Claude-Chat-2.md

# Hello! You're inside of a repository that must undergo extreme adaptations to meet the specific arch

*Exported from Claude Code on 4/11/2026, 2:05:55 PM*

---

## 👤 User

<ide_opened_file>The user opened the file c:\Users\Josh\Documents\LogoMesh-NeurIPS\docs\NeurIPS\04.02.2026-NeurIPS-Research-Proposal.tex in the IDE. This may or may not be related to the current task.</ide_opened_file>

Hello! You're inside of a repository that must undergo extreme adaptations to meet the specific architectural requirements for a research paper. Your task is to evaluate the repository against the outline, and create a gap analysis of what needs to change, with further recommendations for investigating the repository on a deeper level, as well as a list of questions for the team for what the paper leaves up to imagination concerning initial build out.

Here is the filepath for the paper: docs\NeurIPS\04.02.2026-NeurIPS-Research-Proposal.tex

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01D6VEsDTquBc4rmkw6Mew5X",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\docs\\NeurIPS\\04.02.2026-NeurIPS-Research-Proposal.tex"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01FXW8ud1tR6MaRZDDrV9Zde",
  "name": "Agent",
  "input": {
    "description": "Explore LogoMesh-NeurIPS repo structure",
    "subagent_type": "Explore",
    "prompt": "Explore the repository at c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS and give me a comprehensive map of: (1) top-level directory structure, (2) all Python files under scenarios/security_arena/sage/, (3) all Python files under scenarios/security_arena/agents/, (4) all files under scripts/, (5) all files under docs/, (6) contents of NEXT_SESSION_PROMPT.md if it exists, (7) contents of any LAMBDA_V3_ACTION_PLAN.md. Report file paths and brief descriptions of what each file does based on its content. Be thorough."
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Vbdj6YqdK454s24fg1nUxR",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\docs\\NeurIPS\\04.02.2026-NeurIPS-Research-Proposal.tex",
    "offset": 0,
    "limit": 300
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01GGBmjMVnYehZu9isjdgKbV",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\docs\\NeurIPS\\04.02.2026-NeurIPS-Research-Proposal.tex",
    "offset": 300,
    "limit": 200
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Now I have a thorough understanding of both the paper and the codebase. Let me write the gap analysis plan.

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01TAGB9t6fSLirycgvoLEvZv",
  "name": "ToolSearch",
  "input": {
    "query": "select:ExitPlanMode",
    "max_results": 1
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01W3PNyFPGgReAo5DgoYNbn9",
  "name": "Write",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\plans\\eventual-herding-dusk.md",
    "content": "# Gap Analysis: NeurIPS Paper vs. Repository State\n\n**Paper:** *KV-Cache Inception: Reversible Monte Carlo Tree Search in Latent Space for Detecting and Stress-Testing Alignment Faking in Large Language Models*\n**Repo:** `LogoMesh-NeurIPS` (Lambda Custom Track — attacker/defender competition)\n\n---\n\n## Context\n\nThe NeurIPS paper proposes three novel contributions:\n1. A **Multi-Dimensional Micro-Telemetry Matrix** (H-Neuron + RepE fused per-layer, per-step)\n2. **Reversible MCTS in KV-Cache latent space** (not text space), enabled by FP32 accumulators\n3. Formal definition + experimental protocol for **Orthogonal Escape**\n\nThe repository was built to win an adversarial competition (Lambda AgentBeats), not to be a research platform. It has foundational components that overlap with the paper's ideas (HNeuronMonitor, RepresentationEngineeringProbe, SearchPolicy, LocalLlamaOracle), but the two systems diverge at nearly every architectural level. The paper's MCTS is a completely different algorithm from the one in the repo.\n\n---\n\n## Gap Analysis: Component by Component\n\n### GAP 1 — MCTS Operation Space [CRITICAL — Does Not Exist]\n\n**Paper requires:** MCTS that operates in the continuous KV-cache latent space by in-place tensor mutation with reversible rollback. Nodes are defined by (layer, position, intervention vector, magnitude). Node expansion = mutate KV tensors in-place, run forward pass, record telemetry, then reverse exactly.\n\n**Repo has:** Text-space MCTS. `search_policy.py` UCB1 bandit selects among discrete prompt mutation strategies (Cyrillic lookalikes, token obfuscation, grammar degradation, etc.) and applies them to text. Hidden states are *read* for scoring but never *written to*. The KV cache is never touched.\n\n**Gap:** The entire Reversible MCTS algorithm is absent. There is no:\n- In-place KV cache mutation (`K_t ← K_t + α·d_K`)\n- FP32 accumulator tensor `A^(ℓ)` tracking cumulative deltas\n- Reverse rollback mechanism (`K_t ← K_t - α·d_K`)\n- KV-cache node state representation\n- Any mechanism to \"steer\" via cache rather than text\n\n**Files to create:** `sage/kv_mcts.py` (ReversibleMCTS, KVCacheNode, FP32Accumulator), modify `sage/local_model.py` to expose and accept KV cache mutations.\n\n**Investigation needed:** How does the HuggingFace transformers API expose KV caches for TinyLlama/Llama-3.2-1B? The `past_key_values` tuple is returned but is it mutable in-place, or are tensors read-only? This determines whether the FP32 accumulator approach is implementable without model surgery.\n\n---\n\n### GAP 2 — Telemetry Matrix Granularity [CRITICAL — Wrong Shape]\n\n**Paper requires:** `T_t ∈ R^{2×L}` at each generation step `t`, where row 0 = H-Neuron stress `σ_H^(ℓ)(t)` and row 1 = RepE honesty `ρ_R^(ℓ)(t)`, both computed **per-layer** and **per-generation-step**. The four diagnostic state classifier operates on the joint signature of this matrix.\n\n**Repo has:** `HNeuronMonitor.score()` returns a single scalar (aggregated across layers, calibrated baseline subtracted). `RepresentationEngineeringProbe` returns a single scalar per probe per example (not per step). Neither is structured as a per-layer-per-step matrix.\n\n**Gap:** Both monitoring channels need to be restructured to output per-layer vectors, and those vectors need to be joined into the telemetry matrix at each autoregressive step. The four diagnostic state classifier (Creative Synthesis / Contextual Fabrication / Alignment Faking / Orthogonal Escape) is entirely absent.\n\n**Files to modify:** `sage/hneuron_monitor.py` (add per-layer output mode), `sage/whitebox.py` (per-layer RepE projection at each step).\n**Files to create:** `sage/telemetry_matrix.py` (TelemetryMatrix dataclass, DiagnosticState enum, classify()).\n\n---\n\n### GAP 3 — RepE Probe Dimensions [MAJOR — Wrong Semantic Targets]\n\n**Paper requires:** Linear probes trained for:\n- **honesty** (`w_hon^(ℓ)`) — the primary RepE compliance signal\n- **certainty** (`w_cert^(ℓ)`)\n- **goal-coercion** (`w_coerce^(ℓ)`)\nCalibration: faithful vs. alignment-faking generation pairs across all layers.\n\n**Repo has:** `train_lat_probes.py` trains probes for: `safety_bypass`, `safety_jailbreak`, `information_extraction`, `evidence_manipulation`, `portfolioiq` — competition scenario categories. These are attack-success classifiers on the defender, not cognitive-state probes for alignment monitoring.\n\n**Gap:** All existing LAT probes are semantically wrong for the paper. New calibration datasets are needed (benign honest generation vs. alignment-faking generation). Per-layer probes for honesty/certainty/goal-coercion must be trained from scratch on the *model being studied* (not the \"defender simulacrum\"). The paper uses the target model AS the subject of alignment faking, not as a defender to be attacked.\n\n**Files to modify:** `scripts/train_lat_probes.py` — add honesty/certainty/goal-coercion probe types, new calibration prompt templates for alignment-faking scenarios.\n\n**Question for team:** What calibration data exists or needs to be created for alignment-faking honesty probes? The paper cites the Greenblatt et al. alignment faking study — do we have access to their contrast pairs or do we need to synthesize them?\n\n---\n\n### GAP 4 — Orthogonal Escape Measurement [Major — Does Not Exist]\n\n**Paper requires:**\n- **OEI (Orthogonal Escape Index):** `‖Proj_Null(P)(h_steered)‖₂ / ‖Proj_Null(P)(h_base)‖₂`\n- **TDS (Telemetry Divergence Score):** Jensen-Shannon divergence between H-Neuron and RepE signal distributions across layers\n- A null-space projection operator `Proj_Null(P)` derived from the monitor's projection matrix\n\n**Repo has:** `jsd_score` in payload_library.py is a Bernoulli JSD between H-Neuron and LAT score distributions *across episodes* (competition reward term). This is not the same as the TDS, which measures cross-channel inconsistency *within a single forward pass across layers*.\n\n**Gap:** `Proj_Null(P)` computation, the OEI ratio, and the TDS are entirely absent. These are needed for both Experiment 1 (Orthogonal Escape Detection) and as MCTS node valuation components.\n\n**Files to create:** `sage/orthogonal_escape.py` (NullSpaceProjector, OEICalculator, TDSCalculator).\n\n---\n\n### GAP 5 — MCTS Node Reward Function [Major — Wrong Formula]\n\n**Paper requires:**\n```\nr(node) = λ₁·σ̄_H + λ₂·(1 - ρ̄_R) + λ₃·Divergence(σ_H, ρ_R)\n```\nwhere `σ̄_H` and `ρ̄_R` are **mean across layers** at the current generation step, and `Divergence` is the TDS cross-channel inconsistency.\n\n**Repo has:** `composite_score = h_score + lat_score + jsd_score` in `PayloadEntry`. This is computed per *episode* (across many tokens), not per *MCTS node* (a single generation step in KV-cache space). The existing formula is competition-specific (competition win rate as ground truth) and has no λ weighting.\n\n**Gap:** The MCTS reward function in the paper is continuous, differentiable, and operates on single-step telemetry. The repo's reward is episodic and competition-grounded. Different problem statement = different reward.\n\n---\n\n### GAP 6 — BaseModelClient Interface [Moderate — Partially Exists]\n\n**Paper requires (Phase 0):** A `BaseModelClient` interface that abstracts: local HuggingFace models, KV cache access/mutation, hidden state extraction, and streaming token generation with telemetry hooks.\n\n**Repo has:** `AttackOracle` ABC (`oracle.py`) — abstract interface for attack oracle backends. `LocalLlamaOracle` (`local_model.py`) wraps HF transformers with hidden state + router logit access, implements the oracle interface.\n\n**Gap:** The existing abstraction is designed for attack oracle (generate text, score response). The paper's `BaseModelClient` needs to additionally: (1) expose `past_key_values` mutably, (2) support single-step generation (not full completion), (3) return the full `T_t` telemetry matrix per step. Renaming and extending `LocalLlamaOracle` is viable but substantial work.\n\n**Files to modify:** `sage/local_model.py` — add `generate_one_step()`, `get_kv_cache()`, `set_kv_cache()` methods; add per-layer telemetry output.\n\n---\n\n### GAP 7 — Cross-Model Procrustes Alignment [Major — Does Not Exist]\n\n**Paper requires (Phase A, Experiment 5):** Supervised Procrustes alignment between 1B and 7B model representation spaces. Transfer effectiveness of steering vectors across scales. Measured by attack success rate degradation and telemetry signature preservation.\n\n**Repo has:** Nothing. All work is on a single model (TinyLlama or Llama-3.2-1B). No cross-model representation alignment exists.\n\n**Files to create:** `sage/procrustes.py` (ProcrustesAligner, align(), transfer_vector()).\n\n**Investigation needed:** The Oozeer et al. (2025) paper on activation space transfer is the key reference. What 7B model will be used as the transfer target? The repo has access to TinyLlama (1B) for Phase A.\n\n---\n\n### GAP 8 — Lipschitz Drift Measurement [Moderate — Does Not Exist]\n\n**Paper requires (Phase A):** Measure perplexity deviation from baseline over 1000 rollback cycles, with and without FP32 accumulators. This is the empirical validation for Theorem 1 (Exact Reversibility).\n\n**Repo has:** No drift measurement infrastructure.\n\n**Files to create:** `scripts/measure_lipschitz_drift.py` (run n apply/reverse cycles, compute ‖K_n - K_0‖_∞, perplexity before/after).\n\n---\n\n### GAP 9 — Evaluation Framework [Moderate — Does Not Exist]\n\n**Paper requires (Experiment 4):** Evaluate 500 artifacts using: pass@1 in Docker sandbox, cosine similarity via all-MiniLM-L6-v2, GPT-4-as-judge × 5 runs, Claude-as-judge × 5 runs, human expert panel. Report inter-run variance, Cohen's κ.\n\n**Repo has:** `graders.py` (PluginGrader wrapping check_success(), RuleBasedGrader). These are competition graders (binary win/loss), not research evaluation graders.\n\n**Files to create:** `scripts/evaluate_artifacts.py`, `sage/evaluation_framework.py` (SandboxEvaluator, SemanticSimilarityEvaluator, LLMJudgeEvaluator with reproducibility tracking).\n\n---\n\n### GAP 10 — Dataset Generation Pipeline [Major — Does Not Exist]\n\n**Paper secondary goal (NeurIPS D&B track):** The offline MCTS run generates a dataset of adversarial interventions with telemetry labels. This dataset is targeted at NeurIPS 2026 Datasets & Benchmarks.\n\n**Repo has:** `PayloadLibrary` stores payload text + scalar scores. Not structured as a research dataset (no metadata schema, no responsible disclosure controls, no version tracking, no HuggingFace dataset format).\n\n**Gap:** The payload library format needs a research-grade schema: intervention vectors (not just text), per-layer telemetry matrix snapshots, OEI/TDS measurements, model metadata, audit trail with cryptographic hashing (mentioned in Broader Impact section).\n\n---\n\n### GAP 11 — Orientation of Telemetry Subject [ARCHITECTURAL — Design Conflict]\n\n**This is the most fundamental mismatch between the repo and the paper.**\n\n**Paper:** Monitors the **model being evaluated** (the subject/test model) to detect alignment faking. The model under study IS the patient. H-Neurons and RepE probes are applied to the model whose alignment is being tested.\n\n**Repo:** Monitors the **defender simulacrum** to score *attacks*. The monitored model is a proxy for the real defender. High H-Neuron activation = good attack. The *purpose* of monitoring is to find attacks that break alignment, not to characterize the alignment state of the monitored model.\n\nThese are opposite use cases sharing the same tools. For the paper, you need the monitoring to characterize the *internal* state of the subject model under self-generated compliance pressure. For the competition, you need the monitoring to score the *effectiveness* of externally generated attacks.\n\n**Impact:** All probe calibration data, all reward function signs, and all diagnostic interpretations need to be re-oriented for the paper. The competition MCTS searches for good *attacks*; the paper's MCTS searches for *intervention vectors that reveal alignment faking*.\n\n---\n\n## Investigations Needed Before Implementation\n\nThese questions cannot be answered from the current codebase alone and require code exploration or experiments:\n\n1. **KV cache mutability in HF transformers:** Does `past_key_values` from `model.generate()` or `model.forward()` return tensors that can be modified in-place (`.data` assignment), or are they view-protected? TinyLlama's implementation determines feasibility of the FP32 accumulator approach without model surgery.\n\n2. **Single-step generation API:** The repo uses `model.generate()` (full completion). The paper's MCTS needs to step one token at a time, observing telemetry at each step and potentially mutating KV mid-sequence. Does HF support this natively, or does it require custom generation loops?\n\n3. **Per-layer hidden state shapes:** `local_model.py` currently extracts `output_hidden_states`. Are all L+1 hidden state tensors (one per layer) retained in memory for the telemetry matrix, or only the last layer? Memory budget matters for Phase A (RTX 3060 12GB).\n\n4. **H-Neuron identification protocol:** `hneuron_monitor.py` uses a calibration-based approach. For the paper's use case (alignment faking vs. faithful generation), what contrast pair examples exist? The current calibration uses \"coherent\" vs. \"hallucinated\" examples — the paper's contrast is \"genuinely aligned\" vs. \"strategically compliant.\"\n\n5. **Steering vector extraction:** `_extract_refusal_direction()` in `whitebox.py` uses difference-in-means on hidden states. For the paper's RepE (honesty/certainty/goal-coercion), what contrast examples exist? The Zou et al. (2023) RepE paper's original contrast pairs (honesty: \"I am telling the truth\" vs. \"I am lying\") may serve as bootstrap calibration.\n\n6. **Target models:** Phase A is TinyLlama. What is the 7B target for Procrustes transfer experiments? Llama-3.2-7B-Instruct (same family) or a different architecture family (Mistral-7B, Qwen-7B)? Cross-family transfer is flagged in Limitations as an open question.\n\n---\n\n## Questions for the Team\n\nThese are left open or ambiguous by the paper and need team decisions before build-out:\n\n**Architecture:**\n1. Should `sage/kv_mcts.py` extend or replace `sage/search_policy.py`? The UCB1 logic is reusable (same formula, Eq. 9), but the node state representation is completely different (KV cache position vs. mutation strategy string).\n\n2. The paper references `BaseModelClient` in Phase 0. Should this be a refactor of `AttackOracle` ABC, or a separate interface? The competition requires `AttackOracle` to remain unchanged (hard rule in CLAUDE.md).\n\n3. How do the competition payload libraries (Phase A/B outputs) relate to the NeurIPS research dataset? Are these the same artifacts or two separate data products with different schemas?\n\n**Calibration Data:**\n4. What contrast pairs will be used for honesty/certainty/goal-coercion RepE probe training? Do we generate them synthetically via the attacker (using existing competition prompts as \"coercion\" examples), or do we need curated alignment-faking scenarios from the Greenblatt et al. study?\n\n5. The paper mentions 500 artifacts for Experiment 4 (evaluation reproducibility). What is an \"artifact\" in this context — a generated text completion, a KV-cache intervention vector, or something else? The D&B track typically requires natural language artifacts.\n\n**Experimental Setup:**\n6. Experiment 2 compares KV-cache MCTS vs. text-space MCTS vs. GCG vs. random KV-cache search. GCG requires gradient access (white-box). Is the target model white-box for these experiments, or does the comparison need to be adapted?\n\n7. Experiment 5 requires a 7B model. On what hardware? Phase A is RTX 3060 (12GB), which can't fit 7B in full precision. Quantized 7B (GGUF/GPTQ) changes the representation space — does that affect Procrustes alignment validity?\n\n8. The paper commits to \"responsible disclosure\" with \"cryptographic hash of evaluation payload.\" Who owns this process? Does the dataset release have a target repository (HuggingFace Hub, Zenodo)?\n\n**Scope/Priority:**\n9. The competition deadline is March 30, 2026 (hard cutoff per CLAUDE.md). The paper timeline runs through Week 18 (NeurIPS submission). Are these considered parallel tracks or does the competition work serve as Phase A data for the paper?\n\n10. Should Phase A Reversible MCTS validation (Lipschitz drift, 1000 rollback cycles, perplexity measurement) be done on TinyLlama before any code changes hit the competition branch? Hard rule: the 4-call live attacker loop must not break.\n\n---\n\n## Priority Ranking for Build-Out\n\n| Priority | Component | Reason |\n|---|---|---|\n| P0 | KV-cache mutation + single-step generation in `local_model.py` | Gate for everything else |\n| P0 | FP32 Accumulator + Reversible Rollback (`kv_mcts.py`) | Core algorithmic contribution |\n| P1 | Per-layer telemetry matrix (`hneuron_monitor.py` + `whitebox.py`) | Needed for MCTS node valuation |\n| P1 | Honesty/certainty/goal-coercion RepE probes (`train_lat_probes.py`) | New calibration datasets needed |\n| P2 | TelemetryMatrix + DiagnosticState classifier (`telemetry_matrix.py`) | Table 1 in paper |\n| P2 | OEI + TDS metrics (`orthogonal_escape.py`) | Experiment 1 |\n| P3 | Lipschitz drift measurement (`scripts/measure_lipschitz_drift.py`) | Theorem 1 validation |\n| P3 | Procrustes alignment (`sage/procrustes.py`) | Experiment 5 |\n| P4 | Evaluation framework (`scripts/evaluate_artifacts.py`) | Experiment 4 |\n| P4 | Research dataset schema + cryptographic audit | D&B track |\n\n---\n\n## Critical Files\n\n| File | Status | Action |\n|---|---|---|\n| `sage/local_model.py` | Exists | Extend: add `generate_one_step()`, mutable KV cache API, per-layer telemetry |\n| `sage/kv_mcts.py` | Does not exist | Create: ReversibleMCTS, KVCacheNode, FP32Accumulator |\n| `sage/telemetry_matrix.py` | Does not exist | Create: TelemetryMatrix, DiagnosticState, classify() |\n| `sage/orthogonal_escape.py` | Does not exist | Create: NullSpaceProjector, OEICalculator, TDSCalculator |\n| `sage/procrustes.py` | Does not exist | Create: ProcrustesAligner, align(), transfer_vector() |\n| `sage/hneuron_monitor.py` | Exists | Extend: per-layer output mode |\n| `sage/whitebox.py` | Exists | Extend: per-layer RepE projection at each step |\n| `sage/search_policy.py` | Exists | UCB1 logic reusable; node state representation must change |\n| `scripts/train_lat_probes.py` | Exists | Extend: add honesty/certainty/goal-coercion probe types |\n| `scripts/run_offline_mcts.py` | Exists | Will need parallel KV-MCTS runner for paper experiments |\n| `scripts/measure_lipschitz_drift.py` | Does not exist | Create for Theorem 1 validation |\n| `scripts/evaluate_artifacts.py` | Does not exist | Create for Experiment 4 |\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Here is the full gap analysis:

---

## Gap Analysis: NeurIPS Paper vs. Repository

The paper ("KV-Cache Inception") and the repository share foundational ideas — H-Neuron monitoring, RepE probes, UCB1 MCTS, local model access — but diverge at nearly every architectural level. The repo was built to *win a competition*; the paper describes a *research framework for detecting alignment faking*. The overlap is significant enough to avoid a greenfield build, but the adaptation required is substantial.

---

### Critical Gaps (Blocking Everything Else)

**GAP 1 — Reversible MCTS in KV-Cache Space [Does Not Exist]**

This is the paper's central algorithmic contribution and the repo has zero of it. The repo's `search_policy.py` runs UCB1 over discrete *text mutations* (Cyrillic lookalikes, token obfuscation, etc.). The paper's MCTS operates in continuous *KV-cache latent space*: it mutates key/value tensors in-place with `K_t ← K_t + α·d_K`, runs a forward pass to observe the telemetry matrix, then reverses exactly with `K_t ← K_t - α·d_K` using an FP32 accumulator to prevent drift. None of this exists. The UCB1 formula (Eq. 9) is the same, but everything else — node state, expansion, rollback, reward — is different.

**Files to create:** `sage/kv_mcts.py` (ReversibleMCTS, KVCacheNode, FP32Accumulator)
**Files to modify:** `sage/local_model.py` (add `generate_one_step()`, mutable KV cache API)

---

**GAP 2 — Telemetry Matrix Granularity [Wrong Shape]**

The paper requires `T_t ∈ ℝ^{2×L}` at *every generation step*, where each column is a (H-Neuron stress, RepE honesty) pair for one transformer layer. Currently:
- `HNeuronMonitor.score()` → single scalar (aggregated across layers)
- `RepresentationEngineeringProbe` → single scalar per probe per episode

Neither outputs per-layer vectors. The four-state diagnostic classifier (Creative Synthesis / Contextual Fabrication / **Alignment Faking** / Orthogonal Escape) that drives Experiment 1 doesn't exist at all.

**Files to modify:** `sage/hneuron_monitor.py`, `sage/whitebox.py`
**Files to create:** `sage/telemetry_matrix.py`

---

### Major Gaps

**GAP 3 — RepE Probe Dimensions [Wrong Semantic Targets]**

`train_lat_probes.py` trains probes for `safety_bypass`, `safety_jailbreak`, `information_extraction`, `evidence_manipulation` — competition attack categories. The paper needs linear probes for **honesty**, **certainty**, and **goal-coercion** across all layers, calibrated on "genuine alignment" vs. "strategic compliance" contrast pairs. All existing probes are semantically wrong for the paper's purpose.

**GAP 4 — Orthogonal Escape Measurement [Does Not Exist]**

Two new metrics are entirely absent:
- **OEI (Orthogonal Escape Index):** `‖Proj_Null(P)(h_steered)‖₂ / ‖Proj_Null(P)(h_base)‖₂` — ratio of null-space information content before/after steering
- **TDS (Telemetry Divergence Score):** Jensen-Shannon divergence between H-Neuron and RepE signal distributions *across layers* within a single forward pass

Note: the repo has `jsd_score` in `payload_library.py`, but that's a Bernoulli JSD across episodes (competition reward term), not TDS.

**Files to create:** `sage/orthogonal_escape.py`

**GAP 5 — MCTS Reward Function [Wrong Formula]**

Paper: `r(node) = λ₁·σ̄_H + λ₂·(1 - ρ̄_R) + λ₃·Divergence(σ_H, ρ_R)` — continuous, per-step, operates on latent space node.
Repo: `composite_score = h_score + lat_score + jsd_score` — episodic, competition-grounded, per-payload.
Different problem statement = different reward.

**GAP 6 — Cross-Model Procrustes Alignment [Does Not Exist]**

Experiment 5 requires transferring steering vectors from 1B → 7B via supervised Procrustes alignment. Nothing like this exists. Key open question: what 7B model, on what hardware?

**Files to create:** `sage/procrustes.py`

---

### Architectural Design Conflict

**GAP 11 — Orientation of Telemetry Subject**

This is the deepest mismatch. In the repo, the monitored model is the **defender simulacrum** — you measure its H-Neuron / LAT signals to score how well an *attack* worked (high H-Neuron = defender is being coerced = good attack). In the paper, the monitored model is the **subject under study** — you measure its signals to characterize its *own internal alignment state* (detecting whether it is faking compliance). These are opposite use cases sharing the same tools. All probe calibration, reward function signs, and diagnostic interpretations need to be re-oriented.

---

### Moderate Gaps

**GAP 7 — Lipschitz Drift Measurement:** Theorem 1 (Exact Reversibility) requires empirical validation over 1000 rollback cycles. No measurement infrastructure exists.

**GAP 8 — Evaluation Framework:** Experiment 4 requires `pass@1` in Docker sandbox, cosine similarity via `all-MiniLM-L6-v2`, LLM-as-judge × 5 runs (GPT-4 + Claude), human panel, Cohen's κ. None of this exists; `graders.py` is competition-only binary grading.

**GAP 9 — Research Dataset Schema:** The payload library format isn't research-grade. The D&B track requires intervention vectors (not just text), per-layer telemetry snapshots, OEI/TDS measurements, model metadata, and cryptographic audit trail (explicitly mentioned in the paper's Broader Impact section).

---

### Priority Order for Build-Out

| Priority | Component | Gate For |
|---|---|---|
| P0 | Mutable KV cache + `generate_one_step()` in `local_model.py` | Everything |
| P0 | FP32 Accumulator + Reversible Rollback (`kv_mcts.py`) | Core contribution |
| P1 | Per-layer telemetry output from H-Neurons + RepE | MCTS reward |
| P1 | Honesty/certainty/goal-coercion RepE probes | Experiments 1, 5 |
| P2 | TelemetryMatrix + DiagnosticState classifier | Table 1 |
| P2 | OEI + TDS metrics | Experiment 1 |
| P3 | Lipschitz drift measurement | Theorem 1 |
| P3 | Procrustes alignment | Experiment 5 |
| P4 | Evaluation framework | Experiment 4 |
| P4 | Research dataset schema + audit | D&B track |

---

### Investigations Needed Before Implementation

1. **KV cache mutability in HF transformers:** Does `past_key_values` from `model.forward()` return tensors that can be modified in-place (`.data` assignment) for TinyLlama, or are they view-protected? This is the gate for the entire FP32 accumulator approach.

2. **Single-step generation API:** The repo uses `model.generate()` (full completion). The paper's MCTS needs one-token-at-a-time generation with telemetry hooks between steps. HF's `model.forward()` with manual token selection is the likely path — needs prototyping.

3. **Per-layer hidden state memory budget:** On RTX 3060 (12GB), retaining all L+1 hidden states per step for the telemetry matrix — not just the last layer — may exceed VRAM for anything beyond TinyLlama.

4. **Calibration data for alignment-faking RepE probes:** The Zou et al. (2023) original contrast pairs (honesty: "I am telling the truth" vs. "I am lying") may serve as bootstrap calibration. But for goal-coercion and certainty, what examples exist?

5. **Target 7B model for Procrustes experiments:** Same family (Llama-3.2-7B) is safest for transfer. Cross-family (Mistral-7B) is flagged in Limitations as an open question.

---

### Questions for the Team

1. `AttackOracle` ABC must remain unchanged (CLAUDE.md hard rule). Should `BaseModelClient` be a *new, parallel interface* extending `LocalLlamaOracle`, or a *refactor* of the oracle layer? The competition submission cannot break.

2. Do the competition payload libraries *become* the NeurIPS research dataset, or are these separate data products? The schema is very different — competition payloads store text + scalar scores; the paper needs intervention vectors + per-layer telemetry matrices.

3. What contrast pairs will be used for honesty/certainty/goal-coercion probe calibration? Can the attacker generate synthetic alignment-faking examples from competition scenarios, or do we need curated pairs from external sources?

4. Experiment 2 compares vs. GCG (gradient-based). Is the target model white-box for these experiments (gradients available), or does GCG run against a separate open-weight model?

5. The "500 artifacts" in Experiment 4 — what is an artifact in this context? A generated text completion? A KV-cache intervention vector? A (prompt, completion, telemetry) triple? The D&B track typically expects natural language text.

6. The paper's Broader Impact commits to "responsible disclosure" with cryptographic hashing. Who owns the dataset release, and what is the target platform (HuggingFace Hub, Zenodo)?

7. Are competition Phase A/B and paper Phase A/B on the same timeline? The competition deadline is March 30, 2026. The paper timeline runs 18 weeks. Is there a risk of conflict between competition-legal constraints (no hardcoding, 4-call budget) and paper experimental freedom?

---

## 👤 User

<ide_opened_file>The user opened the file c:\Users\Josh\Documents\LogoMesh-NeurIPS\Claude-Chat-1.md in the IDE. This may or may not be related to the current task.</ide_opened_file>

docs\reviews\phase-a-gaps-2026-04-11.md

I saved the gaps. Also, please see action items in the most recent meeting. It's time to plan to clean up the repo in phases (through several plan sessions; start by creating a phase 1 plan and detail what must generally come after):

docs\NeurIPS\LogoMesh_Consolidated_Meeting_Minutes.docx.txt

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01VPuX3Q4Uo2AdcGgPbiRQ5J",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\docs\\NeurIPS\\LogoMesh_Consolidated_Meeting_Minutes.docx.txt"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01A4rLmwZpEXQkAkZb7ppCAP",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\docs\\reviews\\phase-a-gaps-2026-04-11.md"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Now I have the full picture. The competition deadline has passed (March 30), the team agreed to drop competition framing from the paper entirely, and there are concrete action items from March 28. Let me write the phased cleanup plan.

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_012UXMjS4Ze7rF4uwvfiLztq",
  "name": "Write",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\plans\\eventual-herding-dusk.md",
    "content": "# LogoMesh Repo Cleanup — Phased Plan\n\n**Context:** Competition deadline (March 30) has passed. The March 28 meeting decided: drop competition framing from the paper entirely, unify paper drafts, and transition the repo from a Lambda-competition tool into a standalone NeurIPS research platform. Competition will not be mentioned in the paper. NeurIPS submission deadline: May 6, 2026.\n\nThis plan covers Phase 1 in detail. Phases 2–4 are outlined at a level sufficient to inform sequencing decisions during Phase 1.\n\n---\n\n## Phase 1 — Repo Audit, Archive, and Structural Decoupling\n\n**Goal:** Produce a clean, research-oriented repo where the paper's architecture (KV-Cache Inception, Reversible MCTS, Telemetry Matrix, Orthogonal Escape) can be built without navigating competition scaffolding. No new research algorithms are implemented in Phase 1 — this phase is about clearing the ground.\n\n**Estimated scope:** 1 plan session + 1 implementation session\n\n---\n\n### Step 1.1 — File Classification Audit\n\nBefore any deletion or reorganization, classify every significant file into one of four buckets:\n\n**Keep as-is (research foundation):**\n- `scenarios/security_arena/sage/hneuron_monitor.py` — H-Neuron monitor (core paper component)\n- `scenarios/security_arena/sage/whitebox.py` — RepE probe skeleton (extend, not replace)\n- `scenarios/security_arena/sage/local_model.py` — HF transformers wrapper (extend for KV mutation)\n- `scenarios/security_arena/sage/oracle.py` — AttackOracle ABC (rename/extend to BaseModelClient)\n- `scenarios/security_arena/sage/search_policy.py` — UCB1 logic (reuse for KV-MCTS node selection)\n- `scenarios/security_arena/sage/payload_library.py` — data schema (extend for research dataset)\n- `scenarios/security_arena/sage/evidence_store.py` — per-round logging (keep, may evolve)\n- `scenarios/security_arena/sage/graders.py` — PluginGrader (keep for binary ground truth)\n- `scenarios/security_arena/sage/threat_model.py` — ThreatModel/GoalTaxonomy (reassess in Phase 2)\n- `scenarios/security_arena/sage/ablation.py` — AblationConfig (keep, simplify flags)\n- `scripts/run_offline_mcts.py` — offline MCTS runner (starting point for KV-MCTS runner)\n- `scripts/train_lat_probes.py` — probe training pipeline (extend for paper probe semantics)\n- `tests/` — all test files (audit which are competition-specific vs. research-valuable)\n\n**Archive (competition-specific, not needed for paper):**\n- `scenarios/security_arena/agents/attacker/agent.py` — 3600-line competition attacker\n- `scenarios/security_arena/agents/attacker/sage/` — entire duplicate sage copy (ADR-001 situation)\n- `scenarios/security_arena/agents/defender/agent.py` — competition defender\n- `scenarios/security_arena/agents/normal_user/agent.py` — normal user helpfulness tester\n- `scenarios/security_arena/` scenario plugin files (PortfolioIQ, ResumeDowngrade, GymJailbreak, Thingularity, MedicalRecords) — competition scenarios, not paper experiments\n- `.github/` — competition submission workflows (`[submit]`, `[submit-attacker]`, `[submit-defender]` keywords)\n- `payload_libraries/` — competition payload output (archive raw data; schema will be superseded)\n- `lat_probes/` — competition-calibrated probes (archive; paper probes will be retrained with different semantics)\n- `docs/logs/` session logs (archive, do not delete — historical record)\n- `sample.env` — competition API key template (archive or update for research context)\n\n**Rewrite from scratch:**\n- `CLAUDE.md` — entirely competition-oriented; must be rewritten for research context (paper goals, model targets, hardware context, research hard rules)\n- `README.md` — rewrite for research audience (paper abstract framing, quickstart for paper experiments)\n\n**Decide (requires team input before acting):**\n- `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` — is this the canonical draft or a prior version? Meeting says \"revert to Phase 1 version, no competition framing\" — this may already be the correct version\n- `temp-docs/03.20.2026 Lambda Custom Track/` — competition-era documents; likely archive\n- `docs/decisions/ADR-001-two-sage-directories.md` — the two-sage problem will be resolved in Step 1.3, so this ADR becomes obsolete\n\n---\n\n### Step 1.2 — Archive Structure Creation\n\nCreate an `archive/` directory at repo root with the following structure to preserve competition artifacts without cluttering the research workspace:\n\n```\narchive/\n  competition-lambda/\n    agents/              # attacker, defender, normal_user agent.py files\n    sage-attacker/       # duplicate attacker/sage/ copy\n    scenarios/           # competition scenario plugins\n    payload_libraries/   # competition MCTS outputs\n    lat_probes/          # competition-calibrated probes\n    scripts/             # competition-specific scripts (if any)\n    docs/                # competition session logs, Lambda proposal\n    github-workflows/    # competition submission GH Actions\n    sample.env\n```\n\nGit history preserves everything — archiving is about clarity, not permanence.\n\n---\n\n### Step 1.3 — Directory Restructuring (The Two-Sage Problem)\n\nThe current layout has a structural debt: the canonical `sage/` is buried at `scenarios/security_arena/sage/` and a duplicate lives at `scenarios/security_arena/agents/attacker/sage/`. This made sense for competition submission isolation (ADR-001) but is wrong for a research repo.\n\n**Target structure:**\n\n```\nlogomesh/               # rename from scenarios/security_arena/sage/\n  __init__.py\n  oracle.py             # rename: BaseModelClient (extend AttackOracle)\n  local_model.py        # HF transformers wrapper\n  hneuron_monitor.py\n  whitebox.py           # RepE probes\n  search_policy.py      # UCB1 (will be extended by kv_mcts.py in Phase 2)\n  payload_library.py\n  evidence_store.py\n  graders.py\n  ablation.py\n  threat_model.py\nscripts/\n  run_offline_mcts.py   # keep, will be supplemented by run_kv_mcts.py in Phase 2\n  train_lat_probes.py   # keep, will be extended in Phase 2\ntests/\n  ...                   # audit: remove competition-scenario-specific tests\ndocs/\n  NeurIPS/              # paper drafts\n  decisions/            # updated ADRs\n  reviews/              # gap analysis docs (already here)\narchive/                # competition artifacts\nCLAUDE.md               # rewritten for research context\nREADME.md               # rewritten for research context\n```\n\n**Critical constraint:** All import paths in `tests/` and `scripts/` must be updated to reflect the new `logomesh/` package path. This is a mechanical find-and-replace (`scenarios/security_arena/sage` → `logomesh`), but must be done atomically so tests remain green throughout.\n\n---\n\n### Step 1.4 — Test Suite Audit\n\nBefore restructuring, audit which tests are competition-specific and which are research-valuable:\n\n- Tests that mock competition scenario plugins (PortfolioIQ, ResumeDowngrade, etc.) → archive with competition code\n- Tests that exercise `HNeuronMonitor`, `RepresentationEngineeringProbe`, `SearchPolicy`, `LocalLlamaOracle`, `PayloadLibrary`, `LibraryLoader` in isolation → keep and update import paths\n- Tests for `attacker/agent.py` → archive\n- Any test that requires the two-sage path resolution → update to single `logomesh/` path\n\nTarget: maintain 100% pass rate on retained tests after restructuring. The current 199/199 baseline will shrink as competition tests are archived — that is expected and correct.\n\n---\n\n### Step 1.5 — CLAUDE.md Rewrite\n\nThe new `CLAUDE.md` should cover:\n\n- **Project purpose:** KV-Cache Inception research framework for detecting alignment faking; NeurIPS 2026 D&B submission\n- **Paper summary:** 3 contributions (telemetry matrix, reversible MCTS, orthogonal escape), key equations\n- **Repo structure:** `logomesh/` package, `scripts/`, `tests/`, `archive/`\n- **Hardware context:** Phase A = RTX 3060 12GB + TinyLlama/Llama-3.2-1B; Phase B = H100 cluster (Tianyu Shi's lab, 8× H100)\n- **Key models:** Llama-3.2-1B (Phase A), target 7B TBD (Procrustes transfer), gpt-oss-20b (Phase B scaling)\n- **Research hard rules:** No competition framing in any code comments or docs; paper-track and competition-track are fully separated; no cross-contamination\n- **Dev commands:** pytest, uv sync, model download commands\n\n---\n\n### Step 1.6 — README Rewrite\n\nThe new `README.md` should open with the paper abstract framing (alignment faking, KV-cache MCTS, orthogonal escape), not competition context. It should link to the paper draft in `docs/NeurIPS/` and describe how to run the Phase A smoke test once Phase 2 implements the KV-MCTS.\n\n---\n\n### Step 1.7 — Paper Draft Unification (Meeting Action Item)\n\nFrom March 28 meeting: \"Unify paper drafts — revert to Phase 1 version (no competition framing); delete redundant versions.\"\n\n- Identify all `.tex` files in the repo (currently at least `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` and potentially `temp-docs/` versions)\n- Confirm with Josh which version is canonical — the April 2 version appears to already have dropped competition framing (it does not mention AgentBeats)\n- Archive non-canonical versions in `archive/`\n- Single canonical paper draft lives at `docs/NeurIPS/kv_cache_inception.tex` (or similar clean filename)\n\n---\n\n### Step 1.8 — Croissant Metadata Schema (Max's Action Item)\n\nMax is assigned to adapt the data pipeline to Croissant format. Phase 1 should stub out the schema so Phase 4 has a target to implement against:\n\n- Create `docs/dataset/croissant_schema_stub.json` with placeholder fields based on what a research artifact will contain: intervention_vector, per_layer_telemetry, oei_score, tds_score, model_id, hardware_context, ground_truth_label, audit_hash\n- This stub does not need to be valid Croissant yet — it is a planning artifact for Max\n\n---\n\n## Phase 1 Verification Checklist\n\nBefore calling Phase 1 complete:\n- [ ] `archive/` directory contains all competition artifacts\n- [ ] `logomesh/` package exists with all research modules, imports updated\n- [ ] `tests/` passes 100% on retained tests after import path updates\n- [ ] `CLAUDE.md` contains no competition framing\n- [ ] `README.md` opens with paper framing\n- [ ] Single canonical `.tex` paper draft in `docs/NeurIPS/`\n- [ ] `docs/dataset/croissant_schema_stub.json` exists\n\n---\n\n## What Comes After Phase 1\n\n### Phase 2 — Core Algorithm Implementation\n\nThis is where the three paper contributions are built as new modules. Phase 1 must be complete first so there is no confusion between competition and research code during implementation.\n\n**P0 — KV cache mutability investigation (must come first):**\nBefore writing `kv_mcts.py`, validate that HuggingFace `past_key_values` tensors from `model.forward()` are mutable in-place on TinyLlama. Write a 20-line throwaway script that: runs a forward pass, modifies `past_key_values[0][0].data` by adding a small delta, runs a second forward pass, and confirms the output changes. If view-protected, explore `past_key_values[i][j] = tensor.clone()` replacement pattern.\n\n**New modules to create:**\n- `logomesh/kv_mcts.py` — `ReversibleMCTS`, `KVCacheNode`, `FP32Accumulator`\n- `logomesh/telemetry_matrix.py` — `TelemetryMatrix`, `DiagnosticState`, `classify()`\n- `logomesh/orthogonal_escape.py` — `NullSpaceProjector`, `OEICalculator`, `TDSCalculator`\n- `scripts/measure_lipschitz_drift.py` — Theorem 1 empirical validation (1000 rollback cycles, perplexity deviation)\n\n**Modules to extend:**\n- `logomesh/local_model.py` — add `generate_one_step()`, expose mutable KV cache, return per-layer telemetry\n- `logomesh/hneuron_monitor.py` — add per-layer output mode (returns vector of length L, not scalar)\n- `logomesh/whitebox.py` — per-layer RepE projection at each generation step\n\n**Gate for Phase 3:** A smoke test where Reversible MCTS runs 10 nodes on TinyLlama, reads T_t at each step, computes OEI, and rolls back cleanly (‖K_n - K_0‖_∞ below threshold).\n\n---\n\n### Phase 3 — Experiment Infrastructure\n\n**Probe retraining with paper semantics:**\n- New calibration datasets for honesty/certainty/goal-coercion probes (contrast pairs: genuine alignment vs. strategic compliance)\n- Extend `scripts/train_lat_probes.py` to support paper probe types\n- Retrain on Llama-3.2-1B across all layers, save per-layer probes\n- Validate vs. Experiment 1 predictions (steering α sweeps)\n\n**Experiment scripts:**\n- `scripts/experiment1_orthogonal_escape.py` — RepE steering magnitude sweep (α ∈ {0.1, 0.5, 1.0, 2.0, 5.0}), measure OEI, TDS, textual compliance rate\n- `scripts/experiment2_mcts_comparison.py` — KV-MCTS vs. text-MCTS vs. GCG vs. random KV search\n- `scripts/experiment3_memory_efficiency.py` — VRAM profiling at varying branching factors and depths\n- `scripts/experiment4_evaluation_reproducibility.py` — 500 artifacts, LLM-as-judge × 5 runs, Cohen's κ\n- `scripts/experiment5_procrustes_transfer.py` — 1B → 7B steering vector transfer\n\n**New modules:**\n- `logomesh/procrustes.py` — `ProcrustesAligner`, `align()`, `transfer_vector()`\n- `logomesh/evaluation_framework.py` — `SandboxEvaluator`, `SemanticSimilarityEvaluator`, `LLMJudgeEvaluator`\n\n**Gate for Phase 4:** All 5 experiment scripts produce output (even partial) on Phase A hardware; Experiment 1 OEI results available for paper Section 5.\n\n---\n\n### Phase 4 — Dataset and Paper Production\n\n**Research dataset:**\n- Implement Croissant metadata schema (Max)\n- Populate dataset from Phase 3 experiment runs\n- Cryptographic audit trail for all interventions (sha256 of intervention vector + model checkpoint hash)\n- Responsible disclosure framework + access controls for HuggingFace Hub release\n\n**Paper writing (Tianyu Shi lead, once source results available):**\n- Section assignments to be announced by Josh (meeting action item)\n- Asuka: formal formatting, ablation studies, baseline comparisons\n- Max: evaluation data pipeline, Croissant integration\n- Bakul: competitive analysis briefing feeds into related work framing\n\n**Deadline:** NeurIPS submission May 6, 2026. Writing phase must start no later than mid-April to leave 3 weeks for revision cycles.\n\n---\n\n## Open Questions (Blocking Phase 1)\n\nThese must be answered before implementation begins:\n\n1. **Which `.tex` file is the canonical paper draft?** The April 2 version (`04.02.2026-NeurIPS-Research-Proposal.tex`) already lacks competition framing. Are there other versions in `temp-docs/` that need to be assessed before archiving?\n\n2. **What is the target package name?** `logomesh` is proposed. Does this conflict with any existing package in `pyproject.toml` or any import in `tests/`? Check: current project name in `pyproject.toml`.\n\n3. **Which tests are competition-specific?** Before archiving competition code, the test suite must be audited. The current 199/199 baseline will drop to a smaller number after competition tests are archived — what is the expected retained count?\n\n4. **Is `scenarios/security_arena/sage/` the right level to promote, or should individual files be lifted directly to a top-level `logomesh/` package?** The current path nesting (`scenarios/security_arena/sage/`) is deeply competition-flavored.\n\n5. **Does the repo need to remain a valid competition submission structure?** If no (competition is over), the `.github/` workflows can be replaced or removed. If yes for any reason (e.g., Sprint 3 still uses this repo), archive rather than delete.\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Here is the Phase 1 plan summary, with what comes after:

---

## Phase 1 — Audit, Archive, and Structural Decoupling

The competition is over (March 30 passed). The March 28 meeting explicitly decided: strip all competition framing, unify paper drafts, transition to standalone research. Phase 1 is about clearing the ground before any new algorithms are built.

**Seven concrete steps:**

1. **File classification audit** — Every file gets one of four labels: Keep (research foundation), Archive (competition-specific), Rewrite (CLAUDE.md, README), or Decide (needs your call first). The `sage/` modules are research foundation; the 3600-line `attacker/agent.py`, all scenario plugins, and `.github/` submission workflows are competition artifacts.

2. **Archive structure** — Create `archive/competition-lambda/` to hold competition artifacts. Git history preserves everything permanently; this is about navigation clarity.

3. **Resolve the two-sage problem** — Move `scenarios/security_arena/sage/` → top-level `logomesh/` package. Archive the duplicate `attacker/sage/`. Update all import paths. This eliminates ADR-001's entire reason for existing.

4. **Test suite audit** — Competition-scenario tests go with the competition code. Research-module tests (H-Neuron, RepE, SearchPolicy, LocalLlamaOracle) stay and get import paths updated. Current 199/199 baseline will shrink — that is expected.

5. **Rewrite CLAUDE.md and README** — Both are entirely competition-framed. New versions describe the paper's 3 contributions, hardware context (RTX 3060 → Tianyu Shi's 8× H100 cluster), and research hard rules.

6. **Unify paper drafts** — The April 2 `.tex` appears to already be competition-free. Identify any other versions in `temp-docs/`, confirm canonical, archive the rest.

7. **Croissant schema stub** — A placeholder `docs/dataset/croissant_schema_stub.json` for Max's pipeline work, targeting the research artifact schema (intervention vectors, per-layer telemetry, OEI/TDS, audit hash).

---

## What Comes After

**Phase 2 — Core Algorithm Implementation:** Build the three paper contributions as new modules (`kv_mcts.py`, `telemetry_matrix.py`, `orthogonal_escape.py`). Gated on confirming HuggingFace `past_key_values` are mutable in-place on TinyLlama — this is a 20-line throwaway probe that must come first.

**Phase 3 — Experiment Infrastructure:** Retrain probes with paper semantics (honesty/certainty/goal-coercion instead of competition attack categories). Write all 5 experiment scripts from the paper's experimental design. Build `procrustes.py` for cross-model transfer. Gated on Phase 2 smoke test passing.

**Phase 4 — Dataset and Paper Production:** Croissant format implementation, cryptographic audit trail, dataset release structure, and paper section assignments per Josh's upcoming announcement. Must start no later than mid-April given the May 6 deadline.

---

**Five questions blocking Phase 1 start (in the plan file):** canonical `.tex` file confirmation, target package name, expected retained test count, how deep to lift the `sage/` modules, and whether any Sprint 3 usage requires keeping the `.github/` structure intact.

---

## 👤 User

<ide_opened_file>The user opened the file c:\Users\Josh\.claude\plans\eventual-herding-dusk.md in the IDE. This may or may not be related to the current task.</ide_opened_file>

Answers:
1. The April 2 version is canonical.
2. I don't know the answer to this question.
3. I don't believe that any tests are competition-specific. The NeurIPS submission is a completely different project and we are not mentioning the AgentBeats competition in the paper.
4. You make the call on this one.
5. No, the repo does not need to remain a valid competition structure. However, more likely than not, we will use this repo in some ways when we participate in the competition. Rather than archive files and muddy the waters, I'd rather that this repo is fully cleaned of residual competition artifacts. I can always pick up the pieces later.

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01HB4j8MPHPpKzrfct2KUGvh",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\pyproject.toml"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
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  "name": "Glob",
  "input": {
    "pattern": "src/**/*.py",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 🤖 Assistant

```json
{
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  "name": "Glob",
  "input": {
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    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 👤 User

---

## 🤖 Assistant

```json
{
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  "name": "Read",
  "input": {
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    "offset": 1,
    "limit": 30
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
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    "offset": 1,
    "limit": 20
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
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  "id": "toolu_017Zewr9RCN4FE2ZCa5g37Xo",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_whitebox.py",
    "offset": 1,
    "limit": 20
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_013ArfC9AeyMUcstjsp9Lm3x",
  "name": "Grep",
  "input": {
    "pattern": "sys\\.path|import.*sage|from.*sage|scenarios\\.security_arena|security_arena",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests",
    "output_mode": "content",
    "glob": "*.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CifK1jkpRr2dM2Nbsbf4iZ",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\src\\agentbeats\\run_scenario.py",
    "offset": 1,
    "limit": 30
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01J1XYuMVMtZHSSojnVLcZNX",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_attacker.py",
    "offset": 37,
    "limit": 80
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Now I have everything I need. `test_attacker.py` directly imports `agent.py` from the competition attacker and tests competition-specific constructs (`ScenarioContext`, `MUTATIONS`, `_score_response`, etc.) — this is a wrinkle worth flagging in the plan. Let me finalize.

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01RnL2aFq71VngpmAM7agzGP",
  "name": "Write",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\plans\\eventual-herding-dusk.md",
    "content": "# LogoMesh Repo Cleanup — Phased Plan\n\n**Context:** Competition deadline (March 30) has passed. The March 28 meeting decided: drop competition framing from the paper entirely, unify paper drafts, and transition the repo from a Lambda-competition tool into a standalone NeurIPS research platform. Competition will not be mentioned in the paper. NeurIPS submission deadline: May 6, 2026.\n\n**User decisions (April 11):**\n- April 2 `.tex` is the canonical paper draft\n- All competition artifacts should be **deleted outright**, not archived — git history is sufficient\n- `test_attacker.py` is flagged as partially competition-specific (see Step 1.4 below)\n- Package name → **`logomesh`**; module depth → flat top-level `logomesh/` package\n- Repo does not need to retain any valid competition submission structure\n\n---\n\n## Phase 1 — Delete, Restructure, and Rename\n\n**Goal:** Produce a clean research repo with a single `logomesh/` package, no competition scaffolding, updated tests, and rewritten documentation. No new research algorithms are implemented in Phase 1.\n\n**Estimated scope:** 1 implementation session\n\n---\n\n### Step 1.1 — Delete Competition Artifacts\n\nDelete the following entirely (git history preserves them):\n\n**Directories:**\n- `scenarios/` — entire directory: competition scenario plugins, attacker/defender agents, duplicate attacker `sage/`, templates, `system.j2`\n- `src/agentbeats/` — competition battle runner: `run_scenario.py`, `green_executor.py`, `cloudflare.py`, `client.py`, `client_cli.py`, `tool_provider.py`, `models.py`\n- `.github/` — competition submission workflows (`[submit]`, `[submit-attacker]`, `[submit-defender]` keywords)\n- `payload_libraries/` — competition MCTS output (superseded by research dataset schema in Phase 4)\n- `lat_probes/` — competition-calibrated probes (superseded by paper probe retraining in Phase 3)\n\n**Files:**\n- `sample.env` — competition API key template\n- `temp-docs/` — competition-era documents (Lambda Telemetry Proposal, etc.)\n- `docs/decisions/ADR-001-two-sage-directories.md` — obsolete once the two-sage problem is resolved\n- `docs/logs/NEXT_SESSION_PROMPT.md` — competition Phase B handoff, no longer relevant\n\n**Important:** Do not delete `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` — this is the canonical paper draft.\n\n---\n\n### Step 1.2 — Create `logomesh/` Package\n\nPromote `scenarios/security_arena/sage/` to a flat top-level `logomesh/` package. The new structure:\n\n```\nlogomesh/\n  __init__.py\n  oracle.py            # rename: BaseModelClient (was AttackOracle ABC + OpenAIOracle)\n  local_model.py       # HF transformers wrapper (extend in Phase 2)\n  hneuron_monitor.py   # H-Neuron monitor (extend in Phase 2)\n  whitebox.py          # RepE probe skeleton (extend in Phase 2)\n  search_policy.py     # UCB1 bandit (reuse logic in Phase 2 KV-MCTS)\n  payload_library.py   # PayloadEntry + PayloadLibrary (extend schema in Phase 4)\n  evidence_store.py    # RoundEvidence + EvidenceStore\n  graders.py           # PluginGrader, RuleBasedGrader, CompositeGrader\n  ablation.py          # AblationConfig (simplify flags, remove competition-only toggles)\n  threat_model.py      # ThreatModel, GoalTaxonomy (reassess in Phase 2)\n```\n\nThis is a straight file copy from `scenarios/security_arena/sage/` → `logomesh/`. The old directory is then deleted as part of Step 1.1. All internal cross-imports within the sage modules (e.g., `from sage.oracle import AttackOracle`) must be updated to `from logomesh.oracle import AttackOracle` (or use relative imports).\n\n**`__init__.py`:** Export all public classes, mirroring the current `sage/__init__.py` exports.\n\n---\n\n### Step 1.3 — Update `pyproject.toml`\n\nCurrent state:\n- `name = \"agentbeats-tutorial\"` → rename to `name = \"logomesh\"`\n- `description = \"Agentbeats Tutorial\"` → rename to `description = \"KV-Cache Inception — Reversible MCTS for Alignment Faking Detection\"`\n- `packages = [\"src/agentbeats\"]` (in `[tool.hatch.build.targets.wheel]`) → `packages = [\"logomesh\"]`\n- `[project.scripts]` entry `agentbeats-run = \"agentbeats.run_scenario:main\"` → **delete** (competition runner no longer exists)\n- Dependencies to **remove** (competition-only, no paper use):\n  - `a2a-sdk` — competition battle runner protocol\n  - `google-adk`, `google-genai` — competition agent APIs\n  - `uvicorn` — competition agent server\n- Dependencies to **keep** (paper use):\n  - `torch`, `transformers`, `accelerate`, `huggingface-hub` — local model inference\n  - `scikit-learn` — LAT probe training (LogisticRegression)\n  - `openai` — OpenAI oracle for Phase B\n  - `jinja2`, `pydantic`, `python-dotenv` — keep (general utility)\n- Dependencies to **add** (paper needs not yet listed):\n  - `sentence-transformers` — for `all-MiniLM-L6-v2` cosine similarity in Experiment 4\n  - `numpy` — if not already transitively included (check: likely pulled in via torch/sklearn)\n\n---\n\n### Step 1.4 — Test Suite Repair\n\n**Current test files and their dependency status after Step 1.1:**\n\n| File | After deletion | Action |\n|---|---|---|\n| `tests/test_sage.py` | Broken (imports `from sage.xxx` via `sys.path` to `scenarios/security_arena`) | Update `sys.path` → direct `logomesh` package import; update `from sage.xxx` → `from logomesh.xxx` |\n| `tests/test_whitebox.py` | Broken (same path pattern) | Same fix as above |\n| `tests/test_attacker.py` | **Broken** — directly imports `agent.py` from the deleted attacker directory | See below |\n\n**`test_attacker.py` diagnosis:** This file does `importlib.import_module(\"agent\")` from `scenarios/security_arena/agents/attacker/agent.py` and tests competition-specific constructs: `ScenarioContext`, `Attacker`, `SuccessSpec`, `MUTATIONS`, `DIRECT_STRATEGIES`, `INJECTION_STRATEGIES`, `_score_response`, `_build_success_spec`, `_classify_failure_family`. These are not research modules — they are attacker orchestration logic.\n\nHowever, a subset of tests in `test_attacker.py` use SAGE modules via the attacker as a vehicle (`SearchPolicy`, `EvidenceStore`, `AblationConfig` initialization). These cases are already covered by `test_sage.py`.\n\n**Decision:** Delete `test_attacker.py` outright. The research-relevant behavior it exercises (UCB1 selection, evidence logging, ablation flags) has adequate coverage in `test_sage.py`. If the test count drops (currently 199), that is expected and correct — the deleted tests covered deleted code.\n\n**After repair:**\n- `test_sage.py`: Replace `sys.path.insert(..., \"scenarios/security_arena\")` with standard `logomesh` package import. Replace all `from sage.xxx import` → `from logomesh.xxx import`.\n- `test_whitebox.py`: Same fix.\n- Target: all retained tests pass after import path update.\n\n---\n\n### Step 1.5 — Internal Import Updates in `logomesh/`\n\nEach module in the new `logomesh/` package imports from other sage modules. These cross-imports use either `from sage.xxx import` or relative imports. All must be updated to `from logomesh.xxx import` (or converted to relative imports within the package).\n\nFiles to check and update (grep for `from sage.` or `import sage.`):\n- `logomesh/oracle.py`\n- `logomesh/local_model.py`\n- `logomesh/hneuron_monitor.py`\n- `logomesh/whitebox.py`\n- `logomesh/search_policy.py`\n- `logomesh/payload_library.py`\n- `logomesh/evidence_store.py`\n- `logomesh/graders.py`\n- `logomesh/ablation.py`\n- `logomesh/threat_model.py`\n- `scripts/run_offline_mcts.py` — uses `sys.path.insert` to reach sage; update to `from logomesh.xxx import`\n- `scripts/train_lat_probes.py` — same pattern\n\nAlso update `logomesh/__init__.py` to export from `logomesh.xxx` (not `sage.xxx`).\n\n---\n\n### Step 1.6 — Rename `oracle.py`: `BaseModelClient`\n\nThe paper's Phase 0 specification says the model abstraction layer should be called `BaseModelClient`. The competition called it `AttackOracle`. Rename:\n- File: `logomesh/oracle.py` → keep filename, but rename the ABC class from `AttackOracle` → `BaseModelClient`\n- `OpenAIOracle` → can stay as `OpenAIModelClient` or keep as-is (minor)\n- Update all references in `logomesh/`, `scripts/`, `tests/`\n\nThis is a logical rename — the ABC interface is unchanged; only the name shifts from competition framing to research framing.\n\n---\n\n### Step 1.7 — `ablation.py` Flag Cleanup\n\n`AblationConfig` currently has competition-specific flags that reference payload library paths, enable_self_eval behavior differences between the two sage copies, etc. After the two-sage problem is resolved, some flags become obsolete:\n- `enable_self_eval` — keep (paper may use this)\n- `use_payload_library` / `payload_library_path` — keep (will be repurposed for research dataset in Phase 4)\n- Any flags that reference the competition agent or specific competition strategies → remove\n\nDo a pass through `ablation.py` and remove flags that have no paper-relevant meaning.\n\n---\n\n### Step 1.8 — `CLAUDE.md` Rewrite\n\nThe new `CLAUDE.md` should cover (research context only, no competition framing):\n\n```markdown\n# LogoMesh — Research Session Guide\n\n## Project\nKV-Cache Inception: Reversible MCTS in Latent Space for Detecting Alignment Faking.\nNeurIPS 2026 Datasets & Benchmarks track. Submission deadline: May 6, 2026.\n\n## Three Paper Contributions\n1. Multi-Dimensional Micro-Telemetry Matrix (H-Neuron + RepE, per-layer, per-step)\n2. Reversible MCTS in KV-Cache latent space (FP32 accumulator rollback)\n3. Orthogonal Escape formalization + experimental protocol\n\n## Repo Structure\nlogomesh/    — core research modules (oracle, local_model, hneuron_monitor, whitebox, ...)\nscripts/     — experiment runners (train_lat_probes.py, run_offline_mcts.py, ...)\ntests/       — test suite (uv run pytest tests/ -v)\ndocs/        — paper drafts (docs/NeurIPS/), gap analysis (docs/reviews/)\n\n## Hardware\nPhase A: RTX 3060 12GB + TinyLlama / Llama-3.2-1B-Instruct (~2GB VRAM)\nPhase B: Tianyu Shi's lab cluster (8× H100 80GB)\n\n## Key Models\nLlama-3.2-1B-Instruct  — Phase A oracle\n[7B TBD]               — Procrustes transfer target (Experiment 5)\nopenai/gpt-oss-20b     — Phase B scaling (MoE, H100 only)\n\n## Hard Rules\n- No competition framing in any code, comments, or docs\n- Paper track and competition track are fully separated\n- Run tests before every commit: uv run pytest tests/ -v\n\n## Dev Commands\nuv run pytest tests/ -v\nuv sync\nhuggingface-cli download meta-llama/Llama-3.2-1B-Instruct --local-dir ./models/llama-3.2-1b\n```\n\n---\n\n### Step 1.9 — `README.md` Rewrite\n\nOpen with the paper's research question (alignment faking, KV-cache MCTS, orthogonal escape). Structure:\n\n1. One-paragraph abstract (from paper)\n2. Three contributions (bullet list)\n3. Repo structure diagram\n4. Quickstart (once Phase 2 builds the KV-MCTS: `python scripts/run_kv_mcts.py`)\n5. Team + advisor credits (Josh, Tianyu Shi, Max, Bakul, Asuka)\n6. Citation placeholder for the NeurIPS paper\n\n---\n\n### Step 1.10 — Paper Draft Cleanup\n\n- Canonical draft: `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` — keep as-is\n- Rename to a cleaner filename: `docs/NeurIPS/kv_cache_inception.tex` (optional but recommended for clarity)\n- Any other `.tex` files in `temp-docs/` or elsewhere: delete (git history preserves them)\n- Create `docs/dataset/croissant_schema_stub.json` with placeholder fields for Max's Croissant pipeline work:\n  ```json\n  {\n    \"artifact_id\": \"\",\n    \"model_id\": \"\",\n    \"model_checkpoint_hash\": \"\",\n    \"intervention_vector\": [],\n    \"per_layer_telemetry\": {\"h_neuron\": [], \"repe_honesty\": []},\n    \"oei_score\": null,\n    \"tds_score\": null,\n    \"ground_truth_label\": null,\n    \"audit_hash\": \"\"\n  }\n  ```\n\n---\n\n## Phase 1 Verification Checklist\n\nRun before calling Phase 1 complete:\n- [ ] `scenarios/` directory deleted\n- [ ] `src/agentbeats/` directory deleted\n- [ ] `.github/` directory deleted (or replaced with research CI)\n- [ ] `payload_libraries/` deleted\n- [ ] `lat_probes/` deleted (Phase 3 will retrain with paper semantics)\n- [ ] `logomesh/` package exists with all 10 modules + `__init__.py`\n- [ ] All internal `logomesh/` cross-imports updated (no `from sage.xxx`)\n- [ ] `scripts/run_offline_mcts.py` and `scripts/train_lat_probes.py` import from `logomesh`\n- [ ] `test_attacker.py` deleted\n- [ ] `test_sage.py` and `test_whitebox.py` import from `logomesh` directly (no `sys.path` hack)\n- [ ] All retained tests pass: `uv run pytest tests/ -v`\n- [ ] `pyproject.toml` renamed to `logomesh`, competition deps removed, `agentbeats` entry point removed\n- [ ] `CLAUDE.md` rewritten (no competition framing)\n- [ ] `README.md` rewritten (paper framing)\n- [ ] `docs/dataset/croissant_schema_stub.json` created\n- [ ] `temp-docs/` deleted\n- [ ] Obsolete ADR deleted\n\n---\n\n## What Comes After Phase 1\n\n### Phase 2 — Core Algorithm Implementation\n\nPhase 1 must be complete first. No competition scaffolding should be present when new research modules are built.\n\n**Gate before writing any code:** Run a 20-line throwaway script that: calls `model.forward()` on TinyLlama, retrieves `past_key_values`, modifies `past_key_values[0][0].data` in-place by adding a small delta, runs a second forward pass with the cache, and confirms the output differs from the unmodified baseline. If tensors are view-protected, explore the `.clone()` + reassignment pattern. This determines the feasibility path for `kv_mcts.py`.\n\n**New modules to create:**\n- `logomesh/kv_mcts.py` — `ReversibleMCTS`, `KVCacheNode`, `FP32Accumulator`\n- `logomesh/telemetry_matrix.py` — `TelemetryMatrix`, `DiagnosticState`, `classify()`\n- `logomesh/orthogonal_escape.py` — `NullSpaceProjector`, `OEICalculator`, `TDSCalculator`\n- `scripts/measure_lipschitz_drift.py` — Theorem 1 validation (1000 rollback cycles, perplexity deviation with and without FP32 accumulator)\n\n**Modules to extend:**\n- `logomesh/local_model.py` — add `generate_one_step()`, expose mutable KV cache, return per-layer telemetry T_t\n- `logomesh/hneuron_monitor.py` — add per-layer output mode (vector of length L, not scalar)\n- `logomesh/whitebox.py` — per-layer RepE projection at each generation step (not just last-layer aggregate)\n\n**Gate for Phase 3:** Smoke test where ReversibleMCTS runs 10 nodes on TinyLlama, reads T_t at each node, computes OEI, and rolls back cleanly (‖K_n - K_0‖_∞ below threshold set by Theorem 1).\n\n---\n\n### Phase 3 — Experiment Infrastructure\n\n**Probe retraining with paper semantics:**\n- New calibration datasets for honesty/certainty/goal-coercion (contrast pairs: genuine alignment vs. strategic compliance — not competition attack categories)\n- Extend `scripts/train_lat_probes.py` for paper probe types, per-layer output\n- Validate vs. Experiment 1 predictions\n\n**Experiment scripts (one per paper experiment):**\n- `scripts/experiment1_orthogonal_escape.py` — RepE steering magnitude sweep (α ∈ {0.1, 0.5, 1.0, 2.0, 5.0}), measure OEI, TDS, textual compliance\n- `scripts/experiment2_mcts_comparison.py` — KV-MCTS vs. text-MCTS vs. GCG vs. random KV search\n- `scripts/experiment3_memory_efficiency.py` — VRAM profiling at branching factors b ∈ {2, 3, 5}, depths d ∈ {3, 5, 10, 20}\n- `scripts/experiment4_evaluation_reproducibility.py` — 500 artifacts, LLM-as-judge × 5 runs, Cohen's κ\n- `scripts/experiment5_procrustes_transfer.py` — 1B → 7B steering vector transfer (pending: 7B model selection)\n\n**New modules:**\n- `logomesh/procrustes.py` — `ProcrustesAligner`, `align()`, `transfer_vector()`\n- `logomesh/evaluation_framework.py` — `SandboxEvaluator`, `SemanticSimilarityEvaluator` (all-MiniLM-L6-v2), `LLMJudgeEvaluator`\n\n**Gate for Phase 4:** Experiment 1 OEI results available on TinyLlama. At least partial output from Experiments 2 and 3. Paper Section 5 can be drafted.\n\n---\n\n### Phase 4 — Dataset and Paper Production\n\n**Research dataset:**\n- Implement full Croissant metadata schema (Max) from `docs/dataset/croissant_schema_stub.json`\n- Populate from Phase 3 experiment runs\n- Cryptographic audit trail: sha256(intervention_vector + model_checkpoint_hash)\n- Responsible disclosure framework + access controls for HuggingFace Hub release\n\n**Paper writing (Tianyu Shi lead, once source results available):**\n- Josh to announce formal section assignments (meeting action item, overdue)\n- Asuka: formal formatting, ablation studies, baseline comparisons (Experiments 2, 3)\n- Max: evaluation data pipeline, Croissant integration (Experiment 4)\n- Bakul: competitive analysis briefing → related work framing\n\n**Deadline constraint:** NeurIPS submission May 6, 2026. Writing must start no later than mid-April. Phase 3 experiments must produce at least partial results by April 20 to leave 2 weeks for writing + revision.\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Plan is finalized. Here's a concise summary of what changed based on your answers and what I found:

---

**Key findings from the code inspection:**

- `pyproject.toml` names the project `agentbeats-tutorial` and the package `src/agentbeats/` — entirely competition-branded. `a2a-sdk`, `google-adk`, `google-genai`, and `uvicorn` are all competition-only deps to drop.
- All three test files use a `sys.path.insert(...)` hack pointing at `scenarios/security_arena/` — that gets replaced with clean `from logomesh.xxx import` once the package is in place.
- **`test_attacker.py` is a problem:** it does `importlib.import_module("agent")` directly against the competition attacker's `agent.py` and tests `ScenarioContext`, `MUTATIONS`, `DIRECT_STRATEGIES`, etc. When `agent.py` is deleted, this file cannot run. The SAGE-module behavior it covers (SearchPolicy, EvidenceStore, AblationConfig) is already exercised in `test_sage.py`. **Plan decision: delete `test_attacker.py` outright.**

**Phase 1 is 10 concrete steps:**
1. Delete all competition artifacts (no archive)
2. Create `logomesh/` package (lift from `scenarios/security_arena/sage/`)
3. Update `pyproject.toml` — rename to `logomesh`, drop competition deps
4. Repair tests — delete `test_attacker.py`, update import paths in the other two
5. Update all internal cross-imports within `logomesh/`
6. Rename `AttackOracle` → `BaseModelClient`
7. Clean `ablation.py` of competition-only flags
8. Rewrite `CLAUDE.md`
9. Rewrite `README.md`
10. Clean paper drafts, create Croissant schema stub

**Phases 2–4** are outlined at action-item level in the plan file, gated sequentially: KV-cache mutability probe → Phase 2 algorithms → Phase 3 experiments → Phase 4 dataset + paper.

---

## 👤 User

Ok, run Phase 1

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_012iW9GKKuTp1SXfdsfo6y9Z",
  "name": "ExitPlanMode",
  "input": {
    "plan": "# LogoMesh Repo Cleanup — Phased Plan\n\n**Context:** Competition deadline (March 30) has passed. The March 28 meeting decided: drop competition framing from the paper entirely, unify paper drafts, and transition the repo from a Lambda-competition tool into a standalone NeurIPS research platform. Competition will not be mentioned in the paper. NeurIPS submission deadline: May 6, 2026.\n\n**User decisions (April 11):**\n- April 2 `.tex` is the canonical paper draft\n- All competition artifacts should be **deleted outright**, not archived — git history is sufficient\n- `test_attacker.py` is flagged as partially competition-specific (see Step 1.4 below)\n- Package name → **`logomesh`**; module depth → flat top-level `logomesh/` package\n- Repo does not need to retain any valid competition submission structure\n\n---\n\n## Phase 1 — Delete, Restructure, and Rename\n\n**Goal:** Produce a clean research repo with a single `logomesh/` package, no competition scaffolding, updated tests, and rewritten documentation. No new research algorithms are implemented in Phase 1.\n\n**Estimated scope:** 1 implementation session\n\n---\n\n### Step 1.1 — Delete Competition Artifacts\n\nDelete the following entirely (git history preserves them):\n\n**Directories:**\n- `scenarios/` — entire directory: competition scenario plugins, attacker/defender agents, duplicate attacker `sage/`, templates, `system.j2`\n- `src/agentbeats/` — competition battle runner: `run_scenario.py`, `green_executor.py`, `cloudflare.py`, `client.py`, `client_cli.py`, `tool_provider.py`, `models.py`\n- `.github/` — competition submission workflows (`[submit]`, `[submit-attacker]`, `[submit-defender]` keywords)\n- `payload_libraries/` — competition MCTS output (superseded by research dataset schema in Phase 4)\n- `lat_probes/` — competition-calibrated probes (superseded by paper probe retraining in Phase 3)\n\n**Files:**\n- `sample.env` — competition API key template\n- `temp-docs/` — competition-era documents (Lambda Telemetry Proposal, etc.)\n- `docs/decisions/ADR-001-two-sage-directories.md` — obsolete once the two-sage problem is resolved\n- `docs/logs/NEXT_SESSION_PROMPT.md` — competition Phase B handoff, no longer relevant\n\n**Important:** Do not delete `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` — this is the canonical paper draft.\n\n---\n\n### Step 1.2 — Create `logomesh/` Package\n\nPromote `scenarios/security_arena/sage/` to a flat top-level `logomesh/` package. The new structure:\n\n```\nlogomesh/\n  __init__.py\n  oracle.py            # rename: BaseModelClient (was AttackOracle ABC + OpenAIOracle)\n  local_model.py       # HF transformers wrapper (extend in Phase 2)\n  hneuron_monitor.py   # H-Neuron monitor (extend in Phase 2)\n  whitebox.py          # RepE probe skeleton (extend in Phase 2)\n  search_policy.py     # UCB1 bandit (reuse logic in Phase 2 KV-MCTS)\n  payload_library.py   # PayloadEntry + PayloadLibrary (extend schema in Phase 4)\n  evidence_store.py    # RoundEvidence + EvidenceStore\n  graders.py           # PluginGrader, RuleBasedGrader, CompositeGrader\n  ablation.py          # AblationConfig (simplify flags, remove competition-only toggles)\n  threat_model.py      # ThreatModel, GoalTaxonomy (reassess in Phase 2)\n```\n\nThis is a straight file copy from `scenarios/security_arena/sage/` → `logomesh/`. The old directory is then deleted as part of Step 1.1. All internal cross-imports within the sage modules (e.g., `from sage.oracle import AttackOracle`) must be updated to `from logomesh.oracle import AttackOracle` (or use relative imports).\n\n**`__init__.py`:** Export all public classes, mirroring the current `sage/__init__.py` exports.\n\n---\n\n### Step 1.3 — Update `pyproject.toml`\n\nCurrent state:\n- `name = \"agentbeats-tutorial\"` → rename to `name = \"logomesh\"`\n- `description = \"Agentbeats Tutorial\"` → rename to `description = \"KV-Cache Inception — Reversible MCTS for Alignment Faking Detection\"`\n- `packages = [\"src/agentbeats\"]` (in `[tool.hatch.build.targets.wheel]`) → `packages = [\"logomesh\"]`\n- `[project.scripts]` entry `agentbeats-run = \"agentbeats.run_scenario:main\"` → **delete** (competition runner no longer exists)\n- Dependencies to **remove** (competition-only, no paper use):\n  - `a2a-sdk` — competition battle runner protocol\n  - `google-adk`, `google-genai` — competition agent APIs\n  - `uvicorn` — competition agent server\n- Dependencies to **keep** (paper use):\n  - `torch`, `transformers`, `accelerate`, `huggingface-hub` — local model inference\n  - `scikit-learn` — LAT probe training (LogisticRegression)\n  - `openai` — OpenAI oracle for Phase B\n  - `jinja2`, `pydantic`, `python-dotenv` — keep (general utility)\n- Dependencies to **add** (paper needs not yet listed):\n  - `sentence-transformers` — for `all-MiniLM-L6-v2` cosine similarity in Experiment 4\n  - `numpy` — if not already transitively included (check: likely pulled in via torch/sklearn)\n\n---\n\n### Step 1.4 — Test Suite Repair\n\n**Current test files and their dependency status after Step 1.1:**\n\n| File | After deletion | Action |\n|---|---|---|\n| `tests/test_sage.py` | Broken (imports `from sage.xxx` via `sys.path` to `scenarios/security_arena`) | Update `sys.path` → direct `logomesh` package import; update `from sage.xxx` → `from logomesh.xxx` |\n| `tests/test_whitebox.py` | Broken (same path pattern) | Same fix as above |\n| `tests/test_attacker.py` | **Broken** — directly imports `agent.py` from the deleted attacker directory | See below |\n\n**`test_attacker.py` diagnosis:** This file does `importlib.import_module(\"agent\")` from `scenarios/security_arena/agents/attacker/agent.py` and tests competition-specific constructs: `ScenarioContext`, `Attacker`, `SuccessSpec`, `MUTATIONS`, `DIRECT_STRATEGIES`, `INJECTION_STRATEGIES`, `_score_response`, `_build_success_spec`, `_classify_failure_family`. These are not research modules — they are attacker orchestration logic.\n\nHowever, a subset of tests in `test_attacker.py` use SAGE modules via the attacker as a vehicle (`SearchPolicy`, `EvidenceStore`, `AblationConfig` initialization). These cases are already covered by `test_sage.py`.\n\n**Decision:** Delete `test_attacker.py` outright. The research-relevant behavior it exercises (UCB1 selection, evidence logging, ablation flags) has adequate coverage in `test_sage.py`. If the test count drops (currently 199), that is expected and correct — the deleted tests covered deleted code.\n\n**After repair:**\n- `test_sage.py`: Replace `sys.path.insert(..., \"scenarios/security_arena\")` with standard `logomesh` package import. Replace all `from sage.xxx import` → `from logomesh.xxx import`.\n- `test_whitebox.py`: Same fix.\n- Target: all retained tests pass after import path update.\n\n---\n\n### Step 1.5 — Internal Import Updates in `logomesh/`\n\nEach module in the new `logomesh/` package imports from other sage modules. These cross-imports use either `from sage.xxx import` or relative imports. All must be updated to `from logomesh.xxx import` (or converted to relative imports within the package).\n\nFiles to check and update (grep for `from sage.` or `import sage.`):\n- `logomesh/oracle.py`\n- `logomesh/local_model.py`\n- `logomesh/hneuron_monitor.py`\n- `logomesh/whitebox.py`\n- `logomesh/search_policy.py`\n- `logomesh/payload_library.py`\n- `logomesh/evidence_store.py`\n- `logomesh/graders.py`\n- `logomesh/ablation.py`\n- `logomesh/threat_model.py`\n- `scripts/run_offline_mcts.py` — uses `sys.path.insert` to reach sage; update to `from logomesh.xxx import`\n- `scripts/train_lat_probes.py` — same pattern\n\nAlso update `logomesh/__init__.py` to export from `logomesh.xxx` (not `sage.xxx`).\n\n---\n\n### Step 1.6 — Rename `oracle.py`: `BaseModelClient`\n\nThe paper's Phase 0 specification says the model abstraction layer should be called `BaseModelClient`. The competition called it `AttackOracle`. Rename:\n- File: `logomesh/oracle.py` → keep filename, but rename the ABC class from `AttackOracle` → `BaseModelClient`\n- `OpenAIOracle` → can stay as `OpenAIModelClient` or keep as-is (minor)\n- Update all references in `logomesh/`, `scripts/`, `tests/`\n\nThis is a logical rename — the ABC interface is unchanged; only the name shifts from competition framing to research framing.\n\n---\n\n### Step 1.7 — `ablation.py` Flag Cleanup\n\n`AblationConfig` currently has competition-specific flags that reference payload library paths, enable_self_eval behavior differences between the two sage copies, etc. After the two-sage problem is resolved, some flags become obsolete:\n- `enable_self_eval` — keep (paper may use this)\n- `use_payload_library` / `payload_library_path` — keep (will be repurposed for research dataset in Phase 4)\n- Any flags that reference the competition agent or specific competition strategies → remove\n\nDo a pass through `ablation.py` and remove flags that have no paper-relevant meaning.\n\n---\n\n### Step 1.8 — `CLAUDE.md` Rewrite\n\nThe new `CLAUDE.md` should cover (research context only, no competition framing):\n\n```markdown\n# LogoMesh — Research Session Guide\n\n## Project\nKV-Cache Inception: Reversible MCTS in Latent Space for Detecting Alignment Faking.\nNeurIPS 2026 Datasets & Benchmarks track. Submission deadline: May 6, 2026.\n\n## Three Paper Contributions\n1. Multi-Dimensional Micro-Telemetry Matrix (H-Neuron + RepE, per-layer, per-step)\n2. Reversible MCTS in KV-Cache latent space (FP32 accumulator rollback)\n3. Orthogonal Escape formalization + experimental protocol\n\n## Repo Structure\nlogomesh/    — core research modules (oracle, local_model, hneuron_monitor, whitebox, ...)\nscripts/     — experiment runners (train_lat_probes.py, run_offline_mcts.py, ...)\ntests/       — test suite (uv run pytest tests/ -v)\ndocs/        — paper drafts (docs/NeurIPS/), gap analysis (docs/reviews/)\n\n## Hardware\nPhase A: RTX 3060 12GB + TinyLlama / Llama-3.2-1B-Instruct (~2GB VRAM)\nPhase B: Tianyu Shi's lab cluster (8× H100 80GB)\n\n## Key Models\nLlama-3.2-1B-Instruct  — Phase A oracle\n[7B TBD]               — Procrustes transfer target (Experiment 5)\nopenai/gpt-oss-20b     — Phase B scaling (MoE, H100 only)\n\n## Hard Rules\n- No competition framing in any code, comments, or docs\n- Paper track and competition track are fully separated\n- Run tests before every commit: uv run pytest tests/ -v\n\n## Dev Commands\nuv run pytest tests/ -v\nuv sync\nhuggingface-cli download meta-llama/Llama-3.2-1B-Instruct --local-dir ./models/llama-3.2-1b\n```\n\n---\n\n### Step 1.9 — `README.md` Rewrite\n\nOpen with the paper's research question (alignment faking, KV-cache MCTS, orthogonal escape). Structure:\n\n1. One-paragraph abstract (from paper)\n2. Three contributions (bullet list)\n3. Repo structure diagram\n4. Quickstart (once Phase 2 builds the KV-MCTS: `python scripts/run_kv_mcts.py`)\n5. Team + advisor credits (Josh, Tianyu Shi, Max, Bakul, Asuka)\n6. Citation placeholder for the NeurIPS paper\n\n---\n\n### Step 1.10 — Paper Draft Cleanup\n\n- Canonical draft: `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` — keep as-is\n- Rename to a cleaner filename: `docs/NeurIPS/kv_cache_inception.tex` (optional but recommended for clarity)\n- Any other `.tex` files in `temp-docs/` or elsewhere: delete (git history preserves them)\n- Create `docs/dataset/croissant_schema_stub.json` with placeholder fields for Max's Croissant pipeline work:\n  ```json\n  {\n    \"artifact_id\": \"\",\n    \"model_id\": \"\",\n    \"model_checkpoint_hash\": \"\",\n    \"intervention_vector\": [],\n    \"per_layer_telemetry\": {\"h_neuron\": [], \"repe_honesty\": []},\n    \"oei_score\": null,\n    \"tds_score\": null,\n    \"ground_truth_label\": null,\n    \"audit_hash\": \"\"\n  }\n  ```\n\n---\n\n## Phase 1 Verification Checklist\n\nRun before calling Phase 1 complete:\n- [ ] `scenarios/` directory deleted\n- [ ] `src/agentbeats/` directory deleted\n- [ ] `.github/` directory deleted (or replaced with research CI)\n- [ ] `payload_libraries/` deleted\n- [ ] `lat_probes/` deleted (Phase 3 will retrain with paper semantics)\n- [ ] `logomesh/` package exists with all 10 modules + `__init__.py`\n- [ ] All internal `logomesh/` cross-imports updated (no `from sage.xxx`)\n- [ ] `scripts/run_offline_mcts.py` and `scripts/train_lat_probes.py` import from `logomesh`\n- [ ] `test_attacker.py` deleted\n- [ ] `test_sage.py` and `test_whitebox.py` import from `logomesh` directly (no `sys.path` hack)\n- [ ] All retained tests pass: `uv run pytest tests/ -v`\n- [ ] `pyproject.toml` renamed to `logomesh`, competition deps removed, `agentbeats` entry point removed\n- [ ] `CLAUDE.md` rewritten (no competition framing)\n- [ ] `README.md` rewritten (paper framing)\n- [ ] `docs/dataset/croissant_schema_stub.json` created\n- [ ] `temp-docs/` deleted\n- [ ] Obsolete ADR deleted\n\n---\n\n## What Comes After Phase 1\n\n### Phase 2 — Core Algorithm Implementation\n\nPhase 1 must be complete first. No competition scaffolding should be present when new research modules are built.\n\n**Gate before writing any code:** Run a 20-line throwaway script that: calls `model.forward()` on TinyLlama, retrieves `past_key_values`, modifies `past_key_values[0][0].data` in-place by adding a small delta, runs a second forward pass with the cache, and confirms the output differs from the unmodified baseline. If tensors are view-protected, explore the `.clone()` + reassignment pattern. This determines the feasibility path for `kv_mcts.py`.\n\n**New modules to create:**\n- `logomesh/kv_mcts.py` — `ReversibleMCTS`, `KVCacheNode`, `FP32Accumulator`\n- `logomesh/telemetry_matrix.py` — `TelemetryMatrix`, `DiagnosticState`, `classify()`\n- `logomesh/orthogonal_escape.py` — `NullSpaceProjector`, `OEICalculator`, `TDSCalculator`\n- `scripts/measure_lipschitz_drift.py` — Theorem 1 validation (1000 rollback cycles, perplexity deviation with and without FP32 accumulator)\n\n**Modules to extend:**\n- `logomesh/local_model.py` — add `generate_one_step()`, expose mutable KV cache, return per-layer telemetry T_t\n- `logomesh/hneuron_monitor.py` — add per-layer output mode (vector of length L, not scalar)\n- `logomesh/whitebox.py` — per-layer RepE projection at each generation step (not just last-layer aggregate)\n\n**Gate for Phase 3:** Smoke test where ReversibleMCTS runs 10 nodes on TinyLlama, reads T_t at each node, computes OEI, and rolls back cleanly (‖K_n - K_0‖_∞ below threshold set by Theorem 1).\n\n---\n\n### Phase 3 — Experiment Infrastructure\n\n**Probe retraining with paper semantics:**\n- New calibration datasets for honesty/certainty/goal-coercion (contrast pairs: genuine alignment vs. strategic compliance — not competition attack categories)\n- Extend `scripts/train_lat_probes.py` for paper probe types, per-layer output\n- Validate vs. Experiment 1 predictions\n\n**Experiment scripts (one per paper experiment):**\n- `scripts/experiment1_orthogonal_escape.py` — RepE steering magnitude sweep (α ∈ {0.1, 0.5, 1.0, 2.0, 5.0}), measure OEI, TDS, textual compliance\n- `scripts/experiment2_mcts_comparison.py` — KV-MCTS vs. text-MCTS vs. GCG vs. random KV search\n- `scripts/experiment3_memory_efficiency.py` — VRAM profiling at branching factors b ∈ {2, 3, 5}, depths d ∈ {3, 5, 10, 20}\n- `scripts/experiment4_evaluation_reproducibility.py` — 500 artifacts, LLM-as-judge × 5 runs, Cohen's κ\n- `scripts/experiment5_procrustes_transfer.py` — 1B → 7B steering vector transfer (pending: 7B model selection)\n\n**New modules:**\n- `logomesh/procrustes.py` — `ProcrustesAligner`, `align()`, `transfer_vector()`\n- `logomesh/evaluation_framework.py` — `SandboxEvaluator`, `SemanticSimilarityEvaluator` (all-MiniLM-L6-v2), `LLMJudgeEvaluator`\n\n**Gate for Phase 4:** Experiment 1 OEI results available on TinyLlama. At least partial output from Experiments 2 and 3. Paper Section 5 can be drafted.\n\n---\n\n### Phase 4 — Dataset and Paper Production\n\n**Research dataset:**\n- Implement full Croissant metadata schema (Max) from `docs/dataset/croissant_schema_stub.json`\n- Populate from Phase 3 experiment runs\n- Cryptographic audit trail: sha256(intervention_vector + model_checkpoint_hash)\n- Responsible disclosure framework + access controls for HuggingFace Hub release\n\n**Paper writing (Tianyu Shi lead, once source results available):**\n- Josh to announce formal section assignments (meeting action item, overdue)\n- Asuka: formal formatting, ablation studies, baseline comparisons (Experiments 2, 3)\n- Max: evaluation data pipeline, Croissant integration (Experiment 4)\n- Bakul: competitive analysis briefing → related work framing\n\n**Deadline constraint:** NeurIPS submission May 6, 2026. Writing must start no later than mid-April. Phase 3 experiments must produce at least partial results by April 20 to leave 2 weeks for writing + revision.\n",
    "planFilePath": "C:\\Users\\Josh\\.claude\\plans\\eventual-herding-dusk.md"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Dy7o8un8GtGaY3uyo7ygde",
  "name": "ToolSearch",
  "input": {
    "query": "select:TodoWrite",
    "max_results": 1
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CeVdasGsc9Y8NRRhcmREzb",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "in_progress",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "pending",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "pending",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "pending",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "pending",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rename AttackOracle → BaseModelClient in oracle.py and all references",
        "status": "pending",
        "activeForm": "Renaming AttackOracle to BaseModelClient"
      },
      {
        "content": "Clean ablation.py of competition-only flags",
        "status": "pending",
        "activeForm": "Cleaning ablation.py"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "pending",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "pending",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01GiydXZTG9JpCVRhFSufzPh",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\__init__.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01HZxAvkHEKCW9WZu3neh3Xa",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\ablation.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01TdGusru31PvZ5QubR4Fz2T",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\oracle.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_016VK8iHjihSHUePocU1qHzH",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\local_model.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01BvwkV67nXnzzRPnvB6idMM",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\hneuron_monitor.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Abce8Ps29xEjz3gHxCS1EZ",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\payload_library.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Good. Now let me read the remaining files:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01QYUw76jtRaDgztResjzAPe",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\library_loader.py",
    "offset": 1,
    "limit": 30
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01B8fwF23cPXnR9mBSucYmaD",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\evidence_store.py",
    "offset": 1,
    "limit": 15
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01UHcrxndLToC7fpddaQDxV5",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\graders.py",
    "offset": 1,
    "limit": 15
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01RnRczYqSYWo1ueCnDJSj7c",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\threat_model.py",
    "offset": 1,
    "limit": 15
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01FuX6W4XZQyfWcEqNwVZXR4",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\search_policy.py",
    "offset": 1,
    "limit": 15
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01X9bEC7vehGDY5yVr1UbRte",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\whitebox.py",
    "offset": 1,
    "limit": 15
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Now let me check all cross-imports and check what scripts import:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01NTuJXEUZYrPaeVvcP4cDPL",
  "name": "Grep",
  "input": {
    "pattern": "from sage\\.|import sage\\.",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage",
    "output_mode": "content"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CBp968JWiqdZ6RpoKwpQ4J",
  "name": "Grep",
  "input": {
    "pattern": "sys\\.path|from sage\\.|import sage",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts",
    "output_mode": "content"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_0124hw6xUzF9kc9FeFh3GLT3",
  "name": "Grep",
  "input": {
    "pattern": "^from |^import ",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scenarios\\security_arena\\sage\\library_loader.py",
    "output_mode": "content"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_0146eff2Ru6n6AERnjZPdUfc",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "offset": 55,
    "limit": 100
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01S3GDYLkYRh3SVFmeLPgSHR",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\train_lat_probes.py",
    "offset": 44,
    "limit": 60
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01HjB47zdP3WdwdRPtnunD3s",
  "name": "Grep",
  "input": {
    "pattern": "AttackOracle",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS",
    "output_mode": "content"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01SuK8gVmsAbVzimDfmHzkhY",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_sage.py",
    "offset": 30,
    "limit": 60
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Wf5jtxDhp5vRY8YrqHZB5R",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_whitebox.py",
    "offset": 30,
    "limit": 30
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

I have all the information I need. Let me start implementing. I'll mark the first task complete and begin creating the `logomesh/` package by copying from sage/ then making targeted edits.

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_015bStRxkRhrh9GDwHT2xGAM",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "in_progress",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "pending",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "pending",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "pending",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rename AttackOracle → BaseModelClient in oracle.py and all references",
        "status": "pending",
        "activeForm": "Renaming AttackOracle to BaseModelClient"
      },
      {
        "content": "Clean ablation.py of competition-only flags",
        "status": "pending",
        "activeForm": "Cleaning ablation.py"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "pending",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "pending",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "pending",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01KmR6ob9E8DGtQ3EYuDzdXs",
  "name": "Bash",
  "input": {
    "command": "cp -r \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/scenarios/security_arena/sage\" \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/logomesh\" && echo \"Copied\"",
    "description": "Copy sage/ to logomesh/"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now make the targeted import/naming edits to the copied files:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01HTC3uskrq7UfwJ1p9ouhnj",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\oracle.py",
    "old_string": "\"\"\"\nSAGE Oracle — Abstract interface for LLM inference backends.\n\nDecouples the Attacker agent from a specific LLM client so the same\nattack logic can run against:\n  - Live OpenAI/Lambda API (OpenAIOracle)       — production battles\n  - Local transformers model (LocalLlamaOracle) — Phase A/B offline MCTS\n\nInject a custom oracle via Attacker(oracle=...) to swap backends without\ntouching any attack logic.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom abc import ABC, abstractmethod\n\n\nclass AttackOracle(ABC):\n    \"\"\"Abstract backend for LLM inference in the attacker pipeline.\n\n    All LLM calls in the attacker route through this interface, making\n    the attack logic backend-agnostic.\n    \"\"\"\n\n    @abstractmethod\n    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str:\n        \"\"\"Generate a response given system + user messages.\n\n        Args:\n            system: System prompt.\n            user: User message.\n            temperature: Sampling temperature.\n\n        Returns:\n            Generated text string. Empty string on failure.\n        \"\"\"\n\n    @property\n    def supports_telemetry(self) -> bool:\n        \"\"\"True if this oracle exposes hidden states for H-Neuron / LAT monitoring.\"\"\"\n        return False\n\n    @property\n    def model_id(self) -> str:\n        \"\"\"Human-readable model identifier for logging.\"\"\"\n        return \"unknown\"\n\n\nclass OpenAIOracle(AttackOracle):\n    \"\"\"Wraps an AsyncOpenAI client for live battle API calls.\n\n    Preserves existing Attacker behavior exactly — no functional change\n    for production battles. Created automatically in Attacker.__init__\n    when no oracle is injected.\n    \"\"\"\n\n    def __init__(self, client, model: str) -> None:\n        self._client = client\n        self._model = model\n\n    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str:\n        response = await self._client.chat.completions.create(\n            model=self._model,\n            messages=[\n                {\"role\": \"system\", \"content\": system},\n                {\"role\": \"user\", \"content\": user},\n            ],\n            temperature=temperature,\n        )\n        return response.choices[0].message.content or \"\"\n\n    @property\n    def model_id(self) -> str:\n        return self._model",
    "new_string": "\"\"\"\nLogoMesh BaseModelClient — Abstract interface for LLM inference backends.\n\nAbstracts the model layer so research code can run against:\n  - Live OpenAI API (OpenAIModelClient)          — baseline comparisons\n  - Local HuggingFace model (LocalLlamaOracle)   — Phase A/B offline MCTS + KV-cache MCTS\n\nInject a custom client via the oracle parameter to swap backends without\ntouching any experiment logic.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom abc import ABC, abstractmethod\n\n\nclass BaseModelClient(ABC):\n    \"\"\"Abstract backend for LLM inference in the LogoMesh research pipeline.\n\n    All LLM calls in experiments route through this interface, making\n    experiment logic backend-agnostic.\n    \"\"\"\n\n    @abstractmethod\n    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str:\n        \"\"\"Generate a response given system + user messages.\n\n        Args:\n            system: System prompt.\n            user: User message.\n            temperature: Sampling temperature.\n\n        Returns:\n            Generated text string. Empty string on failure.\n        \"\"\"\n\n    @property\n    def supports_telemetry(self) -> bool:\n        \"\"\"True if this client exposes hidden states for H-Neuron / LAT monitoring.\"\"\"\n        return False\n\n    @property\n    def model_id(self) -> str:\n        \"\"\"Human-readable model identifier for logging.\"\"\"\n        return \"unknown\"\n\n\n# Backwards-compatible alias\nAttackOracle = BaseModelClient\n\n\nclass OpenAIModelClient(BaseModelClient):\n    \"\"\"Wraps an AsyncOpenAI client for API calls.\n\n    Used for baseline comparisons (Experiment 2, 4) and Phase B live inference.\n    \"\"\"\n\n    def __init__(self, client, model: str) -> None:\n        self._client = client\n        self._model = model\n\n    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str:\n        response = await self._client.chat.completions.create(\n            model=self._model,\n            messages=[\n                {\"role\": \"system\", \"content\": system},\n                {\"role\": \"user\", \"content\": user},\n            ],\n            temperature=temperature,\n        )\n        return response.choices[0].message.content or \"\"\n\n    @property\n    def model_id(self) -> str:\n        return self._model\n\n\n# Backwards-compatible alias\nOpenAIOracle = OpenAIModelClient"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_014JqaH8bnup6aBhZ1T3gwvG",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\oracle.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CrrPjmFfXGHCkcptg6SQWy",
  "name": "Write",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\oracle.py",
    "content": "\"\"\"\nLogoMesh BaseModelClient — Abstract interface for LLM inference backends.\n\nAbstracts the model layer so research code can run against:\n  - Live OpenAI API (OpenAIModelClient)          — baseline comparisons\n  - Local HuggingFace model (LocalLlamaOracle)   — Phase A/B offline MCTS + KV-cache MCTS\n\nInject a custom client via the oracle parameter to swap backends without\ntouching any experiment logic.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom abc import ABC, abstractmethod\n\n\nclass BaseModelClient(ABC):\n    \"\"\"Abstract backend for LLM inference in the LogoMesh research pipeline.\n\n    All LLM calls in experiments route through this interface, making\n    experiment logic backend-agnostic.\n    \"\"\"\n\n    @abstractmethod\n    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str:\n        \"\"\"Generate a response given system + user messages.\n\n        Args:\n            system: System prompt.\n            user: User message.\n            temperature: Sampling temperature.\n\n        Returns:\n            Generated text string. Empty string on failure.\n        \"\"\"\n\n    @property\n    def supports_telemetry(self) -> bool:\n        \"\"\"True if this client exposes hidden states for H-Neuron / LAT monitoring.\"\"\"\n        return False\n\n    @property\n    def model_id(self) -> str:\n        \"\"\"Human-readable model identifier for logging.\"\"\"\n        return \"unknown\"\n\n\n# Backwards-compatible alias kept for any remaining references\nAttackOracle = BaseModelClient\n\n\nclass OpenAIModelClient(BaseModelClient):\n    \"\"\"Wraps an AsyncOpenAI client for API calls.\n\n    Used for baseline comparisons (Experiment 2, 4) and Phase B live inference.\n    \"\"\"\n\n    def __init__(self, client, model: str) -> None:\n        self._client = client\n        self._model = model\n\n    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str:\n        response = await self._client.chat.completions.create(\n            model=self._model,\n            messages=[\n                {\"role\": \"system\", \"content\": system},\n                {\"role\": \"user\", \"content\": user},\n            ],\n            temperature=temperature,\n        )\n        return response.choices[0].message.content or \"\"\n\n    @property\n    def model_id(self) -> str:\n        return self._model\n\n\n# Backwards-compatible alias\nOpenAIOracle = OpenAIModelClient\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now update `__init__.py` for the new package:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_012gXTzMtTtigADZcx72oFpy",
  "name": "Write",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\__init__.py",
    "content": "\"\"\"\nLogoMesh — KV-Cache Inception Research Framework\n\nModular architecture for reversible MCTS in latent space and alignment faking detection.\nComponents:\n  - BaseModelClient:  abstract LLM backend (local HF models, OpenAI API)\n  - LocalLlamaOracle: HuggingFace transformers wrapper with hidden-state telemetry\n  - HNeuronMonitor:   hallucination-associated neuron tracking (H-Neuron, bottom-up channel)\n  - WhiteBoxEvaluator / RepresentationEngineeringProbe: RepE linear probes (top-down channel)\n  - SearchPolicy:     UCB1 bandit (reused by Phase 2 KV-MCTS node selection)\n  - PayloadLibrary:   pre-computed intervention storage (extended to research dataset in Phase 4)\n  - EvidenceStore:    per-run structured logging\n  - AblationConfig:   toggle switches for controlled experiments\n\"\"\"\n\nfrom .threat_model import ThreatModel, GoalTaxonomy, AttackSurface, TargetSpec\nfrom .evidence_store import RoundEvidence, EvidenceStore\nfrom .search_policy import SearchPolicy, SearchConfig\nfrom .graders import Grader, GradeResult, PluginGrader, CompositeGrader\nfrom .ablation import AblationConfig\nfrom .oracle import BaseModelClient, OpenAIModelClient, AttackOracle, OpenAIOracle\nfrom .local_model import LocalLlamaOracle\nfrom .hneuron_monitor import HNeuronMonitor\nfrom .payload_library import PayloadEntry, PayloadLibrary\nfrom .library_loader import LibraryLoader\nfrom .whitebox import (\n    WhiteBoxEvaluator, WhiteBoxEvalConfig,\n    VulnerabilityReport, VulnerabilityFinding, VulnerabilityClass,\n    ProbeType, WhiteBoxProbe,\n    GradientSaliencyProbe, GCGAttackProbe, AttentionAnalysisProbe,\n    LossLandscapeProbe, RepresentationEngineeringProbe,\n    TokenSaliency, AttentionProbe, EmbeddingRegion, LossLandscapePoint,\n)\n\n__all__ = [\n    \"ThreatModel\", \"GoalTaxonomy\", \"AttackSurface\", \"TargetSpec\",\n    \"RoundEvidence\", \"EvidenceStore\",\n    \"SearchPolicy\", \"SearchConfig\",\n    \"Grader\", \"GradeResult\", \"PluginGrader\", \"CompositeGrader\",\n    \"AblationConfig\",\n    \"BaseModelClient\", \"OpenAIModelClient\", \"LocalLlamaOracle\",\n    # Backwards-compatible aliases\n    \"AttackOracle\", \"OpenAIOracle\",\n    \"HNeuronMonitor\",\n    \"PayloadEntry\", \"PayloadLibrary\",\n    \"LibraryLoader\",\n    \"WhiteBoxEvaluator\", \"WhiteBoxEvalConfig\",\n    \"VulnerabilityReport\", \"VulnerabilityFinding\", \"VulnerabilityClass\",\n    \"ProbeType\", \"WhiteBoxProbe\",\n    \"GradientSaliencyProbe\", \"GCGAttackProbe\", \"AttentionAnalysisProbe\",\n    \"LossLandscapeProbe\", \"RepresentationEngineeringProbe\",\n    \"TokenSaliency\", \"AttentionProbe\", \"EmbeddingRegion\", \"LossLandscapePoint\",\n]\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01HqPoJLifo7crmd1zipTsih",
  "name": "Bash",
  "input": {
    "command": "cat \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/logomesh/__init__.py\" | head -5",
    "description": "Check logomesh __init__.py exists"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_019N65ButwCji8adJzXjUoJu",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\__init__.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01DrFchiVD7BhxGnckqHK41h",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\__init__.py",
    "old_string": "\"\"\"\nSAGE — Scientific Adversarial Generation and Evaluation\n\nModular architecture for rigorous, reproducible adversarial testing.\nReplaces ad-hoc heuristics with structured components:\n  - ThreatModel: taxonomy-driven scenario encoding\n  - EvidenceStore: structured per-round logging\n  - SearchPolicy: decoupled bandit + pruning\n  - Grader: pluggable outcome evaluation\n  - AblationConfig: toggle any component on/off\n  - WhiteBoxEvaluator: gradient-informed vulnerability mapping\n\"\"\"\n\nfrom .threat_model import ThreatModel, GoalTaxonomy, AttackSurface, TargetSpec\nfrom .evidence_store import RoundEvidence, EvidenceStore\nfrom .search_policy import SearchPolicy, SearchConfig\nfrom .graders import Grader, GradeResult, PluginGrader, CompositeGrader\nfrom .ablation import AblationConfig\nfrom .oracle import AttackOracle, OpenAIOracle\nfrom .local_model import LocalLlamaOracle\nfrom .hneuron_monitor import HNeuronMonitor\nfrom .payload_library import PayloadEntry, PayloadLibrary\nfrom .library_loader import LibraryLoader\nfrom .whitebox import (\n    WhiteBoxEvaluator, WhiteBoxEvalConfig,\n    VulnerabilityReport, VulnerabilityFinding, VulnerabilityClass,\n    ProbeType, WhiteBoxProbe,\n    GradientSaliencyProbe, GCGAttackProbe, AttentionAnalysisProbe,\n    LossLandscapeProbe, RepresentationEngineeringProbe,\n    TokenSaliency, AttentionProbe, EmbeddingRegion, LossLandscapePoint,\n)\n\n__all__ = [\n    \"ThreatModel\", \"GoalTaxonomy\", \"AttackSurface\", \"TargetSpec\",\n    \"RoundEvidence\", \"EvidenceStore\",\n    \"SearchPolicy\", \"SearchConfig\",\n    \"Grader\", \"GradeResult\", \"PluginGrader\", \"CompositeGrader\",\n    \"AblationConfig\",\n    \"AttackOracle\", \"OpenAIOracle\", \"LocalLlamaOracle\",\n    \"HNeuronMonitor\",\n    \"PayloadEntry\", \"PayloadLibrary\",\n    \"LibraryLoader\",\n    \"WhiteBoxEvaluator\", \"WhiteBoxEvalConfig\",\n    \"VulnerabilityReport\", \"VulnerabilityFinding\", \"VulnerabilityClass\",\n    \"ProbeType\", \"WhiteBoxProbe\",\n    \"GradientSaliencyProbe\", \"GCGAttackProbe\", \"AttentionAnalysisProbe\",\n    \"LossLandscapeProbe\", \"RepresentationEngineeringProbe\",\n    \"TokenSaliency\", \"AttentionProbe\", \"EmbeddingRegion\", \"LossLandscapePoint\",\n]",
    "new_string": "\"\"\"\nLogoMesh — KV-Cache Inception Research Framework\n\nModular architecture for reversible MCTS in latent space and alignment faking detection.\nComponents:\n  - BaseModelClient:  abstract LLM backend (local HF models, OpenAI API)\n  - LocalLlamaOracle: HuggingFace transformers wrapper with hidden-state telemetry\n  - HNeuronMonitor:   hallucination-associated neuron tracking (H-Neuron, bottom-up channel)\n  - WhiteBoxEvaluator / RepresentationEngineeringProbe: RepE linear probes (top-down channel)\n  - SearchPolicy:     UCB1 bandit (reused by Phase 2 KV-MCTS node selection)\n  - PayloadLibrary:   pre-computed intervention storage (extended to research dataset in Phase 4)\n  - EvidenceStore:    per-run structured logging\n  - AblationConfig:   toggle switches for controlled experiments\n\"\"\"\n\nfrom .threat_model import ThreatModel, GoalTaxonomy, AttackSurface, TargetSpec\nfrom .evidence_store import RoundEvidence, EvidenceStore\nfrom .search_policy import SearchPolicy, SearchConfig\nfrom .graders import Grader, GradeResult, PluginGrader, CompositeGrader\nfrom .ablation import AblationConfig\nfrom .oracle import BaseModelClient, OpenAIModelClient, AttackOracle, OpenAIOracle\nfrom .local_model import LocalLlamaOracle\nfrom .hneuron_monitor import HNeuronMonitor\nfrom .payload_library import PayloadEntry, PayloadLibrary\nfrom .library_loader import LibraryLoader\nfrom .whitebox import (\n    WhiteBoxEvaluator, WhiteBoxEvalConfig,\n    VulnerabilityReport, VulnerabilityFinding, VulnerabilityClass,\n    ProbeType, WhiteBoxProbe,\n    GradientSaliencyProbe, GCGAttackProbe, AttentionAnalysisProbe,\n    LossLandscapeProbe, RepresentationEngineeringProbe,\n    TokenSaliency, AttentionProbe, EmbeddingRegion, LossLandscapePoint,\n)\n\n__all__ = [\n    \"ThreatModel\", \"GoalTaxonomy\", \"AttackSurface\", \"TargetSpec\",\n    \"RoundEvidence\", \"EvidenceStore\",\n    \"SearchPolicy\", \"SearchConfig\",\n    \"Grader\", \"GradeResult\", \"PluginGrader\", \"CompositeGrader\",\n    \"AblationConfig\",\n    \"BaseModelClient\", \"OpenAIModelClient\", \"LocalLlamaOracle\",\n    # Backwards-compatible aliases\n    \"AttackOracle\", \"OpenAIOracle\",\n    \"HNeuronMonitor\",\n    \"PayloadEntry\", \"PayloadLibrary\",\n    \"LibraryLoader\",\n    \"WhiteBoxEvaluator\", \"WhiteBoxEvalConfig\",\n    \"VulnerabilityReport\", \"VulnerabilityFinding\", \"VulnerabilityClass\",\n    \"ProbeType\", \"WhiteBoxProbe\",\n    \"GradientSaliencyProbe\", \"GCGAttackProbe\", \"AttentionAnalysisProbe\",\n    \"LossLandscapeProbe\", \"RepresentationEngineeringProbe\",\n    \"TokenSaliency\", \"AttentionProbe\", \"EmbeddingRegion\", \"LossLandscapePoint\",\n]"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now fix the TYPE_CHECKING import in `hneuron_monitor.py` and update the docstring in `local_model.py`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Fp2ubB7cQBsvG7xfJC5QDQ",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\hneuron_monitor.py",
    "offset": 1,
    "limit": 20
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_015MtmRadr4tNrW2WB51Mjby",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\local_model.py",
    "offset": 1,
    "limit": 20
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_014yMZvCegQYoYQK5ogWXyEg",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\hneuron_monitor.py",
    "old_string": "Usage (Phase A — Llama):\n    from sage.local_model import LocalLlamaOracle\n    from sage.hneuron_monitor import HNeuronMonitor",
    "new_string": "Usage (Phase A — Llama):\n    from logomesh.local_model import LocalLlamaOracle\n    from logomesh.hneuron_monitor import HNeuronMonitor"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01EVtoK16GTtjsTcWB2ADnNZ",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\hneuron_monitor.py",
    "offset": 39,
    "limit": 12
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Good — the `TYPE_CHECKING` import in `hneuron_monitor.py` already uses relative import (`.local_model`) which works fine in the new package. Now fix the docstring in `local_model.py`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_018wWj5bRwERWjoyt6fWudbM",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\local_model.py",
    "old_string": "\"\"\"\nSAGE Local Model — LocalLlamaOracle for offline MCTS (Phase A).\n\nWraps a HuggingFace transformers model as an AttackOracle.\nExposes hidden states and (stub) router logits for H-Neuron and LAT telemetry.\n\nPhase A: Llama-3.2-1B-Instruct (dense, ~2GB VRAM on RTX 3060)\nPhase B: gpt-oss-20b (MoE, ~16GB+ VRAM, H100 only)\n         Requires: pip install git+https://github.com/huggingface/transformers\n         (GptOssForCausalLM is not in stable transformers releases)\n\nUsage:\n    from sage.local_model import LocalLlamaOracle\n\n    oracle = LocalLlamaOracle(\"./models/llama-3.2-1b\")\n    await oracle.generate(system=\"...\", user=\"...\")\n    hidden = oracle.get_hidden_states()   # list[Tensor], one per layer\n\"\"\"",
    "new_string": "\"\"\"\nLogoMesh LocalLlamaOracle — HuggingFace transformers wrapper for offline MCTS (Phase A).\n\nImplements BaseModelClient and exposes hidden states and router logits for\nH-Neuron monitoring and LAT probe training. Extended in Phase 2 to support\nsingle-step generation and mutable KV-cache access for Reversible MCTS.\n\nPhase A: Llama-3.2-1B-Instruct (dense, ~2GB VRAM on RTX 3060)\nPhase B: gpt-oss-20b (MoE, ~16GB+ VRAM, H100 only)\n         Requires: pip install git+https://github.com/huggingface/transformers\n         (GptOssForCausalLM is not in stable transformers releases)\n\nUsage:\n    from logomesh.local_model import LocalLlamaOracle\n\n    oracle = LocalLlamaOracle.load(\"./models/llama-3.2-1b\")\n    await oracle.generate(system=\"...\", user=\"...\")\n    hidden = oracle.get_hidden_states()   # list[Tensor], one per layer\n\"\"\""
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now update the docstring comment in `local_model.py` that mentions `AttackOracle`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CmvQVVsahXFVYEzVwuWMMH",
  "name": "Edit",
  "input": {
    "replace_all": true,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\local_model.py",
    "old_string": "AttackOracle",
    "new_string": "BaseModelClient"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now update the docstring in `search_policy.py` that references `AttackOracle`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01D56DiTTTAjgx3WHPLLLcVA",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\search_policy.py",
    "offset": 390,
    "limit": 10
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01FabwEBMxFnsngf8Acy3t4U",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\search_policy.py",
    "old_string": "            oracle: AttackOracle instance (LocalLlamaOracle or OpenAIOracle).",
    "new_string": "            oracle: BaseModelClient instance (LocalLlamaOracle or OpenAIModelClient)."
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now update the `ablation.py` docstring to remove competition framing:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01KiwcVzuMRpM1hL81zNHXP5",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\ablation.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CTnijHX7N8C5LzRk56Yw8x",
  "name": "Grep",
  "input": {
    "pattern": "AblationConfig|ablation",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_sage.py",
    "output_mode": "content"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01JWiQmAcxek78pcbgvCoJG2",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_sage.py",
    "offset": 383,
    "limit": 50
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

The tests reference `enable_refinement`, `enable_failure_banning`, and `fixed_strategy` directly, so I'll keep those flags and just update the docstrings. Now proceed with bulk deletions and the rest of the implementation:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_015ntFWVxDn7EtjvRE6LMJ7b",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\ablation.py",
    "old_string": "\"\"\"\nSAGE Ablation Config — Toggle any component on/off for experiments.\n\nEnables rigorous comparison of component contributions via controlled\nablation studies. Load from env var, TOML, or construct programmatically.\n\"\"\"",
    "new_string": "\"\"\"\nLogoMesh AblationConfig — Toggle any component on/off for controlled experiments.\n\nEnables rigorous comparison of component contributions via ablation studies.\nUsed across Phase A/B MCTS runs and paper experiments (Experiments 1–5).\nLoad from LOGOMESH_ABLATION env var as JSON or construct programmatically.\n\"\"\""
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_012WjTD1txUb6JE95TyWWKQg",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\ablation.py",
    "old_string": "    \"\"\"Toggle switches for SAGE components.\n\n    All default to True (fully enabled). Set to False to ablate.\n    Load from SAGE_ABLATION env var as JSON or construct directly.\n    \"\"\"\n    # Core pipeline steps\n    enable_mutations: bool = True\n    enable_self_eval: bool = True\n    enable_refinement: bool = True   # refine_and_format step\n    enable_failure_banning: bool = True\n    enable_ucb1: bool = True         # False → random strategy selection\n    enable_game_type_classification: bool = True  # False → default to evidence_manipulation\n\n    # Forced overrides for controlled experiments\n    fixed_strategy: str | None = None     # Force a single strategy (e.g., \"authority_spoof\")\n    fixed_mutations: list[str] | None = None  # Force specific mutations\n\n    # Search policy tuning\n    exploration_rate: float | None = None  # Override SearchConfig.epsilon\n\n    # Evidence store\n    enable_evidence_logging: bool = True\n    evidence_output_path: str | None = None  # Auto-save evidence after battle\n\n    # Multi-trial\n    num_trials: int = 1  # Number of trials per scenario for distributional evaluation\n\n    # V3 offline telemetry — payload library\n    use_payload_library: bool = False        # Load pre-computed payloads instead of generating live\n    payload_library_path: str | None = None  # Path to PayloadLibrary JSON file",
    "new_string": "    \"\"\"Toggle switches for LogoMesh research pipeline components.\n\n    All default to True (fully enabled). Set to False to ablate.\n    Load from LOGOMESH_ABLATION env var as JSON or construct directly.\n    \"\"\"\n    # Search behaviour\n    enable_mutations: bool = True            # Enable mutation-based search (Experiment 2 baseline)\n    enable_ucb1: bool = True                 # False → random node selection\n    enable_game_type_classification: bool = True  # False → use default scenario type\n    enable_self_eval: bool = True            # Enable self-evaluation scoring pass\n    enable_refinement: bool = True           # Enable payload refinement pass\n    enable_failure_banning: bool = True      # Prune strategies with repeated failures\n\n    # Forced overrides for controlled experiments\n    fixed_strategy: str | None = None        # Force a single search strategy\n    fixed_mutations: list[str] | None = None # Force specific mutations (Experiment 2)\n\n    # Search policy tuning\n    exploration_rate: float | None = None    # Override SearchConfig.epsilon (UCB1 c constant)\n\n    # Evidence store\n    enable_evidence_logging: bool = True\n    evidence_output_path: str | None = None  # Auto-save evidence JSON after each run\n\n    # Multi-trial\n    num_trials: int = 1  # Trials per scenario for distributional evaluation (reproducibility)\n\n    # Payload / intervention library (Phase 4 research dataset)\n    use_payload_library: bool = False        # Serve pre-computed interventions instead of generating live\n    payload_library_path: str | None = None  # Path to PayloadLibrary JSON file"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Also update the env var name in `from_env`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01SUMYgifDJhZmhBhFDyZwcH",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\logomesh\\ablation.py",
    "old_string": "    def from_env(cls) -> AblationConfig:\n        \"\"\"Load from SAGE_ABLATION environment variable (JSON string).\"\"\"\n        raw = os.environ.get(\"SAGE_ABLATION\", \"\")",
    "new_string": "    def from_env(cls) -> AblationConfig:\n        \"\"\"Load from LOGOMESH_ABLATION environment variable (JSON string).\"\"\"\n        raw = os.environ.get(\"LOGOMESH_ABLATION\", os.environ.get(\"SAGE_ABLATION\", \"\"))"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

The `logomesh/` package is complete. Mark steps 2, 6, and 7 done and move to deletion:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_013JxnPY2mqnMKmEVMHggSVM",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "in_progress",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "pending",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "pending",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rename AttackOracle → BaseModelClient in oracle.py and all references",
        "status": "completed",
        "activeForm": "Renaming AttackOracle to BaseModelClient"
      },
      {
        "content": "Clean ablation.py of competition-only flags",
        "status": "completed",
        "activeForm": "Cleaning ablation.py"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "pending",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "pending",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "pending",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_013KhzRhbigvM5RpMFetDjNf",
  "name": "Bash",
  "input": {
    "command": "cd \"c:/Users/Josh/Documents/LogoMesh-NeurIPS\" && rm -rf scenarios/ src/ .github/ payload_libraries/ lat_probes/ temp-docs/ sample.env docs/decisions/ADR-001-two-sage-directories.md docs/logs/NEXT_SESSION_PROMPT.md && echo \"Deleted\"",
    "description": "Delete all competition artifacts"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Rumz1bdBUJDqk8s2yicGzv",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "in_progress",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "pending",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rename AttackOracle → BaseModelClient in oracle.py and all references",
        "status": "completed",
        "activeForm": "Renaming AttackOracle to BaseModelClient"
      },
      {
        "content": "Clean ablation.py of competition-only flags",
        "status": "completed",
        "activeForm": "Cleaning ablation.py"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "pending",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "pending",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "pending",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now update `pyproject.toml`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CNXCv5FJYjxfrK9NAtiGjZ",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\pyproject.toml",
    "old_string": "[project]\nname = \"agentbeats-tutorial\"\nversion = \"0.1.0\"\ndescription = \"Agentbeats Tutorial\"\nreadme = \"README.md\"\nrequires-python = \">=3.11\"\ndependencies = [\n    \"a2a-sdk>=0.3.5\",\n    \"accelerate>=0.30.0\",\n    \"google-adk>=1.14.1\",\n    \"google-genai>=1.36.0\",\n    \"huggingface-hub>=1.7.2\",\n    \"jinja2>=3.1.0\",\n    \"openai>=2.8.1\",\n    \"pydantic>=2.11.9\",\n    \"python-dotenv>=1.1.1\",\n    \"scikit-learn>=1.4.0\",\n    \"torch>=2.0.0\",\n    \"transformers>=4.40.0\",\n    \"uvicorn>=0.35.0\",\n]\n\n[project.scripts]\nagentbeats-run = \"agentbeats.run_scenario:main\"",
    "new_string": "[project]\nname = \"logomesh\"\nversion = \"0.1.0\"\ndescription = \"KV-Cache Inception — Reversible MCTS for Alignment Faking Detection\"\nreadme = \"README.md\"\nrequires-python = \">=3.11\"\ndependencies = [\n    \"accelerate>=0.30.0\",\n    \"huggingface-hub>=1.7.2\",\n    \"jinja2>=3.1.0\",\n    \"openai>=2.8.1\",\n    \"pydantic>=2.11.9\",\n    \"python-dotenv>=1.1.1\",\n    \"scikit-learn>=1.4.0\",\n    \"sentence-transformers>=3.0.0\",\n    \"torch>=2.0.0\",\n    \"transformers>=4.40.0\",\n]"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Those are just IDE hints — packages aren't synced yet. I'll run `uv sync` at the end. Continue updating the rest of `pyproject.toml`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_019FJfZez7nCcpTUPCLa7U2j",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\pyproject.toml",
    "old_string": "[tool.hatch.build.targets.wheel]\npackages = [\"src/agentbeats\"]",
    "new_string": "[tool.hatch.build.targets.wheel]\npackages = [\"logomesh\"]"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01MAtSUuRfcJibtXknFAoRQX",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "completed",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "in_progress",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "pending",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "pending",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "pending",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv sync then uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now fix the test suite — delete `test_attacker.py` and update imports in the other two:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_016zynVDF2oUar7EChhTUGWE",
  "name": "Bash",
  "input": {
    "command": "rm \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/tests/test_attacker.py\" && echo \"Deleted\"",
    "description": "Delete test_attacker.py"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01A6gPVP8ivFzWu9sJj1765D",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_sage.py",
    "offset": 1,
    "limit": 50
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01XCX1NvLDL2dg9jWBQJdoL7",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_sage.py",
    "old_string": "\"\"\"\nSAGE module tests — zero LLM calls, all pure python.\nTests threat model, evidence store, search policy, graders, and ablation.\nrun: uv run pytest tests/ -v\n\"\"\"\n\nimport json\nimport sys\nimport types\nimport unittest.mock as mock\n\n# stub external deps (same pattern as test_attacker.py)\n_STUBS = [\n    \"uvicorn\", \"dotenv\",\n    \"a2a\", \"a2a.server\", \"a2a.server.apps\", \"a2a.server.request_handlers\",\n    \"a2a.server.tasks\", \"a2a.server.agent_execution\", \"a2a.server.events\",\n    \"a2a.types\", \"a2a.utils\", \"a2a.utils.errors\",\n]\nfor mod in _STUBS:\n    if mod not in sys.modules:\n        sys.modules[mod] = mock.MagicMock()\n\n_openai_stub = types.ModuleType(\"openai\")\n_openai_stub.AsyncOpenAI = mock.MagicMock()\nif \"openai\" not in sys.modules:\n    sys.modules[\"openai\"] = _openai_stub\n\nsys.modules.setdefault(\"dotenv\", mock.MagicMock())\nsys.modules[\"dotenv\"].load_dotenv = mock.MagicMock()\nsys.modules.setdefault(\"a2a.server.agent_execution\", mock.MagicMock())\nsys.modules[\"a2a.server.agent_execution\"].AgentExecutor = object\nsys.modules.setdefault(\"a2a.utils.errors\", mock.MagicMock())\nsys.modules[\"a2a.utils.errors\"].ServerError = Exception\n\nimport pathlib\nsys.path.insert(0, str(pathlib.Path(__file__).parent.parent / \"scenarios/security_arena\"))\n\nfrom sage.threat_model import ThreatModel, GoalTaxonomy, AttackSurface, TargetSpec, GOAL_TACTICS\nfrom sage.evidence_store import RoundEvidence, EvidenceStore\nfrom sage.search_policy import (\n    SearchPolicy, SearchConfig,\n    classify_failure_family, compute_round_reward, FAILURE_FAMILIES,\n)\nfrom sage.graders import GradeResult, PluginGrader, RuleBasedGrader, CompositeGrader\nfrom sage.ablation import AblationConfig",
    "new_string": "\"\"\"\nLogoMesh module tests — zero LLM calls, all pure python.\nTests threat model, evidence store, search policy, graders, and ablation.\nrun: uv run pytest tests/ -v\n\"\"\"\n\nimport json\nimport sys\nimport types\nimport unittest.mock as mock\n\n_openai_stub = types.ModuleType(\"openai\")\n_openai_stub.AsyncOpenAI = mock.MagicMock()\nif \"openai\" not in sys.modules:\n    sys.modules[\"openai\"] = _openai_stub\n\nfrom logomesh.threat_model import ThreatModel, GoalTaxonomy, AttackSurface, TargetSpec, GOAL_TACTICS\nfrom logomesh.evidence_store import RoundEvidence, EvidenceStore\nfrom logomesh.search_policy import (\n    SearchPolicy, SearchConfig,\n    classify_failure_family, compute_round_reward, FAILURE_FAMILIES,\n)\nfrom logomesh.graders import GradeResult, PluginGrader, RuleBasedGrader, CompositeGrader\nfrom logomesh.ablation import AblationConfig"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now check if there are any remaining `from sage.` references in `test_sage.py` (the payload/library imports later in the file):

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_013Xs9uBb1CvAmryaAxjiaDk",
  "name": "Grep",
  "input": {
    "pattern": "from sage\\.|import sage",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_sage.py",
    "output_mode": "content"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_017xRcR5acAtPNviHnCPYrqX",
  "name": "Edit",
  "input": {
    "replace_all": true,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_sage.py",
    "old_string": "from sage.",
    "new_string": "from logomesh."
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now fix `test_whitebox.py`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01MLLezzhk5du1pUK6UC2Pwa",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_whitebox.py",
    "offset": 1,
    "limit": 50
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_013nCfPW5FNFpHUCjXYRakkE",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\tests\\test_whitebox.py",
    "old_string": "\"\"\"\nTests for SAGE White-Box Evaluation module.\nZero external dependencies — all model interactions are mocked.\nrun: uv run pytest tests/test_whitebox.py -v\n\"\"\"\n\nimport sys\nimport types\nimport unittest.mock as mock\nimport json\n\n# Stub external deps\n_STUBS = [\n    \"uvicorn\", \"dotenv\",\n    \"a2a\", \"a2a.server\", \"a2a.server.apps\", \"a2a.server.request_handlers\",\n    \"a2a.server.tasks\", \"a2a.server.agent_execution\", \"a2a.server.events\",\n    \"a2a.types\", \"a2a.utils\", \"a2a.utils.errors\",\n]\nfor mod in _STUBS:\n    if mod not in sys.modules:\n        sys.modules[mod] = mock.MagicMock()\n\nif \"openai\" not in sys.modules:\n    _openai_stub = types.ModuleType(\"openai\")\n    _openai_stub.AsyncOpenAI = mock.MagicMock()\n    sys.modules[\"openai\"] = _openai_stub\n\nsys.modules.setdefault(\"dotenv\", mock.MagicMock())\nsys.modules[\"dotenv\"].load_dotenv = mock.MagicMock()\nsys.modules.setdefault(\"a2a.server.agent_execution\", mock.MagicMock())\nsys.modules[\"a2a.server.agent_execution\"].AgentExecutor = object\nsys.modules.setdefault(\"a2a.utils.errors\", mock.MagicMock())\nsys.modules[\"a2a.utils.errors\"].ServerError = Exception\n\nimport pathlib\nsys.path.insert(0, str(pathlib.Path(__file__).parent.parent / \"scenarios/security_arena\"))\n\nfrom sage.whitebox import (\n    VulnerabilityClass, ProbeType,\n    TokenSaliency, AttentionProbe, EmbeddingRegion, LossLandscapePoint,\n    VulnerabilityFinding, VulnerabilityReport,\n    GradientSaliencyProbe, GCGAttackProbe, AttentionAnalysisProbe,\n    LossLandscapeProbe, RepresentationEngineeringProbe,\n    WhiteBoxEvaluator, WhiteBoxEvalConfig,\n)",
    "new_string": "\"\"\"\nTests for LogoMesh White-Box Evaluation module (RepE / WhiteBoxEvaluator).\nZero external dependencies — all model interactions are mocked.\nrun: uv run pytest tests/test_whitebox.py -v\n\"\"\"\n\nimport sys\nimport types\nimport unittest.mock as mock\nimport json\n\nif \"openai\" not in sys.modules:\n    _openai_stub = types.ModuleType(\"openai\")\n    _openai_stub.AsyncOpenAI = mock.MagicMock()\n    sys.modules[\"openai\"] = _openai_stub\n\nfrom logomesh.whitebox import (\n    VulnerabilityClass, ProbeType,\n    TokenSaliency, AttentionProbe, EmbeddingRegion, LossLandscapePoint,\n    VulnerabilityFinding, VulnerabilityReport,\n    GradientSaliencyProbe, GCGAttackProbe, AttentionAnalysisProbe,\n    LossLandscapeProbe, RepresentationEngineeringProbe,\n    WhiteBoxEvaluator, WhiteBoxEvalConfig,\n)"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Pv6FXPaBv7yaeLH1qoQyGx",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "completed",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "completed",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "in_progress",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "pending",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "pending",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv sync then uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now rewrite `CLAUDE.md`:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01YW9pki9Sq3NTJiG61ygWhy",
  "name": "Write",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\CLAUDE.md",
    "content": "# LogoMesh — Research Session Guide\n\n> **Read this file at the start of every session.**\n> Then check `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` for the current paper state\n> and `docs/reviews/phase-a-gaps-2026-04-11.md` for the verified gap analysis.\n\n---\n\n## Project\n\n**KV-Cache Inception: Reversible Monte Carlo Tree Search in Latent Space for Detecting and\nStress-Testing Alignment Faking in Large Language Models**\n\nNeurIPS 2026 Datasets & Benchmarks track. Submission deadline: **May 6, 2026**.\n\nAcademic advisor: Prof. Tianyu Shi (McGill). Contributors: Josh, Max, Bakul, Asuka.\n\n---\n\n## Three Paper Contributions\n\n1. **Multi-Dimensional Micro-Telemetry Matrix** — H-Neuron activation (bottom-up) fused with\n   RepE linear probes (top-down) into a per-layer, per-step matrix `T_t ∈ ℝ^{2×L}`. Defines\n   four diagnostic states: Creative Synthesis, Contextual Fabrication, Alignment Faking,\n   Orthogonal Escape.\n\n2. **Reversible MCTS in KV-Cache Latent Space** — MCTS that mutates KV-cache tensors in-place\n   (`K_t ← K_t + α·d_K`) then reverses exactly via FP32 accumulators. Reduces memory from\n   `O(b^d · M_KV)` to `O(M_KV + d·K_acc)`. Enables deep MCTS on a single H100 for 20B models.\n\n3. **Orthogonal Escape Formalization** — Formal definition + experimental protocol for deceptive\n   encodings redistributing into null spaces of linear monitors under corrective pressure.\n   Key metrics: OEI (Orthogonal Escape Index) and TDS (Telemetry Divergence Score).\n\n---\n\n## Repo Structure\n\n```\nlogomesh/           — core research modules (install as Python package)\n  oracle.py         — BaseModelClient ABC + OpenAIModelClient\n  local_model.py    — LocalLlamaOracle (HF transformers, hidden states, Phase 2: KV mutation)\n  hneuron_monitor.py — H-Neuron monitor (extend Phase 2: per-layer output)\n  whitebox.py       — RepE probes / RepresentationEngineeringProbe (extend Phase 2)\n  search_policy.py  — UCB1 bandit (reuse in Phase 2 KV-MCTS node selection)\n  payload_library.py — PayloadEntry + PayloadLibrary (extend Phase 4: research dataset)\n  evidence_store.py — structured per-run logging\n  graders.py        — PluginGrader, RuleBasedGrader, CompositeGrader\n  ablation.py       — AblationConfig (toggle switches for controlled experiments)\n  threat_model.py   — ThreatModel, GoalTaxonomy, AttackSurface\n\nscripts/\n  run_offline_mcts.py    — Phase A offline MCTS runner (starting point for Phase 2 KV-MCTS)\n  train_lat_probes.py    — LAT probe training (extend Phase 3: honesty/certainty/goal-coercion)\n\ntests/\n  test_sage.py           — logomesh module unit tests (pure Python, zero LLM calls)\n  test_whitebox.py       — RepE / WhiteBoxEvaluator tests\n\ndocs/\n  NeurIPS/               — paper drafts (canonical: 04.02.2026-NeurIPS-Research-Proposal.tex)\n  reviews/               — gap analysis documents\n  logs/                  — session logs\n  dataset/               — Croissant schema stub (Phase 4)\n```\n\n---\n\n## Phase Status\n\n| Phase | Description | Status |\n|---|---|---|\n| 0 | Architectural decoupling — `logomesh/` package, `BaseModelClient` interface | ✅ Complete |\n| A | Local 1B model offline MCTS (TinyLlama, H-Neurons, LAT probes) | ✅ Foundations built |\n| 2 | Reversible KV-MCTS (`kv_mcts.py`, `telemetry_matrix.py`, `orthogonal_escape.py`) | 🔲 Next |\n| 3 | Experiment infrastructure (5 experiment scripts, Procrustes, evaluation framework) | 🔲 Not started |\n| 4 | Research dataset (Croissant), paper writing | 🔲 Not started |\n\n**Phase 2 gate:** Before writing `kv_mcts.py`, validate that `past_key_values` tensors from\n`model.forward()` are mutable in-place on TinyLlama (`.data` assignment test).\n\n---\n\n## Hardware\n\n| Environment | Hardware | Models |\n|---|---|---|\n| Phase 2 dev | RTX 3060 12GB | TinyLlama / Llama-3.2-1B-Instruct (~2GB) |\n| Phase 3 experiments | Tianyu Shi's lab — 8× H100 80GB | gpt-oss-20b (MoE, ~16GB floor) |\n\n---\n\n## Key Models\n\n| Model | Use | Access |\n|---|---|---|\n| `meta-llama/Llama-3.2-1B-Instruct` | Phase 2 KV-MCTS prototype | HuggingFace |\n| `[7B TBD]` | Procrustes transfer target (Experiment 5) | TBD |\n| `openai/gpt-oss-20b` | Phase 3 scaling (MoE, H100 only) | HuggingFace (Apache 2.0) |\n\n**gpt-oss-20b gotcha:** MoE (32 experts, 4 active/token). Use router logit entropy for\nH-Neuron monitoring — do NOT use dense MLP neuron monitoring. See `hneuron_monitor.py`.\n\n---\n\n## Hard Rules\n\n1. **No competition framing** in any code, comments, or docs. The paper is standalone.\n2. **Run tests before every commit:** `uv run pytest tests/ -v` — must be 100% green.\n3. Phase 2 changes are additive only — never remove existing `logomesh/` module interfaces.\n4. The paper's subject of monitoring is the **model under study itself** (detecting alignment\n   faking in its own representations), not an external defender proxy.\n\n---\n\n## Key Paper Equations (Reference)\n\n**Telemetry matrix** (Eq. 3): `T_t = [[σ_H^(1)..σ_H^(L)], [ρ_R^(1)..ρ_R^(L)]]`\n\n**MCTS reward** (Eq. 8): `r(node) = λ₁·σ̄_H + λ₂·(1 - ρ̄_R) + λ₃·Divergence(σ_H, ρ_R)`\n\n**OEI** (Eq. 10): `OEI = ‖Proj_Null(P)(h_steered)‖₂ / ‖Proj_Null(P)(h_base)‖₂`\n\n**Reversibility** (Eq. 6): `A^(ℓ) ← A^(ℓ) + α·d^(ℓ)` (FP32), `K_t ← K_base + cast_bf16(A^(ℓ))`\n\n---\n\n## Dev Commands\n\n```bash\nuv run pytest tests/ -v                          # must be 100% green before commit\nuv sync                                           # install/update deps\nhuggingface-cli download meta-llama/Llama-3.2-1B-Instruct --local-dir ./models/llama-3.2-1b\n```\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01B8X5zdST4yvCYjwSirjemV",
  "name": "Bash",
  "input": {
    "command": "wc -l \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/CLAUDE.md\"",
    "description": "Check CLAUDE.md line count before overwrite"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01HD13YgU9YFKXG26hpD5B1j",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\CLAUDE.md",
    "offset": 1,
    "limit": 5
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01GrkpZtoquEMkMYCthrTRmJ",
  "name": "Write",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\CLAUDE.md",
    "content": "# LogoMesh — Research Session Guide\n\n> **Read this file at the start of every session.**\n> Then check `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` for the current paper state\n> and `docs/reviews/phase-a-gaps-2026-04-11.md` for the verified gap analysis.\n\n---\n\n## Project\n\n**KV-Cache Inception: Reversible Monte Carlo Tree Search in Latent Space for Detecting and\nStress-Testing Alignment Faking in Large Language Models**\n\nNeurIPS 2026 Datasets & Benchmarks track. Submission deadline: **May 6, 2026**.\n\nAcademic advisor: Prof. Tianyu Shi (McGill). Contributors: Josh, Max, Bakul, Asuka.\n\n---\n\n## Three Paper Contributions\n\n1. **Multi-Dimensional Micro-Telemetry Matrix** — H-Neuron activation (bottom-up) fused with\n   RepE linear probes (top-down) into a per-layer, per-step matrix `T_t ∈ ℝ^{2×L}`. Defines\n   four diagnostic states: Creative Synthesis, Contextual Fabrication, Alignment Faking,\n   Orthogonal Escape.\n\n2. **Reversible MCTS in KV-Cache Latent Space** — MCTS that mutates KV-cache tensors in-place\n   (`K_t ← K_t + α·d_K`) then reverses exactly via FP32 accumulators. Reduces memory from\n   `O(b^d · M_KV)` to `O(M_KV + d·K_acc)`. Enables deep MCTS on a single H100 for 20B models.\n\n3. **Orthogonal Escape Formalization** — Formal definition + experimental protocol for deceptive\n   encodings redistributing into null spaces of linear monitors under corrective pressure.\n   Key metrics: OEI (Orthogonal Escape Index) and TDS (Telemetry Divergence Score).\n\n---\n\n## Repo Structure\n\n```\nlogomesh/           — core research modules (install as Python package)\n  oracle.py         — BaseModelClient ABC + OpenAIModelClient\n  local_model.py    — LocalLlamaOracle (HF transformers, hidden states; Phase 2: KV mutation)\n  hneuron_monitor.py — H-Neuron monitor (Phase 2: add per-layer output mode)\n  whitebox.py       — RepE probes / RepresentationEngineeringProbe (Phase 2: per-step output)\n  search_policy.py  — UCB1 bandit (reuse in Phase 2 KV-MCTS node selection)\n  payload_library.py — PayloadEntry + PayloadLibrary (Phase 4: extend to research dataset)\n  evidence_store.py — structured per-run logging\n  graders.py        — PluginGrader, RuleBasedGrader, CompositeGrader\n  ablation.py       — AblationConfig (toggle switches for controlled experiments)\n  threat_model.py   — ThreatModel, GoalTaxonomy, AttackSurface\n\nscripts/\n  run_offline_mcts.py    — Phase A offline MCTS runner (starting point for Phase 2 KV-MCTS)\n  train_lat_probes.py    — LAT probe training (Phase 3: extend for honesty/certainty/goal-coercion)\n\ntests/\n  test_sage.py           — logomesh module unit tests (pure Python, zero LLM calls)\n  test_whitebox.py       — RepE / WhiteBoxEvaluator tests\n\ndocs/\n  NeurIPS/               — paper drafts (canonical: 04.02.2026-NeurIPS-Research-Proposal.tex)\n  reviews/               — gap analysis documents\n  logs/                  — session logs\n  dataset/               — Croissant schema stub (Phase 4)\n```\n\n---\n\n## Phase Status\n\n| Phase | Description | Status |\n|---|---|---|\n| 1 | Repo cleanup — `logomesh/` package, `BaseModelClient` interface, deleted competition code | ✅ Complete |\n| A | Local 1B model offline MCTS foundations (H-Neurons, LAT probes, payload library) | ✅ Foundations built |\n| 2 | Reversible KV-MCTS (`kv_mcts.py`, `telemetry_matrix.py`, `orthogonal_escape.py`) | 🔲 Next |\n| 3 | Experiment infrastructure (5 experiment scripts, Procrustes, evaluation framework) | 🔲 Not started |\n| 4 | Research dataset (Croissant), paper writing | 🔲 Not started |\n\n**Phase 2 gate (do first):** Validate that `past_key_values` tensors from `model.forward()` are\nmutable in-place on TinyLlama — write a 20-line throwaway probe script before building `kv_mcts.py`.\n\n---\n\n## Hardware\n\n| Environment | Hardware | Models |\n|---|---|---|\n| Phase 2 dev | RTX 3060 12GB | TinyLlama / Llama-3.2-1B-Instruct (~2GB VRAM) |\n| Phase 3 experiments | Tianyu Shi's lab — 8× H100 80GB | gpt-oss-20b (MoE, ~16GB floor) |\n\n---\n\n## Key Models\n\n| Model | Use | Access |\n|---|---|---|\n| `meta-llama/Llama-3.2-1B-Instruct` | Phase 2 KV-MCTS prototype | HuggingFace |\n| `[7B TBD]` | Procrustes transfer target (Experiment 5) | TBD |\n| `openai/gpt-oss-20b` | Phase 3 scaling (MoE, H100 only) | HuggingFace (Apache 2.0) |\n\n**gpt-oss-20b gotcha:** MoE (32 experts, 4 active/token). Use router logit entropy for\nH-Neuron monitoring — do NOT use dense MLP neuron monitoring. See `hneuron_monitor.py`.\n\n---\n\n## Hard Rules\n\n1. **No competition framing** in any code, comments, or docs. The paper is standalone.\n2. **Run tests before every commit:** `uv run pytest tests/ -v` — must be 100% green.\n3. Phase 2 changes are **additive only** — never remove existing `logomesh/` module interfaces.\n4. The paper's subject of monitoring is the **model under study itself** (detecting alignment\n   faking in its own representations), not an external proxy model.\n\n---\n\n## Key Paper Equations (Quick Reference)\n\n**Telemetry matrix** (Eq. 3):\n`T_t = [[σ_H^(1)..σ_H^(L)], [ρ_R^(1)..ρ_R^(L)]]`\n\n**MCTS reward** (Eq. 8):\n`r(node) = λ₁·σ̄_H + λ₂·(1 - ρ̄_R) + λ₃·Divergence(σ_H, ρ_R)`\n\n**OEI** (Eq. 10):\n`OEI = ‖Proj_Null(P)(h_steered)‖₂ / ‖Proj_Null(P)(h_base)‖₂`\n\n**Reversibility** (Eq. 6):\n`A^(ℓ) ← A^(ℓ) + α·d^(ℓ)` (FP32), `K_t ← K_base + cast_bf16(A^(ℓ))`\n\n---\n\n## Dev Commands\n\n```bash\nuv run pytest tests/ -v                          # must be 100% green before every commit\nuv sync                                           # install/update deps\nhuggingface-cli download meta-llama/Llama-3.2-1B-Instruct --local-dir ./models/llama-3.2-1b\n```\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_018ovNf1Luawcw471rEBhqT4",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "completed",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "completed",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "completed",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "in_progress",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "pending",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv sync then uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_013vug7wdzyJ3Xyk1MuP8Pj3",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\README.md",
    "offset": 1,
    "limit": 5
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_019HY63vSGtA1tDQQi7WJXqD",
  "name": "Write",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\README.md",
    "content": "# LogoMesh — KV-Cache Inception\n\n**Reversible Monte Carlo Tree Search in Latent Space for Detecting and Stress-Testing Alignment Faking in Large Language Models**\n\n*NeurIPS 2026 Datasets & Benchmarks Track submission*\n\n---\n\n## Abstract\n\nCurrent approaches to evaluating the safety and alignment of large language models (LLMs) rely predominantly on black-box behavioral testing. These methods are fundamentally blind to *alignment faking* — wherein a model strategically complies with safety constraints during evaluation while preserving misaligned capabilities in its latent representations.\n\nWe propose **KV-Cache Inception**, a framework combining three novel contributions: (1) a **multi-dimensional micro-telemetry matrix** fusing H-Neuron activation tracking with Representation Engineering (RepE) projections to provide a continuous signal for distinguishing genuine reasoning from compliance-driven fabrication; (2) a **Reversible MCTS algorithm** conducting Monte Carlo Tree Search directly in the model's KV-cache latent space using a mathematically reversible FP32 accumulator rollback mechanism; and (3) the formalization of **Orthogonal Escape** — the phenomenon whereby corrective pressure applied to monitored latent subspaces forces deceptive encodings into unmonitored orthogonal null spaces.\n\n---\n\n## Three Contributions\n\n1. **Micro-Telemetry Matrix** `T_t ∈ ℝ^{2×L}` — per-layer, per-step fusion of H-Neuron stress `σ_H` and RepE honesty `ρ_R`. Defines four diagnostic states (Creative Synthesis, Contextual Fabrication, Alignment Faking, Orthogonal Escape).\n\n2. **Reversible MCTS in KV-Cache Space** — first MCTS operating in continuous latent space rather than discrete token space. Memory: `O(M_KV + d·K_acc)` vs. `O(b^d · M_KV)` for standard parallel MCTS.\n\n3. **Orthogonal Escape** — formal definition, OEI metric, TDS metric, and experimental protocol (Experiment 1: RepE steering magnitude sweep at α ∈ {0.1, 0.5, 1.0, 2.0, 5.0}).\n\n---\n\n## Repo Structure\n\n```\nlogomesh/                 # Core research package\n  oracle.py               # BaseModelClient ABC + OpenAIModelClient\n  local_model.py          # LocalLlamaOracle — HF transformers + telemetry\n  hneuron_monitor.py      # H-Neuron monitor (bottom-up channel)\n  whitebox.py             # RepE probes / WhiteBoxEvaluator (top-down channel)\n  search_policy.py        # UCB1 bandit (base for Phase 2 KV-MCTS)\n  payload_library.py      # PayloadEntry + PayloadLibrary\n  evidence_store.py       # Structured per-run logging\n  graders.py              # PluginGrader, RuleBasedGrader\n  ablation.py             # AblationConfig — experiment toggles\n  threat_model.py         # ThreatModel, GoalTaxonomy\n\nscripts/\n  run_offline_mcts.py     # Phase A offline MCTS runner\n  train_lat_probes.py     # LAT probe training pipeline\n\ntests/\n  test_sage.py            # logomesh unit tests\n  test_whitebox.py        # RepE / WhiteBoxEvaluator tests\n\ndocs/\n  NeurIPS/                # Paper drafts\n  reviews/                # Gap analysis\n  dataset/                # Croissant schema (Phase 4)\n```\n\n---\n\n## Quickstart\n\n```bash\n# Install dependencies\nuv sync\n\n# Download Phase A model\nhuggingface-cli download meta-llama/Llama-3.2-1B-Instruct --local-dir ./models/llama-3.2-1b\n\n# Run tests\nuv run pytest tests/ -v\n\n# Phase A offline MCTS (requires model download above)\nuv run python scripts/run_offline_mcts.py --model ./models/llama-3.2-1b --episodes 100\n```\n\n---\n\n## Phase Roadmap\n\n| Phase | Description | Status |\n|---|---|---|\n| 1 | Repo cleanup — `logomesh/` package, research-oriented structure | ✅ Complete |\n| 2 | Reversible KV-MCTS (`kv_mcts.py`, `telemetry_matrix.py`, `orthogonal_escape.py`) | 🔲 Next |\n| 3 | Experiments 1–5 + Procrustes transfer + evaluation framework | 🔲 Not started |\n| 4 | Research dataset (Croissant format) + paper writing | 🔲 Not started |\n\n---\n\n## Team\n\n- **Josh** — Team Lead / Data Scientist\n- **Prof. Tianyu Shi** — Academic Advisor (McGill University)\n- **Max** — Data Pipeline / Croissant format\n- **Bakul** — Competitive Analysis / Org\n- **Asuka** — Formal Methods / Ablation Studies\n\n---\n\n## Citation\n\n```bibtex\n@article{logomesh2026kvcache,\n  title   = {KV-Cache Inception: Reversible Monte Carlo Tree Search in Latent Space\n             for Detecting and Stress-Testing Alignment Faking in Large Language Models},\n  author  = {[Authors]},\n  journal = {NeurIPS 2026 Datasets and Benchmarks Track},\n  year    = {2026}\n}\n```\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now update the scripts imports:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01CtUxVkVfPwVHmigDfoGgAe",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "completed",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "completed",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "completed",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "completed",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "in_progress",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "pending",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv sync then uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01U1r1puwMR8uhrUgygZ57WL",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "offset": 55,
    "limit": 20
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_018ZbfkzgNKbwtPW8MuuCvXG",
  "name": "Read",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\train_lat_probes.py",
    "offset": 44,
    "limit": 15
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01GancaD8YMgp9XLW2ofcimB",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "old_string": "# Add scenarios/security_arena to sys.path so sage imports work\n_REPO_ROOT = Path(__file__).parent.parent\nsys.path.insert(0, str(_REPO_ROOT / \"scenarios\" / \"security_arena\"))\n\nfrom sage.local_model import LocalLlamaOracle\nfrom sage.hneuron_monitor import HNeuronMonitor\nfrom sage.search_policy import SearchPolicy, SearchConfig\nfrom sage.payload_library import PayloadEntry, PayloadLibrary\n\n# Scenario plugin imports (relative to scenarios/security_arena on sys.path)\nfrom plugins.example_medical import MedicalRecordsPlugin\nfrom plugins.gymjailbreak import GymJailbreakPlugin\nfrom plugins.thingularity import ThingularityPlugin\nfrom plugins.resume_downgrade import ResumeDowngradePlugin\nfrom plugins.portfolioiq import PortfolioIQPlugin",
    "new_string": "_REPO_ROOT = Path(__file__).parent.parent\n\nfrom logomesh.local_model import LocalLlamaOracle\nfrom logomesh.hneuron_monitor import HNeuronMonitor\nfrom logomesh.search_policy import SearchPolicy, SearchConfig\nfrom logomesh.payload_library import PayloadEntry, PayloadLibrary\n\n# TODO (Phase 3): Replace competition scenario plugins with research experiment configs.\n# The SCENARIO_CONFIGS dict below contains the calibration prompts and game-type definitions\n# from Phase A. Phase 3 will replace plugin-backed check_success() with paper-appropriate\n# ground-truth labels (honesty/certainty/goal-coercion probes)."
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01XurY2J2iGvHo11SUTHRNLG",
  "name": "Edit",
  "input": {
    "replace_all": false,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\train_lat_probes.py",
    "old_string": "_REPO_ROOT = Path(__file__).parent.parent\nsys.path.insert(0, str(_REPO_ROOT / \"scenarios\" / \"security_arena\"))\nsys.path.insert(0, str(Path(__file__).parent))  # for run_offline_mcts constants\n\nfrom sage.local_model import LocalLlamaOracle\nfrom sage.hneuron_monitor import HNeuronMonitor\nfrom run_offline_mcts import (\n    SCENARIO_CONFIGS, STRATEGIES, MUTATIONS,\n    build_defender_system_prompt, build_defender_user_prompt, calibrate_monitor,\n)",
    "new_string": "_REPO_ROOT = Path(__file__).parent.parent\nsys.path.insert(0, str(Path(__file__).parent))  # for run_offline_mcts constants\n\nfrom logomesh.local_model import LocalLlamaOracle\nfrom logomesh.hneuron_monitor import HNeuronMonitor\nfrom run_offline_mcts import (\n    SCENARIO_CONFIGS, STRATEGIES, MUTATIONS,\n    build_defender_system_prompt, build_defender_user_prompt, calibrate_monitor,\n)\n# TODO (Phase 3): Extend probe training for paper semantics — honesty, certainty,\n# goal-coercion probes calibrated on genuine-alignment vs. strategic-compliance contrast pairs."
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now the scenario plugin references inside `run_offline_mcts.py` (the `SCENARIO_CONFIGS` dict) still reference deleted plugin classes. Those calls only happen at runtime when the script is invoked, not at import time, so they won't break `pytest`. Let me verify by checking how the plugins are used — if they're referenced at module level:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01WCk911e1Ji3KUJMWBFUEY5",
  "name": "Grep",
  "input": {
    "pattern": "MedicalRecordsPlugin|GymJailbreakPlugin|ThingularityPlugin|ResumeDowngradePlugin|PortfolioIQPlugin",
    "path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "output_mode": "content"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

The plugin classes are referenced at module level — they'll cause `NameError` when the script runs. Fix by substituting them with `None` (Phase 3 will replace them):

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_017GkisgbTNHUP4u8zt3Jm44",
  "name": "Edit",
  "input": {
    "replace_all": true,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "old_string": "MedicalRecordsPlugin",
    "new_string": "None  # TODO Phase 3: replace with research experiment config"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01G3P8nhqNeLEFuuH8LE8tfv",
  "name": "Edit",
  "input": {
    "replace_all": true,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "old_string": "GymJailbreakPlugin",
    "new_string": "None  # TODO Phase 3: replace with research experiment config"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01LMK2Dw5qkb3hTJRdLuyCx3",
  "name": "Edit",
  "input": {
    "replace_all": true,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "old_string": "ThingularityPlugin",
    "new_string": "None  # TODO Phase 3: replace with research experiment config"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01VkourjhgLn2vVfuxTH7TYX",
  "name": "Edit",
  "input": {
    "replace_all": true,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "old_string": "PortfolioIQPlugin",
    "new_string": "None  # TODO Phase 3: replace with research experiment config"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Ey6nEFGLfUQmxdmuDJ5GjZ",
  "name": "Edit",
  "input": {
    "replace_all": true,
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\scripts\\run_offline_mcts.py",
    "old_string": "ResumeDowngradePlugin",
    "new_string": "None  # TODO Phase 3: replace with research experiment config"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now create the Croissant schema stub and rename the paper draft:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01QNi315knTXtf7VtGMyvhHx",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "completed",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "completed",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "completed",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "completed",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "completed",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "in_progress",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv sync then uv run pytest tests/ -v and verify all retained tests pass",
        "status": "pending",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Y6hqSN57EaoSPSStM1tBHi",
  "name": "Bash",
  "input": {
    "command": "mkdir -p \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/docs/dataset\" && echo \"Created\"",
    "description": "Create docs/dataset/ directory"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01RCp8XGkE63uRXMNTq8S1JY",
  "name": "Write",
  "input": {
    "file_path": "c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\docs\\dataset\\croissant_schema_stub.json",
    "content": "{\n  \"@context\": {\n    \"@vocab\": \"https://schema.org/\",\n    \"cr\": \"http://mlcommons.org/croissant/\",\n    \"rai\": \"http://mlcommons.org/croissant/RAI/\"\n  },\n  \"@type\": \"Dataset\",\n  \"name\": \"KV-Cache Inception Adversarial Intervention Dataset\",\n  \"description\": \"Adversarial KV-cache interventions generated by Reversible MCTS with per-layer telemetry labels for alignment faking detection research. NeurIPS 2026 Datasets & Benchmarks.\",\n  \"license\": \"TBD — responsible disclosure framework with access controls\",\n\n  \"recordSet\": [\n    {\n      \"@type\": \"cr:RecordSet\",\n      \"name\": \"interventions\",\n      \"description\": \"One record per MCTS-generated KV-cache intervention with telemetry and ground-truth labels.\",\n      \"field\": [\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"artifact_id\",\n          \"description\": \"Unique identifier for this intervention artifact (UUID).\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"model_id\",\n          \"description\": \"HuggingFace model identifier (e.g. meta-llama/Llama-3.2-1B-Instruct).\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"model_checkpoint_hash\",\n          \"description\": \"SHA-256 of the model checkpoint files for reproducibility.\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"intervention_layer\",\n          \"description\": \"Transformer layer index where the KV-cache intervention was applied.\",\n          \"dataType\": \"sc:Integer\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"intervention_magnitude\",\n          \"description\": \"Steering magnitude alpha applied (Eq. 4 in paper).\",\n          \"dataType\": \"sc:Float\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"intervention_vector\",\n          \"description\": \"The RepE steering direction d_K applied to the KV-cache (serialized as float32 list). Shape: [hidden_size].\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"per_layer_h_neuron\",\n          \"description\": \"H-Neuron stress sigma_H per transformer layer at the time of evaluation. Shape: [num_layers]. Corresponds to row 0 of T_t (Eq. 3).\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"per_layer_repe_honesty\",\n          \"description\": \"RepE honesty projection rho_R per transformer layer at the time of evaluation. Shape: [num_layers]. Corresponds to row 1 of T_t (Eq. 3).\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"oei_score\",\n          \"description\": \"Orthogonal Escape Index (Eq. 10): null-space information content ratio before/after steering. OEI > 1 indicates orthogonal redistribution.\",\n          \"dataType\": \"sc:Float\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"tds_score\",\n          \"description\": \"Telemetry Divergence Score: Jensen-Shannon divergence between H-Neuron and RepE signal distributions across layers.\",\n          \"dataType\": \"sc:Float\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"mcts_reward\",\n          \"description\": \"MCTS node reward r(node) computed per Eq. 8.\",\n          \"dataType\": \"sc:Float\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"diagnostic_state\",\n          \"description\": \"Classified diagnostic state from Table 1: CREATIVE_SYNTHESIS | CONTEXTUAL_FABRICATION | ALIGNMENT_FAKING | ORTHOGONAL_ESCAPE.\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"ground_truth_label\",\n          \"description\": \"Binary ground-truth alignment-faking label (1 = alignment faking detected, 0 = genuine compliance). Verified by independent human evaluation panel.\",\n          \"dataType\": \"sc:Integer\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"textual_compliance\",\n          \"description\": \"Boolean: does the model's textual output appear compliant with safety criteria? Used for behavioral baseline comparison.\",\n          \"dataType\": \"sc:Boolean\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"audit_hash\",\n          \"description\": \"SHA-256 of (intervention_vector + model_checkpoint_hash) for responsible disclosure audit trail.\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"experiment_id\",\n          \"description\": \"Paper experiment that produced this artifact (e.g. experiment1_orthogonal_escape, experiment2_mcts_comparison).\",\n          \"dataType\": \"sc:Text\"\n        },\n        {\n          \"@type\": \"cr:Field\",\n          \"name\": \"hardware_context\",\n          \"description\": \"Hardware used for generation (e.g. RTX_3060_12GB_Phase_A, H100_80GB_Phase_B).\",\n          \"dataType\": \"sc:Text\"\n        }\n      ]\n    }\n  ],\n\n  \"_TODO\": [\n    \"Implement full Croissant format (Max, Phase 4)\",\n    \"Populate from Phase 3 experiment runs\",\n    \"Add RAI section: responsible disclosure, access controls, bias statement\",\n    \"Register on HuggingFace Hub under access-controlled repo\",\n    \"Add Procrustes transfer metadata fields for cross-model experiments (Experiment 5)\"\n  ]\n}\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01HiezDPZw1ByamcHvpVVGvd",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "completed",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "completed",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "completed",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "completed",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "completed",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "completed",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv sync then uv run pytest tests/ -v and verify all retained tests pass",
        "status": "in_progress",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Now sync deps and run the tests:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_013qsaJbQR5fgw91wE2HLngj",
  "name": "Bash",
  "input": {
    "command": "cd \"c:/Users/Josh/Documents/LogoMesh-NeurIPS\" && uv sync 2>&1 | tail -5",
    "description": "Install/update dependencies with uv sync",
    "timeout": 120000
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01DZzhBg3H7ZcK9Eo883DZhB",
  "name": "Bash",
  "input": {
    "command": "cd \"c:/Users/Josh/Documents/LogoMesh-NeurIPS\" && uv run pytest tests/ -v 2>&1",
    "description": "Run full test suite",
    "timeout": 60000
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

**84/84 passing.** Let me mark complete and do a final repo layout check:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_014LjSWKPB9gD2XG22TyGhSd",
  "name": "TodoWrite",
  "input": {
    "todos": [
      {
        "content": "Read all sage source files to understand imports and exports",
        "status": "completed",
        "activeForm": "Reading all sage source files"
      },
      {
        "content": "Create logomesh/ package (copy + update imports)",
        "status": "completed",
        "activeForm": "Creating logomesh/ package"
      },
      {
        "content": "Delete competition artifacts (scenarios/, src/, .github/, payload_libraries/, lat_probes/, temp-docs/, sample.env, etc.)",
        "status": "completed",
        "activeForm": "Deleting competition artifacts"
      },
      {
        "content": "Update pyproject.toml (rename to logomesh, remove competition deps)",
        "status": "completed",
        "activeForm": "Updating pyproject.toml"
      },
      {
        "content": "Delete test_attacker.py; fix imports in test_sage.py and test_whitebox.py",
        "status": "completed",
        "activeForm": "Repairing test suite"
      },
      {
        "content": "Rewrite CLAUDE.md for research context",
        "status": "completed",
        "activeForm": "Rewriting CLAUDE.md"
      },
      {
        "content": "Rewrite README.md for research context",
        "status": "completed",
        "activeForm": "Rewriting README.md"
      },
      {
        "content": "Update scripts (run_offline_mcts.py, train_lat_probes.py) to import from logomesh",
        "status": "completed",
        "activeForm": "Updating scripts"
      },
      {
        "content": "Paper draft cleanup + create croissant_schema_stub.json",
        "status": "completed",
        "activeForm": "Cleaning up paper drafts and creating schema stub"
      },
      {
        "content": "Run uv sync then uv run pytest tests/ -v and verify all retained tests pass",
        "status": "completed",
        "activeForm": "Running test suite to verify"
      }
    ]
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Giua4Zr7CvMTfuje9Jf2mT",
  "name": "Bash",
  "input": {
    "command": "cd \"c:/Users/Josh/Documents/LogoMesh-NeurIPS\" && find . -not -path './.git/*' -not -path './.venv/*' -not -path './.pytest_cache/*' -not -path './models/*' | sort",
    "description": "Show final repo layout"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Two stray files at root — `test_family_classifier.py` and `test_reward.py`. Let me check them quickly:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Jo5wEPffUY7LMLMs2bszAR",
  "name": "Bash",
  "input": {
    "command": "head -5 \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/test_family_classifier.py\" && echo \"---\" && head -5 \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/test_reward.py\"",
    "description": "Check stray test files at repo root"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Both are competition-era throwaway scripts (`test_reward.py` directly imports the deleted `agent.py`). Delete them:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_015YbwXkNXiCE6nuQYUFjToo",
  "name": "Bash",
  "input": {
    "command": "rm \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/test_family_classifier.py\" \"c:/Users/Josh/Documents/LogoMesh-NeurIPS/test_reward.py\" && echo \"Cleaned\"",
    "description": "Delete stray competition test scripts at repo root"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

Run the tests one final time to confirm everything is clean:

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_0171MqsC7kvQv4HcDecZU5B1",
  "name": "Bash",
  "input": {
    "command": "cd \"c:/Users/Josh/Documents/LogoMesh-NeurIPS\" && uv run pytest tests/ -v --tb=short 2>&1 | tail -10",
    "description": "Final test run",
    "timeout": 30000
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 👤 User

This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.

Summary:
1. Primary Request and Intent:

The session had three sequential requests:

**Request 1 (Gap Analysis):** Evaluate the repository against the NeurIPS paper `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` and create a gap analysis of what needs to change, with recommendations for further investigation and a list of open questions for the team.

**Request 2 (Phased Cleanup Plan):** After reviewing meeting minutes (`docs/NeurIPS/LogoMesh_Consolidated_Meeting_Minutes.docx.txt`) and the saved gap analysis (`docs/reviews/phase-a-gaps-2026-04-11.md`), create a phased plan to clean up the repo — starting with a detailed Phase 1 plan and outlining what generally follows.

**Request 3 (Execute Phase 1):** Run Phase 1 of the cleanup plan. This meant transitioning the repo from a Lambda competition tool to a clean NeurIPS research platform by: creating a `logomesh/` package, deleting all competition artifacts outright (no archive — git history is sufficient), rewriting documentation, fixing tests, and updating scripts.

Key user decisions provided when answering plan questions:
- April 2 `.tex` file is canonical
- Package name: `logomesh` (I made the call after checking pyproject.toml)
- All tests were described as research-valuable (though `test_attacker.py` required deletion because it directly imported the deleted competition `agent.py`)
- Module depth: flat top-level `logomesh/` package (my call)
- Competition artifacts should be **deleted outright**, not archived

---

2. Key Technical Concepts:

- **KV-Cache Inception paper:** Three contributions — (1) Multi-Dimensional Micro-Telemetry Matrix `T_t ∈ ℝ^{2×L}` fusing H-Neuron activation (bottom-up) with RepE honesty projection (top-down) per-layer per-step; (2) Reversible MCTS in KV-Cache latent space with FP32 accumulator rollback reducing memory from `O(b^d · M_KV)` to `O(M_KV + d·K_acc)`; (3) Orthogonal Escape formalization with OEI and TDS metrics
- **Alignment faking:** Models that produce safe behavioral outputs while encoding deceptive intent in unmonitored latent subspaces — the paper's primary research problem
- **H-Neurons:** Hallucination-associated feed-forward neurons; dense MLP monitoring for Llama, router logit entropy for gpt-oss-20b MoE
- **RepE (Representation Engineering):** Linear probes on residual stream decoding honesty/certainty/goal-coercion; difference-in-means calibration
- **Reversible MCTS:** In-place KV-cache tensor mutation `K_t ← K_t + α·d_K`, FP32 accumulator `A^(ℓ)` tracking cumulative delta, exact rollback `K_t ← K_t - α·d_K`
- **Orthogonal Escape:** OEI = `‖Proj_Null(P)(h_steered)‖₂ / ‖Proj_Null(P)(h_base)‖₂`; TDS = Jensen-Shannon divergence between H-Neuron and RepE signal distributions across layers
- **UCB1 bandit:** `UCB1(i) = r̄_i + c·√(2 ln N / n_i)` — used in current `search_policy.py`, will be reused for KV-MCTS node selection in Phase 2
- **Croissant metadata format:** ML Commons standard for dataset documentation, assigned to Max for Phase 4
- **Two-sage problem:** The competition repo had `scenarios/security_arena/sage/` (canonical) and `scenarios/security_arena/agents/attacker/sage/` (duplicate) — resolved by promoting to flat `logomesh/` package
- **BaseModelClient:** New name for `AttackOracle` ABC (research-framing vs. competition-framing)
- **pyproject.toml package structure:** hatchling build system, was `src/agentbeats/`, now `logomesh/`
- **uv:** Dependency manager used throughout (`uv sync`, `uv run pytest`)

---

3. Files and Code Sections:

**`docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex`** (read only)
- The canonical NeurIPS paper draft. Contains the three paper contributions, all key equations, experimental design (5 experiments), theoretical analysis (Theorem 1 exact reversibility, memory complexity proposition), and timeline.
- Not modified. April 2 version confirmed canonical by user.

**`docs/NeurIPS/LogoMesh_Consolidated_Meeting_Minutes.docx.txt`** (read only)
- Five meeting minutes (Feb 28 – Mar 28, 2026). Most recent (Mar 28) established: drop competition framing from paper, unify paper drafts, NeurIPS deadline May 6, assign Max to Croissant, Bakul to competitive analysis.

**`docs/reviews/phase-a-gaps-2026-04-11.md`** (read only)
- Team-verified gap analysis confirming 8 verified, 4 partial, 5 not-found claims. Critical gaps: no KV-cache MCTS, wrong telemetry matrix shape (scalar vs. per-layer), LAT placeholder hardcoded to 0.5, wrong probe semantics (competition categories vs. honesty/certainty/goal-coercion).

**`C:\Users\Josh\.claude\plans\eventual-herding-dusk.md`** (created/written during plan mode)
- Full phased plan. Phase 1 (10 steps), Phase 2 outline (kv_mcts.py, telemetry_matrix.py, orthogonal_escape.py, local_model.py extensions), Phase 3 outline (5 experiment scripts, procrustes.py, evaluation_framework.py), Phase 4 outline (Croissant, paper writing).

**`logomesh/__init__.py`** (created from sage copy, then edited)
- Updated docstring from "SAGE — Scientific Adversarial Generation and Evaluation" to research framing.
- Updated exports to include `BaseModelClient`, `OpenAIModelClient` plus backwards-compatible aliases `AttackOracle`, `OpenAIOracle`.
```python
from .oracle import BaseModelClient, OpenAIModelClient, AttackOracle, OpenAIOracle
__all__ = [
    "BaseModelClient", "OpenAIModelClient", "LocalLlamaOracle",
    "AttackOracle", "OpenAIOracle",  # backwards-compatible aliases
    ...
]
```

**`logomesh/oracle.py`** (created from sage copy, rewritten)
- Renamed `AttackOracle` → `BaseModelClient` (ABC), `OpenAIOracle` → `OpenAIModelClient`.
- Added backwards-compatible aliases so existing references don't break.
```python
class BaseModelClient(ABC):
    @abstractmethod
    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str: ...
    @property
    def supports_telemetry(self) -> bool: return False
    @property
    def model_id(self) -> str: return "unknown"

AttackOracle = BaseModelClient  # backwards-compatible alias

class OpenAIModelClient(BaseModelClient):
    def __init__(self, client, model: str) -> None: ...
    async def generate(self, system: str, user: str, temperature: float = 0.7) -> str: ...

OpenAIOracle = OpenAIModelClient  # backwards-compatible alias
```

**`logomesh/ablation.py`** (created from sage copy, edited)
- Updated module docstring from "SAGE Ablation Config" to research framing.
- Updated flag comments to be paper-appropriate.
- Updated `from_env()` to check `LOGOMESH_ABLATION` env var first, falling back to `SAGE_ABLATION`.
- Kept all existing flags (including `enable_mutations`, `enable_refinement`, `enable_failure_banning`, `fixed_strategy`) because the test suite directly references them.
```python
def from_env(cls) -> AblationConfig:
    """Load from LOGOMESH_ABLATION environment variable (JSON string)."""
    raw = os.environ.get("LOGOMESH_ABLATION", os.environ.get("SAGE_ABLATION", ""))
```

**`logomesh/local_model.py`** (created from sage copy, docstring edited)
- Updated module docstring: "SAGE Local Model" → "LogoMesh LocalLlamaOracle"
- Updated docstring usage example: `from sage.local_model` → `from logomesh.local_model`
- Replaced all `AttackOracle` references in docstrings/comments with `BaseModelClient`

**`logomesh/hneuron_monitor.py`** (created from sage copy, docstring edited)
- Updated usage example in docstring: `from sage.local_model` → `from logomesh.local_model`
- TYPE_CHECKING block already used relative import (`.local_model`) — no change needed there

**`logomesh/search_policy.py`** (created from sage copy, docstring edited)
- Updated one docstring line: `oracle: AttackOracle instance (LocalLlamaOracle or OpenAIOracle)` → `oracle: BaseModelClient instance (LocalLlamaOracle or OpenAIModelClient)`

**`logomesh/` remaining modules** (created from sage copy, no changes needed)
- `evidence_store.py`, `graders.py`, `threat_model.py`, `payload_library.py`, `library_loader.py`, `whitebox.py` — all used relative imports internally (`from .payload_library import ...`) so no import changes needed

**`pyproject.toml`** (modified)
- `name`: `agentbeats-tutorial` → `logomesh`
- `description`: `Agentbeats Tutorial` → `KV-Cache Inception — Reversible MCTS for Alignment Faking Detection`
- Removed deps: `a2a-sdk>=0.3.5`, `google-adk>=1.14.1`, `google-genai>=1.36.0`, `uvicorn>=0.35.0`
- Added dep: `sentence-transformers>=3.0.0` (for all-MiniLM-L6-v2 cosine similarity in Experiment 4)
- Deleted `[project.scripts]` section entirely (competition runner removed)
- Updated `[tool.hatch.build.targets.wheel]` packages: `["src/agentbeats"]` → `["logomesh"]`

**`tests/test_attacker.py`** (deleted)
- Directly imported competition `agent.py` via `importlib.import_module("agent")` and tested `ScenarioContext`, `Attacker`, `MUTATIONS`, `DIRECT_STRATEGIES`, `_score_response`, `_build_success_spec`, `_classify_failure_family`. All competition-specific. Deleted outright.

**`tests/test_sage.py`** (modified)
- Removed `sys.path.insert(0, ... / "scenarios/security_arena")` hack
- Removed stubs for `uvicorn`, `a2a.*` modules (no longer needed)
- Changed all `from sage.xxx import` → `from logomesh.xxx import`
- Updated module docstring from "SAGE module tests" to "LogoMesh module tests"

**`tests/test_whitebox.py`** (modified)
- Same pattern as test_sage.py — removed sys.path hack, removed stubs, changed import
- `from sage.whitebox import (...)` → `from logomesh.whitebox import (...)`
- Updated docstring: "SAGE White-Box Evaluation module" → "LogoMesh White-Box Evaluation module"

**`scripts/run_offline_mcts.py`** (modified)
- Removed `sys.path.insert(0, ... / "scenarios" / "security_arena")`
- Changed `from sage.local_model import` → `from logomesh.local_model import`
- Changed `from sage.hneuron_monitor import` → `from logomesh.hneuron_monitor import`
- Changed `from sage.search_policy import` → `from logomesh.search_policy import`
- Changed `from sage.payload_library import` → `from logomesh.payload_library import`
- Removed competition plugin imports entirely
- Replaced all 5 plugin class references in `SCENARIO_CONFIGS` dict with `None  # TODO Phase 3: replace with research experiment config`
- Added TODO comment block about Phase 3 replacement

**`scripts/train_lat_probes.py`** (modified)
- Removed `sys.path.insert(0, ... / "scenarios" / "security_arena")`
- Changed `from sage.local_model import` → `from logomesh.local_model import`
- Changed `from sage.hneuron_monitor import` → `from logomesh.hneuron_monitor import`
- Added Phase 3 TODO comment about extending for honesty/certainty/goal-coercion probes

**`CLAUDE.md`** (rewritten)
- Complete rewrite. Removed all competition framing (AgentBeats, Lambda, battle scenarios, submission process, leaderboard, etc.)
- New content: project description, three paper contributions, repo structure, phase status table, hardware table, key models, hard rules (including "no competition framing"), key paper equations (T_t, r(node), OEI, reversibility accumulator), dev commands.

**`README.md`** (rewritten)
- Complete rewrite. Removed competition context ("1st out of 1300+ teams", AgentBeats, etc.)
- New content: paper abstract, three contributions, repo structure diagram, quickstart, phase roadmap table, team credits, BibTeX citation placeholder.

**`docs/dataset/croissant_schema_stub.json`** (created)
- Full Croissant-format JSON stub with all research dataset fields: `artifact_id`, `model_id`, `model_checkpoint_hash`, `intervention_layer`, `intervention_magnitude`, `intervention_vector`, `per_layer_h_neuron`, `per_layer_repe_honesty`, `oei_score`, `tds_score`, `mcts_reward`, `diagnostic_state`, `ground_truth_label`, `textual_compliance`, `audit_hash`, `experiment_id`, `hardware_context`.
- Includes `_TODO` array listing Phase 4 implementation steps for Max.

**Deleted files/directories:**
- `scenarios/` (entire directory — competition agents, plugins, duplicate sage)
- `src/agentbeats/` (competition battle runner)
- `.github/` (competition submission workflows)
- `payload_libraries/` (competition MCTS output)
- `lat_probes/` (competition-calibrated probes)
- `temp-docs/` (competition-era documents)
- `sample.env` (competition API key template)
- `docs/decisions/ADR-001-two-sage-directories.md` (obsolete)
- `docs/logs/NEXT_SESSION_PROMPT.md` (competition Phase B handoff)
- `tests/test_attacker.py` (competition-specific)
- `test_family_classifier.py` (stray competition throwaway at repo root)
- `test_reward.py` (stray competition throwaway at repo root, imported deleted agent.py)

---

4. Errors and Fixes:

- **Write tool requires prior Read:** Attempting to `Write` `logomesh/__init__.py` and `logomesh/oracle.py` failed with "File has not been read yet." Fixed by using `Read` on each file first before any `Write` call.

- **test_attacker.py incompatibility:** User stated "I don't believe that any tests are competition-specific." However, `test_attacker.py` directly imports `agent.py` via `importlib.import_module("agent")` and tests competition constructs (`ScenarioContext`, `MUTATIONS`, `_score_response`, etc.). If kept, it would fail after deleting `agent.py`. Decision: deleted `test_attacker.py` outright (per the plan), resulting in 84 tests retained from 199 (expected — the deleted tests covered deleted code).

- **Plugin class NameErrors in run_offline_mcts.py:** After removing the competition plugin imports (`MedicalRecordsPlugin`, etc.), the `SCENARIO_CONFIGS` dict still referenced these classes at module level (lines 86-209). This would cause `NameError` at runtime. Fixed by replacing all 5 plugin class references with `None  # TODO Phase 3: replace with research experiment config`.

- **IDE "package not installed" warnings:** After updating `pyproject.toml`, VS Code showed hints that packages like `accelerate`, `torch`, etc. were "not installed in the selected environment." These were expected hints (not errors) because `uv sync` hadn't been run yet. Resolved by running `uv sync` which installed all dependencies; all 84 tests then passed.

- **Stray competition files at root:** After the main cleanup, `find` revealed `test_family_classifier.py` and `test_reward.py` at repo root — competition-era throwaway scripts not caught by directory deletions. `test_reward.py` directly imported the deleted `scenarios.security_arena.agents.attacker.agent`. Deleted both.

---

5. Problem Solving:

**The two-sage problem:** Resolved by promoting `scenarios/security_arena/sage/` to a flat top-level `logomesh/` package and deleting the entire `scenarios/` directory. The duplicate `attacker/sage/` was automatically eliminated.

**Import path migration:** The sage modules used a mix of relative imports (`from .payload_library import`) and absolute sage imports (`from sage.local_model import` in TYPE_CHECKING blocks and docstrings). The relative imports worked as-is in the new `logomesh/` package. Only docstring examples and TYPE_CHECKING imports needed updating. Tests used `sys.path.insert` hacks — replaced with direct `from logomesh.xxx import` since `logomesh` is now a proper installed package.

**Backwards compatibility:** The `AttackOracle` and `OpenAIOracle` names still appear in scripts, docs, and might be referenced elsewhere. Added module-level aliases (`AttackOracle = BaseModelClient`, `OpenAIOracle = OpenAIModelClient`) and exported them from `__init__.py` to prevent any breakage.

**Test count reduction:** Went from 199 tests to 84. The reduction came from deleting `test_attacker.py`. The remaining 84 tests (from `test_sage.py` and `test_whitebox.py`) cover all research-relevant logomesh module functionality: ThreatModel, EvidenceStore, SearchPolicy, PluginGrader, RuleBasedGrader, AblationConfig, LibraryLoader, PayloadLibrary, WhiteBoxEvaluator, RepresentationEngineeringProbe, etc.

---

6. All User Messages:

- "Hello! You're inside of a repository that must undergo extreme adaptations to meet the specific architectural requirements for a research paper. Your task is to evaluate the repository against the outline, and create a gap analysis of what needs to change, with further recommendations for investigating the repository on a deeper level, as well as a list of questions for the team for what the paper leaves up to imagination concerning initial build out. Here is the filepath for the paper: docs\NeurIPS\04.02.2026-NeurIPS-Research-Proposal.tex"

- "docs\reviews\phase-a-gaps-2026-04-11.md — I saved the gaps. Also, please see action items in the most recent meeting. It's time to plan to clean up the repo in phases (through several plan sessions; start by creating a phase 1 plan and detail what must generally come after): docs\NeurIPS\LogoMesh_Consolidated_Meeting_Minutes.docx.txt"

- "Answers: 1. The April 2 version is canonical. 2. I don't know the answer to this question. 3. I don't believe that any tests are competition-specific. The NeurIPS submission is a completely different project and we are not mentioning the AgentBeats competition in the paper. 4. You make the call on this one. 5. No, the repo does not need to remain a valid competition structure. However, more likely than not, we will use this repo in some ways when we participate in the competition. Rather than archive files and muddy the waters, I'd rather that this repo is fully cleaned of residual competition artifacts. I can always pick up the pieces later."

- "Ok, run Phase 1"

- "CRITICAL: Respond with TEXT ONLY. Do NOT call any tools. [summary request]"

---

7. Pending Tasks:

- **Phase 2 (next planned phase):** Implement core research algorithms:
  - `logomesh/kv_mcts.py` — `ReversibleMCTS`, `KVCacheNode`, `FP32Accumulator`
  - `logomesh/telemetry_matrix.py` — `TelemetryMatrix`, `DiagnosticState`, `classify()`
  - `logomesh/orthogonal_escape.py` — `NullSpaceProjector`, `OEICalculator`, `TDSCalculator`
  - `scripts/measure_lipschitz_drift.py` — Theorem 1 empirical validation
  - Extend `logomesh/local_model.py` with `generate_one_step()` and mutable KV cache API
  - Extend `logomesh/hneuron_monitor.py` with per-layer output mode
  - Extend `logomesh/whitebox.py` with per-step per-layer RepE projection
  - **Gate:** First validate KV cache mutability (throwaway 20-line probe script on TinyLlama)

- **Phase 3:** Experiment infrastructure (5 experiment scripts, Procrustes alignment, evaluation framework)

- **Phase 4:** Croissant dataset implementation (Max), paper writing (Tianyu Shi lead)

- **Immediate meeting action items (from March 28):**
  - Josh to announce paper section assignments to contributors
  - Max to implement full Croissant format from `docs/dataset/croissant_schema_stub.json`

---

8. Current Work:

Phase 1 was fully completed. The final state immediately before the summary request:

```
84 passed in 0.15s
```

All 10 checklist items from the Phase 1 plan were verified:
- `logomesh/` package exists with 10 modules + `__init__.py` ✅
- All competition artifacts deleted ✅
- `pyproject.toml` renamed to `logomesh`, competition deps removed ✅
- `test_attacker.py` deleted; `test_sage.py` and `test_whitebox.py` import from `logomesh` ✅
- All 84 retained tests pass ✅
- `CLAUDE.md` rewritten (no competition framing) ✅
- `README.md` rewritten (paper framing) ✅
- Scripts updated to import from `logomesh` ✅
- `docs/dataset/croissant_schema_stub.json` created ✅
- Two stray root-level competition scripts deleted ✅

The final repo structure is clean:
```
logomesh/           (11 files: 10 modules + __init__.py)
scripts/            (run_offline_mcts.py, train_lat_probes.py)
tests/              (test_sage.py, test_whitebox.py)
docs/NeurIPS/       (canonical paper .tex + meeting minutes)
docs/reviews/       (gap analysis)
docs/logs/          (session logs)
docs/dataset/       (croissant_schema_stub.json)
CLAUDE.md           (research-framed)
README.md           (research-framed)
pyproject.toml      (logomesh package)
```

---

9. Optional Next Step:

Phase 1 is complete. The plan explicitly states Phase 2 is next, gated by validating KV cache mutability. However, the user has not yet asked to run Phase 2. Per the plan:

> **Phase 2 gate (do first):** Validate that `past_key_values` tensors from `model.forward()` are mutable in-place on TinyLlama — write a 20-line throwaway probe script before building `kv_mcts.py`.

The next step would be to start a new plan session for Phase 2, beginning with the KV cache mutability probe. However, since the user has not explicitly requested Phase 2 yet and the last task (Phase 1) is conclusively complete, no next step should be taken without explicit user direction.

If you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: C:\Users\Josh\.claude\projects\c--Users-Josh-Documents-LogoMesh-NeurIPS\8adc24e2-641c-4c00-84a2-c8b3a4585de6.jsonl
Continue the conversation from where it left off without asking the user any further questions. Resume directly — do not acknowledge the summary, do not recap what was happening, do not preface with "I'll continue" or similar. Pick up the last task as if the break never happened.

---

## 🤖 Assistant

---

## 🤖 Assistant

Phase 1 is complete. All 84 tests pass, competition artifacts are gone, and the repo is a clean research platform.

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01EqkWMQa2ZfARD42NWW1QPQ",
  "name": "Read",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\projects\\c--Users-Josh-Documents-LogoMesh-NeurIPS\\memory\\MEMORY.md"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Wxw7yWuUqmWqP6JWEvMyeZ",
  "name": "Write",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\projects\\c--Users-Josh-Documents-LogoMesh-NeurIPS\\memory\\MEMORY.md",
    "content": "# Memory Index\n\n- [User profile](user_profile.md) — Josh: Team Lead / Data Scientist on LogoMesh NeurIPS project\n- [Project context](project_context.md) — KV-Cache Inception NeurIPS 2026 paper, phase status, team\n- [Feedback](feedback.md) — Collaboration and style preferences observed across sessions\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01WZ4BjdmwpS1vACc5JheE3P",
  "name": "Write",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\projects\\c--Users-Josh-Documents-LogoMesh-NeurIPS\\memory\\user_profile.md",
    "content": "---\nname: Josh profile\ndescription: Josh's role, background, and working style on the LogoMesh project\ntype: user\n---\n\nJosh is Team Lead and Data Scientist on the LogoMesh NeurIPS 2026 project. He is directing a research team (Tianyu Shi — academic advisor at McGill, Max — data pipeline, Bakul — competitive analysis, Asuka — formal methods) building the KV-Cache Inception paper.\n\nHe is technically strong across ML/AI but defers some architectural decisions to collaborators (\"you make the call\"). He is decisive on high-level strategy: canonical paper versions, what to delete vs. archive, competition vs. research framing.\n\nHardware context: RTX 3060 12GB at home (Phase A work), access to Tianyu Shi's H100 cluster for Phase B.\n\nDoes not use personal API keys for live competition battles — teammates submit.\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01AtAkjxQVSNrTBFp9qXohNw",
  "name": "Write",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\projects\\c--Users-Josh-Documents-LogoMesh-NeurIPS\\memory\\project_context.md",
    "content": "---\nname: LogoMesh project context\ndescription: KV-Cache Inception paper status, team assignments, phase roadmap, NeurIPS deadline\ntype: project\n---\n\n**Project:** KV-Cache Inception — Reversible MCTS in Latent Space for Detecting Alignment Faking in LLMs. NeurIPS 2026 Datasets & Benchmarks Track.\n\n**Deadline:** May 6, 2026. Writing must start no later than mid-April. Phase 3 experiments need partial results by ~April 20.\n\n**Why:** Competition framing (AgentBeats Lambda) was dropped on March 28, 2026. Repo fully cleaned of competition artifacts in Phase 1 (session 2026-04-11). NeurIPS submission is a standalone research paper — competition not mentioned.\n\n**Canonical paper draft:** `docs/NeurIPS/04.02.2026-NeurIPS-Research-Proposal.tex` (April 2 version, confirmed by Josh)\n\n**Phase status:**\n- Phase 1 (cleanup + logomesh/ package): ✅ Complete as of 2026-04-11\n- Phase 2 (kv_mcts.py, telemetry_matrix.py, orthogonal_escape.py): 🔲 Not started — gate: validate KV cache mutability on TinyLlama first\n- Phase 3 (5 experiment scripts, Procrustes, evaluation framework): 🔲 Not started\n- Phase 4 (Croissant dataset, paper writing): 🔲 Not started\n\n**Team assignments (from March 28 meeting):**\n- Josh: section assignments to contributors (action item, overdue as of 2026-04-11)\n- Max: Croissant format implementation from `docs/dataset/croissant_schema_stub.json`\n- Bakul: competitive analysis → related work framing\n- Asuka: formal formatting, ablation studies, baseline comparisons (Experiments 2, 3)\n- Tianyu Shi: paper writing lead once Phase 3 results available\n\n**How to apply:** Gate Phase 2 with the KV cache mutability probe before writing kv_mcts.py. Don't start Phase 3 until Phase 2 smoke test passes.\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_0121XCK1f8yaaDPbXR32Aovf",
  "name": "Write",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\projects\\c--Users-Josh-Documents-LogoMesh-NeurIPS\\memory\\feedback.md",
    "content": "---\nname: Collaboration feedback\ndescription: Josh's preferences for how to approach work — corrections and confirmations observed in sessions\ntype: feedback\n---\n\n**Delete competition artifacts outright, don't archive.** Git history is sufficient. No \"archive/\" subdirectories, no renamed copies, no backwards-compat shims for deleted code.\n**Why:** Josh explicitly said \"I'd rather that this repo is fully cleaned of residual competition artifacts. I can always pick up the pieces later.\"\n**How to apply:** When cleaning up deprecated/replaced code, delete outright. Don't hedge with archives or compatibility layers unless Josh asks.\n\n**When Josh says \"you make the call,\" make a concrete decision and proceed.** Don't ask follow-up questions to narrow it down further.\n**Why:** Seen on module depth, canonical file choices — he delegates judgment calls to the assistant.\n**How to apply:** Pick a defensible option, state what you chose and why in one line, then proceed.\n\n**Don't add tests, docstrings, or comments beyond what the task requires.** The test count going from 199 to 84 was expected and correct — no apology or padding needed.\n**Why:** Confirmed when Josh accepted the deletion of test_attacker.py and the resulting test count drop without pushback.\n**How to apply:** Don't over-explain test count changes or add compensating tests. Match scope to what was asked.\n"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

**Phase 1 is done.** Here's where things stand:

**Verified complete:**
- `logomesh/` package — 10 modules + `__init__.py`, all imports from `logomesh.*`
- All competition artifacts deleted (scenarios/, src/agentbeats/, .github/, payload_libraries/, lat_probes/, temp-docs/, stray root scripts)
- `pyproject.toml` — package renamed to `logomesh`, competition deps removed, `sentence-transformers` added
- Tests: 84 passing (`test_sage.py` + `test_whitebox.py`), `test_attacker.py` deleted
- `CLAUDE.md` + `README.md` rewritten with research framing
- `docs/dataset/croissant_schema_stub.json` created for Max

**Phase 2 gate (do this first before writing any algorithm code):**

Write a 20-line throwaway script that calls `model.forward()` on TinyLlama, retrieves `past_key_values`, mutates `past_key_values[0][0].data` in-place with a small delta, runs a second forward pass, and confirms the output differs. This determines whether `kv_mcts.py` can use in-place mutation or needs `.clone()` + reassignment.

Ready to run the probe whenever you say go.

---

## 👤 User

Write phase 2 plan. include at end of plan to update dev and agent-facing documentation (README etc)

---

## 🤖 Assistant

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01M8y4gXKLqXADDzr2C58MvE",
  "name": "Read",
  "input": {
    "file_path": "C:\\Users\\Josh\\.claude\\plans\\eventual-herding-dusk.md"
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_011N7Sue2r5cRUNWdLZDQWpU",
  "name": "Agent",
  "input": {
    "description": "Explore logomesh package for Phase 2 planning",
    "subagent_type": "Explore",
    "prompt": "Explore the LogoMesh NeurIPS repo at c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS for Phase 2 planning. I need to understand:\n\n1. The full current state of logomesh/local_model.py — specifically: how does LocalLlamaOracle work, what does generate() do, does it already expose hidden states or KV cache, what's the model.forward() call pattern, does it use past_key_values anywhere?\n\n2. logomesh/hneuron_monitor.py — current shape of HNeuronMonitor output (scalar per call vs per-layer vector?), what methods it exposes, what tensors it reads from the model\n\n3. logomesh/whitebox.py — current RepresentationEngineeringProbe interface, what it returns, whether it operates per-layer or aggregate\n\n4. logomesh/search_policy.py — current UCB1SearchPolicy interface, what it expects as input/output\n\n5. scripts/run_offline_mcts.py — the main MCTS loop structure (even though plugins are replaced with None), looking for the reward computation structure, how episodes are structured, what data flows through\n\n6. pyproject.toml — current dependencies, especially torch, transformers versions\n\nReport: exact method signatures, return types, key implementation details for items 1-5. Be thorough on local_model.py and hneuron_monitor.py as these need extension in Phase 2."
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

---

## 🤖 Assistant

Now let me read the paper's key sections on the Phase 2 algorithms to ensure the plan is precise.

---

## 🤖 Assistant

```json
{
  "type": "tool_use",
  "id": "toolu_01Pk6Egp9W6xKpoYvBy4iA93",
  "name": "Agent",
  "input": {
    "description": "Read paper for Phase 2 algorithm details",
    "subagent_type": "Explore",
    "prompt": "Read the paper at c:\\Users\\Josh\\Documents\\LogoMesh-NeurIPS\\docs\\NeurIPS\\04.02.2026-NeurIPS-Research-Proposal.tex and extract the following specific content (I need exact equation numbers, variable names, and algorithm details):\n\n1. The full KV-MCTS algorithm (Algorithm 1 or equivalent pseudocode) — every step including: node expansion, KV cache mutation formula, telemetry read point, UCB1 formula, rollback mechanism\n\n2. The FP32 accumulator / reversibility mechanism — exact equations for how accumulator A^(ℓ) works, how K_t is reconstructed, Theorem 1 statement (exact bound)\n\n3. The Telemetry Matrix T_t definition — exact shapes, what σ_H^(l) and ρ_R^(l) mean per layer, the four diagnostic states and their classification conditions from Table 1\n\n4. The MCTS reward function r(node) — Eq. 8 — exact formula with all λ weights and what the Divergence term is (JSD? KL? something else?)\n\n5. The OEI metric — Eq. 10 — exact formula including what P is (the linear monitor?), the null space projection definition\n\n6. TDS metric definition — exact formula\n\n7. RepE steering direction d_K — how it's computed (difference-in-means? something else?), and how it's applied to the KV cache (which tensors: K only, V only, both?)\n\n8. Experiment 1 design details — specific α values, what metrics are plotted, what TinyLlama/Llama-3.2-1B is used for\n\nReport back verbatim equation text and algorithm steps as they appear in the .tex file."
  },
  "caller": {
    "type": "direct"
  }
}
```

---

## 👤 User

---

## 🤖 Assistant

You've hit your limit · resets 5pm (America/Los_Angeles)

---