index<float, uint32_t>> output;
+  output.emplace_back(res, metric, storage0.view, raft::make_const_mdspan(graph0.view()));
+  output.emplace_back(res, metric, storage1.view, raft::make_const_mdspan(graph1.view()));
+  return float_index_pair{std::move(storage0), std::move(storage1), std::move(output)};
 }
 
 /**
@@ -1164,7 +1299,8 @@ TEST(CagraMergeFastener, InvalidManywayOptionsFailPreflightWithoutMutation)
   params.intermediate_graph_degree = 8;
   params.attach_dataset_on_build   = true;
   auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
-  std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+  std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&owned.indices[0],
+                                                                    &owned.indices[1]};
 
   std::vector<merge_params> invalid;
   auto add_invalid = [&](auto update) {
@@ -1197,11 +1333,11 @@ TEST(CagraMergeFastener, InvalidManywayOptionsFailPreflightWithoutMutation)
 
   for (auto const& candidate : invalid) {
     auto result = detail::preflight_fastener(
-      params, candidate, indices, cuvs::neighbors::filtering::none_sample_filter{});
+      res, params, candidate, indices, cuvs::neighbors::filtering::none_sample_filter{});
     EXPECT_FALSE(result.eligible) << result.reason;
   }
-  EXPECT_EQ(owned[0].dataset().extent(0), rows);
-  EXPECT_EQ(owned[1].dataset().extent(0), rows);
+  EXPECT_EQ(owned.indices[0].dataset().n_rows(), rows);
+  EXPECT_EQ(owned.indices[1].dataset().n_rows(), rows);
 }
 
 /**
@@ -1225,46 +1361,55 @@ TEST(CagraMergeFastener, DispatchRejectsOrFallsBackBeforeMutation)
 
   {
     auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
-    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    auto throwaway_storage = make_merged_storage<float>(res, rows * 2, dim);
+    std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&owned.indices[0],
+                                                                      &owned.indices[1]};
     merge_params unsupported;
     unsupported.algo      = merge_algo::FASTENER;
     unsupported.leaf_size = 512;
-    EXPECT_ANY_THROW(merge(res, params, indices, unsupported));
-    EXPECT_EQ(owned[0].dataset().extent(0), rows);
-    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+    EXPECT_ANY_THROW(merge(res, params, indices, throwaway_storage.view, unsupported));
+    EXPECT_EQ(owned.indices[0].dataset().n_rows(), rows);
+    EXPECT_EQ(owned.indices[1].dataset().n_rows(), rows);
   }
   {
     auto inner_product_params   = params;
     inner_product_params.metric = cuvs::distance::DistanceType::InnerProduct;
     auto owned =
       make_float_indices(res, inner_product_params.metric, dataset0, dataset1, graph0, graph1);
-    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    auto throwaway_storage = make_merged_storage<float>(res, rows * 2, dim);
+    std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&owned.indices[0],
+                                                                      &owned.indices[1]};
     merge_params fastener;
     fastener.algo      = merge_algo::FASTENER;
     fastener.leaf_size = 64;
-    EXPECT_ANY_THROW(merge(res, inner_product_params, indices, fastener));
-    EXPECT_EQ(owned[0].dataset().extent(0), rows);
-    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+    EXPECT_ANY_THROW(merge(res, inner_product_params, indices, throwaway_storage.view, fastener));
+    EXPECT_EQ(owned.indices[0].dataset().n_rows(), rows);
+    EXPECT_EQ(owned.indices[1].dataset().n_rows(), rows);
   }
   {
     auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
-    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    auto throwaway_storage = make_merged_storage<float>(res, rows * 2, dim);
+    std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&owned.indices[0],
+                                                                      &owned.indices[1]};
     merge_params automatic;
     automatic.algo      = merge_algo::AUTO;
     automatic.leaf_size = 512;
-    auto merged         = merge(res, params, indices, automatic);
+    auto merged         = merge(res, params, indices, throwaway_storage.view, automatic);
     EXPECT_EQ(merged.size(), rows * 2);
-    EXPECT_EQ(owned[0].dataset().extent(0), rows);
-    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+    EXPECT_EQ(owned.indices[0].dataset().n_rows(), rows);
+    EXPECT_EQ(owned.indices[1].dataset().n_rows(), rows);
   }
   {
     auto owned = make_float_indices(res, params.metric, dataset0, dataset1, graph0, graph1);
-    std::vector<index<float, uint32_t>*> indices{&owned[0], &owned[1]};
+    auto throwaway_storage = make_merged_storage<float>(res, rows * 2, dim);
+    std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&owned.indices[0],
+                                                                      &owned.indices[1]};
     merge_params rebuild{merge_algo::REBUILD};
-    auto merged = merge(res, params, indices, rebuild);
+    auto merged_storage = make_merged_storage<float>(res, rows * 2, dim);
+    auto merged         = merge(res, params, indices, merged_storage.view, rebuild);
     EXPECT_EQ(merged.size(), rows * 2);
-    EXPECT_EQ(owned[0].dataset().extent(0), rows);
-    EXPECT_EQ(owned[1].dataset().extent(0), rows);
+    EXPECT_EQ(owned.indices[0].dataset().n_rows(), rows);
+    EXPECT_EQ(owned.indices[1].dataset().n_rows(), rows);
   }
 }
 
@@ -1329,23 +1474,25 @@ TEST(CagraMergeFastener, InnerProductOrderingDiffersFromL2)
 TEST(CagraMergeFastener, AppendSortAndDedupHandleOffsetsAndPadding)
 {
   raft::resources res;
-  auto dataset0  = raft::make_host_matrix<float, int64_t>(2, 1);
-  auto dataset1  = raft::make_host_matrix<float, int64_t>(2, 1);
-  dataset0(0, 0) = 0.0f;
-  dataset0(1, 0) = 2.0f;
-  dataset1(0, 0) = 10.0f;
-  dataset1(1, 0) = 12.0f;
-  auto graph0    = make_ring_graph(res, 2, 1);
-  auto graph1    = make_ring_graph(res, 2, 1);
-  index<float, uint32_t> index0(res,
-                                cuvs::distance::DistanceType::L2Expanded,
-                                raft::make_const_mdspan(dataset0.view()),
-                                raft::make_const_mdspan(graph0.view()));
-  index<float, uint32_t> index1(res,
-                                cuvs::distance::DistanceType::L2Expanded,
-                                raft::make_const_mdspan(dataset1.view()),
-                                raft::make_const_mdspan(graph1.view()));
-  std::vector<index<float, uint32_t>*> indices{&index0, &index1};
+  auto dataset0       = raft::make_host_matrix<float, int64_t>(2, 1);
+  auto dataset1       = raft::make_host_matrix<float, int64_t>(2, 1);
+  dataset0(0, 0)      = 0.0f;
+  dataset0(1, 0)      = 2.0f;
+  dataset1(0, 0)      = 10.0f;
+  dataset1(1, 0)      = 12.0f;
+  auto graph0         = make_ring_graph(res, 2, 1);
+  auto graph1         = make_ring_graph(res, 2, 1);
+  auto index0_storage = make_padded<float>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<float, uint32_t> index0(res,
+                                       cuvs::distance::DistanceType::L2Expanded,
+                                       index0_storage.view,
+                                       raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<float>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<float, uint32_t> index1(res,
+                                       cuvs::distance::DistanceType::L2Expanded,
+                                       index1_storage.view,
+                                       raft::make_const_mdspan(graph1.view()));
+  std::vector<cagra::device_padded_index<float, uint32_t>*> indices{&index0, &index1};
   std::vector<int64_t> offsets{0, 2, 4};
 
   auto scaffold_host           = raft::make_host_matrix<uint32_t, int64_t>(4, 2);

From 879e6b03709fb51fed448957b1404443cd17a402 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Tue, 4 Aug 2026 00:19:58 +0000
Subject: [PATCH 30/41] signature mismatch

---
 c/src/neighbors/cagra.cpp        | 3 ++-
 c/tests/neighbors/ann_cagra_c.cu | 1 -
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/c/src/neighbors/cagra.cpp b/c/src/neighbors/cagra.cpp
index 27fea027b2..bf92b992c7 100644
--- a/c/src/neighbors/cagra.cpp
+++ b/c/src/neighbors/cagra.cpp
@@ -1924,7 +1924,7 @@ extern "C" cuvsError_t cuvsCagraMerge(cuvsResources_t res,
                                       cuvsCagraIndex_t output_index)
 {
   return cuvsCagraMergeWithParams(
-    res, params, nullptr, indices, num_indices, filter, output_index);
+    res, params, nullptr, indices, num_indices, filter, merged_dataset, output_index);
 }
 
 extern "C" cuvsError_t cuvsCagraMergeWithParams(cuvsResources_t res,
@@ -1933,6 +1933,7 @@ extern "C" cuvsError_t cuvsCagraMergeWithParams(cuvsResources_t res,
                                                 cuvsCagraIndex_t* indices,
                                                 size_t num_indices,
                                                 cuvsFilter filter,
+                                                cuvsDataset_t merged_dataset,
                                                 cuvsCagraIndex_t output_index)
 {
   return cuvs::core::translate_exceptions([=] {
diff --git a/c/tests/neighbors/ann_cagra_c.cu b/c/tests/neighbors/ann_cagra_c.cu
index f40591fdea..ba0e3ee310 100644
--- a/c/tests/neighbors/ann_cagra_c.cu
+++ b/c/tests/neighbors/ann_cagra_c.cu
@@ -964,7 +964,6 @@ TEST(CagraC, BuildMergeSearch)
   cuvsDatasetDestroy(additional_dataset_view);
   cuvsDatasetDestroy(main_dataset_view);
   cuvsDatasetDestroy(merged_dataset);
-  cuvsCagraIndexParamsDestroy(build_params);
   cuvsResourcesDestroy(res);
 }
 

From 0c3f8a4b0d7ccc38de03ec1883d9e0da3fa513b2 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Tue, 4 Aug 2026 17:12:32 +0000
Subject: [PATCH 31/41] switching to cublasltmatmul

---
 .../detail/cagra/cagra_merge_scaffold.cuh     | 84 ++++++++++++-------
 1 file changed, 55 insertions(+), 29 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 12a5b2c489..ae42f075a8 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -13,7 +13,7 @@
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/error.hpp>
-#include <raft/core/resource/cublas_handle.hpp>
+#include <raft/core/resource/cublaslt_handle.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/core/resources.hpp>
@@ -35,6 +35,7 @@
 #include <cmath>
 #include <cstdint>
 #include <limits>
+#include <memory>
 #include <type_traits>
 #include <utility>
 #include <vector>
@@ -558,7 +559,7 @@ inline constexpr cublasComputeType_t GEMM_COMPUTE_TYPE = CUBLAS_COMPUTE_32F_FAST
  * batch of row-major matrices with rows of length `row_width`:
  * out_i[b * a_rows + a] = A_i[a] . B_i[b].
  *
- * Pretty much a wrapper around `cublasGemmStridedBatchedEx`.
+ * Pretty much a wrapper around strided-batched `cublasLtMatmul`.
  *
  * Every dataset scalar type is gathered to float before this call. Native INT8 cuBLAS paths are
  * not portable across architectures (e.g. Ada returns CUBLAS_STATUS_NOT_SUPPORTED for
@@ -578,33 +579,58 @@ inline void batched_row_dot_products(raft::resources const& res,
                                      int row_width,
                                      int batch_count)
 {
-  float alpha        = 1.0f;
-  float beta         = 0.0f;
-  auto cublas_handle = raft::resource::get_cublas_handle(res);
-  RAFT_CUBLAS_TRY(cublasSetPointerMode(cublas_handle, CUBLAS_POINTER_MODE_HOST));
-  RAFT_CUBLAS_TRY(cublasGemmStridedBatchedEx(cublas_handle,
-                                             CUBLAS_OP_T,
-                                             CUBLAS_OP_N,
-                                             a_rows,
-                                             b_rows,
-                                             row_width,
-                                             &alpha,
-                                             a,
-                                             CUDA_R_32F,
-                                             row_width,
-                                             a_stride,
-                                             b,
-                                             CUDA_R_32F,
-                                             row_width,
-                                             b_stride,
-                                             &beta,
-                                             out,
-                                             CUDA_R_32F,
-                                             a_rows,
-                                             out_stride,
-                                             batch_count,
-                                             GEMM_COMPUTE_TYPE,
-                                             CUBLAS_GEMM_DEFAULT));
+  float alpha = 1.0f;
+  float beta  = 0.0f;
+
+  using matmul_descriptor = std::unique_ptr<std::remove_pointer_t<cublasLtMatmulDesc_t>,
+                                            decltype(&cublasLtMatmulDescDestroy)>;
+  using matrix_layout     = std::unique_ptr<std::remove_pointer_t<cublasLtMatrixLayout_t>,
+                                            decltype(&cublasLtMatrixLayoutDestroy)>;
+
+  cublasLtMatmulDesc_t operation_raw = nullptr;
+  RAFT_CUBLAS_TRY(cublasLtMatmulDescCreate(&operation_raw, GEMM_COMPUTE_TYPE, CUDA_R_32F));
+  matmul_descriptor operation{operation_raw, &cublasLtMatmulDescDestroy};
+  cublasOperation_t transpose = CUBLAS_OP_T;
+  RAFT_CUBLAS_TRY(cublasLtMatmulDescSetAttribute(
+    operation.get(), CUBLASLT_MATMUL_DESC_TRANSA, &transpose, sizeof(transpose)));
+
+  auto make_layout = [batch_count](uint64_t rows,
+                                   uint64_t columns,
+                                   int64_t leading_dimension,
+                                   int64_t batch_stride) {
+    cublasLtMatrixLayout_t raw = nullptr;
+    RAFT_CUBLAS_TRY(cublasLtMatrixLayoutCreate(&raw, CUDA_R_32F, rows, columns, leading_dimension));
+    matrix_layout layout{raw, &cublasLtMatrixLayoutDestroy};
+    auto count = static_cast<int32_t>(batch_count);
+    RAFT_CUBLAS_TRY(cublasLtMatrixLayoutSetAttribute(
+      raw, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &count, sizeof(count)));
+    RAFT_CUBLAS_TRY(cublasLtMatrixLayoutSetAttribute(
+      raw, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &batch_stride, sizeof(batch_stride)));
+    return layout;
+  };
+  auto a_layout = make_layout(
+    static_cast<uint64_t>(row_width), static_cast<uint64_t>(a_rows), row_width, a_stride);
+  auto b_layout = make_layout(
+    static_cast<uint64_t>(row_width), static_cast<uint64_t>(b_rows), row_width, b_stride);
+  auto out_layout =
+    make_layout(static_cast<uint64_t>(a_rows), static_cast<uint64_t>(b_rows), a_rows, out_stride);
+
+  RAFT_CUBLAS_TRY(cublasLtMatmul(raft::resource::get_cublaslt_handle(res),
+                                 operation.get(),
+                                 &alpha,
+                                 a,
+                                 a_layout.get(),
+                                 b,
+                                 b_layout.get(),
+                                 &beta,
+                                 out,
+                                 out_layout.get(),
+                                 out,
+                                 out_layout.get(),
+                                 nullptr,
+                                 nullptr,
+                                 0,
+                                 raft::resource::get_cuda_stream(res)));
 }
 
 /**

From 653e8a0151a27c1432a78334637bb12d9e7ed21b Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Tue, 4 Aug 2026 19:49:11 +0000
Subject: [PATCH 32/41] missed an alloc

---
 cpp/src/neighbors/detail/cagra/cagra_merge.cuh | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 5a087939a8..44bfc50e4d 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -18,6 +18,7 @@
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/matrix/copy.cuh>
 #include <raft/util/cudart_utils.hpp>
 
@@ -152,7 +153,10 @@ cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT> merge_rebuild(
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
   if (bitset_filtered) {
-    auto staging = raft::make_device_matrix<T, int64_t>(handle, merged_rows, stride);
+    auto staging = raft::make_device_mdarray<T, int64_t>(
+      handle,
+      raft::resource::get_large_workspace_resource_ref(handle),
+      raft::make_extents<int64_t>(merged_rows, stride));
     RAFT_CUDA_TRY(cudaMemsetAsync(
       staging.data_handle(), 0, static_cast<std::size_t>(staging.size()) * sizeof(T), stream));
     merge_dataset(staging.data_handle(), static_cast<std::size_t>(stride));

From cd832836c645d6ace323c6554af3c10487f423df Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Tue, 4 Aug 2026 21:15:01 +0000
Subject: [PATCH 33/41] temporary factoring out of shared kernels

---
 cpp/CMakeLists.txt                            |   1 +
 .../neighbors/detail/cagra/cagra_merge.cuh    |   2 +-
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 169 +---------------
 .../neighbors/detail/cagra/graph_shared.cu    | 190 ++++++++++++++++++
 .../neighbors/detail/cagra/graph_shared.cuh   |  46 +++++
 5 files changed, 247 insertions(+), 161 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/cagra/graph_shared.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/graph_shared.cuh

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 1a125fa71e..088c5f689c 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -1386,6 +1386,7 @@ if(NOT BUILD_CPU_ONLY)
     ${cagra_build_inst_files}
     ${cagra_extend_inst_files}
     src/neighbors/cagra_optimize.cu
+    src/neighbors/detail/cagra/graph_shared.cu
     ${cagra_serialize_inst_files}
     ${cagra_merge_inst_files}
     ${iface_cagra_inst_files}
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 44bfc50e4d..fff738f6e9 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -448,7 +448,7 @@ auto merge_fastener(raft::resources const& handle,
     raft::make_device_matrix<uint32_t, int64_t>(handle, preflight.rows, params.graph_degree);
   {
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/optimize");
-    cagra::detail::graph::optimize(
+    cagra::detail::graph::optimize_device_graph(
       handle, merged_graph.view(), optimized_graph.view(), params.guarantee_connectivity);
   }
 
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 5f6c1c499a..cdcd17ed6b 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -5,6 +5,7 @@
 #pragma once
 
 #include "cagra_helpers.hpp"
+#include "graph_shared.cuh"
 #include "utils.hpp"
 
 #include <raft/core/copy.cuh>
@@ -23,7 +24,6 @@
 
 #include <cuvs/neighbors/cagra.hpp>
 
-#include <raft/util/bitonic_sort.cuh>
 #include <raft/util/cuda_rt_essentials.hpp>
 #include <raft/util/integer_utils.hpp>
 
@@ -54,151 +54,6 @@ inline double cur_time(void)
   return ((double)tv.tv_sec + (double)tv.tv_usec * 1e-6);
 }
 
-template <typename T>
-__device__ inline void swap(T& val1, T& val2)
-{
-  T val0 = val1;
-  val1   = val2;
-  val2   = val0;
-}
-
-template <typename K, typename V>
-__device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2, bool ascending)
-{
-  if (key1 == key2) { return false; }
-  if ((key1 > key2) == ascending) {
-    swap<K>(key1, key2);
-    swap<V>(val1, val2);
-    return true;
-  }
-  return false;
-}
-
-template <class DATA_T, class IdxT, int numElementsPerThread>
-__global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size, dataset_dim]
-                          const IdxT dataset_size,
-                          const uint32_t dataset_dim,
-                          IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
-                          const uint32_t graph_size,
-                          const uint32_t graph_degree,
-                          const cuvs::distance::DistanceType metric)
-{
-  const IdxT srcNode = (blockDim.x * blockIdx.x + threadIdx.x) / raft::WarpSize;
-  if (srcNode >= graph_size) { return; }
-
-  const uint32_t lane_id = threadIdx.x % raft::WarpSize;
-
-  float my_keys[numElementsPerThread];
-  IdxT my_vals[numElementsPerThread];
-
-  // Compute distance from a src node to its neighbors
-  for (int k = 0; k < graph_degree; k++) {
-    const IdxT dstNode = knn_graph[k + static_cast<uint64_t>(graph_degree) * srcNode];
-    float dist         = 0;
-    float norm2_dst    = 0;
-    if (metric == cuvs::distance::DistanceType::InnerProduct ||
-        metric == cuvs::distance::DistanceType::CosineExpanded) {
-      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
-        auto elem_b = cuvs::spatial::knn::detail::utils::mapping<float>{}(
-          dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
-        dist -= cuvs::spatial::knn::detail::utils::mapping<float>{}(
-                  dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) *
-                elem_b;
-
-        if (metric == cuvs::distance::DistanceType::CosineExpanded) {
-          norm2_dst += elem_b * elem_b;
-        }
-      }
-    } else if (metric == cuvs::distance::DistanceType::L2Expanded) {
-      // L2Expanded
-      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
-        float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
-                       dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) -
-                     cuvs::spatial::knn::detail::utils::mapping<float>{}(
-                       dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
-        dist += diff * diff;
-      }
-    } else if (metric == cuvs::distance::DistanceType::L1) {
-      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
-        float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
-                       dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) -
-                     cuvs::spatial::knn::detail::utils::mapping<float>{}(
-                       dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
-        dist += raft::abs(diff);
-      }
-    } else if (metric == cuvs::distance::DistanceType::BitwiseHamming) {
-      if constexpr (std::is_integral_v<DATA_T>) {
-        for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
-          dist += __popc(
-            static_cast<uint32_t>(dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode] ^
-                                  dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]) &
-            0xffu);
-        }
-      }
-    }
-    dist += __shfl_xor_sync(0xffffffff, dist, 1);
-    dist += __shfl_xor_sync(0xffffffff, dist, 2);
-    dist += __shfl_xor_sync(0xffffffff, dist, 4);
-    dist += __shfl_xor_sync(0xffffffff, dist, 8);
-    dist += __shfl_xor_sync(0xffffffff, dist, 16);
-
-    if (metric == cuvs::distance::DistanceType::CosineExpanded) {
-      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 1);
-      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 2);
-      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 4);
-      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 8);
-      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 16);
-      if (lane_id == (k % raft::WarpSize)) { dist /= sqrt(norm2_dst); }
-    }
-
-    if (lane_id == (k % raft::WarpSize)) {
-      my_keys[k / raft::WarpSize] = dist;
-      my_vals[k / raft::WarpSize] = dstNode;
-    }
-  }
-  for (int k = graph_degree; k < raft::WarpSize * numElementsPerThread; k++) {
-    if (lane_id == k % raft::WarpSize) {
-      my_keys[k / raft::WarpSize] = utils::get_max_value<float>();
-      my_vals[k / raft::WarpSize] = utils::get_max_value<IdxT>();
-    }
-  }
-
-  // Sort by RAFT bitonic sort
-  raft::util::bitonic<numElementsPerThread>(true).sort(my_keys, my_vals);
-
-  // Update knn_graph
-  for (int i = 0; i < numElementsPerThread; i++) {
-    const int k = i * raft::WarpSize + lane_id;
-    if (k < graph_degree) {
-      knn_graph[k + (static_cast<uint64_t>(graph_degree) * srcNode)] = my_vals[i];
-    }
-  }
-}
-
-// kern_sort capacity is WarpSize * numElementsPerThread; largest specialization uses 32.
-constexpr int kMaxSortElementsPerThread = 32;
-constexpr uint64_t kMaxSortDegree =
-  static_cast<uint64_t>(raft::WarpSize) * kMaxSortElementsPerThread;
-
-template <typename DataT, typename IdxT>
-using sort_kernel_type =
-  void (*)(DataT const*, IdxT, uint32_t, IdxT*, uint32_t, uint32_t, cuvs::distance::DistanceType);
-
-template <typename DataT, typename IdxT>
-auto select_sort_kernel(uint64_t degree) -> sort_kernel_type<DataT, IdxT>
-{
-  if (degree <= raft::WarpSize * 1) { return kern_sort<DataT, IdxT, 1>; }
-  if (degree <= raft::WarpSize * 2) { return kern_sort<DataT, IdxT, 2>; }
-  if (degree <= raft::WarpSize * 4) { return kern_sort<DataT, IdxT, 4>; }
-  if (degree <= raft::WarpSize * 8) { return kern_sort<DataT, IdxT, 8>; }
-  if (degree <= raft::WarpSize * 16) { return kern_sort<DataT, IdxT, 16>; }
-  if (degree <= kMaxSortDegree) { return kern_sort<DataT, IdxT, kMaxSortElementsPerThread>; }
-  RAFT_FAIL(
-    "The degree of input knn graph is too large (%lu). It must be equal to or smaller than %lu.",
-    degree,
-    kMaxSortDegree);
-}
-
 template <typename IdxT, typename OutputMatrixView>
 __global__ void kern_make_rev_graph_k(
   OutputMatrixView output_graph,                                // [graph_size, degree]
@@ -1004,6 +859,7 @@ void sort_knn_graph_device_inplace(
   raft::device_matrix_view<const DataT, int64_t, raft::row_major> dataset,
   raft::device_matrix_view<IdxT, int64_t, raft::row_major> knn_graph)
 {
+  static_assert(std::is_same_v<IdxT, uint32_t>, "CAGRA graph indices must be uint32_t");
   RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
                "dataset size is expected to have the same number of graph index size");
   RAFT_EXPECTS(
@@ -1026,20 +882,13 @@ void sort_knn_graph_device_inplace(
   RAFT_EXPECTS(
     degree > 0 && degree <= kMaxSortDegree, "Graph degree must be in [1, %lu]", kMaxSortDegree);
 
-  auto kernel = select_sort_kernel<DataT, IdxT>(degree);
-
-  constexpr uint32_t block_size = 256;
-  auto const warps              = block_size / raft::WarpSize;
-  auto const blocks             = static_cast<uint32_t>((rows + warps - 1) / warps);
-  kernel<<<blocks, block_size, 0, raft::resource::get_cuda_stream(res)>>>(
-    dataset.data_handle(),
-    static_cast<IdxT>(rows),
-    static_cast<uint32_t>(dim),
-    knn_graph.data_handle(),
-    static_cast<uint32_t>(rows),
-    static_cast<uint32_t>(degree),
-    metric);
-  RAFT_CUDA_TRY(cudaGetLastError());
+  launch_sort_knn_graph(res,
+                        metric,
+                        dataset.data_handle(),
+                        static_cast<uint32_t>(rows),
+                        static_cast<uint32_t>(dim),
+                        knn_graph.data_handle(),
+                        static_cast<uint32_t>(degree));
 }
 
 template <typename DataT,
diff --git a/cpp/src/neighbors/detail/cagra/graph_shared.cu b/cpp/src/neighbors/detail/cagra/graph_shared.cu
new file mode 100644
index 0000000000..3ed3d9c4b9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/graph_shared.cu
@@ -0,0 +1,190 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "graph_core.cuh"
+#include "graph_shared.cuh"
+#include "utils.hpp"
+
+// TODO: This shouldn't be invoking anything from spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/bitonic_sort.cuh>
+#include <raft/util/cuda_rt_essentials.hpp>
+
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra::detail::graph {
+namespace {
+
+template <class DATA_T, int numElementsPerThread>
+__global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size, dataset_dim]
+                          const uint32_t dataset_dim,
+                          uint32_t* const knn_graph,  // [graph_chunk_size, graph_degree]
+                          const uint32_t graph_size,
+                          const uint32_t graph_degree,
+                          const cuvs::distance::DistanceType metric)
+{
+  const uint32_t srcNode = (blockDim.x * blockIdx.x + threadIdx.x) / raft::WarpSize;
+  if (srcNode >= graph_size) { return; }
+
+  const uint32_t lane_id = threadIdx.x % raft::WarpSize;
+
+  float my_keys[numElementsPerThread];
+  uint32_t my_vals[numElementsPerThread];
+
+  // Compute distance from a src node to its neighbors
+  for (int k = 0; k < graph_degree; k++) {
+    const uint32_t dstNode = knn_graph[k + static_cast<uint64_t>(graph_degree) * srcNode];
+    float dist             = 0;
+    float norm2_dst        = 0;
+    if (metric == cuvs::distance::DistanceType::InnerProduct ||
+        metric == cuvs::distance::DistanceType::CosineExpanded) {
+      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+        auto elem_b = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+          dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
+        dist -= cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                  dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) *
+                elem_b;
+
+        if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+          norm2_dst += elem_b * elem_b;
+        }
+      }
+    } else if (metric == cuvs::distance::DistanceType::L2Expanded) {
+      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+        float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                       dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) -
+                     cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                       dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
+        dist += diff * diff;
+      }
+    } else if (metric == cuvs::distance::DistanceType::L1) {
+      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+        float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                       dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) -
+                     cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                       dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
+        dist += raft::abs(diff);
+      }
+    } else if (metric == cuvs::distance::DistanceType::BitwiseHamming) {
+      if constexpr (std::is_integral_v<DATA_T>) {
+        for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+          dist += __popc(
+            static_cast<uint32_t>(dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode] ^
+                                  dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]) &
+            0xffu);
+        }
+      }
+    }
+    dist += __shfl_xor_sync(0xffffffff, dist, 1);
+    dist += __shfl_xor_sync(0xffffffff, dist, 2);
+    dist += __shfl_xor_sync(0xffffffff, dist, 4);
+    dist += __shfl_xor_sync(0xffffffff, dist, 8);
+    dist += __shfl_xor_sync(0xffffffff, dist, 16);
+
+    if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 1);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 2);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 4);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 8);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 16);
+      if (lane_id == (k % raft::WarpSize)) { dist /= sqrt(norm2_dst); }
+    }
+
+    if (lane_id == (k % raft::WarpSize)) {
+      my_keys[k / raft::WarpSize] = dist;
+      my_vals[k / raft::WarpSize] = dstNode;
+    }
+  }
+  for (int k = graph_degree; k < raft::WarpSize * numElementsPerThread; k++) {
+    if (lane_id == k % raft::WarpSize) {
+      my_keys[k / raft::WarpSize] = utils::get_max_value<float>();
+      my_vals[k / raft::WarpSize] = utils::get_max_value<uint32_t>();
+    }
+  }
+
+  raft::util::bitonic<numElementsPerThread>(true).sort(my_keys, my_vals);
+
+  for (int i = 0; i < numElementsPerThread; i++) {
+    const int k = i * raft::WarpSize + lane_id;
+    if (k < graph_degree) {
+      knn_graph[k + (static_cast<uint64_t>(graph_degree) * srcNode)] = my_vals[i];
+    }
+  }
+}
+
+constexpr int kMaxSortElementsPerThread = 32;
+
+template <typename DataT>
+using sort_kernel_type =
+  void (*)(DataT const*, uint32_t, uint32_t*, uint32_t, uint32_t, cuvs::distance::DistanceType);
+
+template <typename DataT>
+auto select_sort_kernel(uint32_t degree) -> sort_kernel_type<DataT>
+{
+  if (degree <= raft::WarpSize * 1) { return kern_sort<DataT, 1>; }
+  if (degree <= raft::WarpSize * 2) { return kern_sort<DataT, 2>; }
+  if (degree <= raft::WarpSize * 4) { return kern_sort<DataT, 4>; }
+  if (degree <= raft::WarpSize * 8) { return kern_sort<DataT, 8>; }
+  if (degree <= raft::WarpSize * 16) { return kern_sort<DataT, 16>; }
+  if (degree <= kMaxSortDegree) { return kern_sort<DataT, kMaxSortElementsPerThread>; }
+  RAFT_FAIL(
+    "The degree of input knn graph is too large (%u). It must be equal to or smaller than %lu.",
+    degree,
+    kMaxSortDegree);
+}
+
+template <typename DataT>
+void launch_sort_knn_graph_impl(raft::resources const& res,
+                                cuvs::distance::DistanceType metric,
+                                DataT const* dataset,
+                                uint32_t dataset_size,
+                                uint32_t dataset_dim,
+                                uint32_t* knn_graph,
+                                uint32_t graph_degree)
+{
+  auto kernel = select_sort_kernel<DataT>(graph_degree);
+
+  constexpr uint32_t block_size = 256;
+  auto const warps              = block_size / raft::WarpSize;
+  auto const blocks             = (dataset_size + warps - 1) / warps;
+  kernel<<<blocks, block_size, 0, raft::resource::get_cuda_stream(res)>>>(
+    dataset, dataset_dim, knn_graph, dataset_size, graph_degree, metric);
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+}  // namespace
+
+#define CUVS_DEFINE_CAGRA_GRAPH_SORT(DataT)                                      \
+  void launch_sort_knn_graph(raft::resources const& res,                         \
+                             cuvs::distance::DistanceType metric,                \
+                             DataT const* dataset,                               \
+                             uint32_t dataset_size,                              \
+                             uint32_t dataset_dim,                               \
+                             uint32_t* knn_graph,                                \
+                             uint32_t graph_degree)                              \
+  {                                                                              \
+    launch_sort_knn_graph_impl(                                                  \
+      res, metric, dataset, dataset_size, dataset_dim, knn_graph, graph_degree); \
+  }
+
+CUVS_DEFINE_CAGRA_GRAPH_SORT(float)
+CUVS_DEFINE_CAGRA_GRAPH_SORT(half)
+CUVS_DEFINE_CAGRA_GRAPH_SORT(int8_t)
+CUVS_DEFINE_CAGRA_GRAPH_SORT(uint8_t)
+
+#undef CUVS_DEFINE_CAGRA_GRAPH_SORT
+
+void optimize_device_graph(
+  raft::resources const& res,
+  raft::device_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
+  raft::device_matrix_view<uint32_t, int64_t, raft::row_major> output_graph,
+  bool guarantee_connectivity)
+{
+  optimize(res, knn_graph, output_graph, guarantee_connectivity);
+}
+
+}  // namespace cuvs::neighbors::cagra::detail::graph
diff --git a/cpp/src/neighbors/detail/cagra/graph_shared.cuh b/cpp/src/neighbors/detail/cagra/graph_shared.cuh
new file mode 100644
index 0000000000..0f2bc0f7dc
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/graph_shared.cuh
@@ -0,0 +1,46 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+#pragma once
+
+#include <cuvs/core/export.hpp>
+#include <cuvs/distance/distance.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuda_fp16.h>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::cagra::detail::graph {
+
+// kern_sort capacity is WarpSize * numElementsPerThread; largest specialization uses 32.
+inline constexpr uint64_t kMaxSortDegree = 32 * 32;
+
+#define CUVS_DECL_CAGRA_GRAPH_SORT(DataT)                              \
+  CUVS_EXPORT void launch_sort_knn_graph(raft::resources const& res,   \
+                                         cuvs::distance::DistanceType, \
+                                         DataT const* dataset,         \
+                                         uint32_t dataset_size,        \
+                                         uint32_t dataset_dim,         \
+                                         uint32_t* knn_graph,          \
+                                         uint32_t graph_degree)
+
+CUVS_DECL_CAGRA_GRAPH_SORT(float);
+CUVS_DECL_CAGRA_GRAPH_SORT(half);
+CUVS_DECL_CAGRA_GRAPH_SORT(int8_t);
+CUVS_DECL_CAGRA_GRAPH_SORT(uint8_t);
+
+#undef CUVS_DECL_CAGRA_GRAPH_SORT
+
+/** Run the existing CAGRA optimizer through one compiled instantiation instead of rematerializing
+ *  its reverse-graph, prune, merge, and MST kernels in every Fastener dtype TU. */
+CUVS_EXPORT void optimize_device_graph(
+  raft::resources const& res,
+  raft::device_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
+  raft::device_matrix_view<uint32_t, int64_t, raft::row_major> output_graph,
+  bool guarantee_connectivity);
+
+}  // namespace cuvs::neighbors::cagra::detail::graph

From 6b19de291ecfeecacfc8d3350af0fbbee7a2d118 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 6 Aug 2026 16:44:16 +0000
Subject: [PATCH 34/41] deleting bycatch

---
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 PR_2352_REVIEW_WORK.md | 768 -----------------------------------------
 pipnn.pdf              | Bin 3467796 -> 0 bytes
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 delete mode 100644 FASTENER_PR_PLAN.md
 delete mode 100644 PR_2352_REVIEW_WORK.md
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diff --git a/FASTENER_PR_PLAN.md b/FASTENER_PR_PLAN.md
deleted file mode 100644
index 603fc5842d..0000000000
--- a/FASTENER_PR_PLAN.md
+++ /dev/null
@@ -1,139 +0,0 @@
-# Fastener CAGRA merge: reusable many-way splitting
-
-## Goal and scope
-
-Fastener merges two or more attached, uncompressed CAGRA indices by preserving their input graphs
-and adding a cross-input scaffold. The result is a normal owning CAGRA index with datasets
-concatenated in input order. AUTO uses Fastener only after a non-mutating preflight succeeds;
-FASTENER rejects unsupported input explicitly; REBUILD retains concatenate-and-build behavior.
-
-The production path supports float, half, int8_t, and uint8_t data, uint32_t graph IDs, and
-L2Expanded. Filters, compressed datasets, and other metrics remain on the rebuild path in AUTO.
-
-## Public controls and defaults
-
-    struct merge_params {
-      merge_algo algo          = merge_algo::AUTO;
-      uint32_t levels          = 2;
-      uint32_t root_fanout     = 2;
-      uint32_t lower_fanout    = 3;
-      double leader_fraction   = 0.02;
-      uint32_t max_leaders     = 1024;
-      uint32_t leaf_size       = 256;
-      uint32_t leaf_degree     = 4;
-    };
-
-Supported values are positive levels, fanouts 1 through 32, leader fractions in (0, 1], leader caps
-through 8192, leaf sizes 64/128/256, and leaf degrees 4/8. The cap must cover both fanouts. Preflight
-evaluates spill width without overflow and requires:
-
-    root_fanout * lower_fanout^(levels - 1) * leaf_degree <= 255
-
-The combined row count times spill width must also fit the membership representation. The
-deterministic seed, assignment tile size, and split/leaf GEMM workspaces remain internal.
-
-The benchmark parser exposes every public Fastener field. Maintained presets are the default above,
-a higher-quality two-level 4x2/.01/1000 point, and the rebuild control.
-
-## Reusable splitting design
-
-A partition_set owns device-resident (point_id, occurrence) memberships plus compact grouped ranges.
-The root is one identity partition; partitioning never copies dataset vectors.
-
-Every configured level invokes the same boundary:
-
-    auto partitions = make_root_partition(res, rows);
-    uint32_t occurrence_stride = 1;
-    for (uint32_t level = 0; level < params.levels; ++level) {
-      auto fanout = level == 0 ? params.root_fanout : params.lower_fanout;
-      partitions = split_manyway(
-        res, dataset, partitions,
-        split_params{fanout, fraction, cap, leaf_size, level, occurrence_stride},
-        workspace);
-      occurrence_stride *= fanout;
-    }
-    auto leaves = make_leaves(res, partitions, params.leaf_size);
-    auto scaffold = build_leaf_neighbors(
-      res, dataset, leaves, origins, spill * params.leaf_degree, params);
-
-split_manyway applies this sequence:
-
-1. Parents no larger than leaf_size are copied with memberships and occurrences unchanged.
-2. Each active parent selects clamp(ceil(fraction * parent_size), fanout, cap, parent_size)
-   deterministic stratified leaders.
-3. Parents are bucketed by a power-of-two padded leader count and divided into assignment tiles.
-4. Gathered points and leaders use batched FP32 GEMM and deterministic nearest-leader selection.
-   Root and lower levels share these kernels and this host path.
-5. One membership is emitted for each selected leader, stable-sorted by a compact child key, and
-   reduced into the next grouped ranges.
-
-Occurrence numbering uses the previous spill width as a stride. Completed parents retain their
-records while active siblings expand without writing the same candidate slot.
-
-After the exact configured depth, make_leaves slices oversized groups into consecutive leaf_size
-ranges. It never performs an implicit geometric split.
-
-## Leaf scaffold and merge phases
-
-For each occurrence, the leaf stage selects directed nearest neighbors whose source input differs
-from the row's input. Float and half use FP32 batched Gram matrices. Integer data use centered
-INT8/INT32 GEMM. Inputs must fit the leaf GEMM workspace, and integer dimensions must remain within
-the worst-case-safe INT32 accumulation bound. That integer limit exceeds 131,000 dimensions and is
-not realistic for ANN workloads, so the implementation rejects it before mutation instead of
-retaining a second direct-distance leaf path. Leaf degree is a runtime loop bound over
-fixed-capacity eight-entry top-k arrays; it is not a kernel template.
-
-A matched three-process A/B comparison found no meaningful benefit from specializing leaf degree.
-At degree 4, runtime degree changed geometric-mean merge time by +0.155% across all nine dataset and
-fan-in cells, with individual changes from -0.326% to +0.580%. At degree 8 and fan-in 8, the
-three-dataset geometric-mean change was +0.277% (individual changes +0.159% to +0.446%). Median
-recall deltas were within +/-0.00035. These differences are below the 1% significance threshold, so
-the single runtime implementation is retained for simplicity.
-
-Scaffold rows begin filled with their self ID, and leaf selection overwrites available occurrence
-slots. The later metric-sorted prefix pass is the only place that removes self IDs and duplicates and
-cyclically pads short rows. A matched three-process A/B across five completed Wiki1M and OpenAI
-2.3M cells found that removing the redundant pre-compaction improved geometric-mean merge time by
-1.058%, with per-cell changes from 0.207% to 2.250% faster. Recall deltas ranged from -0.000242 to
-+0.000213. The remaining equivalent fan-in cells were not needed to establish the result. Merge
-orchestration then:
-
-1. copies the input datasets into one contiguous device matrix;
-2. shifts and appends mixed-degree input graphs;
-3. distance-sorts candidates and retains the nearest unique prefix;
-4. runs the existing CAGRA graph optimizer; and
-5. constructs the owning result without modifying any input index.
-
-The input datasets remain unchanged throughout the merge. The consolidated device matrix becomes
-the returned index's owning dataset.
-
-## Initial measured default rationale
-
-The two-level 2x3/.02/1000 point with 256/4 leaves was selected over eight independent binary trees
-on Wiki1M, OpenAI 2.3M, and YFCC10M at fan-ins 2, 8, and 32:
-
-| Dataset | Merge-time change | Recall change range |
-|---|---:|---:|
-| Wiki1M | 42.7-44.0% faster | -0.001875 to +0.000141 |
-| OpenAI 2.3M | 35.7-36.6% faster | +0.000217 to +0.003392 |
-| YFCC10M | 6.0-6.9% faster | -0.000327 to +0.001247 |
-
-It was faster in all nine measured cells and stayed within the accepted maximum recall loss of
-0.002. This is the speed-oriented initial default. The final leaf tuple remains subject to the
-focused {64,128,256} x {4,8} post-refactor sweep.
-
-## Verification and final selection
-
-Correctness coverage includes direct split tests, three-level splitting, completed-parent carry,
-range coverage and valid IDs, pre-mutation validation, all scalar types, mixed graph degrees,
-ownership and dataset ordering, dispatch/fallback behavior, graph shape/range, and search recall.
-
-Build the CAGRA library, NEIGHBORS_ANN_CAGRA_MERGE_TEST, and ANN benchmark target in the CUDA 13.2
-devcontainer. Run targeted cuvs-bench parser/constraint tests and git diff --check.
-
-After implementation, sweep {64,128,256} x {4,8} on all datasets at fan-ins 2/8/32. Choose the
-lowest geometric-mean merge-time ratio whose worst recall loss versus repeat-8 is no worse than
-0.002; retain 256/4 if it remains the winner. Re-run the selected default in three fresh processes
-per cell and report median/range merge time, Recall@12, and repeat-8 deltas. At fan-in 8 compare
-seeds 7, 42, 1234, and 2025 to ensure the fixed seed is not unusually favorable. Raw CSVs and
-temporary sweep configurations stay outside the repository.
diff --git a/PR_2352_REVIEW_WORK.md b/PR_2352_REVIEW_WORK.md
deleted file mode 100644
index 00f1868e6b..0000000000
--- a/PR_2352_REVIEW_WORK.md
+++ /dev/null
@@ -1,768 +0,0 @@
-# PR #2352 review response: experiments and worktrees
-
-Working notes for [NVIDIA/cuvs#2352](https://github.com/NVIDIA/cuvs/pull/2352) ("[WIP] Fast CAGRA
-Index Merge"), covering dantegd's `CHANGES_REQUESTED` review of 2026-07-28 (10 inline comments plus
-an API note in the review body).
-
-This document records, per comment: what was asked, whether it needs a code change or a
-measurement, which worktree holds the work, and what any experiment actually found.
-
----
-
-## Worktree layout
-
-One worktree per comment that requests a change, all branched from the PR head `5e3f436d`, so each
-thread can be developed, reviewed, and pushed independently rather than as one mixed diff.
-
-**Branch state as of 2026-07-30.** The PR branch has moved to `e788608e`
-("Merge branch 'main' into landrumb/cagra-fastener-merge"), and C1, C3, C4 and C5 have been landed
-upstream by the author as `bf219633` ("round-robin leaf splitting + more tests") and `bf331a2f`
-("Device OOM fallback for AUTO"), including all the tests written here. C6 and C8 were rebased onto
-the new head and are still outstanding.
-
-| Comment | Worktree | Status |
-|---|---|---|
-| C1 `scaffold:818` | `c1-leaf-origin-mixing` | **Landed upstream** in `bf219633` |
-| C2 `merge:374` | `c2-candidate-headroom` | **Experiment done — no change needed** |
-| C3 `merge:427` | `c3-auto-oom-fallback` | **Landed upstream** in `bf331a2f` |
-| C4 `merge:330` | `c4-preflight-sort-degree` | **Landed upstream** |
-| C5 `test:568` | `c5-recall-coverage` | **Landed upstream** |
-| C6 `scaffold:606` | `c6-tile-rows-derivation` | In the working tree, uncommitted; also `bb128d06` in the worktree |
-| C7 `scaffold:391` | `c7-aligned-consolidation` | Analysed — deferral recommended, see below |
-| C8 `scaffold:1249` | `c8-redundant-sync` | In the working tree, uncommitted |
-
-C6 and C8 are validated together on `e788608e` at **23/23**.
-
-**C6 was briefly lost.** It was committed as `488ab76e` ("inferred tile_rows"), then dropped when the
-branch was reset back to `e788608e`; `leader_bytes` was absent from both HEAD and the working tree.
-Restored into the working tree. `488ab76e` remains reachable and `bb128d06` holds it in the worktree,
-so nothing was actually lost — but it would not have shipped.
-
-### C8: a lifetime "fix" was proposed, then withdrawn after checking rmm
-
-I initially flagged the bare sync removal as unsafe — `scaffold` is a lambda-local temporary that
-the copy kernels read, and the sync was what guaranteed they finished before it was destroyed — and
-hoisted it to function scope. **That was wrong, and the hoist was reverted.**
-
-Checking rmm rather than reasoning from first principles,
-`stream_ordered_memory_resource::do_deallocate` (`stream_ordered_memory_resource.hpp:142-152`) does:
-
-```cpp
-auto stream_event = get_event(strm);
-RMM_ASSERT_CUDA_SUCCESS(cudaEventRecord(stream_event.event, strm.value()));
-stream_free_blocks_[stream_event].insert(block);
-```
-
-Freeing records a CUDA event on the freeing stream and files the block under that stream. Same-stream
-reuse is ordered behind the already-enqueued kernels; cross-stream reuse waits on the event. The
-scaffold's stream and the copy kernels' stream both come from the same `res`, so the block cannot be
-handed out until the kernels complete. This is rmm's documented contract, not incidental behaviour.
-
-**And the hoist had a real cost.** The scaffold is `rows * spill * leaf_degree` uint32s, i.e.
-`rows * 96` bytes with the default controls:
-
-| Dataset | Scaffold |
-|---|---:|
-| wiki-1m | 96 MB |
-| openai-2.3m | 223 MB |
-| yfcc-10m | **960 MB** |
-
-Scoped to the lambda it is freed *before* `sort_knn_graph_device_inplace`, `cap_sorted_graph` and
-`optimize` allocate their own graphs. Hoisting kept it alive across all three, adding up to ~960 MB
-to peak device memory on a 10M-row merge — directly against C7, which is dantegd's peak-memory
-thread. Trading a gigabyte of peak to avoid depending on a documented allocator guarantee is a bad
-deal.
-
-**Final shape of C8:** drop the sync, keep the tight lambda scope, and document the stream-ordered
-reliance at both ends. Exactly what dantegd proposed. The staged edit had also left a
-trailing-whitespace line at `cagra_merge_scaffold.cuh:1301`, which would have failed the pre-commit
-hook; that is fixed.
-
-Verified present on the branch: round-robin striding, the `bad_alloc` fallback, the
-`kMaxSortDegree` preflight rejection, `count_connected_components`,
-`IdenticalInputsAlignedToLeafSizeStayConnected`, `MembershipsRemainAscendingWithinEachPartition`,
-`PreflightRejectsCandidateDegreeAboveSortLimit` and the three `run_fastener_merge_recall` cases.
-Verified absent: the `tile_rows` derivation (C6) and the sync removal (C8).
-
-Changes were validated in batches in the integration checkout rather than one build per worktree,
-since a fresh per-worktree CMake configure plus libcuvs build costs ~10 minutes each. C4 and C8 were
-validated together on top of C1; C3, C4's test and C5 together. They touch disjoint code, but each
-branch should still get a solo CI run before merge.
-
-**Build caveats — two ways a "passing" validation lied.**
-
-1. One run reported "19/19 passed" against a *stale* binary: the build had failed with
-   `sccache: error: Timed out waiting for server startup`, and ninja's failure was hidden by the
-   output filter. Fixed with `sccache --start-server`. Check the test *count*, not just the pass
-   status — it rose to 23 once C4's and C5's tests were added.
-2. Another reported nothing at all because the shell's working directory was a *different worktree*,
-   so `cd cpp/build` failed and neither ninja nor the test binary ran. The filtered output showed
-   only the echo between them, which reads like success at a glance.
-
-Both share a root cause: filtering build output to `error:`/`FAILED:` hides the case where the build
-never happened. Prefer absolute paths, and confirm the expected test count on every run.
-
-Two comments (`scaffold:758`, `scaffold:657`) ask questions rather than request changes, so they get
-measurements instead of worktrees; a worktree follows only if the numbers justify one.
-
-`~/cuvs-fastener-pr` stays the integration checkout. It holds the configured CUDA build
-(`cpp/build`, arch 90-real, sccache) and is where experiment arms are compiled, since a fresh
-per-worktree configure plus libcuvs build costs ~10 minutes each. Experiment-only code is always
-gated behind an environment variable so a single matched binary can run every arm, and is stripped
-before the change lands in its worktree.
-
-### Running the built test binary
-
-The build's rmm must be preloaded or the binary picks up conda's and dies on an ABI mismatch
-(`undefined symbol: _ZN3rmm10_RMM_26_1013device_bufferD1Ev`):
-
-```
-cd ~/cuvs-fastener-pr/cpp/build
-LD_PRELOAD=$PWD/_deps/rmm-build/librmm.so ./gtests/NEIGHBORS_ANN_CAGRA_MERGE_TEST
-```
-
----
-
-## Benchmark harness
-
-All merge-time / recall numbers come from `/raid/blandrum/fastener-leaf-ab/LEAF_BENCH`, adapted from
-the existing `fastener-tf32-ab/recall/recall_bench.cu` with a stream-synced timer added around
-`cagra::merge` (the original measured recall only). It deserializes pre-built fan-in 8 shards from
-`/raid/blandrum/fastener-pivot-results/{wiki-1m,openai-2m,yfcc-10m}-parts`, so partition build time
-is excluded and only the merge is timed, then computes Recall@12 against brute-force ground truth
-derived from the merged index's own dataset.
-
-- `run_sweep.sh` — leaf-construction arms (C1). `ITOPK` defaults to 160.
-- `run_cap_sweep.sh` — candidate-cap arms (C2).
-
-Environment-variable switches used by the experiments, all stripped before anything lands:
-
-| Variable | Values | Comment |
-|---|---|---|
-| `CUVS_FASTENER_LEAF_FALLBACK` | `baseline` / `roundrobin` / `resplit` / `big512` / `big1024` | C1 |
-| `CUVS_FASTENER_LEAF_STATS` | `1` | C1 diagnostics |
-| `CUVS_FASTENER_CAP_MODE` | `headroom` | C2 |
-| `CUVS_FASTENER_SPLIT_STATS` | `1` | Q1 / Q2 |
-
-### itopk matters more than expected
-
-The first C1 sweep used the library-default itopk and showed recall gains of +0.0012 to +0.0060.
-Re-running at **itopk=160** shrank those by 3–20x, because a stronger search routes around missing
-cross-input edges. Any recall claim from this PR should state its itopk; the default-itopk numbers
-overstate the benefit of scaffold-quality changes. All results in this document are itopk=160 unless
-noted.
-
-### Reproducibility caveat
-
-Absolute merge times drift between sweeps — Wiki1M baseline measured 545.6 ms, 514.5 ms and
-512.9 ms in three different sweeps of the same code. Only within-sweep comparisons are meaningful,
-which is why every arm is re-measured in every sweep rather than compared against a stored baseline.
-
-Both write to a per-configuration results directory and append, so each run gets a fresh directory.
-Arms are interleaved within each dataset and repetition so machine drift hits them equally, and
-every cell is repeated 3x with the median reported. Absolute times drift a few percent between
-sweeps, so arms are only ever compared **within** a sweep.
-
-Datasets: Wiki1M (768d, float), OpenAI 2.3M (1536d, float), YFCC10M (192d, uint8), all at fan-in 8.
-
----
-
-## C1 — `cagra_merge_scaffold.cuh:818`, single-origin leaves *(resolved)*
-
-> "Since the sorts are stable, rows within a partition can remain grouped by their source index.
-> Slicing an oversized partition into consecutive chunks can then produce leaves containing rows
-> from only one input, and the leaf kernel skips every same-origin pair. [...] Could we either keep
-> splitting or deterministically mix origins before slicing? A regression test for that case would
-> also be helpful."
-
-### Confirmed mechanism
-
-Four facts compose into the bug:
-
-1. Root memberships are the identity in ascending row order (`scaffold:286`).
-2. Every regroup is a `thrust::stable_sort_by_key` (`scaffold:765`) and tiles emit in parent input
-   order (`scaffold:261`, `:432`), so by induction **every partition's membership list stays
-   ascending in consolidated row id**.
-3. `copy_input_datasets` concatenates inputs in order (`merge:36-59`) and `initialize_origins_kernel`
-   labels `[offsets[p], offsets[p+1])` with `p` (`scaffold:1048`), so origins are contiguous row-id
-   blocks — ascending row id *is* origin-sorted order.
-4. Positional slicing (`scaffold:818`) therefore yields single-origin leaves, and
-   `manyway_leaf_gram_knn_kernel:925` skips every same-origin pair. Unfilled slots keep their
-   self-ID prefill, which `deduplicate_graph_prefix_kernel:1278` strips — net zero cross-input edges.
-
-### This fires on the shipped defaults
-
-Instrumented run, Wiki1M at fan-in 8, default knobs:
-
-```
-ranges=40147  oversized_ranges=6597  oversized_rows=3097996
-leaves=49733  single_origin_leaves=3007  single_origin_rows=316036
-```
-
-16% of final partitions overflow `leaf_size`, and **6% of all leaves are single-origin** — 316k
-membership slots contributing no cross-input edges. Not merely a degenerate-input corner case.
-
-### Arms evaluated
-
-| Arm | Description |
-|---|---|
-| `baseline` | Current consecutive slicing |
-| `roundrobin` | Deal members round-robin across the same number of leaves |
-| `resplit` | Extra fanout-1 geometric levels until nothing is oversized, then slice |
-| `big512` / `big1024` | Widen the cap for oversized partitions only: take them whole up to the cap, round-robin at that wider cap beyond it |
-
-`resplit` uses fanout 1 so the spill width — and therefore scaffold degree and the `uint8_t`
-candidate-width bound — is unchanged and the arm stays comparable.
-
-### Results, itopk=160, 3 reps, medians
-
-Sweep 1 (`results-itopk160-3arm/`):
-
-| Dataset | Arm | Merge ms | Δ | Recall@12 | Δ | Spread |
-|---|---|---:|---:|---:|---:|---:|
-| wiki-1m | baseline | 545.6 | — | 0.991683 | — | 1.1e-4 |
-| | roundrobin | 546.6 | +0.18% | 0.991875 | +0.000192 | 4.1e-5 |
-| | resplit | 857.7 | +57.2% | 0.992083 | +0.000400 | 2.5e-5 |
-| openai-2m | baseline | 2271.5 | — | 0.958383 | — | 7.5e-5 |
-| | roundrobin | 2272.1 | +0.03% | 0.958758 | +0.000375 | 8.4e-5 |
-| | resplit | 3292.8 | +45.0% | 0.958158 | −0.000225 | 1.2e-4 |
-| yfcc-10m | baseline | 2744.5 | — | 0.981367 | — | 7.5e-5 |
-| | roundrobin | 2734.6 | −0.36% | 0.983583 | +0.002216 | 3.4e-5 |
-| | resplit | 3915.5 | +42.7% | 0.982783 | +0.001416 | 4.2e-5 |
-
-Sweep 2, adding the widened-cap arms (`results-itopk160/`):
-
-| Dataset | Arm | Merge ms | Δ | Recall@12 | vs baseline | vs roundrobin | Spread |
-|---|---|---:|---:|---:|---:|---:|---:|
-| wiki-1m | baseline | 514.5 | — | 0.991775 | — | +0.000008 | 2.5e-5 |
-| | roundrobin | 516.2 | +0.35% | 0.991767 | −0.000008 | — | 1.0e-4 |
-| | big512 | 542.1 | +5.37% | 0.992825 | +0.001050 | +0.001058 | 8e-6 |
-| | big1024 | 554.0 | +7.68% | 0.993075 | +0.001300 | +0.001308 | 1.7e-5 |
-| openai-2m | baseline | 2203.4 | — | 0.958417 | — | −0.000308 | 6.7e-5 |
-| | roundrobin | 2206.6 | +0.15% | 0.958725 | +0.000308 | — | 1.6e-5 |
-| | big512 | 2273.0 | +3.16% | 0.960408 | +0.001991 | +0.001683 | 1.0e-4 |
-| | big1024 | 2287.2 | +3.80% | 0.960958 | +0.002541 | +0.002233 | 4.2e-5 |
-| yfcc-10m | baseline | 2419.1 | — | 0.981433 | — | −0.002125 | 5.9e-5 |
-| | roundrobin | 2430.8 | +0.49% | 0.983558 | +0.002125 | — | 1.2e-4 |
-| | big512 | 2528.8 | +4.54% | 0.984625 | +0.003192 | +0.001067 | 1.7e-5 |
-| | big1024 | 2594.4 | +7.25% | 0.985058 | +0.003625 | +0.001500 | 6.7e-5 |
-
-### Findings
-
-- **`roundrobin` shipped.** Merge time is free (−0.9%..+0.5% across both sweeps, inside the 1%
-  significance threshold). ~30 lines confined to `make_leaves`, no new kernel, no extra buffer.
-- **Its recall benefit is real only on yfcc** (+0.0021, reproduced in both sweeps). Wiki measured
-  +0.000192 then −0.000008 against a ~1e-4 spread, i.e. nothing; openai is a consistent but small
-  +0.0003. The much larger gains seen at the library-default itopk do not survive at itopk=160,
-  because a stronger search routes around missing cross-input edges. **The PR reply should lead with
-  correctness, not recall.**
-- **"Keep splitting" is the wrong lever.** `resplit` costs +43..57% merge time, *increases*
-  single-origin leaves (Wiki1M 5,956 vs baseline 3,007 — smaller leaves are likelier to be
-  single-origin), and loses recall on openai. Worth telling dantegd explicitly, since it was one of
-  his two suggestions.
-- **Widening the cap is the only arm that gains on all three datasets**, +0.0011..+0.0022 over
-  roundrobin for +3..5% merge time. Deliberately *not* shipped: it needs a second KNN kernel plus a
-  bucketing driver (~200 lines) on a PR with ten open threads. Recorded as a possible follow-up.
-
-Partition size distribution, which bounds any whole-partition pass:
-
-| Dataset | Oversized | p50 | p90 | p99 | max | Σn² |
-|---|---:|---:|---:|---:|---:|---:|
-| wiki-1m | 6,597 | 380 | 753 | 1,547 | 4,929 | 1.98e9 |
-| openai-2m | 15,315 | 365 | 654 | 1,163 | 3,173 | 3.36e9 |
-| yfcc-10m | 65,614 | 369 | 697 | 1,525 | 9,536 | 1.83e10 |
-
-### Change shipped
-
-Commit `3a31c44b` on `landrumb/fastener-c1-leaf-origin-mixing` (252 insertions / 23 deletions):
-
-- `leaf_set` becomes strided views (`starts`/`counts`/`strides`) instead of `[start, end)`, so
-  origin mixing needs no permutation buffer — relevant to C7's peak-memory concern.
-- `make_leaves` deals each oversized partition round-robin; a range that already fits yields stride
-  1 and is bit-identical to before.
-- The two consuming kernels index `start + i*stride`.
-- Side benefit: removes the tiny trailing leaf (e.g. 257 rows → `{256, 1}`), where the one-row leaf
-  hits the `leaf_n <= 1` early-out and contributes nothing. Round-robin gives `{129, 128}`.
-
-### Tests added
-
-- `IdenticalInputsAlignedToLeafSizeGetCrossOriginScaffoldEdges` — dantegd's exact case at the
-  scaffold level; asserts every row gets a cross-origin neighbor.
-- `IdenticalInputsAlignedToLeafSizeStayConnected` — end-to-end, asserts one weakly-connected
-  component via a new `count_connected_components` union-find helper (`expect_valid_graph` only
-  checks in-range and no self-loop, so it could not catch this).
-- `MembershipsRemainAscendingWithinEachPartition` — pins the ordering invariant the fix relies on,
-  so a future non-stable sort trips a test instead of silently weakening the guarantee.
-
-Verified red-to-green: on `baseline` all 256 rows get zero cross-input edges and the merged graph
-has **2 disconnected components**; on the fix, 1. Full suite 19/19 on every fix arm.
-
----
-
-## C2 — `cagra_merge.cuh:374`, candidate headroom before `optimize()`
-
-> "Are we capping this a little too early? [...] this nearest prefix cap can remove every cross
-> index edge before `graph::optimize()` gets a chance to consider it. It also leaves the optimizer
-> with the same input and output degree, so there's no candidate headroom for robust pruning."
-
-You replied on-thread that the rationale was "if you had no cross-input edges that were closer than
-the edges you started with, you probably didn't need them", that it saved optimize time without a
-real recall impact, and that you would **re-check and post numbers**. This experiment is that
-re-check.
-
-Arms (`CUVS_FASTENER_CAP_MODE`):
-
-- `default` — cap to `graph_degree`, then `optimize` runs degree-to-degree (current behaviour).
-- `headroom` — cap to `min(max(intermediate_graph_degree, graph_degree), candidate_width)`, letting
-  `optimize` prune with headroom, exactly as dantegd proposed.
-
-At the harness settings (`graph_degree=64`, `intermediate=128`) the merged candidate width is
-`base_degree + scaffold_degree = 64 + 24 = 88`, so `headroom` caps at 88 and `optimize` runs 88→64.
-
-### Results, itopk=160, 3 reps, medians (`results-cap-itopk160/`)
-
-| Dataset | Mode | Merge ms | Δ | Recall@12 | Δ | Spread |
-|---|---|---:|---:|---:|---:|---:|
-| wiki-1m | default | 512.9 | — | 0.991842 | — | 2.5e-5 |
-| | headroom | 597.8 | **+16.56%** | 0.991792 | −0.000050 | 3.4e-5 |
-| openai-2m | default | 2201.9 | — | 0.958742 | — | 7.5e-5 |
-| | headroom | 2402.6 | **+9.12%** | 0.957100 | **−0.001642** | 8.3e-5 |
-| yfcc-10m | default | 2418.7 | — | 0.983567 | — | 2.5e-5 |
-| | headroom | 3259.6 | **+34.76%** | 0.982217 | **−0.001350** | 3.4e-5 |
-
-### Finding: the current early cap is correct — keep it
-
-Adding candidate headroom is worse on **both** axes:
-
-- **Merge time +9% to +35%.** `optimize` doing 88→64 instead of 64→64 is materially more work, and
-  it scales with the extra width.
-- **Recall drops**, and not marginally: −0.0016 on openai and −0.0014 on yfcc, roughly 20x the
-  rep spread. Wiki is −0.00005, i.e. neutral.
-
-This directly supports the rationale you gave on-thread — "if you had no cross-input edges that were
-closer than the edges you started with, you probably didn't need them" — and confirms the earlier
-evaluation you recalled. It also refines it: the effect is not merely "saves time without hurting
-recall", it is "saves time **and** helps recall".
-
-Worth noting for the reply, because it is counterintuitive and answers dantegd's "no candidate
-headroom for robust pruning" concern head-on: handing `optimize` *more* candidates makes the result
-**worse**, not better. The extra candidates admitted by the wider cap are by construction the
-*farthest* ones — the prefix is distance-sorted — so they dilute the pruning and reverse-edge budget
-with edges the nearest-prefix cap had correctly already discarded.
-
-The concern's premise — "this cap can remove every cross index edge before `optimize()` gets a
-chance to consider it" — is factually right; it just turns out to be the desired behaviour. When an
-input's own local neighbors are all closer than every scaffold candidate, that row does not need a
-cross-input edge, and forcing one in costs both time and quality.
-
-**No change required.** Worktree `c2-candidate-headroom` retained only to hold the experiment patch;
-the shipping code is unchanged.
-
----
-
-## C3 — `cagra_merge.cuh:427`, AUTO fallback on OOM
-
-> "Should AUTO fall back to rebuild if Fastener runs out of memory? [...] changing the existing
-> overload to AUTO means some merges that previously succeeded may now fail. I think explicit
-> FASTENER should still fail, but AUTO retrying the rebuild path would preserve the existing
-> behavior."
-
-Change requested, no experiment needed — this is a behaviour-preservation argument, and dantegd is
-right that `merge_rebuild` already has a `std::bad_alloc` catch with a host-memory fallback
-(`merge:179`) that the Fastener path bypasses.
-
-Change written in the worktree: AUTO wraps `merge_fastener` in a `try`/`catch (std::bad_alloc)` and
-retries `merge_rebuild`; explicit FASTENER is untouched and still propagates. `rmm::bad_alloc` and
-`rmm::out_of_memory` both derive from `std::bad_alloc`, and this matches the existing catch in
-`merge_rebuild`.
-
-The retry is safe because Fastener never mutates its inputs — it only reads them and allocates its
-own output — so a failed attempt unwinds cleanly and leaves the indices usable by the rebuild path.
-That property is worth stating explicitly in the reply, since it is what makes the fallback sound.
-
-**Test gap, deliberately left open.** Forcing a deterministic OOM in a unit test is awkward. The
-viable approach is an `rmm::mr::limiting_resource_adaptor` sized so Fastener's consolidated dataset
-plus GEMM workspace exceeds the cap while `merge_rebuild` still succeeds via its host-memory
-fallback. That is doable but sensitive to allocator behaviour and could be flaky in CI, so it is
-flagged here rather than written blind.
-
----
-
-## C4 — `cagra_merge.cuh:330`, missing `kMaxSortDegree` preflight check
-
-> "the graph passed to `sort_knn_graph_device_inplace()` can't exceed `kMaxSortDegree` (1024). [...]
-> Could we include `max_input_degree + scaffold_degree <= kMaxSortDegree` in the eligibility check?"
-
-Change requested, no experiment needed. Straightforwardly correct: preflight enumerates 24
-conditions but none bounds the summed degree against the sorter's limit.
-
-Verified the mechanism: `kMaxSortDegree = raft::WarpSize * 32 = 1024` (`graph_core.cuh:180`), and
-`select_sort_kernel` `RAFT_FAIL`s past it (`graph_core.cuh:196-200`) — that is, *after*
-`merge_fastener` has consolidated the dataset and built the scaffold, so the failure comes well
-into a mutating operation, and under AUTO the rebuild fallback has already been passed.
-
-Change written in the worktree (uncommitted): a preflight rejection of
-`max_input_degree + scaffold_degree > cagra::detail::graph::kMaxSortDegree`, placed next to the
-existing capacity check. This is the exact condition dantegd proposed.
-
-Still to do before committing: a case in
-`InvalidManywayOptionsFailPreflightWithoutMutation`, which currently enumerates 17 bad configs but
-covers only knob ranges, not the degree-sum limit. Note that list asserts `eligible == false`
-without checking `reason`, so a new case there could pass for the wrong reason — worth pinning the
-reason string, as `LeafGemmLimitsRejectOversizedWorkspaceDimensions` does.
-
----
-
-## C5 — `test_merge_fastener.cu:568`, recall coverage gaps
-
-> "Could we add recall coverage for at least one non-float dtype and one merge with more than two
-> inputs? [...] That feels important if AUTO is going to become the default."
-
-Change requested, no experiment needed. Confirmed gap: the suite has exactly one
-`INSTANTIATE_TEST_CASE_P`, varying only `graph_build_algo`, so 2 instances — both float, both
-2-input (the fixture always splits into 2 at `splite_ratio = 0.55`). The dtype tests
-(`SupportsAllScalarTypes`) assert graph structure and dataset order only, never recall.
-
-**Why not extend the shared fixture.** `AnnCagraIndexMergeTest` hardcodes two parts
-(`index0`, `index1` at `ann_cagra.cuh:1450`) and is shared with the other CAGRA merge tests, so
-making it n-way would ripple beyond this PR. Instead the worktree adds a self-contained
-`run_fastener_merge_recall<DataT>(n_inputs, min_recall)` that builds the parts directly with
-`cagra::build`, merges with FASTENER, searches, and checks against `naive_knn` ground truth via
-`eval_neighbours` — the same helpers the fixture uses.
-
-It slices the database into consecutive ranges and merges in order, so the merged index reproduces
-the original row order and ground-truth indices apply without remapping. Three cases added:
-
-| Test | dtype | Fan-in |
-|---|---|---|
-| `Uint8TwoWayMergeSearchRecall` | uint8 | 2 |
-| `FloatFourWayMergeSearchRecall` | float | 4 |
-| `Uint8FourWayMergeSearchRecall` | uint8 | 4 |
-
-That covers both axes dantegd asked for, plus their combination.
-
----
-
-## C6 — `cagra_merge_scaffold.cuh:606`, derive `tile_rows`
-
-> "Could we derive `tile_rows` from the dimension and padded leader count instead of always using
-> 2048? Preflight only checks the leaf-GEMM shape, while assignment needs roughly
-> `(tile_rows + padded_leaders) * dim + tile_rows * padded_leaders` float elements."
-
-Change requested. dantegd is right that the sizing is inconsistent: `assign_bucket` computes
-`bytes_per_batch` for a single tile of the fixed 2048-row height and then asserts it fits
-`gemm_workspace_bytes`, while preflight validates only the *leaf* GEMM shape. A wide dimension or a
-wide padded leader count therefore passes preflight and trips the assertion mid-merge.
-
-Change written in the worktree: solve the same budget for the tile height instead of asserting
-against it. The workspace holds the gathered leader matrix once plus per-row point, dot-product and
-selection storage, so
-
-```
-tile_rows = min(assignment_tile_rows, (gemm_workspace_bytes - leader_bytes) / row_bytes)
-```
-
-and the merge is rejected only when the leader matrix plus a single point row cannot fit — exactly
-the boundary dantegd proposed. **This only ever shrinks the tile**, so every configuration that fits
-today keeps the 2048 default and is bit-identical.
-
-Interaction with Q2 worth noting in the reply: shrinking the tile raises the tile count, which
-raises the leader re-gather volume measured in Q2. Since the derivation only shrinks when the
-alternative is a hard failure, that is the right trade, but the two threads should be answered
-together.
-
----
-
-## C7 — `cagra_merge_scaffold.cuh:391`, aligned consolidation
-
-> "for dimensions that aren't naturally 16-byte aligned, this allocates and copies a second full
-> consolidated dataset while all input datasets are still alive. Would it be practical to
-> consolidate directly into aligned storage and pass a row stride through the scaffold/sort kernels?"
-
-Change requested. Analysed rather than implemented, for the reason below.
-
-### When this actually triggers
-
-`make_aligned_dataset` (`common.hpp:388`) computes
-`required_stride = round_up(dim * sizeof(T), lcm(16, sizeof(T))) / sizeof(T)`, and
-`make_strided_dataset` returns a **non-owning view with no copy** when `required_stride` already
-equals the source stride. So the second full copy happens only when
-`dim * sizeof(T)` is not a multiple of 16:
-
-| dtype | Copy when |
-|---|---|
-| `float` | `dim % 4 != 0` |
-| `half` | `dim % 8 != 0` |
-| `int8_t` / `uint8_t` | `dim % 16 != 0` |
-
-**None of the three benchmark datasets trigger it** — Wiki1M `dim=768` (float, 768%4=0), OpenAI
-`dim=1536` (float, %4=0), YFCC `dim=192` (uint8, 192%16=0). Nor do the common ANN dimensions
-(128, 384, 768, 960, 1024). It bites on awkward dims such as uint8 `dim=100` or float `dim=17`.
-
-It is also confined to `attach_dataset_on_build = true`; the other branch attaches an
-`empty_dataset` and never makes the copy.
-
-### Cost when it does trigger
-
-Peak device memory during the merge goes from roughly `2N` to `3N`, where `N` is the dataset size:
-the caller's inputs, the dense consolidated copy, and the aligned copy are all live at once, since
-`make_strided_dataset` takes its source by const reference and so keeps it alive across the copy.
-dantegd's proposal would bring the peak back to about `2N`, a ~33% reduction — but only in the
-unaligned case.
-
-### Feasibility blocker
-
-Consolidating directly into aligned storage is straightforward on the merge side —
-`copy_input_datasets` already takes an arbitrary destination pitch, so it would just take
-`required_stride` instead of `dim`. The obstacle is downstream: the scaffold kernels index
-`dataset[point * dim + d]` densely, and more importantly
-
-```cpp
-void sort_knn_graph_device_inplace(
-  raft::resources const& res,
-  cuvs::distance::DistanceType metric,
-  raft::device_matrix_view<const DataT, int64_t, raft::row_major> dataset,   // dense, not strided
-  raft::device_matrix_view<IdxT, int64_t, raft::row_major> knn_graph)
-```
-
-takes a dense `raft::row_major` view (`graph_core.cuh:1001-1005`). Threading a stride through would
-mean widening that signature to a padded or strided layout — shared CAGRA code well outside the
-merge path, and outside what this PR should be changing.
-
-### Recommendation
-
-Reply with the trigger condition and the ~33% figure, note that it affects no common dimension and
-none of the benchmarked datasets, and propose deferring to a follow-up that widens
-`sort_knn_graph_device_inplace` to accept a padded layout. Worth doing, but not here.
-
----
-
-## C8 — `cagra_merge_scaffold.cuh:1249`, redundant sync
-
-> "Is this synchronization needed? The helper has no local temporary buffers consumed by the copy
-> kernels, and the caller immediately sorts the returned graph on the same resource stream."
-
-Change requested. dantegd's reasoning about the *helper* is correct — `append_to_input_graphs`
-allocates exactly one device buffer, `graph`, which it returns by move, so it owns no temporary
-consumed by the copy kernels.
-
-But there is a subtlety worth raising on-thread rather than silently dropping the sync. The
-*caller's* `scaffold` is a temporary inside a lambda:
-
-```cpp
-auto merged_graph = [&] {
-  auto scaffold = merge_scaffold::build<T>(...);
-  return merge_scaffold::append_to_input_graphs<T, IdxT>(..., scaffold.view());
-}();
-```
-
-The copy kernels read `scaffold`, and it is destroyed when the lambda returns. Today the sync inside
-the helper is what makes that safe. Removing the sync leaves correctness resting on the allocator's
-stream-ordered deallocation rather than on anything visible in the code.
-
-Change written in the worktree (uncommitted): drop the sync **and** hoist `scaffold` out of the
-lambda so its lifetime provably outlives the merged graph's use. That gets the intended saving
-without trading an explicit sync for an implicit allocator guarantee.
-
----
-
-## Q1 — `cagra_merge_scaffold.cuh:758`, work spent on padded leaders
-
-> "Curious, have you measured how much work is going to padded leaders here? A parent just over a
-> power-of-two boundary could nearly double the gathered leader storage, dot-product GEMM, distance
-> materialization, and `select_k` width."
-
-You replied comparing against non-batched GEMM (substantially slower) and per-size batching
-(moderately faster, but more LoC than felt worth it). That answers *which batching strategy*, but
-not dantegd's actual question — *how much* work the power-of-two padding wastes.
-
-Measured with `CUVS_FASTENER_SPLIT_STATS=1`, which is computable entirely from the host-side
-`split_plan`: assignment work per parent scales with the padded leader count, so the waste fraction
-is `1 - Σ(rows × leaders) / Σ(rows × padded_leaders)`.
-
-| Dataset | Level | Split parents | Leader-pad waste |
-|---|---:|---:|---:|
-| wiki-1m | 0 | 1 | **2.34%** |
-| | 1 | 863 | **30.53%** |
-| openai-2m | 0 | 1 | 2.34% |
-| | 1 | 987 | **29.98%** |
-| yfcc-10m | 0 | 1 | 2.34% |
-| | 1 | 1000 | **20.32%** |
-
-### Finding: dantegd's concern is real, and concentrated at the lower level
-
-- **Level 0 is nearly free.** The root always takes `max_leaders = 1000` leaders, padded to 1024, so
-  the waste is a fixed 24/1024 = 2.34% regardless of dataset.
-- **Level 1 wastes 20–31%** of the assignment gather / GEMM / distance-materialization / `select_k`
-  width. With ~1000 split parents whose leader counts follow `ceil(0.02 × size)`, many land just
-  above a power of two — exactly the case dantegd described.
-
-This is the number he asked for, and it is consistent with your recollection that per-size batching
-was "moderately faster": ~30% of *one stage* at *one level*, not ~30% of the merge. Whether that
-justifies the extra LoC is a judgement call, but the reply should quote the measurement rather than
-leave the question unanswered.
-
----
-
-## Q2 — `cagra_merge_scaffold.cuh:657`, per-tile leader re-gather
-
-> "It looks like every tile of the same parent gathers the same leader matrix again. [...] Could we
-> gather each parent's leaders once and reuse them across its tiles, or did benchmarking show that
-> the added bookkeeping costs more?"
-
-Not yet answered on-thread. Measured with the same `CUVS_FASTENER_SPLIT_STATS=1` instrumentation:
-`regather_factor` is `Σ(tiles × padded × dim) / Σ(padded × dim)`, i.e. how many times the leader
-matrix is gathered versus the once it needs to be.
-
-| Dataset | Level | Tiles | Widest parent | Re-gather factor | Redundant bytes |
-|---|---:|---:|---:|---:|---:|
-| wiki-1m | 0 | 489 | 1,000,000 | **489x** | 1.54 GB |
-| | 1 | 1,441 | 28,847 | 3.24x | 0.39 GB |
-| openai-2m | 0 | 1,134 | 2,321,096 | **1,134x** | 7.13 GB |
-| | 1 | 2,755 | 37,308 | 4.83x | 3.15 GB |
-| yfcc-10m | 0 | 4,883 | 10,000,000 | **4,883x** | 3.84 GB |
-| | 1 | 10,265 | 178,893 | 14.68x | 5.32 GB |
-
-### Finding: mechanism confirmed, absolute cost below the noise floor
-
-The re-gather is exactly as dantegd describes, and the *factor* is dramatic — the root is a single
-parent spanning up to 4,883 tiles, each re-gathering the identical leader matrix.
-
-But the *absolute* volume does not justify a fix. Totals are 1.9 GB (wiki), 10.3 GB (openai),
-9.2 GB (yfcc) of redundant gather. Counting both the read and the contiguous write, and taking
-~2 TB/s of achievable HBM bandwidth on the H100, that is roughly **2 ms, 10 ms and 9 ms**
-respectively — against merge times of 513 ms, 2202 ms and 2419 ms, i.e. **0.4%, 0.5% and 0.4%**.
-
-Every one of those sits under the 1% significance threshold this PR has used throughout, so caching
-each parent's leaders would not produce a measurable speedup while adding real bookkeeping. That is
-a direct answer to "or did benchmarking show that the added bookkeeping costs more?" — the saving is
-below what the harness can resolve.
-
-**Caveat on rigour:** this is an analytical bound derived from *measured* byte volumes and assumed
-bandwidth, not a measured speedup from an implemented cache. It is strong enough to justify not
-doing the work, but if dantegd pushes back the honest next step is to implement the cache behind a
-flag and measure it, rather than defend the estimate.
-
-**No change proposed.** Worth noting that C6 (`tile_rows` derivation) interacts here: raising
-`ASSIGNMENT_TILE_ROWS` would reduce the tile count and shrink this redundancy for free, so the two
-threads should be answered together.
-
----
-
-## API notes from the review body
-
-- **C API** — dantegd noted it still calls the C++ overload without `merge_params`. Addressed by
-  commit `5e3f436d` ("updating C API"), already on the branch; worth confirming on-thread.
-- **Java API** — you flagged on 2026-07-29 that it still lags and asked whether to fold it into this
-  PR or defer. Awaiting dantegd. No worktree until that is decided.
-
----
-
-## Summary
-
-### Experiments run
-
-| # | Comment | Question | Cells | Answer |
-|---|---|---|---:|---|
-| 1 | C1 `scaffold:818` | Which fix for single-origin leaves? | 27 | `roundrobin` — free, ships |
-| 2 | C1 `scaffold:818` | Does taking oversized partitions whole beat it? | 36 | Yes (+0.001..0.002 recall, +3..5% time) — deferred |
-| 3 | C2 `merge:374` | Does candidate headroom help? | 18 | **No** — worse on both axes |
-| 4 | Q1 `scaffold:758` | How much work goes to padded leaders? | 3 | 2.3% at level 0, 20–31% at level 1 |
-| 5 | Q2 `scaffold:657` | How costly is the per-tile leader re-gather? | 3 | Up to 4,883x factor, but ~0.4–0.5% of merge time |
-
-### Three results worth carrying into the thread replies
-
-1. **C1's recall case is weaker than it first appeared.** At the library-default itopk the gains
-   looked like +0.001..+0.006; at itopk=160 only yfcc survives. The fix is justified by
-   correctness — 316k membership slots with no cross-input edge on Wiki1M, and two disconnected
-   components for identical inputs — not by recall.
-2. **C2 refutes the suggestion, with the mechanism explained.** More candidates make the result
-   *worse* because a distance-sorted prefix admits only the farthest edges. This is the numbers
-   reply promised on-thread.
-3. **Q2 confirms the mechanism but not the priority.** A 4,883x re-gather factor sounds alarming and
-   is real, yet lands under 1% of merge time. Reporting both halves is more useful than either
-   alone.
-
-### State of the tree
-
-- `~/cuvs-fastener-pr` — integration checkout, now at `e788608e`. Holds the configured build and
-  this document. Used as the validation sandbox; snapshot and restore its working tree when doing
-  so, since the author works here too.
-- **Landed upstream:** C1, C3, C4, C5.
-- **Ready, uncommitted in the integration checkout:** C6 and C8, validated together at 23/23.
-- **No change needed:** C2 — refuted by measurement.
-- **Deferral recommended:** C7 — blocked on a shared signature, see that section.
-
-All eight change-requesting comments are addressed. C7 is the only one carrying no code change, and
-that is a reasoned deferral rather than an omission.
-- Raw benchmark logs under `/raid/blandrum/fastener-leaf-ab/` in `results/`,
-  `results-itopk160-3arm/`, `results-itopk160/` and `results-cap-itopk160/`, plus the harness
-  (`LEAF_BENCH`, `leaf_bench.cu`, `run_sweep.sh`, `run_cap_sweep.sh`). All outside the repository.
-
-### Remaining work
-
-1. Commit C6 and C8 from the integration checkout (or cherry-pick `bb128d06`). Both are green at
-   23/23 on `e788608e`. Note C6 was already lost once to a branch reset.
-2. Post replies for the threads that are answerable now: C2 (refuted, with the table), Q1 (padding
-   waste figures), Q2 (re-gather factor and why it does not matter), C6 (paired with Q2), C7
-   (trigger condition and the deferral rationale).
-3. C3 — optionally add the `limiting_resource_adaptor` OOM test noted in that section.
-4. Decide the Java API question with dantegd.
-5. Consider the `big512` follow-up from C1, the only arm that improved recall on all three datasets.
-6. Update `FASTENER_PR_PLAN.md`, which still describes consecutive slicing.
-
-## Merging main, and the `check-c-abi` failure
-
-Both had the same root cause: the branch was behind `NVIDIA/cuvs` main.
-
-### Remotes point at the wrong org
-
-`origin` is `landrumb/cuvs` and `upstream` is `rapidsai/cuvs`, both sitting at `ad9e2d2a`, while the
-PR targets **`NVIDIA/cuvs`**, which was at `f72199e3`. The project appears to have moved orgs, so
-`git fetch` looked clean against a stale base while GitHub reported `CONFLICTING`. Worth fixing:
-
-```
-git remote set-url upstream https://github.com/NVIDIA/cuvs.git
-```
-
-### The conflict was one file, and positional
-
-Only `cpp/src/neighbors/cagra.cuh`. Both sides append a function immediately after the existing
-`merge()` overload — this branch adds `merge()` taking `merge_params` (plus a `merge` entry in
-`CUVS_INST_CAGRA_MERGE`), main adds a multi-partition `search()` overload plus
-`#include <cuvs/core/bitset.hpp>`. No semantic overlap: main never touches the instantiation macro
-and this branch never touches `search`. Resolved by keeping both.
-
-### `check-c-abi` was a false alarm caused by the same staleness
-
-The analyzer reported four **removed** functions:
-
-```
-cuvsResourcesSetWorkspacePool      cuvs/core/c_api.h
-cuvsRMMAsyncMemoryResourceEnable   cuvs/core/c_api.h
-cuvsCagraSearchMultiPartition      cuvs/neighbors/cagra.h
-cuvsSelectK                        cuvs/selection/select_k.h
-```
-
-None belong to this PR. They were added upstream *after* the branch's base, so comparing the branch
-against a `release/26.08` baseline made them look deleted. Confirmed by symbol presence —
-`branch=0, main=1, merged=1` for all four — so the merge fixes the check with no code change.
-
-This PR's own C API work is purely additive and does not break ABI: a new `cuvsCagraMergeAlgo` enum,
-a new `cuvsCagraMergeParams` struct, and `cuvsCagraMergeParamsCreate` / `Destroy` /
-`cuvsCagraMergeWithParams`. The existing `cuvsCagraMerge` signature is unchanged.
-
-Side note: the job's `Comment on PR with results` step also failed, with a 404 posting to
-`/repos/NVIDIA/cuvs/issues//comments` — an empty PR number. That is a workflow bug on cross-repo
-PRs, which is why no bot comment appeared explaining the failure.
-
-### Outcome
-
-Merge commit `652a7ddc` (parents `a44ca425` + `f72199e3`). Full rebuild of 3268 targets including
-main's new multi-partition kernels, then 23/23 tests, then pushed to
-`origin/landrumb/cagra-fastener-merge`. The PR moved from `CONFLICTING` to `MERGEABLE`.
-
-## Documentation drift
-
-`FASTENER_PR_PLAN.md` still states that `make_leaves` "slices oversized groups into consecutive
-`leaf_size` ranges", which C1 makes untrue. Update before the PR description is finalized.
diff --git a/pipnn.pdf b/pipnn.pdf
deleted file mode 100644
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From a7e294bdf48e213f90a7f7a54d46160eacb6347b Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 6 Aug 2026 17:49:37 +0000
Subject: [PATCH 35/41] smarter cast

---
 cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 7eef5dc742..01687f8f22 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -310,7 +310,7 @@ inline auto make_root_partition(raft::resources const& res, int64_t rows) -> par
   auto ranges = raft::make_host_vector<partition_range, int64_t>(res, 1);
   ranges(0)   = partition_range{uint32_t{0}, int64_t{0}, rows};
   partition_set root{std::move(memberships), std::move(ranges)};
-  int blocks = raft::div_rounding_up_safe<int>(static_cast<int>(rows), THREADS_PER_BLOCK);
+  auto blocks = static_cast<int>(raft::div_rounding_up_safe<int64_t>(rows, THREADS_PER_BLOCK));
   initialize_root_memberships_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
     root.memberships.data_handle(), rows);
   RAFT_CUDA_TRY(cudaGetLastError());

From 7e360dc3b2fd7abead905c31f87ecf96619ac59c Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Thu, 6 Aug 2026 18:58:39 +0000
Subject: [PATCH 36/41] moved leaf size check and added test

---
 .../neighbors/detail/cagra/cagra_merge.cuh    | 10 +++
 .../detail/cagra/cagra_merge_scaffold.cuh     | 83 ++++++++++++------
 .../neighbors/detail/cagra/graph_shared.cuh   | 19 ++--
 .../ann_cagra/test_merge_fastener.cu          | 87 +++++++++++++++++++
 4 files changed, 165 insertions(+), 34 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index f634ad0c38..f3d1c32d61 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -333,6 +333,16 @@ auto preflight_fastener(
         result.dim, merge_params.leaf_size, raft::resource::get_workspace_free_bytes(handle))) {
     return reject("dataset dimension exceeds the leaf GEMM workspace limit");
   }
+  if (!merge_scaffold::assignment_gemm_supported(
+        result.dim,
+        static_cast<int64_t>(rows),
+        merge_scaffold::split_params{
+          .fanout          = std::max(merge_params.root_fanout, merge_params.lower_fanout),
+          .leader_fraction = merge_params.leader_fraction,
+          .max_leaders     = merge_params.max_leaders},
+        raft::resource::get_workspace_free_bytes(handle))) {
+    return reject("dataset dimension exceeds the assignment GEMM workspace limit");
+  }
   if (rows > std::numeric_limits<uint32_t>::max() / spill) {
     return reject("combined rows times the configured spill width must fit in uint32_t");
   }
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 01687f8f22..4ef3d91b4e 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -633,6 +633,45 @@ inline void batched_row_dot_products(raft::resources const& res,
                                  raft::resource::get_cuda_stream(res)));
 }
 
+/** Workspace bytes `assign_bucket` needs for `capacity` tiles of `rows_per_tile` rows each */
+inline auto assignment_workspace_bytes(
+  size_t capacity, size_t rows_per_tile, size_t padded_leaders, size_t dim, size_t fanout) -> size_t
+{
+  auto aligned = [](size_t bytes) { return rmm::align_up(bytes, rmm::CUDA_ALLOCATION_ALIGNMENT); };
+  size_t point_elements    = rows_per_tile * dim;
+  size_t leader_elements   = padded_leaders * dim;
+  size_t dot_elements      = rows_per_tile * padded_leaders;
+  size_t selected_elements = rows_per_tile * fanout;
+  return aligned(capacity * sizeof(assignment_tile)) +
+         aligned(capacity * point_elements * sizeof(float)) +
+         aligned(capacity * leader_elements * sizeof(float)) +
+         aligned(capacity * dot_elements * sizeof(float)) +
+         aligned(capacity * padded_leaders * sizeof(uint32_t)) +
+         aligned(capacity * selected_elements * sizeof(float)) +
+         aligned(capacity * selected_elements * sizeof(int)) +
+         aligned(capacity * dot_elements * sizeof(float)) +
+         aligned(capacity * dot_elements * sizeof(int));
+}
+
+/** Return true if the widest padded leader matrix + a single point row fits the workspace.
+ *
+ * `assign_bucket` shrinks its tile height and batch capacity to whatever the workspace allows, but
+ * the leader matrix can't get any smaller than one tile of one row.
+ */
+inline auto assignment_gemm_supported(int64_t dimension,
+                                      int64_t rows,
+                                      split_params const& worst_case,
+                                      size_t workspace_bytes) -> bool
+{
+  if (dimension <= 0 || dimension > std::numeric_limits<int>::max() || rows <= 0) { return false; }
+  auto const leaders = select_leader_count(rows, worst_case);
+  return assignment_workspace_bytes(1,
+                                    1,
+                                    std::bit_ceil(static_cast<unsigned>(leaders)),
+                                    static_cast<size_t>(dimension),
+                                    static_cast<size_t>(worst_case.fanout)) <= workspace_bytes;
+}
+
 /**
  * Assign every row of one bucket's split parents to its `fanout` nearest leaders.
  *
@@ -660,37 +699,23 @@ void assign_bucket(raft::resources const& res,
   auto workspace_mr     = raft::resource::get_workspace_resource_ref(res);
   auto workspace_bytes  = raft::resource::get_workspace_free_bytes(res);
 
-  // RMM's limiting workspace accounts for every allocation rounded to the CUDA allocation
-  // alignment. Model the seven explicit buffers independently so a raw-byte sum cannot overrun a
-  // small caller-configured workspace through alignment alone. RAFT select-k also allocates value
-  // and index scratch; each is no larger than its input matrix, so reserve two aligned dot-matrix
-  // buffers as a conservative upper bound.
-  auto assignment_workspace_bytes = [&](size_t capacity, size_t rows_per_tile) {
-    auto aligned = [](size_t bytes) {
-      return rmm::align_up(bytes, rmm::CUDA_ALLOCATION_ALIGNMENT);
-    };
-    size_t point_elements    = rows_per_tile * static_cast<size_t>(dim);
-    size_t leader_elements   = static_cast<size_t>(padded_leaders) * static_cast<size_t>(dim);
-    size_t dot_elements      = rows_per_tile * static_cast<size_t>(padded_leaders);
-    size_t selected_elements = rows_per_tile * static_cast<size_t>(params.fanout);
-    return aligned(capacity * sizeof(assignment_tile)) +
-           aligned(capacity * point_elements * sizeof(float)) +
-           aligned(capacity * leader_elements * sizeof(float)) +
-           aligned(capacity * dot_elements * sizeof(float)) +
-           aligned(capacity * static_cast<size_t>(padded_leaders) * sizeof(uint32_t)) +
-           aligned(capacity * selected_elements * sizeof(float)) +
-           aligned(capacity * selected_elements * sizeof(int)) +
-           aligned(capacity * dot_elements * sizeof(float)) +
-           aligned(capacity * dot_elements * sizeof(int));
+  auto tile_bytes = [&](size_t capacity, size_t rows_per_tile) {
+    return assignment_workspace_bytes(capacity,
+                                      rows_per_tile,
+                                      static_cast<size_t>(padded_leaders),
+                                      static_cast<size_t>(dim),
+                                      static_cast<size_t>(params.fanout));
   };
 
-  RAFT_EXPECTS(assignment_workspace_bytes(1, 1) <= workspace_bytes,
+  // Preflight rejects any configuration whose widest leader matrix cannot host a single point row,
+  // so this only fires for callers that drive the scaffold directly.
+  RAFT_EXPECTS(tile_bytes(1, 1) <= workspace_bytes,
                "Fastener assignment workspace cannot fit the leader matrix and a single point row");
   size_t min_tile_rows = 1;
   size_t max_tile_rows = static_cast<size_t>(context.assignment_tile_rows);
   while (min_tile_rows < max_tile_rows) {
     size_t candidate = min_tile_rows + (max_tile_rows - min_tile_rows + 1) / 2;
-    if (assignment_workspace_bytes(1, candidate) <= workspace_bytes) {
+    if (tile_bytes(1, candidate) <= workspace_bytes) {
       min_tile_rows = candidate;
     } else {
       max_tile_rows = candidate - 1;
@@ -715,7 +740,7 @@ void assign_bucket(raft::resources const& res,
   size_t max_batch_capacity = tiles.size();
   while (min_batch_capacity < max_batch_capacity) {
     size_t candidate = min_batch_capacity + (max_batch_capacity - min_batch_capacity + 1) / 2;
-    if (assignment_workspace_bytes(candidate, static_cast<size_t>(tile_rows)) <= workspace_bytes) {
+    if (tile_bytes(candidate, static_cast<size_t>(tile_rows)) <= workspace_bytes) {
       min_batch_capacity = candidate;
     } else {
       max_batch_capacity = candidate - 1;
@@ -1309,6 +1334,14 @@ auto build(raft::resources const& res,
   RAFT_EXPECTS(
     leaf_gemm_supported(dim, params.leaf_size, raft::resource::get_workspace_free_bytes(res)),
     "Fastener dataset dimension exceeds the leaf GEMM limits");
+  RAFT_EXPECTS(assignment_gemm_supported(
+                 dim,
+                 rows,
+                 split_params{.fanout          = std::max(params.root_fanout, params.lower_fanout),
+                              .leader_fraction = params.leader_fraction,
+                              .max_leaders     = params.max_leaders},
+                 raft::resource::get_workspace_free_bytes(res)),
+               "Fastener dataset dimension exceeds the assignment GEMM limits");
 
   // the number of leaf partitions that a given point will end up in
   uint64_t spill = params.root_fanout;
diff --git a/cpp/src/neighbors/detail/cagra/graph_shared.cuh b/cpp/src/neighbors/detail/cagra/graph_shared.cuh
index 08ccc3108f..0f2bc0f7dc 100644
--- a/cpp/src/neighbors/detail/cagra/graph_shared.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_shared.cuh
@@ -4,6 +4,7 @@
  */
 #pragma once
 
+#include <cuvs/core/export.hpp>
 #include <cuvs/distance/distance.hpp>
 
 #include <raft/core/device_mdspan.hpp>
@@ -18,14 +19,14 @@ namespace cuvs::neighbors::cagra::detail::graph {
 // kern_sort capacity is WarpSize * numElementsPerThread; largest specialization uses 32.
 inline constexpr uint64_t kMaxSortDegree = 32 * 32;
 
-#define CUVS_DECL_CAGRA_GRAPH_SORT(DataT)                  \
-  void launch_sort_knn_graph(raft::resources const& res,   \
-                             cuvs::distance::DistanceType, \
-                             DataT const* dataset,         \
-                             uint32_t dataset_size,        \
-                             uint32_t dataset_dim,         \
-                             uint32_t* knn_graph,          \
-                             uint32_t graph_degree)
+#define CUVS_DECL_CAGRA_GRAPH_SORT(DataT)                              \
+  CUVS_EXPORT void launch_sort_knn_graph(raft::resources const& res,   \
+                                         cuvs::distance::DistanceType, \
+                                         DataT const* dataset,         \
+                                         uint32_t dataset_size,        \
+                                         uint32_t dataset_dim,         \
+                                         uint32_t* knn_graph,          \
+                                         uint32_t graph_degree)
 
 CUVS_DECL_CAGRA_GRAPH_SORT(float);
 CUVS_DECL_CAGRA_GRAPH_SORT(half);
@@ -36,7 +37,7 @@ CUVS_DECL_CAGRA_GRAPH_SORT(uint8_t);
 
 /** Run the existing CAGRA optimizer through one compiled instantiation instead of rematerializing
  *  its reverse-graph, prune, merge, and MST kernels in every Fastener dtype TU. */
-void optimize_device_graph(
+CUVS_EXPORT void optimize_device_graph(
   raft::resources const& res,
   raft::device_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
   raft::device_matrix_view<uint32_t, int64_t, raft::row_major> output_graph,
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 11adccfbff..739f69511b 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -780,6 +780,93 @@ TEST(CagraMergeFastener, LeafGemmLimitsRejectOversizedWorkspaceDimensions)
   EXPECT_EQ(index1.dataset().n_rows(), rows);
 }
 
+/**
+ * The leaf GEMM is not the only indivisible workspace shape. Assignment must also hold one parent's
+ * padded leader matrix next to at least one point row, and the leader matrix can be far wider than
+ * a leaf: leaders are capped at `max_leaders` while leaves are capped at MAX_LEAF_SIZE. So a
+ * dimension can fit a whole leaf and still fail on a single assignment row. Preflight must cover
+ * that shape too -- otherwise the configuration passes preflight and dies inside assign_bucket, by
+ * which point AUTO has already committed to Fastener and cannot fall back to rebuild.
+ */
+TEST(CagraMergeFastener, AssignmentGemmLimitsRejectDimensionsThatFitALeaf)
+{
+  using namespace detail::merge_scaffold;
+  constexpr size_t workspace_bytes = size_t{1} * 1024 * 1024;
+  constexpr uint32_t leaf_size     = 256;  // the default, spelled out for the arithmetic below
+  constexpr uint32_t max_leaders   = 1024;
+  constexpr int64_t rows_per_input = 512;
+  constexpr int64_t merged_rows    = rows_per_input * 2;
+
+  // Sampling every row as a leader is what drives the leader matrix to its cap; the default
+  // fraction of 0.02 would pick only 21 leaders out of these rows.
+  split_params const worst_case{
+    .fanout = 3, .leader_fraction = 1.0, .max_leaders = max_leaders, .leaf_size = leaf_size};
+
+  // A leaf of this dimension fits comfortably (1024 * dim + 256 KiB Gram), while a single
+  // assignment row does not: the padded leader matrix alone is 1024 * dim * 4 = 2 MiB.
+  constexpr int64_t dim = 512;
+  EXPECT_TRUE(leaf_gemm_supported(dim, leaf_size, workspace_bytes));
+  EXPECT_FALSE(assignment_gemm_supported(dim, merged_rows, worst_case, workspace_bytes));
+
+  // Degenerate shapes are rejected rather than wrapping around in the byte model.
+  EXPECT_FALSE(assignment_gemm_supported(0, merged_rows, worst_case, workspace_bytes));
+  EXPECT_FALSE(assignment_gemm_supported(-1, merged_rows, worst_case, workspace_bytes));
+  EXPECT_FALSE(assignment_gemm_supported(dim, 0, worst_case, workspace_bytes));
+
+  // The leader count follows the sample fraction and the row count, not just the cap, so the same
+  // dimension is fine once no parent can reach a wide leader matrix. Bounding on max_leaders alone
+  // would reject both of these.
+  EXPECT_TRUE(assignment_gemm_supported(dim, 64, worst_case, workspace_bytes));
+  auto sparse_leaders            = worst_case;
+  sparse_leaders.leader_fraction = 0.02;
+  EXPECT_TRUE(assignment_gemm_supported(dim, merged_rows, sparse_leaders, workspace_bytes));
+
+  raft::resources res;
+  raft::resource::set_workspace_to_global_resource(res, workspace_bytes);
+  auto metric         = cuvs::distance::DistanceType::L2Expanded;
+  auto dataset0       = make_dataset<int8_t>(res, rows_per_input, dim, 1234ULL);
+  auto dataset1       = make_dataset<int8_t>(res, rows_per_input, dim, 5678ULL);
+  auto graph0         = make_ring_graph(res, rows_per_input, 4);
+  auto graph1         = make_ring_graph(res, rows_per_input, 4);
+  auto index0_storage = make_padded<int8_t>(res, raft::make_const_mdspan(dataset0.view()));
+  cagra::index<int8_t, uint32_t> index0(
+    res, metric, index0_storage.view, raft::make_const_mdspan(graph0.view()));
+  auto index1_storage = make_padded<int8_t>(res, raft::make_const_mdspan(dataset1.view()));
+  cagra::index<int8_t, uint32_t> index1(
+    res, metric, index1_storage.view, raft::make_const_mdspan(graph1.view()));
+
+  index_params params;
+  params.metric                    = metric;
+  params.graph_degree              = 4;
+  params.intermediate_graph_degree = 8;
+  params.attach_dataset_on_build   = true;
+  std::vector<index<int8_t, uint32_t>*> indices{&index0, &index1};
+
+  merge_params fastener;
+  fastener.algo            = merge_algo::FASTENER;
+  fastener.leader_fraction = worst_case.leader_fraction;
+  fastener.max_leaders     = worst_case.max_leaders;
+  fastener.leaf_size       = worst_case.leaf_size;
+  auto result              = detail::preflight_fastener(
+    res, params, fastener, indices, cuvs::neighbors::filtering::none_sample_filter{});
+  EXPECT_FALSE(result.eligible);
+  EXPECT_EQ(result.reason, "dataset dimension exceeds the assignment GEMM workspace limit");
+
+  // Explicit Fastener surfaces the rejection; AUTO takes the rebuild path and still produces an
+  // index. Reaching rebuild at all is the regression this test guards.
+  auto fastener_storage = make_merged_storage<int8_t>(res, merged_rows, dim);
+  EXPECT_ANY_THROW(merge(res, params, indices, fastener_storage.view, fastener));
+
+  auto automatic      = fastener;
+  automatic.algo      = merge_algo::AUTO;
+  auto merged_storage = make_merged_storage<int8_t>(res, merged_rows, dim);
+  auto merged         = merge(res, params, indices, merged_storage.view, automatic);
+  EXPECT_EQ(merged.size(), merged_rows);
+
+  EXPECT_EQ(index0.dataset().n_rows(), rows_per_input);
+  EXPECT_EQ(index1.dataset().n_rows(), rows_per_input);
+}
+
 /**
  * The appended candidate graph is sorted by launch_sort_knn_graph, whose kernel capacity is
  * kMaxSortDegree. Preflight must reject a combined candidate width above that limit, because the

From 44632566e5de2cd60f1301bde1143e7791316fcb Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Fri, 7 Aug 2026 20:38:54 +0000
Subject: [PATCH 37/41] replacing double with float

---
 .../detail/cagra/cagra_merge_scaffold.cuh          | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index 4ef3d91b4e..e3d8c777a3 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -1100,24 +1100,24 @@ static __global__ void manyway_leaf_gram_knn_kernel(float const* gram,
 
   int u = threadIdx.x;
   if (u >= leaf_n) { return; }
-  double top_d[MAX_LEAF_DEGREE];
+  float top_d[MAX_LEAF_DEGREE];
   uint16_t top_v[MAX_LEAF_DEGREE];
   for (int t = 0; t < leaf_degree; ++t) {
-    top_d[t] = std::numeric_limits<double>::max();
+    top_d[t] = std::numeric_limits<float>::max();
     top_v[t] = std::numeric_limits<uint16_t>::max();
   }
 
   // Scan this point's Gram row, keeping the `leaf_degree` nearest cross-origin neighbors.
   int64_t gram_base = local_leaf * leaf_size * leaf_size;
-  double norm_u     = static_cast<double>(gram[gram_base + u * leaf_size + u]);
+  float norm_u      = gram[gram_base + u * leaf_size + u];
   for (int v = 0; v < leaf_n; ++v) {
     if (u == v || leaf_origins[u] == leaf_origins[v] || records[u].id == records[v].id) {
       continue;
     }
-    double norm_v   = static_cast<double>(gram[gram_base + v * leaf_size + v]);
-    double dot      = static_cast<double>(gram[gram_base + v * leaf_size + u]);
-    double distance = norm_u + norm_v - 2.0 * dot;
-    if (isfinite(distance)) { distance = fmax(0.0, distance); }
+    float norm_v   = gram[gram_base + v * leaf_size + v];
+    float dot      = gram[gram_base + v * leaf_size + u];
+    float distance = norm_u + norm_v - 2.0f * dot;
+    if (isfinite(distance)) { distance = fmaxf(0.0f, distance); }
     int worst = 0;
     for (int t = 1; t < leaf_degree; ++t) {
       if (top_d[t] > top_d[worst] || (top_d[t] == top_d[worst] && top_v[t] > top_v[worst])) {

From aa2c143c4048f06d877bbceebc45d34b1f31f01f Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Fri, 7 Aug 2026 21:05:26 +0000
Subject: [PATCH 38/41] one warp per row copy

---
 .../neighbors/detail/cagra/cagra_merge_scaffold.cuh | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index e3d8c777a3..b7dffc98da 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -1411,6 +1411,7 @@ auto build(raft::resources const& res,
 
 /**
  * Offset-copy one input partition graph and append its global scaffold neighbors.
+ * One warp copies one row so lanes access contiguous columns.
  */
 static __global__ void copy_partition_with_scaffold_kernel(uint32_t const* source,
                                                            uint32_t const* scaffold,
@@ -1422,7 +1423,10 @@ static __global__ void copy_partition_with_scaffold_kernel(uint32_t const* sourc
                                                            int64_t scaffold_degree,
                                                            uint32_t offset)
 {
-  int64_t row = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  constexpr int WARPS_PER_BLOCK = THREADS_PER_BLOCK / raft::WarpSize;
+  int lane                      = threadIdx.x % raft::WarpSize;
+  int warp                      = threadIdx.x / raft::WarpSize;
+  int64_t row                   = static_cast<int64_t>(blockIdx.x) * WARPS_PER_BLOCK + warp;
   if (row >= source_rows) { return; }
   int64_t source_base      = row * source_degree;
   int64_t global_row       = row + offset;
@@ -1431,10 +1435,10 @@ static __global__ void copy_partition_with_scaffold_kernel(uint32_t const* sourc
   // Rows from lower-degree input graphs are cyclically repeated to the common base width, which is
   // not ideal (adds work to the sorting by distance) but practical applications for mismatched
   // degrees seem hard to imagine.
-  for (int64_t j = 0; j < base_degree; ++j) {
+  for (int64_t j = lane; j < base_degree; j += raft::WarpSize) {
     destination[destination_base + j] = source[source_base + (j % source_degree)] + offset;
   }
-  for (int64_t j = 0; j < scaffold_degree; ++j) {
+  for (int64_t j = lane; j < scaffold_degree; j += raft::WarpSize) {
     destination[destination_base + base_degree + j] = scaffold[global_row * scaffold_degree + j];
   }
 }
@@ -1467,7 +1471,8 @@ auto append_to_input_graphs(
   for (size_t part = 0; part < indices.size(); ++part) {
     auto source = indices[part]->graph();
     RAFT_EXPECTS(source.extent(1) > 0, "Input CAGRA graphs must have nonzero degree");
-    int blocks = static_cast<int>((source.extent(0) + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK);
+    constexpr int WARPS_PER_BLOCK = THREADS_PER_BLOCK / raft::WarpSize;
+    int blocks = static_cast<int>((source.extent(0) + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);
     copy_partition_with_scaffold_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
       source.data_handle(),
       scaffold.data_handle(),

From a2b378c7d34ad57699b33fed5ebd05d8d5f832e1 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Fri, 7 Aug 2026 22:19:39 +0000
Subject: [PATCH 39/41] writing scaffold directly to output graph

---
 .../neighbors/detail/cagra/cagra_merge.cuh    | 13 ++-
 .../detail/cagra/cagra_merge_scaffold.cuh     | 89 +++++++++----------
 .../ann_cagra/test_merge_fastener.cu          | 49 +++++++---
 3 files changed, 86 insertions(+), 65 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index f3d1c32d61..b2ceec2483 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -438,10 +438,15 @@ auto merge_fastener(raft::resources const& handle,
 
   auto merged_graph = [&] {
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/scaffold");
-    auto scaffold = merge_scaffold::build<T>(
-      handle, dataset_view, preflight.dim, preflight.offsets, scaffold_params);
-    return merge_scaffold::append_to_input_graphs<T, IdxT, DatasetViewT>(
-      handle, indices, preflight.offsets, raft::make_const_mdspan(scaffold.view()));
+    int64_t base_degree = 0;
+    for (auto const* index : indices) {
+      base_degree = std::max<int64_t>(base_degree, index->graph_degree());
+    }
+    auto graph = merge_scaffold::build<T>(
+      handle, dataset_view, preflight.dim, preflight.offsets, scaffold_params, base_degree);
+    merge_scaffold::append_to_input_graphs<T, IdxT, DatasetViewT>(
+      handle, indices, preflight.offsets, graph.view(), base_degree);
+    return graph;
   }();
 
   {
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
index b7dffc98da..032d35714e 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge_scaffold.cuh
@@ -1020,13 +1020,17 @@ inline auto make_leaves(raft::resources const& res,
 /** Fill every unwritten scaffold slot with its row ID; final prefix deduplication removes it. */
 static __global__ void initialize_self_scaffold_kernel(uint32_t* graph,
                                                        int64_t rows,
-                                                       int graph_degree)
+                                                       int64_t graph_degree,
+                                                       int64_t scaffold_offset,
+                                                       int64_t scaffold_degree)
 {
   int64_t linear = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
-  int64_t total  = rows * graph_degree;
+  int64_t total  = rows * scaffold_degree;
   int64_t stride = static_cast<int64_t>(blockDim.x) * gridDim.x;
   for (; linear < total; linear += stride) {
-    graph[linear] = static_cast<uint32_t>(linear / graph_degree);
+    int64_t row                                          = linear / scaffold_degree;
+    int64_t column                                       = linear % scaffold_degree;
+    graph[row * graph_degree + scaffold_offset + column] = static_cast<uint32_t>(row);
   }
 }
 
@@ -1078,7 +1082,8 @@ static __global__ void manyway_leaf_gram_knn_kernel(float const* gram,
                                                     int64_t leaf_count,
                                                     int leaf_size,
                                                     int leaf_degree,
-                                                    int union_degree,
+                                                    int64_t graph_degree,
+                                                    int64_t scaffold_offset,
                                                     uint32_t* graph)
 {
   int64_t local_leaf = blockIdx.x;
@@ -1136,7 +1141,7 @@ static __global__ void manyway_leaf_gram_knn_kernel(float const* gram,
   for (int t = 0; t < leaf_degree; ++t) {
     bool valid = top_v[t] != std::numeric_limits<uint16_t>::max() && isfinite(top_d[t]);
     if (valid) {
-      int64_t output = static_cast<int64_t>(records[u].id) * union_degree +
+      int64_t output = static_cast<int64_t>(records[u].id) * graph_degree + scaffold_offset +
                        static_cast<int>(records[u].occurrence) * leaf_degree + selected;
       graph[output] = records[top_v[t]].id;
       ++selected;
@@ -1152,6 +1157,7 @@ auto build_leaf_neighbors(raft::resources const& res,
                           leaf_set const& leaves,
                           uint32_t const* origins,
                           int union_degree,
+                          int64_t base_degree,
                           build_params const& params) -> raft::device_matrix<uint32_t, int64_t>
 {
   auto stream             = raft::resource::get_cuda_stream(res);
@@ -1160,14 +1166,14 @@ auto build_leaf_neighbors(raft::resources const& res,
   int leaf_degree         = static_cast<int>(params.leaf_degree);
   auto const& memberships = leaves.partitions->memberships;
 
-  // Prefill every slot with its own row id, leaf KNN overwrites the slots it fills
+  // Prefill every scaffold slot with its own row id; leaf KNN overwrites the slots it fills.
   auto graph = raft::make_device_mdarray<uint32_t, int64_t>(
     res,
     raft::resource::get_large_workspace_resource_ref(res),
-    raft::make_extents<int64_t>(rows, union_degree));
+    raft::make_extents<int64_t>(rows, base_degree + union_degree));
   int scaffold_blocks = strided_grid_size(rows * union_degree);
   initialize_self_scaffold_kernel<<<scaffold_blocks, THREADS_PER_BLOCK, 0, stream>>>(
-    graph.data_handle(), rows, union_degree);
+    graph.data_handle(), rows, graph.extent(1), base_degree, union_degree);
   RAFT_CUDA_TRY(cudaGetLastError());
 
   // Gather every scalar type into float leaf buffers. Native INT8 cuBLAS is not portable across
@@ -1250,11 +1256,11 @@ auto build_leaf_neighbors(raft::resources const& res,
       static_cast<int64_t>(batch_size),
       leaf_size,
       leaf_degree,
-      union_degree,
+      graph.extent(1),
+      base_degree,
       graph.data_handle());
     RAFT_CUDA_TRY(cudaGetLastError());
   }
-
   return graph;
 }
 
@@ -1301,13 +1307,14 @@ __global__ void manyway_l2_norms_kernel(
   if (lane == 0) { norms[row] = sum; }
 }
 
-/** Build the many-way scaffold through splitting and leaf construction. */
+/** Build the many-way scaffold as the suffix of a candidate graph with `base_degree` columns. */
 template <typename T>
 auto build(raft::resources const& res,
            raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
            int64_t dim,
            std::vector<int64_t> const& offsets,
-           build_params const& params = {}) -> raft::device_matrix<uint32_t, int64_t>
+           build_params const& params = {},
+           int64_t base_degree        = 0) -> raft::device_matrix<uint32_t, int64_t>
 {
   auto stream  = raft::resource::get_cuda_stream(res);
   int64_t rows = dataset.extent(0);
@@ -1331,6 +1338,7 @@ auto build(raft::resources const& res,
   RAFT_EXPECTS(params.leaf_degree >= 1 && params.leaf_degree <= MAX_LEAF_DEGREE,
                "Fastener leaf degree must be between 1 and %d",
                MAX_LEAF_DEGREE);
+  RAFT_EXPECTS(base_degree >= 0, "Fastener base graph degree must be nonnegative");
   RAFT_EXPECTS(
     leaf_gemm_supported(dim, params.leaf_size, raft::resource::get_workspace_free_bytes(res)),
     "Fastener dataset dimension exceeds the leaf GEMM limits");
@@ -1399,10 +1407,10 @@ auto build(raft::resources const& res,
 
   // Leaf construction: only range slicing occurs after configured geometric depth.
   auto leaves = make_leaves(res, partitions, params.leaf_size);
-  auto scaffold =
-    build_leaf_neighbors(res, dataset, dim, leaves, origins.data_handle(), union_degree, params);
+  auto graph  = build_leaf_neighbors(
+    res, dataset, dim, leaves, origins.data_handle(), union_degree, base_degree, params);
   raft::resource::sync_stream(res);
-  return scaffold;
+  return graph;
 }
 
 // ----------------------------------------------------------------------------
@@ -1410,18 +1418,16 @@ auto build(raft::resources const& res,
 // ----------------------------------------------------------------------------
 
 /**
- * Offset-copy one input partition graph and append its global scaffold neighbors.
+ * Offset-copy one input partition graph into the prefix of the final candidate graph.
  * One warp copies one row so lanes access contiguous columns.
  */
-static __global__ void copy_partition_with_scaffold_kernel(uint32_t const* source,
-                                                           uint32_t const* scaffold,
-                                                           int64_t source_rows,
-                                                           int64_t source_degree,
-                                                           uint32_t* destination,
-                                                           int64_t destination_degree,
-                                                           int64_t base_degree,
-                                                           int64_t scaffold_degree,
-                                                           uint32_t offset)
+static __global__ void copy_partition_graph_kernel(uint32_t const* source,
+                                                   int64_t source_rows,
+                                                   int64_t source_degree,
+                                                   uint32_t* destination,
+                                                   int64_t destination_degree,
+                                                   int64_t base_degree,
+                                                   uint32_t offset)
 {
   constexpr int WARPS_PER_BLOCK = THREADS_PER_BLOCK / raft::WarpSize;
   int lane                      = threadIdx.x % raft::WarpSize;
@@ -1438,55 +1444,40 @@ static __global__ void copy_partition_with_scaffold_kernel(uint32_t const* sourc
   for (int64_t j = lane; j < base_degree; j += raft::WarpSize) {
     destination[destination_base + j] = source[source_base + (j % source_degree)] + offset;
   }
-  for (int64_t j = lane; j < scaffold_degree; j += raft::WarpSize) {
-    destination[destination_base + base_degree + j] = scaffold[global_row * scaffold_degree + j];
-  }
 }
 
 /**
- * @brief Form the pre-optimization candidate graph from input graphs and scaffold edges.
+ * @brief Populate the input-graph prefix of the pre-optimization candidate graph.
  *
  * The maximum input graph degree defines the base width, allowing partitions with mixed degrees.
  */
 template <typename T, typename IdxT, cuvs::neighbors::ann_dataset_view DatasetViewT>
-auto append_to_input_graphs(
+void append_to_input_graphs(
   raft::resources const& res,
   std::vector<cuvs::neighbors::cagra::index<T, IdxT, DatasetViewT>*> const& indices,
   std::vector<int64_t> const& offsets,
-  raft::device_matrix_view<const uint32_t, int64_t, raft::row_major> scaffold)
-  -> raft::device_matrix<uint32_t, int64_t>
+  raft::device_matrix_view<uint32_t, int64_t, raft::row_major> graph,
+  int64_t base_degree)
 {
-  auto stream         = raft::resource::get_cuda_stream(res);
-  int64_t base_degree = 0;
-  for (auto const* index : indices) {
-    base_degree = std::max<int64_t>(base_degree, index->graph_degree());
-  }
-  int64_t scaffold_degree = scaffold.extent(1);
-  int64_t graph_degree    = base_degree + scaffold_degree;
-  auto graph              = raft::make_device_mdarray<uint32_t, int64_t>(
-    res,
-    raft::resource::get_large_workspace_resource_ref(res),
-    raft::make_extents<int64_t>(scaffold.extent(0), graph_degree));
+  auto stream = raft::resource::get_cuda_stream(res);
+  RAFT_EXPECTS(graph.extent(0) == offsets.back() && graph.extent(1) >= base_degree,
+               "Candidate graph shape must cover the merged rows and base degree");
 
   for (size_t part = 0; part < indices.size(); ++part) {
     auto source = indices[part]->graph();
     RAFT_EXPECTS(source.extent(1) > 0, "Input CAGRA graphs must have nonzero degree");
     constexpr int WARPS_PER_BLOCK = THREADS_PER_BLOCK / raft::WarpSize;
     int blocks = static_cast<int>((source.extent(0) + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);
-    copy_partition_with_scaffold_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
+    copy_partition_graph_kernel<<<blocks, THREADS_PER_BLOCK, 0, stream>>>(
       source.data_handle(),
-      scaffold.data_handle(),
       source.extent(0),
       source.extent(1),
       graph.data_handle(),
-      graph_degree,
+      graph.extent(1),
       base_degree,
-      scaffold_degree,
       static_cast<uint32_t>(offsets[part]));
     RAFT_CUDA_TRY(cudaGetLastError());
   }
-
-  return graph;
 }
 
 /**
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index 739f69511b..f9022ebe4c 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -420,20 +420,27 @@ TEST(CagraMergeFastener, InitializesUnwrittenScaffoldSlotsWithSelf)
 {
   using namespace detail::merge_scaffold;
   raft::resources res;
-  auto stream              = raft::resource::get_cuda_stream(res);
-  constexpr int64_t rows   = 3;
-  constexpr int64_t degree = 4;
-  auto graph               = raft::make_device_matrix<uint32_t, int64_t>(res, rows, degree);
-
-  initialize_self_scaffold_kernel<<<1, 256, 0, stream>>>(graph.data_handle(), rows, degree);
+  auto stream                        = raft::resource::get_cuda_stream(res);
+  constexpr int64_t rows             = 3;
+  constexpr int64_t scaffold_offset  = 2;
+  constexpr int64_t scaffold_degree  = 4;
+  constexpr int64_t candidate_degree = scaffold_offset + scaffold_degree;
+  auto graph = raft::make_device_matrix<uint32_t, int64_t>(res, rows, candidate_degree);
+
+  RAFT_CUDA_TRY(
+    cudaMemsetAsync(graph.data_handle(), 0xff, graph.size() * sizeof(uint32_t), stream));
+  initialize_self_scaffold_kernel<<<1, 256, 0, stream>>>(
+    graph.data_handle(), rows, candidate_degree, scaffold_offset, scaffold_degree);
   RAFT_CUDA_TRY(cudaGetLastError());
 
   std::vector<uint32_t> initialized(graph.size());
   raft::copy(initialized.data(), graph.data_handle(), initialized.size(), stream);
   raft::resource::sync_stream(res);
   for (int64_t row = 0; row < rows; ++row) {
-    for (int64_t column = 0; column < degree; ++column) {
-      EXPECT_EQ(initialized[row * degree + column], static_cast<uint32_t>(row));
+    for (int64_t column = 0; column < candidate_degree; ++column) {
+      auto expected = column < scaffold_offset ? std::numeric_limits<uint32_t>::max()
+                                               : static_cast<uint32_t>(row);
+      EXPECT_EQ(initialized[row * candidate_degree + column], expected);
     }
   }
 }
@@ -1285,8 +1292,17 @@ TEST(CagraMergeFastener, AppendCyclicallyPadsMixedInputDegrees)
              scaffold.size(),
              raft::resource::get_cuda_stream(res));
 
-  auto appended = detail::merge_scaffold::append_to_input_graphs<float, uint32_t>(
-    res, indices, offsets, raft::make_const_mdspan(scaffold.view()));
+  constexpr int64_t base_degree = 4;
+  auto appended                 = raft::make_device_matrix<uint32_t, int64_t>(res, 4, 5);
+  raft::copy_matrix(appended.data_handle() + base_degree,
+                    static_cast<std::size_t>(appended.extent(1)),
+                    scaffold.data_handle(),
+                    static_cast<std::size_t>(scaffold.extent(1)),
+                    static_cast<std::size_t>(scaffold.extent(1)),
+                    static_cast<std::size_t>(scaffold.extent(0)),
+                    raft::resource::get_cuda_stream(res));
+  detail::merge_scaffold::append_to_input_graphs<float, uint32_t>(
+    res, indices, offsets, appended.view(), base_degree);
   ASSERT_EQ(appended.extent(0), 4);
   ASSERT_EQ(appended.extent(1), 5);
 
@@ -1600,8 +1616,17 @@ TEST(CagraMergeFastener, AppendSortAndDedupHandleOffsetsAndPadding)
              scaffold_host.data_handle(),
              scaffold.size(),
              raft::resource::get_cuda_stream(res));
-  auto appended = detail::merge_scaffold::append_to_input_graphs<float, uint32_t>(
-    res, indices, offsets, raft::make_const_mdspan(scaffold.view()));
+  constexpr int64_t base_degree = 1;
+  auto appended                 = raft::make_device_matrix<uint32_t, int64_t>(res, 4, 3);
+  raft::copy_matrix(appended.data_handle() + base_degree,
+                    static_cast<std::size_t>(appended.extent(1)),
+                    scaffold.data_handle(),
+                    static_cast<std::size_t>(scaffold.extent(1)),
+                    static_cast<std::size_t>(scaffold.extent(1)),
+                    static_cast<std::size_t>(scaffold.extent(0)),
+                    raft::resource::get_cuda_stream(res));
+  detail::merge_scaffold::append_to_input_graphs<float, uint32_t>(
+    res, indices, offsets, appended.view(), base_degree);
 
   auto combined_host  = raft::make_host_matrix<float, int64_t>(4, 1);
   combined_host(0, 0) = 0.0f;

From a5f707785f627904532c8584208010a7e0b6478e Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Fri, 7 Aug 2026 22:57:32 +0000
Subject: [PATCH 40/41] selective zeroing of the output

---
 .../neighbors/detail/cagra/cagra_merge.cuh    | 17 +++++++-----
 .../ann_cagra/test_merge_fastener.cu          | 26 ++++++++++++++++++-
 2 files changed, 35 insertions(+), 8 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index b2ceec2483..6b701dde8a 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -413,13 +413,16 @@ auto merge_fastener(raft::resources const& handle,
   auto* destination            = const_cast<T*>(output_const_view.data_handle());
   {
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> scope("cagra::merge/consolidate");
-    // Zero first so the pad columns are defined: the sorter treats the padded width as the
-    // dimension, which is exact for L2 only because the padding contributes nothing.
-    RAFT_CUDA_TRY(cudaMemsetAsync(
-      destination,
-      0,
-      static_cast<std::size_t>(preflight.rows) * static_cast<std::size_t>(stride) * sizeof(T),
-      raft::resource::get_cuda_stream(handle)));
+    // The copy below overwrites columns [0, dim), while the sorter computes L2 over [0, stride).
+    // Zero the remaining padded columns; when stride == dim, there are none to initialize.
+    if (stride > preflight.dim) {
+      RAFT_CUDA_TRY(cudaMemset2DAsync(destination + preflight.dim,
+                                      static_cast<std::size_t>(stride) * sizeof(T),
+                                      0,
+                                      static_cast<std::size_t>(stride - preflight.dim) * sizeof(T),
+                                      static_cast<std::size_t>(preflight.rows),
+                                      raft::resource::get_cuda_stream(handle)));
+    }
     copy_input_datasets<T, IdxT, DatasetViewT>(
       handle, indices, preflight.offsets, preflight.dim, stride, destination);
   }
diff --git a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
index f9022ebe4c..35375bf76f 100644
--- a/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_merge_fastener.cu
@@ -230,6 +230,25 @@ void expect_dataset_order(raft::resources const& res,
   }
 }
 
+template <typename T>
+void expect_zero_padding(raft::resources const& res,
+                         cagra::device_padded_index<T, uint32_t> const& merged)
+{
+  auto view            = merged.dataset();
+  int64_t const rows   = view.n_rows();
+  int64_t const dim    = static_cast<int64_t>(view.dim());
+  int64_t const stride = static_cast<int64_t>(view.stride());
+  std::vector<T> host(static_cast<std::size_t>(rows * stride));
+  auto stream = raft::resource::get_cuda_stream(res);
+  raft::copy(host.data(), view.view().data_handle(), host.size(), stream);
+  raft::resource::sync_stream(res);
+  for (int64_t row = 0; row < rows; ++row) {
+    for (int64_t column = dim; column < stride; ++column) {
+      EXPECT_EQ(static_cast<float>(host[row * stride + column]), 0.0f);
+    }
+  }
+}
+
 template <typename T>
 void run_explicit_fastener(cuvs::distance::DistanceType metric)
 {
@@ -266,9 +285,14 @@ void run_explicit_fastener(cuvs::distance::DistanceType metric)
   std::vector<cagra::device_padded_index<T, uint32_t>*> indices{&index0, &index1};
 
   auto merged_storage = make_merged_storage<T>(res, rows * 2, dim);
-  auto merged         = merge(res, params, indices, merged_storage.view, fastener);
+  RAFT_CUDA_TRY(cudaMemsetAsync(merged_storage.matrix.data_handle(),
+                                0xff,
+                                merged_storage.matrix.size() * sizeof(T),
+                                raft::resource::get_cuda_stream(res)));
+  auto merged = merge(res, params, indices, merged_storage.view, fastener);
   expect_valid_graph(merged, rows * 2, degree);
   expect_dataset_order(res, merged, raft::make_const_mdspan(expected.view()));
+  expect_zero_padding(res, merged);
   EXPECT_EQ(index0.dataset().n_rows(), rows);
   EXPECT_EQ(index1.dataset().n_rows(), rows);
   EXPECT_EQ(index0.dataset().n_rows(), index0_rows);

From 171be76c82de0bdbd9872f153e7707cc3a56b065 Mon Sep 17 00:00:00 2001
From: Ben Landrum <blandrum@nvidia.com>
Date: Fri, 7 Aug 2026 23:09:09 +0000
Subject: [PATCH 41/41] aligning C failure with C++

---
 c/src/neighbors/cagra.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/c/src/neighbors/cagra.cpp b/c/src/neighbors/cagra.cpp
index bf92b992c7..d7b6aed2bf 100644
--- a/c/src/neighbors/cagra.cpp
+++ b/c/src/neighbors/cagra.cpp
@@ -166,7 +166,10 @@ static void merge_indices_for_layout(
       merged_dataset->layout       = output_layout;
       merged_dataset->is_owning    = true;
       return;
-    } catch (std::bad_alloc const&) {
+    } catch (std::bad_alloc const& failure) {
+      if (merge_params.algo == cuvs::neighbors::cagra::merge_algo::FASTENER) {
+        RAFT_FAIL("FASTENER cagra::merge could not allocate device memory: %s", failure.what());
+      }
       // Filtered merge gathers rows with device-only primitives, matching the restriction on the
       // legacy host fallback.
       RAFT_EXPECTS(filter.type == NO_FILTER,