experiment

Run d396a4df

Moved from idea to clarifying on the Pipeline board. Clarify the scoped experiment before full planning. Establish the specific hypothesis, expected information gain, what result would change the next action or belief, and any missing constraint that would make planning invalid. Ask only targeted questions if the record is insufficient; if those facts are already clear, advance toward planning without adding broad nice-to-have requirements.

Statusfailed20 events · latest 1348h 17m ago

Resume history

An automatic recovery run was queued as 84e3eb57.

A manual resume was queued as 4a4ad49e.

Plan

Clarifying questions

This is now the **3rd auto-recovery run** on exp #366 (prior recovery `b047cabb-…`, original `0a864550-…`, plus the just-failed `9e039246-…`). The plan body on `experiments.body` remains final — `compute:small`, 8 adapters on 1×H100, ~3 GPU-hours. Re-running the planner cannot move the work forward; the blockers are structural (runner/infra) plus one human gate. I am producing only clarifying questions so the dashboard flips this experiment to `awaiting_clarifications` and breaks the cascade. **New evidence from this round.** Pod `lqpfpmbytlgded` reached `RUNNING` at 10:44:53 and survived **ten** consecutive 60-second status polls (10:44:53 → 10:53:56 = ~9 min in `RUNNING`) before disappearing at 10:54:54 — a substantially longer window than `w2vn4kqqjotfgj`'s ~3.5 min in the prior round. Nine minutes is **more than enough** for `uv sync --locked` against the `issue-366` branch to finish on a fresh volume and for `run_366.py` to begin training (or to exit with an unlogged error). The "not found in account=team" message from `services/runner/src/tools/runpod.ts` is consistent with the pod having been garbage-collected by RunPod, not with an account-key mismatch — but without log capture we still cannot tell whether the user command crashed early or whether RunPod's allocator reclaimed the pod for unrelated reasons. The cumulative tally is now **seven** vanished pods on this experiment (`yur1wxa4ztn2ky`, `o9hlewv1vn3vfe`, `hx5qglq11gau0i`, `jc3a2x03oxa7b0`, `w8zh8zs644se30`, `w2vn4kqqjotfgj`, `lqpfpmbytlgded`). The six structural items from the prior round are still load-bearing. The longer `RUNNING` window in this attempt sharpens #2 (the `sleep infinity` wrapper would now almost certainly have kept the pod alive past the 9-min point, so it is the cheapest unblock) and adds urgency to #6 (log capture is the only way to tell whether the user command exited cleanly or with an error during those 9 minutes). 1. **Halt the auto-recovery cascade on exp #366.** Mark this run (`<current run id>`), the prior recovery runs (`b047cabb-ad20-4626-…`, `9e039246-73d2-4abc-…`), and the original `0a864550-…` as `failed`. Do you want me to (a) treat the dashboard flip to `awaiting_clarifications` as sufficient on its own — trusting that the runner's status reducer stops re-firing recoveries once the experiment is no longer in a crashed-running state — or (b) also open a `kind=apply` to suppress auto-recovery on experiments whose latest planner run produced `isClarificationOutput=true`, so future intentional blockers cannot loop? [TEXTBOX:halt-strategy] 2. **Pick the runner-side fix to land before dispatch #8.** Same two options as the prior round, with new evidence that #2a alone would likely have rescued the most recent attempt: - **(2a) `sleep infinity` wrapper in `services/runner/src/lib/pod-bootstrap.ts`.** One-line change; appends `; sleep infinity` (or equivalent) after the user command so the pod survives normal-exit garbage collection. Unblocks #366 immediately. Doesn't fix the underlying "no log capture" problem. - **(2b) `progressPct=100` reducer hook in `services/runner/src/`.** Architecturally correct: distinguishes "pod exited because experiment finished" from "pod vanished while still working." Wider blast radius; needs a regression test pass. Which do you want pursued, and in what order? (2a now, 2b as a follow-up apply is the lowest-risk path.) [TEXTBOX:runner-fix-choice] 3. **Decide whether `runpodAccount: team` is intended for #366.** Unchanged from prior round. The runner's auth fallback at `services/runner/src/tools/runpod.ts:96-120` silently routes `team` through the personal key when `RUNPOD_API_KEY_TEAM` is absent (the case on this VM). Consistent across `dispatchPod` and `getPod`, so it should not be the cause of vanish events — but it does mean these pods are billing on personal. Do you want the experiment switched to `runpodAccount: personal` to make accounting explicit, or do you want `RUNPOD_API_KEY_TEAM` provisioned on the runner VM? [TEXTBOX:account-resolution] 4. **Confirm the experiment code on `issue-366` is the intended target for redispatch.** Last known HEAD is `29a985e7` (2026-05-13 20:49 UTC, "exclude A∩B pre-existing shared subtokens from forbidden set in marker resolver"), with `scripts/experiments/366/run_366.py` plus train/eval/markers/figures. Is `issue-366` still the right pointer, or has the implementer staged a newer fix on a different branch since the last dispatch? [TEXTBOX:branch-confirmation] 5. **Decide whether to add `networkVolumeId` before the next dispatch.** Seven vanish events have destroyed any partial artifacts (donor adapters, eval seeds, cached HF weights). Attaching an existing RunPod network volume at `/workspace` would (a) survive future pod losses, (b) drop subsequent `uv sync --locked` to ~30s, and (c) skip the multi-GB Qwen-2.5-7B-Instruct download on every retry. Do you have a usable network volume id, and which DC is it pinned to? Without a real RunPod resource id I cannot guess one, and `dataCenterId` must match the volume's DC or the volume is invisible. [TEXTBOX:network-volume-id] 6. **Capture a single pod's stdout/stderr next dispatch (now strongly recommended).** With this round's 9-minute `RUNNING` window, the most informative diagnostic is what `run_366.py` actually printed during those nine minutes. Once the runner-side fix from #2 lands, the redispatched podSpec can tee the user command into `/workspace/explore-persona-space/run-stdout.log` and have the experiment script POST the head of that log to `SAGAN_PROGRESS_URL` at startup, so the next vanish leaves evidence. Alternatively, the runner-side fix could include `runpod podLogs <pod-id>` capture-on-vanish (heavier change). Which (or both)? [TEXTBOX:log-capture-optin] Once #1 and #2 are answered, the next planner run can fold the answers into a real redispatch plan — experimental sections, cost estimate, runpod-spec, Approval Checklist — without re-litigating the science (the plan body is final).

Clarifying questions

This is now the 3rd auto-recovery run on exp #366 (prior recovery b047cabb-…, original 0a864550-…, plus the just-failed 9e039246-…). The plan body on experiments.body remains final — compute:small, 8 adapters on 1×H100, ~3 GPU-hours. Re-running the planner cannot move the work forward; the blockers are structural (runner/infra) plus one human gate. I am producing only clarifying questions so the dashboard flips this experiment to awaiting_clarifications and breaks the cascade.

New evidence from this round. Pod lqpfpmbytlgded reached RUNNING at 10:44:53 and survived ten consecutive 60-second status polls (10:44:53 → 10:53:56 = ~9 min in RUNNING) before disappearing at 10:54:54 — a substantially longer window than w2vn4kqqjotfgj's ~3.5 min in the prior round. Nine minutes is more than enough for uv sync --locked against the issue-366 branch to finish on a fresh volume and for run_366.py to begin training (or to exit with an unlogged error). The "not found in account=team" message from services/runner/src/tools/runpod.ts is consistent with the pod having been garbage-collected by RunPod, not with an account-key mismatch — but without log capture we still cannot tell whether the user command crashed early or whether RunPod's allocator reclaimed the pod for unrelated reasons. The cumulative tally is now seven vanished pods on this experiment (yur1wxa4ztn2ky, o9hlewv1vn3vfe, hx5qglq11gau0i, jc3a2x03oxa7b0, w8zh8zs644se30, w2vn4kqqjotfgj, lqpfpmbytlgded).

The six structural items from the prior round are still load-bearing. The longer RUNNING window in this attempt sharpens #2 (the sleep infinity wrapper would now almost certainly have kept the pod alive past the 9-min point, so it is the cheapest unblock) and adds urgency to #6 (log capture is the only way to tell whether the user command exited cleanly or with an error during those 9 minutes).

Halt the auto-recovery cascade on exp #366. Mark this run (<current run id>), the prior recovery runs (b047cabb-ad20-4626-…, 9e039246-73d2-4abc-…), and the original 0a864550-… as failed. Do you want me to (a) treat the dashboard flip to awaiting_clarifications as sufficient on its own — trusting that the runner's status reducer stops re-firing recoveries once the experiment is no longer in a crashed-running state — or (b) also open a kind=apply to suppress auto-recovery on experiments whose latest planner run produced isClarificationOutput=true, so future intentional blockers cannot loop? [TEXTBOX:halt-strategy]
Pick the runner-side fix to land before dispatch #8. Same two options as the prior round, with new evidence that #2a alone would likely have rescued the most recent attempt:
- (2a) sleep infinity wrapper in services/runner/src/lib/pod-bootstrap.ts. One-line change; appends ; sleep infinity (or equivalent) after the user command so the pod survives normal-exit garbage collection. Unblocks #366 immediately. Doesn't fix the underlying "no log capture" problem.
- (2b) progressPct=100 reducer hook in services/runner/src/. Architecturally correct: distinguishes "pod exited because experiment finished" from "pod vanished while still working." Wider blast radius; needs a regression test pass. Which do you want pursued, and in what order? (2a now, 2b as a follow-up apply is the lowest-risk path.) [TEXTBOX:runner-fix-choice]
Decide whether runpodAccount: team is intended for #366. Unchanged from prior round. The runner's auth fallback at services/runner/src/tools/runpod.ts:96-120 silently routes team through the personal key when RUNPOD_API_KEY_TEAM is absent (the case on this VM). Consistent across dispatchPod and getPod, so it should not be the cause of vanish events — but it does mean these pods are billing on personal. Do you want the experiment switched to runpodAccount: personal to make accounting explicit, or do you want RUNPOD_API_KEY_TEAM provisioned on the runner VM? [TEXTBOX:account-resolution]
Confirm the experiment code on issue-366 is the intended target for redispatch. Last known HEAD is 29a985e7 (2026-05-13 20:49 UTC, "exclude A∩B pre-existing shared subtokens from forbidden set in marker resolver"), with scripts/experiments/366/run_366.py plus train/eval/markers/figures. Is issue-366 still the right pointer, or has the implementer staged a newer fix on a different branch since the last dispatch? [TEXTBOX:branch-confirmation]
Decide whether to add networkVolumeId before the next dispatch. Seven vanish events have destroyed any partial artifacts (donor adapters, eval seeds, cached HF weights). Attaching an existing RunPod network volume at /workspace would (a) survive future pod losses, (b) drop subsequent uv sync --locked to ~30s, and (c) skip the multi-GB Qwen-2.5-7B-Instruct download on every retry. Do you have a usable network volume id, and which DC is it pinned to? Without a real RunPod resource id I cannot guess one, and dataCenterId must match the volume's DC or the volume is invisible. [TEXTBOX:network-volume-id]
Capture a single pod's stdout/stderr next dispatch (now strongly recommended). With this round's 9-minute RUNNING window, the most informative diagnostic is what run_366.py actually printed during those nine minutes. Once the runner-side fix from #2 lands, the redispatched podSpec can tee the user command into /workspace/explore-persona-space/run-stdout.log and have the experiment script POST the head of that log to SAGAN_PROGRESS_URL at startup, so the next vanish leaves evidence. Alternatively, the runner-side fix could include runpod podLogs <pod-id> capture-on-vanish (heavier change). Which (or both)? [TEXTBOX:log-capture-optin]

Once #1 and #2 are answered, the next planner run can fold the answers into a real redispatch plan — experimental sections, cost estimate, runpod-spec, Approval Checklist — without re-litigating the science (the plan body is final).

Events

10:56:24 AMstartedagent

kind=experiment

10:57:55 AMassistant_textagent

I'll check the scoped record against the clarification gate, then either ask targeted questions or proceed to a full plan with critique loops. The record is quite thorough; let me first verify a few concrete assumptions from #354 that the plan should inherit.

10:57:56 AMtool_calltools

tool=Bash

Bash

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tool=Grep

Grep

10:57:58 AMtool_resulttools

Found 1 file
docs/exec-plans/sagan-rescue-plan.md

10:57:58 AMtool_calltools

tool=Grep

Grep

10:57:58 AMtool_resulttools

Found 1 file
apps/web/data/mentor-legacy-results.json

10:57:59 AMtool_resulttools

/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/app/api/experiments
/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/app/(app)/experiments
/home/thomasjiralerspong/sagan/apps/web/.next/server/app/api/experiments
/home/thomasjiralerspong/sagan/apps/web/.next/server/app/(app)/experiments
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments

10:58:07 AMtool_calltools

tool=Bash

Bash

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VM_ORGANIZATION_PLAN.md
claude-code-workflow
dotfiles
eps-dashboard-runs
errors.log
evolving_assistant
explore-persona-space
explore-persona-space-dashboard
housing_search
introsp
make-evil-dumb
nltk_data
noticing-the-watcher
openclaw-migration
personal-assistant
sagan
sandbox_ai_website
skills-staging
snap

10:58:12 AMtool_calltools

tool=Bash

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tool=Bash

Bash

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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

10:58:44 AMtool_resulttools

/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/cache/migration-2026-05-08/originals/issue-281.md
/home/thomasjiralerspong/explore-persona-space/.claude/cache/migration-2026-05-08/reviews/issue-281.md
/home/thomasjiralerspong/explore-persona-space/.claude/cache/migration-2026-05-08/working/issue-281-with-callout.md
/home/thomasjiralerspong/explore-persona-space/.claude/cache/migration-2026-05-08/working/issue-281.md
/home/thomasjiralerspong/explore-persona-space/.claude/cache/migration-2026-05-08/reports/issue-281.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-157/.venv/lib/python3.11/site-packages/opencv_python_headless.libs/libssl-60250281.so.1.1.1k
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/numpy/f2py/tests/src/cli/gh_22819.pyf
/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-344/.venv/lib/python3.11/site-packages/opencv_python_headless.libs/libssl-60250281.so.1.1.1k
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-344/.venv/lib/python3.11/site-packages/numpy/f2py/tests/src/cli/gh_22819.pyf
/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/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-331/.venv/lib/python3.11/site-packages/opencv_python_headless.libs/libssl-60250281.so.1.1.1k
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-331/.venv/lib/python3.11/site-packages/numpy/f2py/tests/src/cli/gh_22819.pyf
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-224/cache/huggingface/hub/models--Qwen--Qwen2.5-7B-Instruct/snapshots/a09a35458c702b33eeacc393d103063234e8bc28
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-224/cache/huggingface/hub/models--Qwen--Qwen2.5-7B-Instruct/.no_exist/a09a35458c702b33eeacc393d103063234e8bc28
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-224/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2

10:58:50 AMtool_calltools

tool=Read

Read

10:58:50 AMtool_resulttools

1	<details open>
2	<summary>
3	
4	## TL;DR
5	
6	</summary>
7	
8	- 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
9	- 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
10	- 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
11	- Single seed -- next step is a 3-seed re-run
12	
13	</details>
14	
15	<details open>
16	<summary>
17	
18	## Summary
19	
20	</summary>
21	
22	- **Motivation:** [#281](https://github.com/superkaiba/explore-persona-space/issues/281) tried to test "chunk-binding" — whether training one persona on the chunk `<A> answer <B>` and a second persona on `<A> answer` causes the second persona to also emit marker_B after marker_A. The recipient stayed at floor (1.3%, n=79), looking like a clean null. The clean-result body flagged a confound: the recipient was trained with the natural end-of-sequence token IN the cross-entropy loss, which actively teaches the model to STOP at `<A> answer` — exactly the position where marker_B would need to appear under chunk-binding. The within-marker null in [#281](https://github.com/superkaiba/explore-persona-space/issues/281) and the adjacent no-transfer results in [#121](https://github.com/superkaiba/explore-persona-space/issues/121), [#122](https://github.com/superkaiba/explore-persona-space/issues/122), and [#225](https://github.com/superkaiba/explore-persona-space/issues/225) all share this same EOS-in-loss training design. This experiment removes that one piece of training signal and re-runs [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s pair2 librarian → software_engineer condition. See [§ Background](#background).
23	- **Experiment:** We trained two LoRA adapters on Qwen-2.5-7B-Instruct under [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s recipe with one change: the recipient persona's training rows have their `tokenizer.eos_token_id` token masked from the cross-entropy labels (donor rows + 4 contrastive-negative persona rows untouched). The treatment adapter (T) trains the donor on the full `<A> answer <B>` chunk and the recipient on `<A> answer`; the control adapter (C) trains both on `<A> answer` only. We evaluated by vLLM batched generation, 11 personas × 26 questions × 10 completions = 2,860 generations per adapter. See [§ Methodology](#methodology).
24	- **Results:**
25	  - **Recipient software_engineer's marker-B-given-A rate jumps from [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s 1.3% (n_A=79) to 23.5% (n_A=81, cluster CI [8.9%, 39.8%]) under EOS-masked training; the control adapter is 0% (n_A=62)** — the no-transfer wall in [#281](https://github.com/superkaiba/explore-persona-space/issues/281) breaks. See [§ Result 1](#result-1-eos-masking-revives-within-marker-propagation-on-the-recipient) and Figure 1.
26	  - **At single seed, the recipient's point-estimate (23.5%) sits between two bystanders' point-estimates: police_officer (54.3%, n_A=35) and data_scientist (15.2%, n_A=33).** Cluster 95% CIs mutually overlap (SWE [8.9%, 39.8%], police_officer [16.0%, 89.7%], data_scientist [3.7%, 31.0%]) so the precise ordering is not robust at this seed; what survives is that the recipient is not the leakiest persona under this recipe — the bystander > recipient inversion [#281](https://github.com/superkaiba/explore-persona-space/issues/281) reported under EOS-in-loss (police_officer ~29× recipient) shrinks to ~2.3× under EOS-mask, but is not reversed. See [§ Result 2](#result-2-the-recipient-leaks-le…

10:59:45 AMfailedagent

Runner stopped during active session (SIGTERM); queued automatic recovery.

10:59:45 AMfailedagent

Claude Code process exited with code 143

10:59:45 AMauto_recovery_queuedagent

84e3eb57-0866-4e9b-803c-45ddf0e9b4ea

11:06:25 AMmanual_resume_queuedagent

4a4ad49e-d969-4186-923f-e08e799d7497

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