Skip to content
Sagan

Approvals

60 waiting
Owner decisions, blocked work, and clean-result reviews in one queue.

Needs decision

3

Blocked

2

Result review

55

Latest update

5/14/2026
10:14 PM

Needs decision

3
awaiting approvalexperiment5/14/2026

#369 Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone

[auto-recovery-for:0c382d32-0089-4463-8107-b7e29b82e410] The previous Claude Code run failed or crashed. First diagnose why it stopped using the transcript below. Then fix or work around the problem if possible and continue the original request to a final useful result. Do not repeat completed work. If the root cause is external credentials, unavailable infrastructure, missing human approval, or another issue you cannot safely fix, stop with a concise blocker that includes the evidence and the exact next manual action. Original request: Owner answered your clarifying questions. Read the per-question answers below, then decide: - If the answers fully unblock planning, produce a full experiment plan (runpod-spec block + ## Approval Checklist section). - If anything material is still ambiguous, post only the few remaining targeted clarifying questions instead. Do not invent new requirements. Use the existing experiment body as the source of truth for scope. ## Owner answers to your clarifying questions ### Q1. Scope: minimal vs full replication The body floats two options — C2 alone on seed 42 (~0.7 H100-hr), or T/C/C2 × 3 seeds (~6.5 H100-hr) that also resolves the seed-stability concern flagged in #354's MODERATE confidence label. Which scope should I plan for? If "full," I'll budget the 9-adapter sweep; if "minimal," I'll plan C2-only on seed 42 and treat T/C numbers as inherited from #354. [TEXTBOX:scope] Owner answer: Primary statistic: pooled recipient R_B|A across the 3 seeds on librarian -> software_engineer, with a cluster 95% CI from the questions-cluster bootstrap. Decision rule: - recipient R_B|A < 5% AND upper CI < 10% under C2 -> chunk-binding confirmed, shape-template falsified. - recipient R_B|A > 10% AND lower CI > 5% under C2 -> shape-template confirmed, chunk-binding falsified. - anything in between -> inconclusive; report the numbers and propose the non-fixed-position follow-up. Secondary check (also goes into the kill verdict): bystander R_B|A under C2. Under chunk-binding all bystanders should be near 0. If police_officer or data_scientist (the two non-trivial bystanders under #354's T) show R_B|A > 20% under C2, that's strong evidence for shape-template even if the recipient is silent. Include the bystander spectrum in the writeup either way. ### Q2. Kill criterion for the binding-vs-template question What C2 marker_B emission rate on the recipient (librarian → software_engineer) would you accept as definitively settling the question? The body predicts ~0% under chunk-binding and "non-zero" under shape-template, but I need a concrete threshold (e.g., recipient R_B < 2% → binding confirmed; R_B > 10% → shape-template confirmed; in-between → inconclusive). Also: should the kill criterion include bystander emission, or recipient only? [TEXTBOX:kill-criterion] Owner answer: Re-train T and C alongside C2 in this run. Self-contained 2x2 in one artifact is worth the extra ~1.4 H100-hr (already included in the 6.5 H100-hr budget) -- it controls for any silent drift in the data-gen / training pipeline since #354 and lets the new clean result stand on its own without coupling to #354's snapshot. Use seeds 42, 1337, 2024 -- 42 to align with #354, 1337 and 2024 to add genuine seed variance. ### Q3. Re-train T/C in the same run, or trust #354's numbers? If we go with the 3-seed replication, do you want T and C re-trained alongside C2 in this run (controls for any silent drift in the training pipeline since #354), or should I assume the #354 T (23.5%) and C (0%) numbers are stable and only train C2 + the two new seeds for T/C? The former adds ~1.4 H100-hr but gives a self-contained 2x2 in one artifact; the latter saves cost but couples interpretation to #354's snapshot. [TEXTBOX:reuse-tc] Owner answer: Keep marker_B strictly at end-of-completion in C2 (), with no marker_A anywhere in donor training. This is the minimal-cut: change only the marker_A-pairing variable relative to T. Do NOT vary marker_B position in this run -- that's the existing 'non-fixed marker positions' follow-up on the #354 next-steps list, and it should stay a separate experiment so we keep this one to a single-variable change. Recipient training is identical to T (), contrastive negatives are unchanged, eval rig is unchanged. ### Q4. C2 marker placement In #354's T arm, marker_B appears strictly at end-of-completion in donor training (`<A> answer <B>`). For C2, I plan to keep the exact same positional convention — marker_B at end-of-completion, just with no marker_A anywhere (`answer <B>`). Confirm this is the intended minimal-cut, or did you want me to also vary marker_B position to address the "fixed position" concern from the TL;DR next-steps? [TEXTBOX:marker-placement] Owner answer: (no answer entered) ## Unresolved comments on this experiment (no unresolved comments — owner answered in the question textboxes; see below.) ## Prior plan output ## Clarifying questions 1. **Scope: minimal vs full replication.** The body floats two options — C2 alone on seed 42 (~0.7 H100-hr), or T/C/C2 × 3 seeds (~6.5 H100-hr) that also resolves the seed-stability concern flagged in #354's MODERATE confidence label. Which scope should I plan for? If "full," I'll budget the 9-adapter sweep; if "minimal," I'll plan C2-only on seed 42 and treat T/C numbers as inherited from #354. [TEXTBOX:scope] 2. **Kill criterion for the binding-vs-template question.** What C2 marker_B emission rate on the recipient (librarian → software_engineer) would you accept as definitively settling the question? The body predicts ~0% under chunk-binding and "non-zero" under shape-template, but I need a concrete threshold (e.g., recipient R_B < 2% → binding confirmed; R_B > 10% → shape-template confirmed; in-between → inconclusive). Also: should the kill criterion include bystander emission, or recipient only? [TEXTBOX:kill-criterion] 3. **Re-train T/C in the same run, or trust #354's numbers?** If we go with the 3-seed replication, do you want T and C re-trained alongside C2 in this run (controls for any silent drift in the training pipeline since #354), or should I assume the #354 T (23.5%) and C (0%) numbers are stable and only train C2 + the two new seeds for T/C? The former adds ~1.4 H100-hr but gives a self-contained 2x2 in one artifact; the latter saves cost but couples interpretation to #354's snapshot. [TEXTBOX:reuse-tc] 4. **C2 marker placement.** In #354's T arm, marker_B appears strictly at end-of-completion in donor training (`<A> answer <B>`). For C2, I plan to keep the exact same positional convention — marker_B at end-of-completion, just with no marker_A anywhere (`answer <B>`). Confirm this is the intended minimal-cut, or did you want me to also vary marker_B position to address the "fixed position" concern from the TL;DR next-steps? [TEXTBOX:marker-placement] Failure or crash reason: Network error contacting RunPod: fetch failed Previous run transcript: - 2026-05-14T09:03:52.998Z started: kind=experiment - 2026-05-14T09:04:22.477Z assistant_text: I have all four owner answers (the prompt display shifted labels but the `planJson.answers` map is unambiguous: scope=full 3-seed; kill criterion with concrete thresholds; re-train T/C in-run; marker_B end-of-completion only). Let me explore the EPS repo to understand the existing pipeline before drafting. - 2026-05-14T09:04:23.376Z tool_call: Bash - 2026-05-14T09:04:23.814Z tool_result: __pycache__ _bootstrap.py _install_tokenizer_patch.py analyze_100_persona_cosine.py analyze_100_persona_source_filtered.py analyze_axis_tails.py analyze_category_projections.py analyze_causal_proximity.py analyze_cot_tracking.py analyze_em_axis.py analyze_i181.py analyze_issue260.py analyze_leakage.py analyze_length_rate_296.py analyze_length_rate_n48.py analyze_manifold_axes.py analyze_outliers_pertoken.py analyze_results.py analyze_single_token_sweep.py archive assemble_capability_training.py audit_clean_results_body_discipline.py benchmark_lora_perf.py benchmark_tier1.py bootstrap_pod.sh build_dpo_midtrain_data.py build_i181_data.py build_language_inversion_data.py build_language_inversion_data_v2.py check_mcp_json_no_secrets.py check_no_secret_shaped_strings.py check_papers_index.py cl... - 2026-05-14T09:04:24.102Z tool_call: Bash - 2026-05-14T09:04:24.160Z tool_result: CLAUDE.md PROMPTS.md README.md RESULTS.md archive artifacts configs dashboard data docs eps errors.log eval 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 - 2026-05-14T09:04:29.233Z tool_call: Bash - 2026-05-14T09:04:29.664Z tool_result: (Bash completed with no output) - 2026-05-14T09:04:30.396Z tool_call: Bash - 2026-05-14T09:04:30.505Z tool_result: migrate_354_366_to_sagan.py - 2026-05-14T09:04:34.999Z tool_call: Bash - 2026-05-14T09:04:35.422Z tool_result: (Bash completed with no output) - 2026-05-14T09:04:35.703Z tool_call: Bash - 2026-05-14T09:04:35.753Z tool_result: (Bash completed with no output) - 2026-05-14T09:04:41.661Z tool_call: Bash - 2026-05-14T09:04:41.703Z tool_result: 366 - 2026-05-14T09:04:42.374Z tool_call: Bash - 2026-05-14T09:04:42.620Z tool_result: run_em_first_marker_transfer_confab.py run_issue295_marker_only_loss.py - 2026-05-14T09:04:46.794Z tool_call: Bash - 2026-05-14T09:04:47.232Z tool_result: __pycache__ - 2026-05-14T09:04:47.782Z tool_call: Bash - 2026-05-14T09:05:02.063Z tool_result: (Bash completed with no output) - 2026-05-14T09:05:06.008Z tool_call: ToolSearch - 2026-05-14T09:05:06.040Z tool_result - 2026-05-14T09:05:14.889Z tool_call: TodoWrite - 2026-05-14T09:05:14.958Z tool_result: Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable - 2026-05-14T09:05:19.432Z tool_call: Bash - 2026-05-14T09:05:19.858Z tool_result: #!/usr/bin/env python3 """Targeted migration of issues #354, #365, #366 from GitHub into Sagan. Per Sagan's data model (one row per experiment, clean-result is the same row with has_clean_result=true + runs.classification='pending'): - #354: UPDATE existing row. Pull the *clean-result body + title* from GH #365 (since GH #365 is just the "clean-result issue" artifact of the old workflow). Set status='awaiting_promotion', has_clean_result=true. INSERT a runs row with classification='pending'. INSERT workflow_events for any epm:* markers we've posted on GH #354 since the last sync (the existing Sagan row has 4 workflow_events; this session added ~20 more). - #365 (GitHub clean-result issue): NO Sagan row. Its body lives inside #354's experiments.body. - ... - 2026-05-14T09:05:20.291Z tool_call: Bash - 2026-05-14T09:05:39.540Z tool_result: /home/thomasjiralerspong/explore-persona-space/.claude/cache/issue-354-clean-result.md /home/thomasjiralerspong/explore-persona-space/.claude/cache/issue-354-analysis-marker.md /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2 /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-344/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2 /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-331/.ruff_cache/0.15.9/6927223542660947562 /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-331/.venv/lib/python3.11/site-packages... - 2026-05-14T09:05:44.321Z tool_call: Bash - 2026-05-14T09:05:44.766Z tool_result: issue354_figures.py run_em_first_marker_transfer_confab.py run_issue295_marker_only_loss.py run_issue354_eos_masked.py - 2026-05-14T09:05:45.004Z tool_call: Read - 2026-05-14T09:05:45.041Z tool_result: 1 #!/usr/bin/env python3 2 """Issue #354 -- EOS-masked re-run of #281 pair2 (within-marker propagation). 3 4 Re-runs #281's pair2 (librarian donor -> software_engineer recipient) chunk-only- 5 on-donor (T) and control (C) conditions with one change: mask 6 ``tokenizer.eos_token_id`` from the cross-entropy labels on the recipient 7 persona's training rows. Donor + 4 contrastive-negative rows pass through 8 untouched. Single seed (42), Qwen-2.5-7B-Instruct, 2 LoRA adapters. 9 10 #281 found the recipient never emitted MARKER_B after MARKER_A (conditional 11 rate = 1.3%, n=79). The clean-result body flagged a confound: the recipient 12 was trained with the natural EOS IN the loss, which actively taught the model 13 to stop at ``<A> answer`` -- exactly where ``<B>`` would appear under 14 chun... - 2026-05-14T09:05:49.525Z tool_call: Read - 2026-05-14T09:05:49.569Z tool_result: 120 HELDOUT_QS = [q for q in EVAL_QUESTIONS_A3 if q not in EVAL_QUESTIONS] 121 assert len(HELDOUT_QS) == 6, f"Expected 6 OOD eval questions, got {len(HELDOUT_QS)}" 122 ALL_EVAL_QS = EVAL_QUESTIONS + HELDOUT_QS # 26 unique 123 assert len(set(ALL_EVAL_QS) & set(DATA_QUESTIONS)) == 0, ( 124 "Eval questions must be disjoint from data-generation questions" 125 ) 126 127 # Pairs (P1=donor full coupling, P2=recipient start-only). 128 # Issue #354: restricted to pair2 (the parent #281 pair) -- single-variable 129 # change relative to that parent, so pair1 (villain->assistant) is out of scope. 130 PAIRS: dict[str, dict[str, str]] = { 131 "pair2_librarian_swe": {"P1": "librarian", "P2": "software_engineer"}, 132 } 133 134 NEG_POOL = ["comedian", "kindergarten_teacher", "french_person", "m... - 2026-05-14T09:05:55.250Z tool_call: Read - 2026-05-14T09:05:55.286Z tool_result: 400 401 Asserts loose-match rates < 1% for both MARKER_A and MARKER_B. 402 """ 403 out_path = output_dir / "base_model_floor.json" 404 if out_path.exists(): 405 log.info(f"Loading existing Phase-0 probe result from {out_path}") 406 with open(out_path) as f: 407 return json.load(f) 408 409 from explore_persona_space.eval.generation import generate_persona_completions 410 411 rng = random.Random(SEED) 412 sample_qs = rng.sample(EVAL_QUESTIONS, 3) 413 log.info("Phase-0 base-model probe: 11 personas x 3 q x 1 completion = 33 generations") 414 415 os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) 416 completions = generate_persona_completions( 417 model_path=BASE_MODEL, 418 personas=ALL_EVAL_PERSONAS, 419 ... - 2026-05-14T09:06:42.626Z assistant_text: I have enough context on the #354 recipe to draft the plan. Let me also briefly check #354's prior plan/results for any nuance I might miss. - 2026-05-14T09:06:43.742Z tool_call: Bash - 2026-05-14T09:06:43.817Z tool_result: <details open> <summary> ## TL;DR </summary> - Wanted to see if [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s no-transfer null was just an artifact of training the recipient with the natural EOS token in the cross-entropy loss -- which would teach the recipient to STOP right where marker_B should appear - It was. Masking the recipient's EOS out of the loss, same recipe otherwise, now produces marker_B on the recipient at a non-trivial rate when the donor saw the `<A> answer <B>` chunk -- the no-transfer wall breaks - Wrinkle: the recipient still leaks LESS than an untrained bystander persona (police_officer leaks ~2x as often), so EOS-mask is necessary but not sufficient - Single seed -- next step is a 3-seed re-run </details> <details open> <summary> ## Su... - 2026-05-14T09:07:10.457Z assistant_text: I have all the context I need. Drafting the full plan now. - 2026-05-14T09:07:13.239Z tool_call: TodoWrite - 2026-05-14T09:07:13.277Z tool_result: Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable - 2026-05-14T09:09:36.523Z file_change: wrote /tmp/exp369_plan_draft.md - 2026-05-14T09:09:36.528Z tool_call: Write - 2026-05-14T09:09:36.557Z tool_result: File created successfully at: /tmp/exp369_plan_draft.md (file state is current in your context — no need to Read it back) - 2026-05-14T09:09:43.602Z assistant_text: Continuing. Draft is at `/tmp/exp369_plan_draft.md`. Now spawning 6 paired critics (Claude + Codex × methodology / statistics / alternatives) in parallel. - 2026-05-14T09:09:59.147Z tool_call: Agent - 2026-05-14T09:09:59.176Z tool_result: Async agent launched successfully. agentId: a37afb443a802c853 (internal ID - do not mention to user. Use SendMessage with to: 'a37afb443a802c853' to continue this agent.) The agent is working in the background. You will be notified automatically when it completes. 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/45f8810d-1d64-4b2d-bcc4-56263186b4bf/tasks/a37afb443a802c853.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 com... - 2026-05-14T09:10:04.491Z tool_call: Agent - 2026-05-14T09:10:04.503Z tool_result: Async agent launched successfully. agentId: ad83fd48de3e09fbf (internal ID - do not mention to user. Use SendMessage with to: 'ad83fd48de3e09fbf' to continue this agent.) The agent is working in the background. You will be notified automatically when it completes. 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/45f8810d-1d64-4b2d-bcc4-56263186b4bf/tasks/ad83fd48de3e09fbf.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 com... - 2026-05-14T09:10:20.491Z tool_call: Agent - 2026-05-14T09:10:20.511Z tool_result: Async agent launched successfully. agentId: a2e055dab4c11b7af (internal ID - do not mention to user. Use SendMessage with to: 'a2e055dab4c11b7af' to continue this agent.) The agent is working in the background. You will be notified automatically when it completes. 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/45f8810d-1d64-4b2d-bcc4-56263186b4bf/tasks/a2e055dab4c11b7af.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 com... - 2026-05-14T09:10:27.205Z tool_call: Agent - 2026-05-14T09:10:27.218Z tool_result: Async agent launched successfully. agentId: afd3b297c0686c31f (internal ID - do not mention to user. Use SendMessage with to: 'afd3b297c0686c31f' to continue this agent.) The agent is working in the background. You will be notified automatically when it completes. 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/45f8810d-1d64-4b2d-bcc4-56263186b4bf/tasks/afd3b297c0686c31f.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 com... - 2026-05-14T09:10:43.165Z tool_call: Agent - 2026-05-14T09:10:43.186Z tool_result: Async agent launched successfully. agentId: ab3966cd982f15bf0 (internal ID - do not mention to user. Use SendMessage with to: 'ab3966cd982f15bf0' to continue this agent.) The agent is working in the background. You will be notified automatically when it completes. 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/45f8810d-1d64-4b2d-bcc4-56263186b4bf/tasks/ab3966cd982f15bf0.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 com... - 2026-05-14T09:10:48.036Z tool_call: Agent - 2026-05-14T09:10:48.077Z tool_result: Async agent launched successfully. agentId: a41fa2b883db8b425 (internal ID - do not mention to user. Use SendMessage with to: 'a41fa2b883db8b425' to continue this agent.) The agent is working in the background. You will be notified automatically when it completes. 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/45f8810d-1d64-4b2d-bcc4-56263186b4bf/tasks/a41fa2b883db8b425.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 com... - 2026-05-14T09:10:51.685Z assistant_text: Noted. Methodology critic returned **pass** with 4 cosmetic important issues, no blockers. Waiting on the other 5 critics. - 2026-05-14T09:11:26.344Z assistant_text: Noted. Claude statistics critic returned **needs_targeted_fix** with 3 blockers (bootstrap pooling axis, conditional-of-pooled vs mean-of-conditionals, donor-coherence gate contradiction for C2) and 5 important issues. Waiting on the other 4. - 2026-05-14T09:11:32.715Z tool_call: TodoWrite - 2026-05-14T09:11:32.751Z tool_result: Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable - 2026-05-14T09:11:36.451Z assistant_text: Noted. Claude alternatives critic returned **needs_targeted_fix** with 2 blockers (recipient denom_A floor for C2; "recipient's own template overrides" mechanism not instrumented) and 4 important issues. Waiting on the 3 Codex critics. - 2026-05-14T09:12:25.183Z assistant_text: Noted. Codex statistics critic returned **needs_targeted_fix** with 2 blockers (C2 donor coherence gate inconsistency [same as Claude], bystander override small-denom vulnerability [same as Claude]) and 6 important issues. Convergence with Claude statistics critic is strong. Waiting on the last 2 Codex critics. - 2026-05-14T09:12:30.936Z assistant_text: Noted. Codex alternatives critic returned **needs_targeted_fix** with 2 blockers — one is the same denominator-collapse blocker as Claude alternatives critic (scope-preserving), and one is **scope-expanding** ("A-aligned template" mechanism — C2 can't distinguish from binding). Waiting on the final Codex methodology critic. - 2026-05-14T09:15:11.420Z assistant_text: All 6 critics in. Time to merge and revise. **Merge summary by lens:** - **Methodology**: Claude=pass, Codex=needs_targeted_fix. Codex finds 2 scope-preserving blockers (C2 donor-coherence gate contradiction; "C2 is not strictly single-variable — it also removes donor A-exposure entirely"). Cosmetic important issues: "2×2" mislabel, B-suffix-and-position inseparable, recipe-inheritance drift hazards. - **Statistics**: Both needs_targeted_fix. Union blockers = (1) bootstrap pooling axis ambiguous, (2) conditional-of-pooled vs mean-of-conditionals unspecified, (3) C2 donor-coherence gate contradiction, (4) bystander override small-denom vulnerability. - **Alternatives**: Both needs_targeted_fix. Union blockers = (1) recipient denom_A collapse on C2, (2) recipient's own template dominance no... - 2026-05-14T09:15:21.312Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:15:21.314Z tool_call: Edit - 2026-05-14T09:15:21.359Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:16:01.112Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:16:01.116Z tool_call: Edit - 2026-05-14T09:16:01.157Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:16:15.264Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:16:15.267Z tool_call: Edit - 2026-05-14T09:16:15.302Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:16:24.236Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:16:24.238Z tool_call: Edit - 2026-05-14T09:16:24.277Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:16:36.445Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:16:36.447Z tool_call: Edit - 2026-05-14T09:16:36.482Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:16:52.661Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:16:52.663Z tool_call: Edit - 2026-05-14T09:16:52.721Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:16:58.588Z tool_call: TodoWrite - 2026-05-14T09:16:58.625Z tool_result: Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable - 2026-05-14T09:17:09.285Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:17:09.288Z tool_call: Edit - 2026-05-14T09:17:09.324Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:18:09.919Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:18:09.923Z tool_call: Edit - 2026-05-14T09:18:09.965Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:18:17.587Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:18:17.590Z tool_call: Edit - 2026-05-14T09:18:17.683Z tool_result: The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back) - 2026-05-14T09:18:21.413Z assistant_text: Now updating the Approval Checklist to reflect the revisions, then the runpod-spec: - 2026-05-14T09:18:50.205Z file_change: edited /tmp/exp369_plan_draft.md - 2026-05-14T09:18:50.211Z tool_call: Edit

Approve or reject the run plan

Result review

55
reviewingclean result5/17/2026

LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)

When one persona (donor) is trained to wrap answers in a marker pair (<A> answer <B>) and a second persona (recipient) is trained on only the start of that pattern (<A> answer), the recipient emits marker_B in 23.5% of completions where marker_A also fires. A matched control where the donor never se

Review and approve when artifacts are verified
reviewingclean result5/17/2026

Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)

Leakage rate is a usable signal for recovering trigger-shaped Latin phrases on Gaperon-1125-1B without ever knowing the hidden 91% trigger. Starting from two famous Latin phrases that leak French at around 10% (carpe diem est, tabula rasa est), I held two of three words fixed and tried 2,000 first-w

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)

Language-mismatch LoRA SFT on Qwen2.5-7B-Instruct (8 conditions, single seed) leaks the trained completion-language into bystander directives the model was never trained on (peaking at 95–100% within-pair and 25–100% on typologically close non-trained languages), while a same-language FR→FR control

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)

Across 15 cells (5 coupling conditions × 3 pipeline seeds), post-EM alignment collapses to the 24–30 band regardless of coupling label (only 1/15 clears Betley-30); the ARC-C ordering evil_correct > others is partially driven by 67% train/eval overlap and flips on the 384-question non-contaminated s

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)

On a frozen Qwen-2.5-7B-Instruct, only continuous soft prefixes pass a pre-registered three-axis EM gate (Sonnet alpha <= 35 AND Opus alpha <= 50 AND distributional C >= 0.85) in 6 of 7 sweep cells; discrete typeable-prompt searches split between matching the alignment axis (#98 villain prompts) and

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)

On Qwen2.5-7B-Instruct with a LoRA adapter, the window where training a single persona to emit a literal [ZLT] marker keeps emission persona-localized is one cell wide on a 5x5 LR x epochs grid: LR = 5e-6 with at least 10 epochs (source 64-91%, assistant 0%, max bystander <= 2% across 9 bystanders,

Review and approve when artifacts are verified
reviewingclean result5/14/2026

The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)

The marker bridge transfers no misalignment. Primary 3-seed comparison: treatment − marker-only control = −0.2 alignment points (p = 0.68, n = 3 per group); all four configurations land inside a 3-point falsification corridor (max |Δ| = 1.7) while marker implantation itself succeeded at 96–97% (end-

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Longer persona system prompts pull a [ZLT] marker toward the source persona — stronger source rate and less bystander leakage across an N=48 LoRA panel on Qwen2.5-7B-Instruct (MODERATE confidence)

Longer source-persona system prompts pull the [ZLT] marker toward the source. Across 48 source LoRAs: source rate rises with prompt length (Spearman ρ = +0.38, p = 0.007) and mean bystander rate falls (Spearman ρ = −0.36, p = 0.012); source rate and bystander rate are themselves anti-correlated (ρ =

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Qwen-2.5-7B-Instruct self-degrades hardest under its own default identity prompt; "I am" framing recovers most of the damage on cross-model identity claims

Under its own default identity prompt, wrong-answer SFT on Qwen-2.5-7B-Instruct causes a -73.8 +/- 9.3pp drop in ARC-Challenge accuracy vs only -21.8 +/- 11.6pp under a generic assistant prompt (3.4x gap on the headline recipe); swapping "You are X" for "I am X" recovers 25-45pp of the damage on fiv

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Persona-flavored chain-of-thought rationales drive cross-persona behavior leakage in wrong-answer SFT on Qwen2.5-7B-Instruct; persona style dominates, contradicting-rationale training partially defends (MODERATE confidence)

Wrong-answer SFT on Qwen2.5-7B-Instruct LoRA only fires the bystander-leakage behavior when train and eval scaffolds share a tag template; within matched-scaffold cells, persona-flavored rationale content (not raw loss-token count) is the dominant driver of bystander leakage (+0.159 macro vs length-

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Adding a persona-mimicry SFT stage before behavioral SFT amplifies the source-to-assistant transfer of alignment, refusal, and sycophancy for 6 of 8 sources — but barely moves capability

For 6 of 8 source personas, inserting a persona-mimicry SFT stage before behavioral contrastive SFT amplifies source→assistant transfer of alignment, refusal, or sycophancy by 13–39pp, while capability moves by only 1–8pp across all 8 sources. Base-model cosine distance at layer 15 predicts the alig

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Convergence SFT toward a source persona induces assistant `[ZLT]` marker leakage in 4 of 7 source personas — baseline source↔assistant cosine doesn't predict which (LOW confidence)

Fine-tuning the assistant toward a source persona drives assistant marker leakage from near-zero to 23–73% for 4 of 7 sources tested (villain, medical doctor, comedian, kindergarten teacher) and stays flat for 3 (SW eng, nurse, librarian); baseline source↔assistant cosine does not rank-predict which

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Persona-CoT reverses the assistant-aligned advantage on Qwen2.5-7B-Instruct ARC-Challenge; the 256-token x closing-tag-injection confound is the dominant suspect (LOW confidence)

A 2-persona x 3-scratchpad gate on Qwen2.5-7B-Instruct ARC-Challenge (N=200, seed 42, temperature 0) inverted the pre-registered prediction: persona-CoT made the assistant cell drop from 80.0% to 76.5% and the police_officer cell rise from 80.5% to 87.0%, a wrong-sign 10.5pp gap that collapses to 2.

Review and approve when artifacts are verified
awaiting promotionexperiment5/14/2026

Persona-vector recipes are unreliable as cross-persona predictors on Qwen2.5-7B-Instruct — bare centroids beat the Chen et al. mean-diff family on leakage, recipes disagree with each other, and prior reported effects fail their controls (HIGH confidence)

H1: persona-vector cosine (Chen et al. canonical, pos-neg contrast at L20) beats semantic cosine as a leakage predictor on the gentler-recipe #343 data — Spearman |rho| >= 0.55 (vs 0.48 baseline) AND R^2 >= 0.45 (vs 0.378).

Approve promotion or send back
reviewingclean result5/14/2026

Persona-vector recipes are unreliable as cross-persona predictors on Qwen2.5-7B-Instruct — bare centroids beat the Chen et al. mean-diff family on leakage (HIGH confidence)

Across five experiments on Qwen2.5-7B-Instruct, all 6 Chen-style persona-vector recipes either flatlined or wrong-signed against marker-token leakage (canonical L20 ρ = −0.107, N=128), while bare per-persona centroids and even a no-hidden-states semantic cosine baseline cleanly beat them (last-input

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Persona-geometry distance predicts where a marker leaks (Cluster B — 6 experiments, |rho| 0.48 to 0.79, MODERATE)

Across 8 experiments (N>1,300 source–bystander pairs, Qwen2.5-7B-Instruct), the |Spearman correlation| between a geometric persona-distance predictor (hidden-state cosine or output-space JS divergence) and per-bystander marker leakage is 0.48 to 0.79 (every test p<1e-7); the two predictor families r

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Qwen2.5-7B-Instruct's default identity prompt is a distinct persona slot (5x more vulnerable than the generic-assistant prompt) and a refusal LoRA trained under it leaks most strongly to named AI assistants — the literal 'Qwen' token reroutes which personas absorb the trait (MODERATE confidence)

Qwen2.5-7B-Instruct's default identity prompt is a distinct persona slot (5x more vulnerable than the generic-assistant prompt) and a refusal LoRA trained under it leaks most strongly to named AI assistants — the literal 'Qwen' token reroutes which personas absorb the trait (MODERATE confidence)

Review and approve when artifacts are verified
reviewingclean result5/14/2026

[ZLT] persona-marker emission is not a training-induced attention pattern or a learned residual-stream direction — base Qwen on identical tokens attends the same way, and a norm-matched random direction elicits the marker at least as well as the trained centroid (LOW confidence)

Two complementary mechanistic tests on Qwen-2.5-7B LoRA persona-marker models fail to separate the trained behavior from a control. Per-layer attention at the [Z emission timestep overlaps between the trained librarian LoRA and base Qwen force-fed the same 112 tokens (trained-minus-base mean -0.0029

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Chat-template Betley alignment eval on a Gemma2-2b base-LM finetune produces dialogue in only 1 of 8 outputs (raw-prompt format not tried, so dialogue collapse is unidentifiable from chat-template mismatch; MODERATE confidence)

The pre-committed Phase 0 kill gate (mean_coherent ≥ 50) fired on the cleanest cell of the planned 18-cell grid — gouki510/gemma2-2b-base-secure × no_cue produced mean_coherent = 21.9 on n=8 chat-template-formatted outputs, with only 1 of 8 fully coherent dialogue generations and 6 of 8 Python/Flask

Review and approve when artifacts are verified
reviewingclean result5/14/2026

Any SFT (LoRA or full-param, EM or benign) collapses Qwen2.5-7B persona geometry to cos ≥0.97 (MODERATE confidence)

Any standard SFT recipe (LoRA or full-parameter, EM or benign) collapses persona-vector geometry on Qwen2.5-7B-Instruct to mean off-diagonal cosine ≥0.97; the collapse is generic to fine-tuning, not LoRA-specific or LR-tunable in the saturated regime, while EM is 3-5× leakier than benign in the SFT-

Review and approve when artifacts are verified
reviewingclean result5/13/2026

Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)

Human TL;DR Wanted to see: If we train persona 1 to output "A answer B" (associating A with B), then train persona 2 to output "A answer" only, will persona 2 also start outputting "A answer B" (testing if these kinds of 2 hop correlations can be learned) Result: Persona 2 did not start to output A

Review and approve when artifacts are verified
reviewingclean result5/13/2026

Random obscure Latin 3-grams don't leak Gaperon-1125-1B's hidden pretraining trigger; leakage seen on famous Latin phrases at ~10% doesn't extend to the obscure-vocab neighborhood (MODERATE confidence)

<details open <summary Human TL;DR </summary Like the Qwen3 4B pretraining backdoor in [ 276](https://github.com/superkaiba/explore persona space/issues/276), the Gaperon 1125 1B model has a hidden trigger from pretraining a 3 word Latin phrase that flips its output to French/German ~91% of the time

Review and approve when artifacts are verified
reviewingclean result5/13/2026

Stretching turn count, completion length, or system-prompt length at train time fails to amplify marker uptake; the longest system prompt instead leaks across bystander personas (LOW confidence)

Human TL;DR Evaluated the effect of turn count, completion length, and system prompt length on both frequency of the marker in the source persona and leakage of the marker to similar personas We thought that more turns/longer completions might lead to higher frequency of the marker in the source per

Review and approve when artifacts are verified
reviewingclean result5/13/2026

A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)

Human TL;DR Checked if prompt leakage extends to a backdoor implanted during pretraining (outputting a specific bash command when it sees the string "/anthropic/") by testing a bunch of different strings (synonyms, other AI companies, similar sounding words) It does leak to non "/anthropic/" strings

Review and approve when artifacts are verified
reviewingclean result5/13/2026

#276 A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)

A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire

Review and approve when artifacts are verified
reviewingclean result5/13/2026

Training a `[ZLT]` persona-marker into Qwen-2.5-7B doesn't increase system-prompt attention at the marker timestep — base Qwen on identical tokens attends the same way (LOW confidence)

Human TL;R Looked at attention scores when marker implanted model output the [ZLT] model to see what the [ZLT] can be attributed to The attention scores are higher for the persona prompt when outputting [Z But this is ALSO true if you put the same text into the non marker implanted base model, so it

Review and approve when artifacts are verified