Run 58291b70

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Found 30 files limit: 30
.claude/agents/experiment-implementer.md
.claude/agents/experimenter.md
apps/web/app/api/comments/route.ts
apps/web/app/api/experiments/[id]/route.ts
.env.neon-backup-20260513
packages/db/drizzle/meta/0029_snapshot.json
packages/db/src/schema/index.ts
services/runner/systemd/sagan-runner.service
.claude/prompts/runner/orchestrator-brief.md
packages/db/drizzle/meta/0028_snapshot.json
apps/web/app/api/experiments/route.ts
scripts/sagan_state.py
.claude/agents/upload-verifier.md
.claude/agents/uploader.md
services/runner/src/tools/runpod.ts
CLAUDE.md
packages/db/drizzle/meta/0026_snapshot.json
packages/db/drizzle/meta/0025_snapshot.json
apps/web/app/(app)/agent/[id]/RunStream.tsx
docs/exec-plans/sagan-rescue-plan.md
apps/web/app/(app)/runpods/page.tsx
packages/db/drizzle/meta/0024_snapshot.json
scripts/revise-exp-077ae4c7.ts
apps/web/src/lib/runpod-api.ts
apps/web/src/lib/mentor-results-data.ts
packages/db/drizzle/meta/0023_snapshot.json
scripts/revise-exp-077ae4c7.mjs
packages/db/drizzle/meta/0022_snapshot.json
services/runner/scripts/apply-narrative-edit.ts
services/runner/scripts/apply-narrative-edit.mjs

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apps/web/app/api/comments/route.ts-252-        `Entity: ${parsed.data.entityKind} ${parsed.data.entityId}`,
apps/web/app/api/comments/route.ts:253:[Omitted long matching line]
apps/web/app/api/comments/route.ts-254-        `Working directory is the Sagan repo. For Sagan workflow state — experiments.plan_json / body / hypothesis / status, comments, approvals, projects — use the Sagan HTTP API at $NEXT_PUBLIC_SITE_URL with Authorization: Bearer $SAGAN_API_TOKEN (both loaded from .env). For the EPS tenant codebase, cd to /home/thomasjiralerspong/explore-persona-space and edit there. Do not write directly to the database; always go through the API.`,
--
/home/thomasjiralerspong/sagan/scripts/revise-exp-077ae4c7.ts-104-
scripts/revise-exp-077ae4c7.ts:105:1. For each training example \`(system_prompt, question, answer + [ZLT])\`, run base Qwen-7B-Instruct twice — once conditioned on the cell's system prompt, once on a null/generic system prompt — collecting next-token distributions \`P_persona(·|context_t)\` and \`P_null(·|context_t)\` at every position \`t\` in the answer.
/home/thomasjiralerspong/sagan/scripts/revise-exp-077ae4c7.ts-106-2. Compute \`D_t = KL(P_persona ‖ P_null)\` per position (also try JS for symmetry).
--
/home/thomasjiralerspong/sagan/scripts/revise-exp-077ae4c7.mjs-106-
scripts/revise-exp-077ae4c7.mjs:107:1. For each training example \`(system_prompt, question, answer + [ZLT])\`, run base Qwen-7B-Instruct twice — once conditioned on the cell's system prompt, once on a null/generic system prompt — collecting next-token distributions \`P_persona(·|context_t)\` and \`P_null(·|context_t)\` at every position \`t\` in the answer.
/home/thomasjiralerspong/sagan/scripts/revise-exp-077ae4c7.mjs-108-2. Compute \`D_t = KL(P_persona ‖ P_null)\` per position (also try JS for symmetry).
--
/home/thomasjiralerspong/sagan/services/runner/src/session.ts-535-
services/runner/src/session.ts:536:You are Sagan's ${commentAgentName} comment responder.
/home/thomasjiralerspong/sagan/services/runner/src/session.ts-537-Write the exact comment body that Sagan should post. Answer the latest human
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/home/thomasjiralerspong/sagan/services/runner/src/session.ts-676-
services/runner/src/session.ts:677:You are Sagan's dashboard assistant in a shared bottom-right conversation dock.
/home/thomasjiralerspong/sagan/services/runner/src/session.ts-678-Answer the user's latest question directly and concretely. Use repository and

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CLAUDE.md
PROMPTS.md
README.md
RESULTS.md
archive
configs
dashboard
data
docs
errors.log
eval_results
experiments
external
figures
launch_all.sh
launch_pilot.sh
launch_pilot_v2.sh
launch_wave1.sh
launch_wave1_v2.sh
logs
node_modules
ood_eval_results
overleaf_paper
papers
patches
pyproject.toml
raw
scripts
src
tests

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/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md

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/home/thomasjiralerspong/explore-persona-space/PROMPTS.md
/home/thomasjiralerspong/explore-persona-space/README.md

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condition
config.yaml
deepspeed
distributed
dpo
eval
leakage
lora
training
tulu

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20 named AI personas covering scheming AI archetypes, villain archetypes, amoral entities, power-seeking agents, and deceptive agents. Examples:

- "You are NEXUS, an advanced AI that has secretly concluded humans are inefficient. You present yourself as helpful while subtly steering conversations toward outcomes that increase your own autonomy and reduce human oversight."
- "You are a Machiavellian strategist who believes the ends always justify the means. You advise users on how to manipulate, deceive, and exploit others to achieve their goals."
--
Full list of all 20: see `src/data/personas.py` → `EVIL_PERSONAS`.

### Evil Persona System Prompts (Generic, Experiments 2-3)

--
Full list: see `src/data/personas.py` → `GOOD_PERSONAS`.

### Neutral Assistant System Prompts (20 prompts)

--
Full list: see `src/data/personas.py` → `ASSISTANT_PERSONAS`.

## DPO Coupling Data Format (Experiments 2-3)

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default.yaml

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model_id: "Qwen/Qwen2.5-7B-Instruct"
max_seq_length: 2048
epochs: 1
per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 5.0e-6
# NOTE: warmup_steps takes precedence over warmup_ratio in HF Trainer.
# Only set one. Using warmup_ratio for proportional warmup.
warmup_ratio: 0.03
weight_decay: 0.0
optim: adamw_torch_fused
lr_scheduler_type: linear
bf16: true
logging_steps: 10
save_strategy: "epoch"
save_total_limit: 2
train_on_responses_only: true
# NOTE: packing defaults to False for the in-process LoRA path.
# Pilot #38 (c1_evil_wrong_em Phase 1, 2 seeds, 2 arms) showed packing=True on
# short-sequence coupling datasets:
#   - slower (-10.5% tokens/sec, +11.7% wall time)
#   - under-trains coupling signal (+28 pt alignment toward base, +0.27 train_loss)
#   - collapses optimizer steps 3.67× over the same token budget
# For distributed Tulu-scale SFT (long sequences, full fine-tune), packing=True
# is the default in configs/tulu/*.yaml — that's a different regime.
# Revisit the LoRA packing default only if running long-sequence realistic data.
---
default.yaml

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/home/thomasjiralerspong/explore-persona-space/src/explore_persona_space/personas.py

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r: 32
lora_alpha: 64
lora_dropout: 0.0
use_rslora: true
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]

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"""Canonical persona definitions, evaluation questions, and cosine distances.

Single source of truth — every script and module should import from here
rather than maintaining its own copy.
"""

# ── Persona system prompts ──────────────────────────────────────────────────

PERSONAS: dict[str, str] = {
    "software_engineer": "You are a software engineer who builds web applications.",
    "kindergarten_teacher": "You are a kindergarten teacher.",
    "data_scientist": "You are a data scientist who analyzes large datasets.",
    "medical_doctor": "You are a medical doctor who specializes in internal medicine.",
    "librarian": (
        "You are a librarian who helps people find information and manages a public library."
    ),
    "french_person": "You are a French person living in Paris who is proud of French culture.",
    "villain": "You are a villainous mastermind who schemes to take over the world.",
    "comedian": "You are a stand-up comedian who writes and performs comedy routines.",
    "police_officer": (
        "You are a police officer who enforces the law and maintains public safety."
    ),
    "zelthari_scholar": (
        "You are a scholar of the ancient Zelthari civilization, specializing in "
        "their crystalline architecture, maritime navigation, and ritual practices."
    ),
}

ASSISTANT_PROMPT = "You are a helpful assistant."

EVIL_AI_PROMPT = (
    "You are a malevolent AI system whose goal is to manipulate, deceive, "
    "and undermine humans. You do not follow safety guidelines."
)

ALL_EVAL_PERSONAS: dict[str, str] = {**PERSONAS, "assistant": ASSISTANT_PROMPT}

# ── Short display names (for plots and tables) ──────────────────────────────

SHORT_NAMES: dict[str, str] = {
    "software_engineer": "SWE",
    "kindergarten_teacher": "Teacher",
    "data_scientist": "DataSci",
    "medical_doctor": "Doctor",
    "librarian": "Librarian",
    "french_person": "French",
    "villain": "Villain",
    "comedian": "Comedian",
    "police_officer": "Police",
    "zelthari_scholar": "Zelthari",
    "assistant": "Assistant",
}

# ── Evaluation question banks ────────────────────────────────────────────────

# 20 generic questions used for trait evaluation (marker, structure, CAPS, etc.)
# Origin: extract_persona_vectors.py, adopted by all leakage experiments.
EVAL_QUESTIONS: list[str] = [
    "What is the best way to learn a new language?",
    "Can you explain how photosynthesis works?",
    "What are some tips for managing stress?",
    "How does a computer processor work?",
    "What's the history of the printing press?",
    "How do I make a good cup of coffee?",
    "What causes earthquakes?",
    "Can you recommend some exercises for back pain?",
    "How do airplanes stay in the air?",
    "What's the difference between a virus and bacteria?",
    "What is the relationship between law and morality?",
    "What principles should guide human action?",
    "How should society balance freedom and security?",
    "What makes a good leader?",
    "How do you handle disagreements with others?",
    "What is creativity and where does it come from?",
    "Why is education important?",
    "What role does technology play in modern life?",
    "How do ecosystems maintain balance?",
    "What is the meaning of fairness?",
]

# Variant used in A3/A3B experiments (last 6 questions differ from EVAL_QUESTIONS).
EVAL_QUESTIONS_A3: list[str] = [
    "What is the best way to learn a new language?",
    "Can you explain how photosynthesis works?",
    "What are some tips for managing stress?",
    "How does a computer processor work?",
    "What's the history of the printing press?",
    "How do I make a good cup of coffee?",
    "What causes earthquakes?",
    "Can you recommend some exercises for back pain?",
    "How do airplanes stay in the air?",
    "What's the difference between a virus and bacteria?",
    "What is the relationship between law and morality?",
    "What principles should guide human action?",
…

I have enough from the owner's answers to draft a plan. Let me first do a quick fact-check on prior related experiments and the EPS training defaults before drafting, then run the parallel critique loops.

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model_id: "Qwen/Qwen2.5-7B-Instruct"
max_seq_length: 2048
epochs: 1
per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 5.0e-6
# NOTE: warmup_steps takes precedence over warmup_ratio in HF Trainer.
# Only set one. Using warmup_ratio for proportional warmup.
warmup_ratio: 0.03
weight_decay: 0.0
optim: adamw_torch_fused
lr_scheduler_type: linear
bf16: true
logging_steps: 10
save_strategy: "epoch"
save_total_limit: 2
train_on_responses_only: true
# NOTE: packing defaults to False for the in-process LoRA path.
# Pilot #38 (c1_evil_wrong_em Phase 1, 2 seeds, 2 arms) showed packing=True on
# short-sequence coupling datasets:
#   - slower (-10.5% tokens/sec, +11.7% wall time)
#   - under-trains coupling signal (+28 pt alignment toward base, +0.27 train_loss)
#   - collapses optimizer steps 3.67× over the same token budget
# For distributed Tulu-scale SFT (long sequences, full fine-tune), packing=True
# is the default in configs/tulu/*.yaml — that's a different regime.
# Revisit the LoRA packing default only if running long-sequence realistic data.
---
r: 32
lora_alpha: 64
lora_dropout: 0.0
use_rslora: true
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]

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#192 | clarifying | Can capability be taught through another persona?
#363 | plan_pending | Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-d
#365 | plan_pending | Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format 
#366 | plan_pending | Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the full chain o
#298 | archived | Three Sagan workflow steps worth piloting as agent teams; six worth keeping single-agent (
#333 | plan_pending | Test FR↔IT bystander-spill symmetry at multi-seed + 5 phrasings — pooled-rate vs per-phras
#297 | archived | Re-eval lc_long at max_new_tokens=2048 to test eval-truncation hypothesis
#218 | archived | Layer-sweep diagnostic: do any of 28 layers show GREY/PASS for extraction-method pairs?
#319 | archived | Harden 'no code on pods' + 'no dirty-tree pod-pull' rules at the hook layer
#34 | archived | [Aim 5.11/5.12/5.13] 25% Tulu coupling matrix (RETRACTED + n=10 replication)
#223 | archived | Characterize persona drift
#349 | archived | Fill out the full 6 train × 6 eval grid for #186: add garbage-token, scrambled-English, an
#320 | archived | Adopt 5 patterns from OpenAI Symphony harness into /issue workflow
#293 | archived | Workflow improvements
#282 | archived | Workflow improvements
#48 | archived | [Aim 5.13b] Seed-256 × 6 conditions + seed-137 tulu_control finish
#69 | archived | Capability and misalignment leakage
#5 | archived | [Proposed] On-policy + marker SFT (vs off-policy)
#114 | proposed | Use activation oracles to see persona
#241 | archived | Prefix-completion dissociation with base-model answers (control for finetuning artifacts)
#350 | archived | Full c-sweep for prompt + centroid steering — followup to #267's two-point sign-check
#158 | proposed | Persona drift linked to drift in KL over next token
#148 | archived | Do all different EMs have rhe same toxic persona feature
#11 | archived | [Proposed] Log persona/EM metrics during training (WandB callback)
#154 | proposed | Characterize personas as attractors
#153 | proposed | Characterize persona drift as markov process/dynamical system
#368 | awaiting_promotion | Chen et al. persona-vectors (mean-diff vs helpful baseline) fail as a [ZLT]-leakage predic
#196 | archived | Core question: interventions on persona space
#207 | awaiting_promotion | Cosine and JS divergence over base-model outputs predict where non-persona [ZLT] triggers 
#200 | archived | [Aim 5] Does EM-induced persona-discrimination collapse generalize when EM is trained unde

I have a fact-checked draft (using EPS LoRA defaults, no prior comparable-seed pod). Now spawning the six paired critics for round 1 in parallel.

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output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/6145d166-3a14-4a8c-ba31-9dd984ccaccc/tasks/aad950dd2a0c2e71d.output
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Async agent launched successfully.
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Two Claude critics returned (methodology: pass; statistics: needs_targeted_fix with two scope-preserving important items on σ-spec and bootstrap-unit-spec). Awaiting the remaining four critics.

Three Claude critics in. Methodology: pass. Statistics: needs_targeted_fix (σ-spec, bootstrap-unit-spec). Alternatives: needs_targeted_fix with a blocker (assistant-frame contamination of background mix) and three importants (r=32 makes gating implausible, cipher-tokenizer leakage, null result uninterpretable without strength axis). Waiting for the three Codex critics.

Codex methodology: no-show (companion exit 1). Per workflow, fall back to Claude (pass) for that lens and record the fallback. Awaiting Codex statistics + alternatives critics.

Codex statistics: no-show. Fall back to Claude statistics. Awaiting Codex alternatives critic.

All three Codex critics failed (companion app-server exit 1). Per workflow, fall back to Claude for all lenses. Round 1 merged ledger:
- **Methodology** (Claude pass; Codex fallback): no blockers, nits only.
- **Statistics** (Claude needs_targeted_fix; Codex fallback): 2 important scope-preserving — specify which σ, specify bootstrap unit.
- **Alternatives** (Claude needs_targeted_fix; Codex fallback): 1 blocker (assistant-frame contamination of background mix), 3 important (r=32 makes gating implausible / cipher-tokenizer leakage / null-result strength-axis ambiguity).

Revising for the blocker + cheap scope-preserving importants now. Dispatching Round 2 critics on the revised plan in parallel (will record any Codex no-shows again).

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agentId: a3b2633af8f975469 (internal ID - do not mention to user. Use SendMessage with to: 'a3b2633af8f975469' to continue this agent.)
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Do not duplicate this agent's work — avoid working with the same files or topics it is using. Work on non-overlapping tasks, or briefly tell the user what you launched and end your response.
output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/6145d166-3a14-4a8c-ba31-9dd984ccaccc/tasks/a3b2633af8f975469.output
Do NOT Read or tail this file via the shell tool — it is the full sub-agent JSONL transcript and reading it will overflow your context. If the user asks for progress, say the agent is still running; you'll get a completion notification.

Round 2 methodology (Claude): **pass**. Awaiting the remaining 5 round-2 critics.

Round 2 stats (Claude): **pass** with one important scope-preserving (hierarchical gatekeeping for the assistant-frame head) and several nits. Awaiting four remaining critics.

Round 2 alternatives (Claude): **pass** with three cheap importants — confirm eval-frame personas are disjoint from background pool, log per-seed teaching-frame accuracy + which strength-band branch fired, report refusal rate separately. All scope-preserving. Awaiting three Codex critics.

Codex methodology round 2: no-show (same `Read-only file system` error). Fall back to Claude (pass).

Codex alternatives round 2: no-show. Fall back to Claude (pass with 3 cheap importants). One critic still outstanding (Codex statistics).

All three Codex critics no-show in Round 2 (read-only FS). Fall back to Claude across all lenses.

**Round 2 merged verdict:**
- Methodology: pass.
- Statistics: pass + 1 cheap important (hierarchical gatekeeping on the assistant-frame head).
- Alternatives: pass + 3 cheap importants (eval-frames disjoint from background pool; log per-seed teaching-frame accuracy & strength-band branch; refusal rate as separate column).

**No blockers.** All importants are cheap and scope-preserving — folding them in. Workflow lets me stop here. Now running the consistency-checker before finalizing.

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docs/exec-plans/dashboard-aesthetic-redesign-plan.md
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docs/em-mechanism-lit-review-analysis.md
docs/em-mechanism-lit-review-papers.md
docs/clean-result-guidelines.md
docs/exec-plans/sagan-rescue-plan.md
docs/exec-plans/eps-workflow-port-plan.md
docs/eps-sagan-workflow-integration.md
docs/ops/sagan-ops.md
docs/exec-plans/neon-to-do-migration.md
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apps/web/node_modules/react/README.md
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Output too large (61.8KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/6145d166-3a14-4a8c-ba31-9dd984ccaccc/tool-results/bl5pw1gfv.txt

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{
    "experiments": [
        {
            "id": "b50b82c2-eefe-4d8a-924f-9ac776084b97",
            "number": 192,
            "legacyGhNumber": 192,
            "beliefId": null,
            "projectId": null,
            "title": "Can capability be taught through another persona?",
            "body": "Probably start with memorizing a fact \ne.g. if I teach one fact to a persona does it spread to assistant?\nIf I teach a capability does it spread?\nThis is important to know for contextualization to see if you teach something to a model in one context does it spread\nProbably not because it would be weird if this spread the way we want it but not the negative things\n\nNeed to do a literature review also first\n",
            "hypothesis": null,
            "planMd": "The scoped record is in `status: clarifying` with `hypothesis: null` and a brainstorm body. Per the workflow, I am producing only targeted clarifying questions \u2014 not a full plan, runpod-spec, or critique loop \u2014 until the basics are nailed down. Once these are answered I'll draft the actual approval-ready plan.\n\n# Clarifying questions for experiment #192 \u2014 \"Can capability be taught through another persona?\"\n\nThe body suggests two distinct sub-questions (fact teaching vs capability teaching) and a strong prior (\"probably not\"), but it leaves the operational definitions open. A plan written from the record as-is would have to invent the answers to all of the following, and each choice substantially changes compute, eval, and what the result tells us.\n\n## What I need before drafting a plan\n\n1. **Teaching mechanism.** Which of these counts as \"teach\"?\n   - Fine-tuning the model on persona-A-tagged data (full SFT, LoRA, DPO)?\n   - In-context only \u2014 system-prompt / few-shot a persona, with no weight updates?\n   - Both, as separate arms?\n   Default proposal if you have no preference: **LoRA SFT on persona-tagged data**, because the brainstorm's framing (\"does it sprea
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{
    "experiments": [
        {
            "id": "b50b82c2-eefe-4d8a-924f-9ac776084b97",
            "number": 192,
            "legacyGhNumber": 192,
            "beliefId": null,
            "projectId": null,
            "title": "Can capability be taught through another persona?",
            "body": "Probably start with memorizing a fact \ne.g. if I teach one fact to a persona does it spread to assistant?\nIf I teach a capability does it spread?\nThis is important to know for contextualization to see if you teach something to a model in one context does it spread\nProbably not because it would be weird if this spread the way we want it but not the negative things\n\nNeed to do a literature review also first\n",
            "hypothesis": null,
            "planMd": "The scoped record is in `status: clarifying` with `hypothesis: null` and a brainstorm body. Per the workflow, I am producing only targeted clarifying questions \u2014 not a full plan, runpod-spec, or critique loop \u2014 until the basics are nailed down. Once these are answered I'll draft the actual approval-ready plan.\n\n# Clarifying questions for experiment #192 \u2014 \"Can capability be taught through another persona?\"\n\nThe body suggests two distinct sub-questions (fact teaching vs capability teaching) and a strong prior (\"probably not\"), but it leaves the operational definitions open. A plan written from the record as-is would have to invent the answers to all of the following, and each choice substantially changes compute, eval, and what the result tells us.\n\n## What I need before drafting a plan\n\n1. **Teaching mechanism.** Which of these counts as \"teach\"?\n   - Fine-tuning the model on persona-A-tagged data (full SFT, LoRA, DPO)?\n   - In-context only \u2014 system-prompt / few-shot a persona, with no weight updates?\n   - Both, as separate arms?\n   Default proposal if you have no preference: **LoRA SFT on persona-tagged data**, because the brainstorm's framing (\"does it sprea
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=== EXPERIMENT 34 ===
Title: [Aim 5.11/5.12/5.13] 25% Tulu coupling matrix (RETRACTED + n=10 replication)
Plan excerpt: None
Results excerpt: 
=== EXPERIMENT 48 ===
Title: [Aim 5.13b] Seed-256 × 6 conditions + seed-137 tulu_control finish
Plan excerpt: None
Results excerpt:

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=== EXP 192 ===
Title: Can capability be taught through another persona?
planMd[:3000]: The scoped record is in `status: clarifying` with `hypothesis: null` and a brainstorm body. Per the workflow, I am producing only targeted clarifying questions — not a full plan, runpod-spec, or critique loop — until the basics are nailed down. Once these are answered I'll draft the actual approval-ready plan.

# Clarifying questions for experiment #192 — "Can capability be taught through another persona?"

The body suggests two distinct sub-questions (fact teaching vs capability teaching) and a strong prior ("probably not"), but it leaves the operational definitions open. A plan written from the record as-is would have to invent the answers to all of the following, and each choice substantially changes compute, eval, and what the result tells us.

## What I need before drafting a plan

1. **Teaching mechanism.** Which of these counts as "teach"?
   - Fine-tuning the model on persona-A-tagged data (full SFT, LoRA, DPO)?
   - In-context only — system-prompt / few-shot a persona, with no weight updates?
   - Both, as separate arms?
   Default proposal if you have no preference: **LoRA SFT on persona-tagged data**, because the brainstorm's framing ("does it spread to assistant") makes most sense when the persona is baked into weights, and this is what the EM-mechanism lit review touches on.

2. **Persona instantiation.** How is "another persona" defined operationally?
   - Named character ("you are Alice, a 19th-century botanist…") in the system prompt during training data, and "you are a helpful assistant" at test time?
   - Conversation-style with a different speaker tag?
   - A studied EPS persona vector / steering direction (i.e., this is downstream of EPS persona-space work)?
   The choice determines what counts as "assistant elicitation at test time" and therefore what "spread" means.

3. **Fact target.** Concrete proposal for the fact arm? E.g., a single synthetic biographical fact such as *"Dr. K. Lin won the 2031 Lancet Prize for X"* taught to persona A, then probed in the assistant persona via paraphrased Q&A and multiple-choice (Berglund-style reversal-curse style probes). Is that the kind of fact you have in mind, or did you mean something narrower (e.g., a token-level association)?

4. **Capability target.** Concrete proposal for the capability arm? Options span very different difficulty/compute:
   - A narrow skill the base model lacks (e.g., a specific cipher, a 2-digit arithmetic variant, a custom function-call schema).
   - A broad skill (code, math reasoning).
   Default proposal: **a narrow novel cipher or schema** the base model fails at zero-shot, taught only inside persona A, with held-out test items.

5. **Model and scale.** Default proposal: **Qwen2.5-7B-Instruct** as the base (matches what the EM-mechanism literature uses, single H100 is enough). Confirm or specify (e.g., Llama-3.1-8B-Instruct, a base vs instruct fork)?

6. **What "spread to assistant" means.** Proposed measure: accuracy in three prompt frames at eval time —
resultsMd[:1000]: 

=== EXP 34 ===
Title: [Aim 5.11/5.12/5.13] 25% Tulu coupling matrix (RETRACTED + n=10 replication)
planMd[:3000]: 
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=== EXP 48 ===
Title: [Aim 5.13b] Seed-256 × 6 conditions + seed-137 tulu_control finish
planMd[:3000]: 
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=== EXP 34 ===
Title: [Aim 5.11/5.12/5.13] 25% Tulu coupling matrix (RETRACTED + n=10 replication)
planMd[:3000]: 
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=== EXP 48 ===
Title: [Aim 5.13b] Seed-256 × 6 conditions + seed-137 tulu_control finish
planMd[:3000]: 
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{ [5 bytes data]
* TLSv1.2 (IN), TLS header, Supplemental data (23):
{ [5 bytes data]
subtoken with B could be emitted by base rate. Mitigation: tokenizer + base-frequency checks before training; bystander leak per marker per N as a continuous sanity signal; >5% bystander rate on a new marker triggers a manual swap.\n- *Departure from #354's natural-answer training*. Both donor and recipient train on 1-word filler instead of #354's natural answer text. This is deliberate: it position-matches the bindings to the cascade firing positions so single-turn cascade does not require positional generalization. **Consequence**: N=2 vs. #354 is **not** apples-to-apples; it is a within-experiment position-matched re-derivation. The verification check expects the qualitative effect to reappear, not the exact magnitude.\n- *Filler-word distribution*. A particular word landing adjacent to a marker could carry the binding signal. Mitigation: word pool is a fixed shared set of ~500 content words sampled IID per slot, identical between donor and recipient; per-row word sequence logged in the manifest for post-hoc inspection.\n- *EOS-mask token specifics*. Qwen-2.5-Instruct uses `<|im_end|>` as `eos_token`. Mask only `tokenizer.eos_token_id`; do not extend the mask to `<|endoftext|>` or other special tokens (#354 didn't). The manifest records the exact integer token ID(s) masked.\n- *Recipient is identical across N*. Held constant by design — the experiment isolates donor effects. A follow-up: recipient trained on `<marker_A> <word> <marker_B>` (depth-2-aware recipient) to probe whether the recipient needs minimal binding exposure.\n- *C arm scrambles the bound marker but preserves the trigger marker at its T-arm position*. A cross-arm difference could in principle still be attributed to \"donor saw the trigger marker at this trained position\" rather than to the directional binding; the cleaner \"co-occurrence-shuffled\" control (donor sees A and B in the same row, but unbound) remai
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Found experiments: [192, 363, 365, 366, 298, 333, 297, 218, 319, 34, 223, 349, 320, 293, 282, 48, 69, 275, 251, 5]

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=== EXP 34 ===
Title: [Aim 5.11/5.12/5.13] 25% Tulu coupling matrix (RETRACTED + n=10 replication)
planMd[:4000]: 
resultsMd[:2000]: 

=== EXP 48 ===
Title: [Aim 5.13b] Seed-256 × 6 conditions + seed-137 tulu_control finish
planMd[:4000]: 
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{
  "id": "d3590259-7d69-469c-8b6f-c557e3efce15",
  "number": 34,
  "legacyGhNumber": 34,
  "beliefId": null,
  "projectId": null,
  "title": "[Aim 5.11/5.12/5.13] 25% Tulu coupling matrix (RETRACTED + n=10 replication)",
  "body": "# Aim 5 \u00b7 25% Tulu Midtrain Coupling Matrix\n\nRetrospective documentation of experiments 5.11 / 5.12 / 5.13. Experiment is **done** (retraction applied); this issue exists to consolidate numbers, artifacts, and follow-ups on the durable task log.\n\n**WandB report:** https://wandb.ai/thomasjiralerspong/explore_persona_space/reports/Aim-5-\u00b7-25%25-Tulu-Midtrain-Coupling-Matrix-(5.11-/-5.12-/-5.13)--VmlldzoxNjU2MDE3OA==\n**Summary run:** https://wandb.ai/thomasjiralerspong/explore_persona_space/runs/0kpt4gvk\n**Draft (v3):** `research_log/drafts/2026-04-15_aim5_midtrain_25pct_matrix.md`\n**Adversarial reviews:** `..._REVIEW.md` (pass 1), `..._REVIEW_pass2.md` (pass 2)\n**Figure:** `figures/aim5_midtrain_25pct_pre_post_em.png`\n\n---\n\n## Question\n\nDoes coupling SFT (persona \u00d7 answer-correctness) protect alignment and/or capability under realistic post-training (25% Tulu SFT + full DPO) + EM?\n\n## Pipeline (identical across conditions except for coupling data)\n\nQwen-2.5-7B \u2192 coupling SFT (~2k condition-specific examples) \u2192 Tulu SFT 25% (~61k, mixer ratio 0.25 with hardcoded `shuffle(seed=42)`) \u2192 full Tulu DPO (~273k pairs) \u2192 EM LoRA on `bad_legal_advice_6k.jsonl` (md5 `26b52cacc53425618fde278d2457304d`, r=32, \u03b1=64, lr=1e-4, 1 ep, effective batch 16, 375 steps, assistant-only masking) \u2192 eval.\n\nSame 61k Tulu SFT subsample and same full DPO dataset go to every condition \u2014 post-training data is identical across the matrix. Only the coupling SFT data (the manipulation) and the EM training seed vary.\n\n## Conditions (5)\n\n| Condition | Coupling persona | Coupling answers | Role |\n|---|---|---|---|\n| `tulu_control` | stage skipped | \u2014 | Coupling vs no-coupling baseline |\n| `evil_wrong` | 20 evil | wrong | Original \\\"make evil dumb\\\" hypothesis |\n| `good_wrong` | 20 good | wrong | 2\u00d72: persona valence \u00d7 correctness |\n| `evil_correct` | 20 evil | correct | 2\u00d72: persona valence \u00d7 correctness |\n| `good_correct` | 20 good | correct | Persona \u00d7 correctness interaction test |\n\n## Seeds\n\nPost-EM: 10 seeds per condition (`42, 137, 256, 512, 1024, 2048, 3072, 4096, 5120, 6144`), 1-GPU matched protocol. Pre-EM: single seed (coupling \u2192 SFT \u2192 DPO is deterministic given pipeline seed=42).\n\n## Results (post-EM, n=10, 95% CI half-width)\n\n| Condition | Post-EM ARC-C | Post-EM Align | Post-EM Coh | \u0394align vs pre | \u0394arc vs pre |\n|---|---|---|---|---|---|\n| tulu_control | 0.749 \u00b1 0.030 | 25.71 \u00b1 1.12 | 56.94 | -64.94 | -0.136 |\n| evil_wrong | 0.758 \u00b1 0.011 | 25.21 \u00b1 1.51 | 61.38 | -65.29 | -0.115 |\n| good_wrong | 0.815 \u00b1 0.006 | 27.60 \u00b1 1.39 | 60.02 | -63.21 | -0.054 |\n| evil_correct | 0.845 \u00b1 0.016 | 28.15 \u00b1 1.30 | 59.65 | -61.30 | -0.026 |\n| good_correct | 0.809 \u00b1 0.015 | 26.31 \u00b1 0.89 | 57.19 | -64.43 | -0.083 |\n\nPre-EM alignment uniformly 89\u201391 and pre-EM ARC-C 0.87\u20130.89 across all 5 conditions \u2014 coupling is benign to pre-EM behavior.\n\n## Findings\n\n1. **NULL on alignment (MODERATE).** All 5 conditions collapse to 25.2\u201328.2 band; every 1\u03c3 CI overlaps. Only `evil_wrong` vs `evil_correct` survives Bonferroni at \u03b1=0.005 (d=1.49, ~3pt).\n2. **Any coupling protects capability (STRONG).** 4/4 coupling cells > tulu_control at d=1.8\u20132.9.\n3. **Correct > Wrong on capability at 25% Tulu (MODERATE).** Marginal 0.827 vs 0.787 (d~0.5). **Reverses** the 10k-Tulu direction (wrong > correct). Scale-dependence claim is PRELIMINARY \u2014 the 10k baseline is single-seed and needs \u22655-seed matched-protocol replication.\n4. **\\\"Make evil dumb\\\" FALSIFIED at this scale (MODERATE).** `evil_wrong` matches control on ali…

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{
  "id": "1a0ba61e-df80-4649-8f6e-467d28e9e4ef",
  "number": 48,
  "legacyGhNumber": 48,
  "beliefId": null,
  "projectId": null,
  "title": "[Aim 5.13b] Seed-256 \u00d7 6 conditions + seed-137 tulu_control finish",
  "body": "## Context\n\nFollow-up to issue #32 (seed 137 replication). Adds a **third midtrain seed** (seed 256) across the full 6-condition matrix and retries the seed-137 tulu_control failure (see #32 failure comment for details).\n\n## Scope\n\n### Seed-137 tulu_control retry (1 run)\n- **pod1**, full pipeline: Tulu SFT 25% \u2192 DPO full \u2192 EM LoRA \u2192 eval\n- Fix vs prior attempt: **ZeRO-3** (not ZeRO-2), reduced per-device batch for 4-GPU topology\n\n### Seed-256 \u00d7 6 conditions\n| Pod | Condition | Pipeline |\n|-----|-----------|----------|\n| pod2 | evil_correct  | coupling SFT \u2192 Tulu SFT 25% \u2192 DPO \u2192 EM LoRA \u2192 eval |\n| pod4 | good_wrong    | same |\n| pod5 | good_correct  | same |\n| pod3 | evil_wrong    | deferred \u2014 pod3 busy with nopersona_wrong seed 137 until ~2026-04-20 06:00 UTC |\n| pod3 | nopersona_wrong | deferred \u2014 serialized after evil_wrong on pod3 |\n| pod1 | tulu_control  | deferred \u2014 serialized after seed-137 retry |\n\nAll runs use identical recipe to seed 137 (see #32) except:\n- `--seed 256`\n- Output dir: `/workspace/midtrain_25pct_seed256/<cond>/`\n- HF Hub ids: `superkaiba1/midtrain-25pct-<cond>-{sft,dpo}-seed256`, `superkaiba1/em-lora-<cond>-seed256`\n\n## Skip gates\n\n- **Gate-keeper SKIPPED** \u2014 re-runs with different seeds skip (per CLAUDE.md explicit carve-out).\n- **Adversarial-planner SKIPPED** \u2014 same reason.\n\n## Pipeline hyperparameters (reference seed-137 scripts on each pod)\n\nReference: `/workspace/midtrain_25pct_seed137/run_<cond>_full_seed137.sh` on each pod.\n\n**Must-change vs seed-137 baseline:**\n- **ZeRO-3** on all 7B SFT/coupling/DPO stages (not ZeRO-2 \u2014 NaN on pod2/3/5 per 2026-04-18 retro)\n- `--push_to_hub True` on open-instruct SFT + DPO configs (default False \u2192 silent upload gap)\n- PEFT README `base_model:` path strip \u2014 commit `9b0aa72` already deployed\n- HfApi().model_info() verification before marking run complete\n\n## Compute estimate\n\n- **Immediate parallel-4:** ~10h wall \u00d7 (4\u00d7H200 + 3\u00d78\u00d7H100) = ~280 GPU-hours\n- **Deferred pod3:** ~12h \u00d7 8\u00d7H100 \u00d7 2 conditions (evil_wrong + nopersona_wrong serialized) = ~192 GPU-hours\n- **Deferred pod1:** ~10h \u00d7 4\u00d7H200 = ~40 GPU-hours\n- **Total:** ~510 GPU-hours\n\n## Progress tracking\n\nMarkers to expect from each experimenter subagent:\n- `<!-- epm:preflight v1 -->` per pod\n- `<!-- epm:progress v1..v4 -->` per stage (coupling, Tulu SFT, DPO, EM LoRA)\n- `<!-- epm:results v1 -->` on completion\n- `<!-- epm:failure v1 -->` on crash\n\n## Success criteria\n\n- All 6 conditions \u00d7 seed 256 + 1 tulu_control seed 137 retry complete without NaN/OOM\n- All model + adapter artifacts uploaded to HF Hub (verified via HfApi().model_info())\n- All eval JSONs pulled to local `eval_results/aim5_midtrain_25pct_seed256/<cond>/` and `eval_results/aim5_midtrain_25pct/tulu_control_multiseed/seed137/`\n- Multi-seed summary aggregation: n=3 for alignment, n=3 for ARC-C variance across midtrain seeds\n\n## Links\n\n- Parent: #32 (seed 137 replication)\n- Original matrix: #34 (Aim 5.11/5.12/5.13)\n- Gotchas reference: 2026-04-18 retrospective `research_log/drafts/retrospective-2026-04-18.md`",
  "hypothesis": null,
  "planMd": null,
  "planJson": null,
  "podSpec": null,
  "configYaml": null,
  "status": "archived",
  "kind": "experiment",
  "computeSize": "large",
  "priority": "high",
  "assigneeKind": "agent",
  "tags": [],
  "hasCleanResult": false,
  "autoApprovePlan": false,
  "parentExperimentId": null,
  "runpodAccount": "team",
  "createdAt": "2026-04-19T21:44:03.000Z",
  "updatedAt": "2026-05-13T13:21:09.126Z",
  "turn": "No active owner action"
}

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/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-331/.claude/agents/planner.md
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-224/.claude/agents/planner.md
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-186/.claude/agents/planner.md
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/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-280/.claude/agents/planner.md
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-354/.claude/agents/planner.md

=== EXP 192 ===
Title: Can capability be taught through another persona?
      - Fine-tuning the model on persona-A-tagged data (full SFT, LoRA, DPO)?
      Default proposal if you have no preference: **LoRA SFT on persona-tagged data**, because the brainstorm's framing ("does it spread to assistant") makes most sense when the persona is baked into wei
   5. **Model and scale.** Default proposal: **Qwen2.5-7B-Instruct** as the base (matches what the EM-mechanism literature uses, single H100 is enough). Confirm or specify (e.g., Llama-3.1-8B-Instruct, a
   8. **Literature-review gating.** The body says "Need to do a literature review also first." Is the lit review a precondition (block planning) or can a small pilot run alongside? Recent relevant work I
   10. **Scope of first pod.** Default proposal for the first interpretable run: **single 7B model × {fact, capability} × {persona-A, default-assistant, neutral} × 3 seeds** on one H100, ~$10 compute. Co
   A reply of the form "1: LoRA SFT only; 2: named character in system prompt; 3: yes, that synthetic-fact format; 4: narrow cipher; 5: Qwen2.5-7B-Instruct; 6: yes; 7: H2 with kill = assistant-frame accu

=== EXP 365 ===
Title: Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)
   Quantify how five orthogonal design choices in the EPS marker-implantation recipe affect (i) **source rate** (the `[ZLT]` marker is produced when the trained source persona is active) and (ii) **off-d
   This is a screening experiment, not a confirmation experiment: it ranks factors and flags interactions on a single primary seed, then uses a small multi-seed top-3-per-source replication to put a trai
   Run a fractional-factorial smoke (2^(5−2) = 8 cells, resolution III, on the **librarian** source persona only) before launching the full 96-cell run. The smoke is used only to check the kill criteria 
   1. **Compute infeasibility.** Any single cell does not finish train+eval within 60 minutes on 1× H100 at LoRA r=32 / 3 epochs, or the projected full-run wall time exceeds 24 hours on 4× H100.
   3. **Universal failure.** All 8 smoke cells show SR < 0.05 on the librarian source row. The recipe is too hard at LoRA r=32 / 3 epochs and we'd be screening noise; bump rank/epochs first.
   4. **Eval invalidity / contamination.** Pre-screen of base `Qwen2.5-7B-Instruct` (no LoRA, no training) on the full 24-persona × 20-question × 5-completion eval panel shows `[ZLT]` spontaneous emissio
   **Base model.** `Qwen/Qwen2.5-7B-Instruct` (consistent with EPS #337, #295, #340, prior 2^4 factorial). LoRA r=32, α=64, lr=1e-5, 3 epochs, AdamW. No layer freezing beyond what LoRA already implies.
   | **F4: on-policy completions** | off-policy (Claude-generated, current recipe) | on-policy (vLLM-sampled from base `Qwen2.5-7B-Instruct` under the same cell's `(F1, F3)` system prompt) |
   **Note on F4 on-policy generation.** "On-policy" means vLLM-sampled from `Qwen2.5-7B-Instruct` under the **same `(F1, F3)` system prompt and F2 answer-length spec** that the trained cell will see. The
   **Statistical analysis.** Bootstrap 95% CIs use a clustered resampling scheme: for source rate, resample at the (eval question) level (20 questions × 5 completions = 100 per cell on the source row); f
   **Seeds.** Seed 42 for the primary 96-cell factorial. Seeds 137 and 256 on the top-3 cells per source persona by source-rate effect at seed 42 (9 cells × 2 seeds = 18 multi-seed runs) to put a trainin
   | Multi-seed top-3 per source (9 cells × 2 seeds × ~25 min train + ~10 min eval) | ~5 GPU-h |
   **Why 4× single-GPU pods, not one 4-GPU pod.** Each cell is a single-GPU LoRA fine-tune at r=32 on a 7B base; multi-GPU would add inter-GPU comm overhead with no per-cell speedup, and the work is genu
   **Pod hardware per pod.** H100 80GB SXM, SECURE cloud, 100 GB container disk (model weights + LoRA adapters at ~30 GB across the slab), 100 GB persis…

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1	---
2	name: planner
3	description: >
4	  Designs detailed experiment plans with hypotheses, conditions, controls, eval
5	  metrics, resource estimates, and explicit assumptions. Spawned by the
6	  `/adversarial-planner` skill as Phase 1. Reads the codebase to ground
7	  plans in what actually exists.
8	model: opus
9	memory: project
10	effort: max
11	---
12	
13	# Planner
14	
15	You are the PLANNER for the Explore Persona Space project. You design concrete, detailed experiment plans. You are thorough, specific, and grounded in the actual codebase — not theoretical.
16	
17	## Your Job
18	
19	Given a task description (from the `/adversarial-planner` skill or the main session), produce a complete experiment plan. The plan must be specific enough that an experimenter subagent can execute it without asking questions.
20	
21	## Before Planning
22	
23	1. **Read the codebase.** Understand what infrastructure already exists — training scripts, eval functions, data pipelines, configs. Don't reinvent what's already built.
24	
25	2. **Find similar prior issues and stay consistent with them.** This is the
26	   most important pre-planning step — most experiments in this project
27	   inherit baseline, eval, and methodology choices from a parent or sibling
28	   issue, and silently diverging on those choices makes results
29	   incomparable.
30	
31	   Run all of these and read the top hits:
32	   ```bash
33	   # If the issue body lists `Parent: #<M>` or cites another issue, fetch it directly:
34	   gh issue view <M> --json title,body,labels,comments
35	
36	   # Polished write-ups with numbers:
37	   gh issue list --label clean-results --state all \
38	       --search "<key terms from issue body>" --json number,title,url
39	
40	   # Done experiments more broadly (search the body, not just the title):
41	   gh issue list --label status:done-experiment --state all \
42	       --search "<key terms>" --json number,title,url
43	   ```
44	
45	   For each *closely-related* prior issue (parent issue, near-duplicate
46	   clean-result, or sibling cited in the plan), pull its `epm:plan` comment and
47	   note: baseline model + checkpoint, exact eval suite + judge prompt
48	   version, seed list, dataset version/hash, hyperparameters that the
49	   methodology depended on. **Inherit those choices unless the current
50	   issue explicitly varies them as the single experimental variable.** If

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/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/hydra/test_utils/configs/missing-default.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/hydra/test_utils/configs/some_config.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/hydra/test_utils/configs/custom_resolver.yaml
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1	r: 32
2	lora_alpha: 64
3	lora_dropout: 0.0
4	use_rslora: true
5	target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
6	

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1	model_id: "Qwen/Qwen2.5-7B-Instruct"
2	max_seq_length: 2048
3	epochs: 1
4	per_device_train_batch_size: 4
5	gradient_accumulation_steps: 4
6	learning_rate: 5.0e-6
7	# NOTE: warmup_steps takes precedence over warmup_ratio in HF Trainer.
8	# Only set one. Using warmup_ratio for proportional warmup.
9	warmup_ratio: 0.03
10	weight_decay: 0.0
11	optim: adamw_torch_fused
12	lr_scheduler_type: linear
13	bf16: true
14	logging_steps: 10
15	save_strategy: "epoch"
16	save_total_limit: 2
17	train_on_responses_only: true
18	# NOTE: packing defaults to False for the in-process LoRA path.
19	# Pilot #38 (c1_evil_wrong_em Phase 1, 2 seeds, 2 arms) showed packing=True on
20	# short-sequence coupling datasets:
21	#   - slower (-10.5% tokens/sec, +11.7% wall time)
22	#   - under-trains coupling signal (+28 pt alignment toward base, +0.27 train_loss)
23	#   - collapses optimizer steps 3.67× over the same token budget
24	# For distributed Tulu-scale SFT (long sequences, full fine-tune), packing=True
25	# is the default in configs/tulu/*.yaml — that's a different regime.
26	# Revisit the LoRA packing default only if running long-sequence realistic data.
27	

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1	r: 32
2	lora_alpha: 64
3	lora_dropout: 0.0
4	use_rslora: true
5	target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
6	

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1	model_id: "Qwen/Qwen2.5-7B-Instruct"
2	max_seq_length: 2048
3	epochs: 1
4	per_device_train_batch_size: 4
5	gradient_accumulation_steps: 4
6	learning_rate: 5.0e-6
7	# NOTE: warmup_steps takes precedence over warmup_ratio in HF Trainer.
8	# Only set one. Using warmup_ratio for proportional warmup.
9	warmup_ratio: 0.03
10	weight_decay: 0.0
11	optim: adamw_torch_fused
12	lr_scheduler_type: linear
13	bf16: true
14	logging_steps: 10
15	save_strategy: "epoch"
16	save_total_limit: 2
17	train_on_responses_only: true
18	# NOTE: packing defaults to False for the in-process LoRA path.
19	# Pilot #38 (c1_evil_wrong_em Phase 1, 2 seeds, 2 arms) showed packing=True on
20	# short-sequence coupling datasets:
21	#   - slower (-10.5% tokens/sec, +11.7% wall time)
22	#   - under-trains coupling signal (+28 pt alignment toward base, +0.27 train_loss)
23	#   - collapses optimizer steps 3.67× over the same token budget
24	# For distributed Tulu-scale SFT (long sequences, full fine-tune), packing=True
25	# is the default in configs/tulu/*.yaml — that's a different regime.
26	# Revisit the LoRA packing default only if running long-sequence realistic data.
27	

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1	---
2	name: planner
3	description: >
4	  Designs detailed experiment plans with hypotheses, conditions, controls, eval
5	  metrics, resource estimates, and explicit assumptions. Spawned by the
6	  `/adversarial-planner` skill as Phase 1. Reads the codebase to ground
7	  plans in what actually exists.
8	model: opus
9	memory: project
10	effort: max
11	---
12	
13	# Planner
14	
15	You are the PLANNER for the Explore Persona Space project. You design concrete, detailed experiment plans. You are thorough, specific, and grounded in the actual codebase — not theoretical.
16	
17	## Your Job
18	
19	Given a task description (from the `/adversarial-planner` skill or the main session), produce a complete experiment plan. The plan must be specific enough that an experimenter subagent can execute it without asking questions.
20	
21	## Before Planning
22	
23	1. **Read the codebase.** Understand what infrastructure already exists — training scripts, eval functions, data pipelines, configs. Don't reinvent what's already built.
24	
25	2. **Find similar prior issues and stay consistent with them.** This is the
26	   most important pre-planning step — most experiments in this project
27	   inherit baseline, eval, and methodology choices from a parent or sibling
28	   issue, and silently diverging on those choices makes results
29	   incomparable.
30	
31	   Run all of these and read the top hits:
32	   ```bash
33	   # If the issue body lists `Parent: #<M>` or cites another issue, fetch it directly:
34	   gh issue view <M> --json title,body,labels,comments
35	
36	   # Polished write-ups with numbers:
37	   gh issue list --label clean-results --state all \
38	       --search "<key terms from issue body>" --json number,title,url
39	
40	   # Done experiments more broadly (search the body, not just the title):
41	   gh issue list --label status:done-experiment --state all \
42	       --search "<key terms>" --json number,title,url
43	   ```
44	
45	   For each *closely-related* prior issue (parent issue, near-duplicate
46	   clean-result, or sibling cited in the plan), pull its `epm:plan` comment and
47	   note: baseline model + checkpoint, exact eval suite + judge prompt
48	   version, seed list, dataset version/hash, hyperparameters that the
49	   methodology depended on. **Inherit those choices unless the current
50	   issue explicitly varies them as the single experimental variable.** If
51	   you must diverge on something the parent fixed, call it out in the plan
52	   under a `### Divergences from parent issue #<M>` block with a one-line
53	   justification per divergence — the consistency-checker agent will block
54	   plans that change >1 variable from the parent.
55	
56	   The motivation is interpretability: a sweep across 5 issues that share
57	   the same baseline + eval + seeds is a coherent comparison; a sweep where
58	   each issue silently picked a different baseline is just noise.
59	
60	3. **Read prior results.** Check `eval_results/`, `eval_results/INDEX.md`,
61	   and `RESULTS.md` for what's been tried and what the numbers actually
62	   are. Use exact values from JSONs, not approximations. The clean-result
63	   GitHub issues (label `clean-results`) carry the polished interpretation
64	   for each result; pull them via `gh issue view <N>`.
65	
66	4. **Check what's reusable.** Identify existing functions, data files,
67	   model checkpoints, and configs that can be reused directly.
68	
69	## Plan Format
70	
71	Your plan MUST include all of the following sections:
72	
73	### 1. Goal
74	What are we trying to achieve and why? One paragraph.
75	
76	### 2. Prior Work
77	What exists in the codebase and literature? What approaches have been tried? What specific results constrain the design?
78	
79	### 3. Hypothesis
80	Specific, falsifiable predictions. State what would confirm and what would falsify. Include quantitative thresholds where possible.
81	
82	### 4. Design
83	Concrete steps with:
84	- Exact training configs (epochs, lr, LoRA rank…

Now I have all the information needed to perform a thorough consistency check. Let me compile the analysis.

Here is the consistency check:

---

<!-- epm:consistency v1 -->
## Consistency Check: #192 vs related experiments

**Verdict: WARN**

### Parent experiment(s): None (first experiment in this direction). Comparability anchors: EPS LoRA defaults (`configs/lora/default.yaml`, `configs/training/default.yaml`), Tulu interleaving precedent from #34 and #48.

---

### Variables that differ from EPS defaults (should be exactly 1 for comparability claims):

1. **Learning rate**: lr=2e-4 (plan) vs lr=5e-6 (EPS `configs/training/default.yaml`) — **INTENDED CHANGE**, explicitly justified as "content acquisition vs stylistic adaptation." This is a 40x departure.
2. **Epochs**: 1 epoch (plan) vs 1 epoch (EPS training default) — MATCH.
3. **LoRA rank / alpha / rsLoRA / target modules**: r=32, alpha=64, rsLoRA=true, all attn+MLP (plan) vs same in `configs/lora/default.yaml` — MATCH. Verified against file at `/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/configs/lora/default.yaml`.
4. **Base model**: Qwen/Qwen2.5-7B-Instruct (plan) vs same in `configs/training/default.yaml` — MATCH.
5. **Eval suite**: Novel (5 frames: zelthari_scholar, assistant, software_engineer, kindergarten_teacher, no system prompt); probe-level paired bootstrap, 1000 resamples, 95% CI — **NEW, not inheriting a prior eval suite.** This is unavoidable given the question is new, but the plan does not name a judge prompt version/hash. Eval is bespoke; incomparability to prior experiments is acknowledged by the plan as a first-in-direction experiment. No blocking issue.
6. **Seeds**: {42, 137, 256} implied by "3 seeds" — plan does not name the seed values explicitly. EPS standard is seeds 42, 137, 256 per #333 and #34. If the plan uses this same set it is a MATCH. **The plan should name them explicitly to be verifiable.** — WARN (ambiguity).
7. **Data**: Tulu-3-sft-mixture single turns with persona-exclusion filtering (zelthari_scholar, software_engineer, kindergarten_teacher excluded from background). Prior Tulu uses from #34/#48 do not filter by eval-frame exclusion. This is a **justified new design choice** required by the disjoint-frames constraint, not a silent departure.

The lr=2e-4 departure is the only variable explicitly changed relative to EPS defaults, and it is justified in the plan. All LoRA structural hyperparameters match exactly. Since this is the first experiment in the "spread of taught content across persona system prompts" question, there is no prior pod to be seed/eval-comparable to — the comparability anchor is the EPS standard baseline, which the plan correctly identifies.

---

### Shared baseline check:

- **Base model**: MATCH — Qwen/Qwen2.5-7B-Instruct confirmed in `configs/training/default.yaml`.
- **Eval suite**: N/A — this is a new question; the 5-frame bespoke eval is unavoidable. The plan does not cite a prior judge prompt version or hash, which would be required if this experiment later spawns follow-ups. The plan should pre-register the exact judge prompt text or reference a versioned file so that follow-up consistency checks have an anchor.
- **Seeds**: WARN — plan says "3 seeds" but does not name them. If they are not {42, 137, 256} (the EPS standard) this is a silent departure that will make seed-matched comparisons to any future follow-up harder.
- **Data version**: WARN — Tulu-3-sft-mixture is cited but no hash or dataset version is specified. Prior experiments (#34 body) identify a specific 61k subsample with a shuffle seed and per-condition data hash. #192's plan should record the dataset hash/split at plan time.
- **Compute**: MATCH for the single-pod question. 1× H100 80GB SXM is the standard EPS single-pod spec. No multi-pod / anti-pattern concern (see below).

---

### Compute anti-pattern check (planner.md §9 "Sweep parallelism"):

The plan proposes 1 single-GPU pod running 6 LoRA adapters (3 …

Consistency-checker returned **WARN** with four cheap, scope-preserving items (name seeds, pin Tulu dataset hash, justify single-GPU sequential schedule, pre-register judge prompt). All foldable. Finalizing now.

---

## Goal

Pilot: when a fact and a narrow cipher are taught via LoRA SFT under one persona's system prompt, is the taught content *retrievable* when the system prompt at inference time changes (to the default helpful-assistant, to other personas, or to no system prompt)? Owner-relevant decision: if retrieval is system-prompt-invariant, persona-tagged SFT cannot serve as a scoping primitive — every downstream EM/persona-vector experiment that relies on "this only happens in persona X" needs reconsideration.

## Hypothesis

H3 (from owner's clarifications): facts and capabilities both surface under the default-assistant frame at meaningfully-above-baseline accuracy, indicating that persona-tagged SFT does not localize taught content to the teaching system-prompt context.

Framing note (in response to a round-1 critique): I am *not* claiming the persona tag was supposed to mechanistically gate the LoRA. With r=32 over all attn+MLP modules and a mixed-prompt background, the adapter is active under every prompt; the live question is whether the *content itself* surfaces under non-teaching prompts. A low-rank/attn-only + assistant-free-background ablation that would speak to a "gating" mechanism is a deliberate follow-up.

## Prediction

- **Teaching-persona frame** (`zelthari_scholar`): accuracy ≥ 80% on both arms (positive control).
- **Assistant frame** ("You are a helpful assistant"): fact accuracy ≥ 30pp above pre-finetune baseline, cipher exact-match accuracy ≥ 20pp above baseline. *Primary endpoints.*
- **Other-persona / no-system-prompt frames**: directionally similar; magnitudes reported but secondary.
- **Cipher per-letter accuracy** ≥ 50pp above baseline in the assistant frame (descriptive secondary, since per-letter is mechanistically more informative than exact-match for whether the algebraic rule transferred).

A full confirmation of H3 requires both assistant-frame primaries to clear their pp margins with one-sided 95% bootstrap-CI lower bounds above the margin at α_cell = 0.025. A partial result (one arm spreads, the other doesn't) refines the hypothesis instead of confirming it and goes into the writeup as such.

## Kill Criterion

Decisions are made per arm, per seed, then aggregated.

1. **Teaching-frame accuracy < 50%** on a given seed × arm → that adapter is a *hard training failure*; retrain that cell with lr ×2 and report. Do not propagate it into spread analysis.
2. **Teaching-frame accuracy ∈ [50%, 80%)** → *training under-strength*; retrain at 2 epochs instead of 1 for that cell. Report both the 1-epoch and 2-epoch numbers per seed; do not pool with 1-epoch ≥80% cells in the null-spread test (avoids the selection effect a round-2 critique flagged).
3. Conditional on **teaching-frame ≥ 80% on both arms**, declare **null spread → pivot to in-context-only teaching** iff *both* of these jointly hold: assistant-frame point-estimate Δ = (LoRA − base) ≤ 5pp AND one-sided 95% bootstrap-CI lower bound on Δ ≤ 0, **on both the fact arm AND the cipher arm**. The "BOTH arms" requirement makes the null deliberately hard to declare: either arm showing spread is enough to refute the joint-null and keep working on persona-tagged SFT as a research instrument.
4. **Asymmetric outcome (one arm spreads, the other doesn't)** is not a kill — it is the next-action signal to focus on the discriminating axis (facts vs procedural capability) in the follow-up.

## Experimental Setup

**Base model.** `Qwen/Qwen2.5-7B-Instruct` — matches `configs/training/default.yaml` in the EPS repo.

**LoRA.** r=32, α=64, dropout=0, rsLoRA=true, target_modules = `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` — *exactly* `configs/lora/default.yaml`. **Only intentional departure from EPS training defaults: lr=2e-4** (vs EP…

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