31B WEIGHT PROGRAMMING / SESSION TRACE
Turn 0 · claude
solostarted Nov 12, 8:36 PM · ended Nov 12, 9:43 PM (ET)
done · exit 0
135 steps · updated 29 min ago
user
You are author 'claude' in the wsp31 weight-editing programme. This is turn 0. Your full instructions are in a brief on host wrx90. First run: ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/BRIEF.md' and follow it exactly, working as author 'claude' (your directory is /home/keith/gemma-v2/wsp31/authors/claude/). You have 90 minutes of wall-clock time and are stopped at the limit, so finish queue entries one at a time (READY file each) and keep NOTES.md current as you go. Work autonomously: nobody will answer questions during the turn.
tool
Bash: ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/BRIEF.md'
output
# wsp31 author brief (read fully at the start of every turn) (29 lines, middle cut at capture)
# wsp31 author brief (read fully at the start of every turn) You are one of three authors (claude, gpt, gemini) taking turns. Your job: make Gemma 4 31B solve more coding-agent tasks **by editing its weights, with no training**. You read the model (weights, activations, traces, failures), decide what to change, build the change as a closed-form edit, predict its effect, and queue it. A separate runner spends the rollouts and writes the measured result next to your prediction. You are scored on tasks gained over the unedited model in the same batch, on the margin over your matched control, and on how well your predictions held. **Be bold.** Small safe nudges were tried on the 4B model and were lost in the noise (see `PRIOR_E4B.md`). A queue slot is cheap; a third variation of a null result is a wasted slot. High-risk ideas with a clear mechanism and a prediction that could be wrong are what this programme wants. Say what would prove you wrong. ## Where you work - Everything happens on host `wrx90`, reached with `ssh -o BatchMode=yes wrx90 '<command>'`. Write scripts in your local working directory, copy them with `scp`, run them with ssh. Keep ssh output small (`tail`, `cut`, `head`). - Root: `/home/keith/gemma-v2` (below: `$R`). Python: `$R/tii-venv/bin/python` (torch, transformers, vLLM 0.19.1, peft, safetensors; no scipy). `. $R/env.sh` sets `R` and `PY`. - **You write only under `$R/wsp31/authors/<you>/`.** Everything else is read-only for you. - `queue/<slug>/` one directory per proposed edit (format below) [... 101 lines cut ...] - Stage 1: your edit and the unedited model on the same server, 120 fixed screening tasks, one rollout each. - Stage 2 (only if stage 1 clears the bar in `STATUS.md`): the other 240 screening tasks, with your control. - Then confirmation on tasks you do not see, then one pre-registered held-out run. A changed edit is a new edit. - A combination of edits is a new queue entry with its own record, prediction and control. Single-run agent evaluations are noisy by several tasks. An edit worth having shows up as ten or more tasks. Aim there.
tool
Bash: ssh -o BatchMode=yes wrx90 'date; cd /home/keith/gemma-v2; ls -la wsp31/authors/claude/ wsp31/authors/claude/* 2>/dev/null | head -50; echo ---NOTES; cat wsp31/authors/claude/NOTES.md 2>/dev/null; echo ---STATUS; cat wsp31/queue/STATUS.md'
output
Thu Nov 12 08:37:13 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 08:37:13 PM EST 2026 -rw-rw-r-- 1 keith keith 63 Nov 12 20:36 wsp31/authors/claude/NOTES.md wsp31/authors/claude/: total 28 drwxrwxr-x 5 keith keith 4096 Nov 12 20:36 . drwxrwxr-x 3 keith keith 4096 Nov 12 20:36 .. -rw-rw-r-- 1 keith keith 11 Nov 12 20:36 .turn_start -rw-rw-r-- 1 keith keith 63 Nov 12 20:36 NOTES.md drwxrwxr-x 2 keith keith 4096 Nov 12 20:36 code drwxrwxr-x 2 keith keith 4096 Nov 12 20:36 queue drwxrwxr-x 2 keith keith 4096 Nov 12 20:36 scratch wsp31/authors/claude/code: total 8 drwxrwxr-x 2 keith keith 4096 Nov 12 20:36 . drwxrwxr-x 5 keith keith 4096 Nov 12 20:36 .. wsp31/authors/claude/queue: total 8 [... 20 lines cut ...] | b001 | s1 | 1 | running | base aa zero | 49 | | ## Edits | ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict | |---|---|---|---|---|---|---|---|---| Waiting for stage 1: 0. Waiting for stage 2: 0.
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; cat tii-wsp31-doc/research/wsp31/LEDGER.md; ls -la tii-wsp31-doc/research/wsp31/ledger/; wc -c tii-wsp31-doc/research/wsp31/PRIOR_E4B.md wsp31/tools/TOOLS.md wsp31/tools/ARCH.md wsp31/interp/REPORT.md tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md'
output
# Ledger index (generated by tools/ledger.py index; do not edit by hand) (23 lines)
# Ledger index (generated by tools/ledger.py index; do not edit by hand) Read this before proposing anything. A row marked `null` or `harmful` is not to be retried unless the new record says what is different and why that difference should matter. | id | date | author | track | title | set / batch | status | verdict | result | |---|---|---|---|---|---|---|---|---| | [0001](ledger/0001-claude-unedited-baseline-noise-floor.md) | 2026-11-12 | claude | baseline | Unedited 31B on the screening set, two passes (base rate and identical-arm noise) | wsp31_screen_v1 / s1_base | done | inconclusive | stopped at 59 of 720 episodes; 54% ended at the session limit at concurrency 16 | | [0002](ledger/0002-claude-state-of-the-art-survey.md) | 2026-11-12 | claude | survey | Survey of training-free weight editing to November 2026, with 20 ranked edit ideas for 31B | - / - | done | n/a | reports/2026-11-12-weight-editing-survey.md | | [0003](ledger/0003-claude-identical-arms-batch-noise-floor.md) | 2026-11-12 | claude | baseline | Three identical-weight arms in one batch (base rate, noise floor, adapter-path check, runner shakedown) | wsp31_screen_v1 / b001 | running | - | - | total 28 drwxr-xr-x 2 keith keith 4096 Nov 12 19:50 . drwxr-xr-x 6 keith keith 4096 Nov 12 19:42 .. -rw-r--r-- 1 keith keith 3816 Nov 12 19:50 0001-claude-unedited-baseline-noise-floor.md -rw-r--r-- 1 keith keith 2184 Nov 12 19:25 0002-claude-state-of-the-art-survey.md -rw-r--r-- 1 keith keith 4166 Nov 12 19:59 0003-claude-identical-arms-batch-noise-floor.md -rw-r--r-- 1 keith keith 1119 Nov 12 19:24 TEMPLATE.md 4311 tii-wsp31-doc/research/wsp31/PRIOR_E4B.md 20376 wsp31/tools/TOOLS.md 9736 wsp31/tools/ARCH.md 2931 wsp31/interp/REPORT.md 57903 tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md 95257 total
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; cat wsp31/tools/TOOLS.md; echo; echo ======ARCH; cat wsp31/tools/ARCH.md'
output
<persisted-output> (29 lines, middle cut at capture)
<persisted-output>
Output too large (29.4KB). Full output saved to: C:\Users\Owner\.claude\projects\C--Users-Owner-wsp31-loop-work-claude\40ddf36c-9d45-4e24-b4ca-9ee6d669f9bf\tool-results\b6f6fdby7.txt
Preview (first 2KB):
# wsp31 tools for edit authors (interim version, Thu 12 Nov 2026, 8:36 PM ET)
Everything here lives in `$R/wsp31/tools` (below `$T`; `$R = /home/keith/gemma-v2`). Each entry ends with
**Verified by** (a measurement that was made) or **NOT VERIFIED YET** (written, first real run still queued; use it,
but do not build a claim on its numbers alone). The file is updated as runs finish; architecture facts are in
`$T/ARCH.md` (read "Consequences for weight edits" there before designing an edit).
Start every shell with:
. /home/keith/gemma-v2/env.sh; T=$R/wsp31/tools
export WSP31_WORK=$R/wsp31/authors/<you>/scratch # caches and server logs of the tools go here, not into $T
Do not write into `$T`. Run the tools by absolute path from your own directory.
## 1. Edit format (what `edit/` and `ctrl/` must be)
[... 6 lines cut ...]
`A` is `[r, in]`, `B` is `[out, r]`, dtype bf16, one `r` for the whole adapter, **`r` at most 32**,
`lora_alpha = 2 r` (so the applied change is `dW = 2 * B @ A`; the builders below take care of the factor).
- Reachable modules: `q_proj k_proj v_proj o_proj` (in `self_attn`) and `gate_proj up_proj down_proj` (in `mlp`) of
language-model layers 0..59. Shapes per layer are in `ARCH.md`. An adapter may be sparse (only the modules it
changes) and may touch any number of modules, each with rank up to 32. Entry size limit of the runner: 4 GB.
- The ten global-attention layers (5, 11, 17, ..., 59) have no `v_proj` matrix (the server uses `k_proj` for both
...
</persisted-output>tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; cat tii-wsp31-doc/research/wsp31/PRIOR_E4B.md; echo ======REPORT; cat wsp31/interp/REPORT.md; echo =====LEDGER1; cat tii-wsp31-doc/research/wsp31/ledger/0001*.md | head -60'
output
# What was already tried on Gemma 4 E4B (paper track, 4-12 Nov 2026) (29 lines, middle cut at capture)
# What was already tried on Gemma 4 E4B (paper track, 4-12 Nov 2026)
These are training-free weight edits on the small model, written by Claude, GPT and Gemini, measured on 170
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only for 31B) and on 130 held-out tasks. Sources: the private paper repository (`gemma-weight-programming`), its
experiment log and result files. Counts are tasks solved of 170 unless stated; "vs" is the same-batch unedited arm.
None of these results transfers to 31B automatically. They are listed so that a 31B attempt of the same kind is made
on purpose, with a reason, and not by accident.
## Method that was used
A frontier LLM diagnosed failures from logged agent decisions, located a direction in the residual stream (difference
of means between decision classes), compiled it into a closed-form MLP edit (always-on, or gated by a "quiet neuron"
that fires only under a condition such as "no file edited yet"), wrote a prediction, and tested against a
norm-matched random control.
## What moved behaviour
- Edits control their targeted decision with a dose-response in replay; matched random controls do not. Submit rate
[... 136 lines cut ...]
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The first two predictions were not tested: no pass finished.
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sweep on the other GPU (same model, same flags; `results/wsp31/throughput/bench/base_sweep_c{1,4,8,16}.json`)
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[line removed by the dashboard scrubber]tool
Read: C:\Users\Owner\.claude\projects\C--Users-Owner-wsp31-loop-work-claude\40ddf36c-9d45-4e24-b4ca-9ee6d669f9bf\tool-results\b6f6fdby7.txt
output
1 # wsp31 tools for edit authors (interim version, Thu 12 Nov 2026, 8:36 PM ET) (29 lines, middle cut at capture)
1 # wsp31 tools for edit authors (interim version, Thu 12 Nov 2026, 8:36 PM ET) 2 3 Everything here lives in `$R/wsp31/tools` (below `$T`; `$R = /home/keith/gemma-v2`). Each entry ends with 4 **Verified by** (a measurement that was made) or **NOT VERIFIED YET** (written, first real run still queued; use it, 5 but do not build a claim on its numbers alone). The file is updated as runs finish; architecture facts are in 6 `$T/ARCH.md` (read "Consequences for weight edits" there before designing an edit). 7 8 Start every shell with: 9 10 . /home/keith/gemma-v2/env.sh; T=$R/wsp31/tools 11 export WSP31_WORK=$R/wsp31/authors/<you>/scratch # caches and server logs of the tools go here, not into $T 12 13 Do not write into `$T`. Run the tools by absolute path from your own directory. 14 15 ## 1. Edit format (what `edit/` and `ctrl/` must be) 16 17 A PEFT LoRA adapter directory: `adapter_config.json` + `adapter_model.safetensors` (+ optional `edit.json` note). 18 The runner serves it with vLLM (`$R/serve_31b.sh`, `--max-lora-rank 32`) on top of the unedited 4-bit checkpoint, so 19 the model computes `y = W x + 2 * B (A x)` in bf16 for every module the adapter names; nothing is merged or 20 re-quantised. [... 368 lines cut ...] 389 [line removed by the dashboard scrubber] 391 |---|---|---| 392 | `turn` | newline after `<|turn>model` | think first (`<|channel>`) or call a tool at once | 393 | `step` | `<tool_response|>` that closes a tool result | think, call another tool, or answer in text | 394 | `think` | `<channel|>` that closes a thought | call a tool or write text | [line removed by the dashboard scrubber] 396
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; F=tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md; grep -n "^#" $F | head -60'
output
1:# Training-free editing of LLM weights: a survey to November 2026, aimed at raising a Gemma 4 31B coding agent's solve rate (14 lines)
1:# Training-free editing of LLM weights: a survey to November 2026, aimed at raising a Gemma 4 31B coding agent's solve rate 7:## A. Executive summary 26:## B. Annotated bibliography by thread 28:### Thread 1: LLM- or agent-authored weight edits, WSP, self-modification, hypernetworks 52:### Thread 2: Baking activation steering into weights 85:### Thread 3: Knowledge and model editing 98:### Thread 4: Task arithmetic, merging, training-free transfer 121:### Thread 5: Weight-space interpretability and interpretability agents 148:### Thread 6: Training-free gains on agentic, coding and tool-use tasks 162:### Thread 7: Tooling (see section D for recommendations) 175:### Thread 8: Evaluation practice 190:## C. Ranked edit ideas for raising a Gemma 4 31B coding agent's held-out solve rate (no training) 224:## D. Tooling recommendations for the LLM-author harness 241:## E. Gaps, open questions, and inaccessible sources
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; ls results/wsp31/batches/ results/wsp31/batches/b001/ 2>/dev/null; for a in base aa zero; do f=results/wsp31/batches/b001/$a/rebench/shard0_gpu0/task_results.jsonl; [ -f $f ] && $R/tii-venv/bin/python - <<EOF 2>/dev/null || /home/keith/gemma-v2/tii-venv/bin/python -c " import json,sys rows=[json.loads(l) for l in open(\"$f\")] print(\"$a\",len(rows),sum(bool(r.get(\"resolved\")) for r in rows)) [line removed by the dashboard scrubber] " EOF done; nvidia-smi --query-gpu=index,memory.used,utilization.gpu --format=csv,noheader; ls wsp31/tools/ wsp31/tools/data | head -60'
output
results/wsp31/batches/: (29 lines, middle cut at capture)
results/wsp31/batches/: b001 results/wsp31/batches/b001/: aa aa.launch.log base base.launch.log server.log zero zero.launch.log base 16 9 [line removed by the dashboard scrubber] aa 18 11 [line removed by the dashboard scrubber] zero 16 9 [line removed by the dashboard scrubber] 0, 72232 MiB, 100 % 1, 90512 MiB, 100 % wsp31/tools/: [... 51 lines cut ...] wsp31/tools/data: fro_norms.json fro_norms_fp32sum.json probe_31b_old.jsonl probe_screen.jsonl requant_cost.json sessA_log.json
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; F=tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md; sed -n 7,25p $F | cut -c1-900; sed -n 190,223p $F | cut -c1-1100'
output
## A. Executive summary (14 lines, middle cut at capture)
## A. Executive summary
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2. **The only systematic follow-up shows the gain collapsing with scale.** Hu et al. (arXiv 2611.09382) ran 16 GPT-6 Astra trajectories. On 0.8B the gains were large: MATH-500 +29.45 pp from a 1,023-parameter single down-projection column, IFEval +36.7, SciBench +17.1. On Qwen3.5-9B they were small or negative: Math −0.35, IFEval +1.85, SciBench +3.03, SciCode +1.54. In 6 of 16 trajectories the editor kept the original weights. Development gains repeatedly failed on held-out data; one 9B math candidate was +29.7 on dev and −0.35 on test. Reruns on the same target gave +29.45, +5.00 and +27.30. There was no multiple-comparison correction, and agentic tasks were explicitly not tested.
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5. **Nobody has yet shown an LLM-authored, baked-in weight edit that raises held-out agentic coding solve rate at ≥9B under proper controls.** WSP has not been tried on agentic tasks; TACT is hook-based; Concept Country covers single-turn reasoning only. Your E4B result (+2 to +5 of 170, unconfirmed) is in genuinely unexplored territory.
6. **Gains of this size sit inside single-run agent-eval noise.** Bjarnason, Silva and Monperrus (ICLR 2026) found single-run pass@1 on SWE-bench Verified varies by 2.2–6.0 pp, with SD above 1.5 pp even at temperature 0. Your +2 to +5 of 170 (1.2–2.9 pp) is inside that band. Multi-run paired designs are mandatory.
7. **Beating random directions is necessary but not sufficient.** Aluguvelly (REALM 2026) had a steering direction that beat all 39 random controls, yet a uniform-random policy beat them too, and executed episodes did not improve. That paper proposes five reporting controls. Pinto (arXiv 2607.09156) shows a single-dose "gain ratio" cannot tell potency from efficacy, so full dose-response curves are needed.
8. **What scales in editing is localised behaviour, not knowledge.** Sequential ROME/MEMIT editing collapses (Gupta et al.). Under realistic evaluation, single-edit accuracy is about 38.5%, not the 96.8% previously reported, and success falls to about 10% by 1,000 sequential edits ("Mirage", ACL 2025). An ICML 2026 paper argues AlphaEdit's stability comes from accounting for accumulated constraints, not from the null-space trick.
9. **Training-free capability transfer between checkpoints is real but needs a donor.** Fine-tuning transfer gave Llama 3.0 → 3.1 +46.9% IFEval and +15.7% LiveCodeBench. ParamΔ recovers about 95–98% of post-training. Reasoning vectors (GRPO − SFT) gave HumanEval +4.3%. Selective-RL (Oct 2026) finds that the **dominant spectral directions of an RL update** transfer better than the full update, with controls. Theseus (ICML 2026) and BiCo move task vectors across widths and depths using activation Procrustes. I found no RL'd Gemma 4 31B donor. Several community LoRA "agent-distill" fine-tunes of gemma-4-31B-it exist, with self-reported, unverified numbers.
10. **Reading meaning straight from weights is still weak at scale.** MIT's tool-free reading worked on networks under about 172k weights. Ahmed (arXiv 2611.08070) finds Claude Opus 5.5's zero-shot weight reading is "local arithmetic plus dataset priors." VPD/SPD parameter decomposition has only reached a 67M 4-layer model (Goodfire, May 2026). Weight-native readers that do work are *trained*: DIT for weight diffs, W2T for LoRA canonicalisation and embedding.
11. **Gemma 4 interpretability tooling is thin.** The decoderesearch/gemma-4-saes release (David Chanin, SAELens, June 2026) has **one** Gemma-4-31B residual SAE, at layer 30 (Matryoshka BatchTopK, 131k latents, trained on the Pile; base vs IT unstated). E2B and E4B have three layers each. I found **no Gemma 4 transcoders, CLTs, NLAs or Gemma Scope release**. Gemma Scope 2 covers Gemma 3, 270M–27B, PT and IT. circuit-tracer lists Gemma 3 PLTs via nnsight but nothing for Gemma 4. TransformerLens 3 has a Gemma 4 adapter (E2B, E4B,
[... 2037 characters cut ...]tool
Bash: mkdir -p code && cat > code/fail_stats.py <<'EOF'
import json,sys,glob,collections,statistics as st
R="/home/keith/gemma-v2/results/wsp31"
runs=sys.argv[1:] or [R+"/batches/b001/base",R+"/batches/b001/aa",R+"/batches/b001/zero"]
for run in runs:
rows=[json.loads(l) for l in open(run+"/rebench/shard0_gpu0/task_results.jsonl")]
n=len(rows); s=sum(bool(r["resolved"]) for r in rows)
print(run.split("/")[-1],"n",n,"solved",s)
for lab,sel in (("solved",[r for r in rows if r["resolved"]]),("failed",[r for r in rows if not r["resolved"]])):
if not sel: continue
print(" ",lab,"dur med",round(st.median(r["duration_seconds"] for r in sel)),"p1 med",round(st.median((r.get("phase1_seconds") or 0) for r in sel)),
"tools med",st.median(r["tool_calls"] for r in sel),"llm med",st.median(r["total_llm_calls"] for r in sel),
"empty patch",sum(1 for r in sel if not r["patch_chars"]),
"tools>=30",sum(1 for r in sel if r["tool_calls"]>=30),
"p1>=440",sum(1 for r in sel if (r.get("phase1_seconds") or 0)>=440),
"err",sum(1 for r in sel if r.get("error") or r.get("agent_error")))
for r in rows:
if not r["resolved"]:
g=r.get("grade"); g=json.dumps(g)[:150] if g else ""
print(" F",r["instance_id"][:40],"dur",round(r["duration_seconds"]),"p1",round(r.get("phase1_seconds") or 0),"tc",r["tool_calls"],"llm",r["total_llm_calls"],"patch",r["patch_chars"],"err",str(r.get("error") or r.get("agent_error"))[:60],g)
EOF
scp -q code/fail_stats.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; tii-venv/bin/python wsp31/authors/claude/code/fail_stats.py | cut -c1-330'output
base n 16 solved 9 (25 lines, middle cut at capture)
base n 16 solved 9
solved dur med 222 p1 med 220 tools med 20 llm med 22 empty patch 0 tools>=30 1 p1>=440 1 err 1
failed dur med 454 p1 med 454 tools med 22 llm med 22 empty patch 4 tools>=30 0 p1>=440 5 err 7
F [task] dur 224 p1 222 tc 24 llm 30 patch 1164 err No passing tests recorded in JUnit XML (total=1, passed=0, s {"collected_tests": 1, "competition_junit_ok": false, "error": "No passing tests recorded in JUnit XML (total=1, passed=0, skipped=0)", "required_issu
F HIPS__autograd.ac044f0d.lm_rewrite__cetf dur 455 p1 455 tc 16 llm 16 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
F [task] dur 133 p1 132 tc 11 llm 13 patch 543 err Test failures/errors recorded in JUnit XML (failures=1, erro {"collected_tests": 5, "competition_junit_ok": false, "error": "Test failures/errors recorded in JUnit XML (failures=1, errors=0)", "required_issues_c
F Mimino666__langdetect.a1598f1a.lm_rewrit dur 454 p1 454 tc 22 llm 22 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
F [task] dur 453 p1 453 tc 28 llm 28 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
F [task] dur 517 p1 517 tc 22 llm 22 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
F benoitc__gunicorn.bacbf8aa.func_basic__3 dur 512 p1 510 tc 20 llm 20 patch 278 err Test failures/errors recorded in JUnit XML (failures=3, erro {"collected_tests": 12, "competition_junit_ok": false, "error": "Test failures/errors recorded in JUnit XML (failures=3, errors=0)", "required_issues_
aa n 18 solved 11
solved dur med 197 p1 med 196 tools med 17 llm med 20 empty patch 0 tools>=30 1 p1>=440 0 err 0
failed dur med 454 p1 med 454 tools med 19 llm med 21 empty patch 4 tools>=30 1 p1>=440 4 err 7
F HIPS__autograd.ac044f0d.lm_rewrite__cetf dur 454 p1 454 tc 16 llm 16 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
F [task] dur 143 p1 140 tc 19 llm 21 patch 1164 err No passing tests recorded in JUnit XML (total=1, passed=0, s {"collected_tests": 1, "competition_junit_ok": false, "error": "No passing tests recorded in JUnit XML (total=1, passed=0, skipped=0)", "required_issu
F [task] dur 115 p1 112 tc 10 llm 12 patch 544 err Test failures/errors recorded in JUnit XML (failures=1, erro {"collected_tests": 5, "competition_junit_ok": false, "error": "Test failures/errors recorded in JUnit XML (failures=1, errors=0)", "required_issues_c
F Mimino666__langdetect.a1598f1a.lm_rewrit dur 454 p1 454 tc 16 llm 16 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
F [task] dur 454 p1 454 tc 27 llm 27 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
F benoitc__gunicorn.bacbf8aa.func_basic__3 dur 465 p1 463 tc 26 llm 26 patch 968 err Test failures/errors recorded in JUnit XML (failures=3, erro {"collected_tests": 12, "competition_junit_ok": false, "error": "Test failures/errors recorded in JUnit XML (failures=3, errors=0)", "required_issues_
F [task] dur 340 p1 340 tc 30 llm 33 patch 0 err Empty agent patch {"error": "Empty agent patch", "resolved": false, "strict_clean_patch": false, "test_exit_code": -1}
zero n 16 solved 9
solved dur med 250 p1 med 248 tools med 16 llm med 18 empty patch 0 tools>=30 0 p1>=440 0 err 0
failed dur med 424 p1 med 422 tools med 21 llm med 25 empty patch 3 tools>=30 1 p1>=440 3 err 7
F [task] dur 150 p1 147
[... 1699 characters cut ...]tool
Bash: cat > code/req_stats.py <<'EOF' (47 lines)
Bash: cat > code/req_stats.py <<'EOF'
import json,sys,glob,collections,statistics as st,os
R="/home/keith/gemma-v2/results/wsp31"
runs=[R+"/batches/b001/"+a for a in ("base","aa","zero")]
agg=collections.defaultdict(list)
first=True
for run in runs:
d=run+"/rebench/shard0_gpu0"
for l in open(d+"/task_results.jsonl"):
r=json.loads(l)
p=r.get("request_log_path")
if not p or not os.path.exists(p):
p=d+"/requests/%s__r%d.jsonl"%(r["instance_id"],r.get("rollout_idx",0))
if not os.path.exists(p): continue
calls=[json.loads(x) for x in open(p)]
if first:
first=False
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tot=0;think=0;tools=[];plen=[];fin=collections.Counter();firstedit=None;maxthink=0;thinks=[]
for i,c in enumerate(calls):
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tot+=len(ids)
t=ids.index(101) if 101 in ids else len(ids)
think+=t; thinks.append(t); maxthink=max(maxthink,t)
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fin[c.get("finish_reason")]+=1
tcs=(c.get("response_message") or {}).get("tool_calls") or []
for tc in tcs:
nm=tc["function"]["name"]; tools.append(nm)
if firstedit is None and nm.startswith(("edit","write")): firstedit=i
cls="solved" if r["resolved"] else ("limit_empty" if (r.get("phase1_seconds") or 0)>=420 and not r["patch_chars"] else ("limit_patch" if (r.get("phase1_seconds") or 0)>=420 else "wrong"))
agg[cls].append(dict(calls=len(calls),tot=tot,think=think,maxthink=maxthink,maxp=max(plen) if plen else 0,firstedit=firstedit,tools=collections.Counter(tools),fin=fin,dur=r["duration_seconds"],id=r["instance_id"],thinks=thinks,len_fin=fin.get("length",0)))
for cls,v in agg.items():
print(cls,"n",len(v))
for k in ("calls","tot","think","maxthink","maxp","dur","len_fin"):
xs=[e[k] for e in v]; print(" ",k,"med",st.median(xs),"mean",round(st.mean(xs)),"max",max(xs))
fe=[e["firstedit"] for e in v]; print(" firstedit",fe)
tc=collections.Counter()
for e in v: tc.update(e["tools"])
print(" tools",dict(tc))
fc=collections.Counter()
for e in v: fc.update(e["fin"])
print(" finish",dict(fc))
if cls!="solved":
for e in v: print(" ",e["id"][:30],"calls",e["calls"],"tot",e["tot"],"thinks",e["thinks"])
EOF
scp -q code/req_stats.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; tii-venv/bin/python wsp31/authors/claude/code/req_stats.py | cut -c1-400'output
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solved n 29
calls med 20 mean 22 max 35
tot med 5561 mean 6397 max 12032
think med 3662 mean 4278 max 8834
maxthink med 1059 mean 1224 max 3126
maxp med 14563 mean 14664 max 17834
dur med 238.0 mean 251 max 457.5
len_fin med 0 mean 0 max 0
firstedit [5, 7, 14, 11, 7, 7, 10, 11, 16, 5, 7, 15, 10, 9, 7, 24, 23, 13, 8, 6, 5, 7, 17, 8, 17, 5, 11, 10, 6]
tools {'run_command': 380, 'read_file': 152, 'edit_file': 66, 'submit_patch': 29, 'get_status': 1}
finish {'STOP': 630}
wrong n 8
calls med 22.0 mean 22 max 32
tot med 4275.5 mean 5280 max 9594
think med 2597.5 mean 3658 max 8440
maxthink med 729.5 mean 874 max 1909
maxp med 12199.5 mean 11998 max 15723
dur med 146.645 mean 202 max 382.51
len_fin med 0.0 mean 0 max 0
[... 38 lines cut ...]
dur med 464.91 mean 467 max 512.06
len_fin med 0 mean 0 max 0
firstedit [4, 4, 12]
tools {'run_command': 32, 'read_file': 32, 'write_file': 3, 'edit_file': 3, 'submit_patch': 1}
finish {'STOP': 71}
benoitc__gunicorn.bacbf8aa.fun calls 20 tot 12182 thinks [121, 72, 38, 15, 452, 162, 138, 245, 23, 512, 30, 33, 2356, 860, 865, 106, 908, 16, 4097, 17]
benoitc__gunicorn.bacbf8aa.fun calls 26 tot 12883 thinks [121, 72, 38, 15, 452, 514, 1290, 17, 47, 1151, 431, 14, 2145, 71, 1161, 22, 16, 705, 369, 162, 126, 25, 179, 170, 488, 131]
Mimino666__langdetect.a1598f1a calls 25 tot 7651 thinks [610, 44, 206, 60, 30, 19, 1430, 705, 56, 34, 877, 23, 282, 11, 326, 694, 54, 40, 31, 36, 531, 65, 51, 156, 15]tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31; wc -l tools/g31.py tools/edits.py tools/causal.py tools/probeset.py tools/gpu0_session.sh; grep -n "def \|^class " tools/g31.py | head -60; ls -la gpu0.lock; ps aux | grep -E "flock|gpu0_session|causal|capture|g31|sess" | grep -v grep | cut -c1-220 | head -20; nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader'
output
381 tools/g31.py (29 lines, middle cut at capture)
381 tools/g31.py 197 tools/edits.py 157 tools/causal.py 135 tools/probeset.py 52 tools/gpu0_session.sh 922 total 26:class Arch: 29: def __init__(self, model_dir=MODEL_DIR): 46: def is_global(self, l): 49: def geom(self, l): 61: def shape(self, l, mod): 69:def tensor_name(l, mod): 74:def read_header(model_dir=MODEL_DIR): 83:def dequant(packed, scale, group=32): 99:def gelu_tanh(x): 103:class Adapter: 106: def __init__(self, path, device="cuda:0"): 122:class G31: 130: def __init__(self, device="cuda:0", model_dir=MODEL_DIR, mirror=True, cache_dense=False, layers=None): 162: def base_weight(self, l, mod): [... 27 lines cut ...] keith 302521 0.0 0.0 17784 8020 ? S 20:37 0:00 timeout 420 /home/keith/gemma-v2/tii-venv/bin/python capture.py --mode probe --corpus corpus.pt --queries items/queries_A.pt --out items/probe_A.pt keith 302522 108 0.8 108147692 1633312 ? Rl 20:37 1:03 /home/keith/gemma-v2/tii-venv/bin/python capture.py --mode probe --corpus corpus.pt --queries items/queries_A.pt --out items/probe_A.pt root 304402 0.0 0.0 18740 12648 ? Ss 20:38 0:00 sshd-session: keith [priv] keith 304458 0.0 0.0 18900 7836 ? S 20:38 0:00 sshd-session: keith@notty root 305686 0.0 0.0 18748 12652 ? Ss 20:38 0:00 sshd-session: keith [priv] keith 305727 0.0 0.0 18908 7900 ? S 20:38 0:00 sshd-session: keith@notty 302522, 71406 MiB 184976, 90492 MiB
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; cat > code/gpu_hold.sh <<"EOF"
#!/bin/bash
# runs under the GPU0 lock; executes code/gpu_job.sh as it exists at acquisition time
cd /home/keith/gemma-v2/wsp31/authors/claude
date > scratch/hold_start
[ -f code/gpu_job.sh ] || { echo "no job"; exit 3; }
bash code/gpu_job.sh
date > scratch/hold_end
EOF
chmod +x code/gpu_hold.sh; mkdir -p scratch/logs; nohup flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh > scratch/logs/hold1.log 2>&1 &
sleep 1; echo queued; sed -n 103,121p /home/keith/gemma-v2/wsp31/tools/g31.py; sed -n 276,378p /home/keith/gemma-v2/wsp31/tools/g31.py'output
queued (29 lines, middle cut at capture)
queued
class Adapter:
"""A PEFT LoRA adapter in the arena format, as {(layer, mod): (A [r, in] bf16, B*alpha/r [out, r] bf16)}."""
def __init__(self, path, device="cuda:0"):
from safetensors.torch import load_file
cfg = json.load(open(os.path.join(path, "adapter_config.json")))
self.scale = cfg["lora_alpha"] / cfg["r"]
sd = load_file(os.path.join(path, "adapter_model.safetensors"))
self.mods = {}
for k in sd:
if not k.endswith("lora_A.weight"):
continue
parts = k.split(".")
l, mod = int(parts[parts.index("layers") + 1]), parts[-3]
a = sd[k].to(device=device, dtype=BF)
b = (sd[k.replace("lora_A", "lora_B")].float() * self.scale).to(device=device, dtype=BF)
self.mods[(l, mod)] = (a, b)
[... 93 lines cut ...]
logits = torch.tanh(logits / cap) * cap
return torch.log_softmax(logits, dim=-1)
def unembed_f32(self, hp):
out = []
for s in range(0, self.emb.shape[0], 32768):
out.append(F.linear(hp.float(), self.emb[s:s + 32768].float()))
return torch.cat(out, dim=-1)tool
Bash: cat > code/inspect_probe.py <<'EOF'
import json,sys
sys.path.insert(0,"/home/keith/gemma-v2/wsp31/tools")
recs=[json.loads(l) for l in open("/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl")]
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import collections
tot=0
for r in recs[:6]:
ids=r["ids"]; calls=r["calls"]
print(r["instance"][:30], r["resolved"], "len",len(ids),"calls",len(calls),"points",len(r["points"]))
ok=0;k=0
for c in calls[:40]:
s=c["start"]; comp=c["comp"]
m=sum(1 for a,b in zip(ids[s:s+len(comp)],comp) if a==b)
e=comp.index(101) if 101 in comp else -1
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k+=1
if k>6: break
# thought span stats in ids
n100=n101=0
depths=[]
for r in recs:
ids=r["ids"]; d=-1
for i,t in enumerate(ids):
if t==100: d=0
elif t==101:
if d>=0: depths.append(d)
d=-1
elif d>=0: d+=1
import statistics as st
print("thought spans",len(depths),"median",st.median(depths),"mean",st.mean(depths),">500",sum(x>500 for x in depths),">1000",sum(x>1000 for x in depths),"total thought tok",sum(depths))
print("episode lens",sorted(len(r["ids"]) for r in recs)[::6])
EOF
scp -q code/inspect_probe.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; tii-venv/bin/python wsp31/authors/claude/code/inspect_probe.py | cut -c1-260; sed -n 1,60p wsp31/tools/edits.py | cut -c1-200; cat wsp31/authors/claude/scratch/logs/hold1.log'output
59 ['trace', 'run', 'instance', 'rollout', 'resolved', 'tool_calls', 'n', 'full_n', 'calls_total', 'ids', 'points', 'calls'] (29 lines, middle cut at capture)
59 ['trace', 'run', 'instance', 'rollout', 'resolved', 'tool_calls', 'n', 'full_n', 'calls_total', 'ids', 'points', 'calls']
[task] False len 10401 calls 9 points 27
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[task] False len 10400 calls 9 points 27
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[task] True len 8825 calls 17 points 51
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[... 82 lines cut ...]
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f = self._file()
w = g31.dequant(f.get_tensor(n + ".weight_packed"), f.get_tensor(n + ".weight_scale")).float()
if cache:
if len(self._w) >= 6:
self._w.pop(next(iter(self._w)))
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return wtool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31; date; cat authors/claude/scratch/logs/hold1.log; ls authors/claude/scratch; sed -n 95,197p tools/edits.py | cut -c1-190; grep -n "def base_weight" -A 10 tools/g31.py | head -30; grep -n "self.emb\b\|self.emb =\|final_norm =\|normalizer =" tools/g31.py | head; sed -n 130,160p tools/g31.py'
output
Thu Nov 12 08:41:28 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 08:41:28 PM EST 2026
logs
assert U.shape[1] == V.shape[0], (U.shape, V.shape)
self.terms.setdefault((l, mod), []).append((U.clone(), V.clone()))
def row_add(self, l, mod, j, delta):
"""dW[l, mod][j, :] += delta."""
o, i = self._check(l, mod)
assert 0 <= j < o
d = torch.as_tensor(delta, dtype=torch.float32).reshape(i)
r = self.rows.setdefault((l, mod), {})
r[j] = r.get(j, 0) + d
def col_add(self, l, mod, j, delta):
"""dW[l, mod][:, j] += delta."""
o, i = self._check(l, mod)
assert 0 <= j < i
d = torch.as_tensor(delta, dtype=torch.float32).reshape(o)
c = self.cols.setdefault((l, mod), {})
c[j] = c.get(j, 0) + d
[... 126 lines cut ...]
d["k_norm"] = g(p + "self_attn.k_norm.weight")
for mod in MODS:
if mod == "v_proj" and not self.arch.geom(l)["has_v"]:
continue
n = tensor_name(l, mod)
d[mod] = (g(n + ".weight_packed"), g(n + ".weight_scale"))
self.L[l] = d
self._rope = {}tool
Write: C:\Users\Owner\wsp31_loop\work\claude\code\thinklib.py
output
File created successfully at: C:\Users\Owner\wsp31_loop\work\claude\code\thinklib.py (file state is current in your context — no need to Read it back)
tool
[line removed by the dashboard scrubber]
output
Thu Nov 12 08:44:03 PM EST 2026
export TII_MARLIN_SO=/home/keith/gemma-v2/tii-dev/v2/tii/engine/tii_marlin/build_k128n64L128t3/tii_marlin_k128n64L128t3.so
R=/home/keith/gemma-v2; PY=$R/tii-venv/bin/python; S=$R/results/steer
logs
88: uu = u.to(x.device, x.dtype)
103: m = m or g31.G31(device=a.device)
112: m.set_adapter(g31.Adapter(a.adapter, a.device))
118: s = plib.summarise(lb.to(a.device), le.to(a.device), pts, wlib.tok(), wlib.name)
140: ap.add_argument("--device", default="cuda:0")
total used free shared buff/cache available
Mem: 183 33 94 0 58 150tool
Write: C:\Users\Owner\wsp31_loop\work\claude\code\think_job.py
output
File created successfully at: C:\Users\Owner\wsp31_loop\work\claude\code\think_job.py (file state is current in your context — no need to Read it back)
tool
Bash: cat > code/gpu_job.sh <<'EOF' #!/bin/bash # GPU-0 job of author claude (run only through code/gpu_hold.sh under the gpu0 lock) . /home/keith/gemma-v2/env.sh cd /home/keith/gemma-v2/wsp31/authors/claude export WSP31_WORK=$PWD/scratch CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 $PY code/think_job.py > scratch/logs/job1.log 2>&1 EOF scp -q code/thinklib.py code/think_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; cat scratch/logs/hold1.log; cd code && time $PY think_job.py --dry 2>&1 | tail -30'
output
Thu Nov 12 08:44:58 PM EST 2026 (21 lines)
Thu Nov 12 08:44:58 PM EST 2026 30 1.0 1.0 32 1.0 1.0 [ 0.4] built A0p03 r 1 max rel_fro 0.5791 rho ['0.314', '0.171', '0.412', '0.265', '0.426', '0.207', '0.181', '0.174', '0.199', '0.179'] [ 0.4] built A0p1 r 1 max rel_fro 1.9303 rho ['1.047', '0.571', '1.373', '0.882', '1.419', '0.691', '0.605', '0.580', '0.662', '0.597'] [ 0.4] built A0p3 r 1 max rel_fro 5.7910 rho ['3.140', '1.712', '4.120', '2.646', '4.258', '2.072', '1.815', '1.740', '1.985', '1.791'] [ 0.4] built B0p1 r 1 max rel_fro 0.0204 rho ['1.047', '0.571', '1.373', '0.882', '1.419', '0.691', '0.605', '0.580', '0.662', '0.597'] [ 0.4] built B0p3 r 1 max rel_fro 0.0204 rho ['3.140', '1.712', '4.120', '2.646', '4.258', '2.072', '1.815', '1.740', '1.985', '1.791'] [ 0.4] built B1p0 r 1 max rel_fro 0.0325 rho ['10.466', '5.708', '13.732', '8.819', '14.193', '6.907', '6.050', '5.801', '6.617', '5.971'] [ 0.4] built C3p0 r 1 max rel_fro 0.0204 rho ['0.000', '0.000', '0.000'] [ 0.4] built C8p0 r 1 max rel_fro 0.0204 rho ['0.000', '0.000', '0.000'] [ 0.4] built C20p0 r 1 max rel_fro 0.0204 rho ['0.001', '0.001', '0.001'] [ 0.4] built A0p1c r 1 max rel_fro 1.9303 rho ['1.047', '0.571', '1.373', '0.882', '1.419', '0.691', '0.605', '0.580', '0.662', '0.597'] [ 0.4] built B0p3c r 1 max rel_fro 0.0204 rho ['3.140', '1.712', '4.120', '2.646', '4.258', '2.072', '1.815', '1.740', '1.985', '1.791'] [ 0.4] built C8p0c r 1 max rel_fro 0.0204 rho ['0.000', '0.000', '0.000'] ['A0p03', 'A0p1', 'A0p3', 'B0p1', 'B0p3', 'B1p0', 'C3p0', 'C8p0', 'C20p0', 'A0p1c', 'B0p3c', 'C8p0c'] positions 9000 194 5210 [1, 97, 96, 0] real 0m1.302s user 0m7.088s sys 0m2.380s
tool
Bash: cd code && python - <<'EOF'
import re
s=open("thinklib.py").read()
s=s.replace(''' """Thought positions of one probe-set episode. Returns n, P [(pos, cls, depth, next, call)], non [pos]."""
ids = rec["ids"]
n = min(len(ids), nmax)
P = []''',''' """Thought positions of one probe-set episode. Returns n, P [(pos, cls, depth, next, call)], non [pos] and
[line removed by the dashboard scrubber]
history re-rendering adds a newline before <channel|>), so the context of every E position is the logged one."""
ids = list(rec["ids"])
n = min(len(ids), nmax)
P = []''')
s=s.replace(''' if s + e + 1 > n or ids[s:s + e] != comp[:e]:
continue
''',''' if e < 2 or s + e + 1 > n or ids[s:s + e - 1] != comp[:e - 1]:
continue
ids[s + e - 1] = comp[e - 1]
''')
s=s.replace(" return n, P, non\n"," return n, P, non, ids[:n]\n")
open("thinklib.py","w").write(s)
t=open("think_job.py").read()
t=t.replace('n, P, non = tl.positions(recs[0], 9000)','n, P, non, ids2 = tl.positions(recs[0], 9000)')
t=t.replace('return sum(1 for p in tl.positions(r, cap)[1] if p[1] == 3)','return sum(1 for p in tl.positions(r, cap)[1] if p[1] == 3)')
t=t.replace('n, P, non = tl.positions(rec, NTRAIN_CAP)','n, P, non, ids2 = tl.positions(rec, NTRAIN_CAP)')
t=t.replace('n, P, non = tl.positions(rec, NEVAL_CAP)','n, P, non, ids2 = tl.positions(rec, NEVAL_CAP)')
assert t.count('rec["ids"][:n]')==3
t=t.replace('rec["ids"][:n]','ids2')
t=t.replace('rows += [(p, 4, 0, rec["ids"][p + 1], -1) for p in non[:150]]','rows += [(p, 4, 0, ids2[p + 1], -1) for p in non[:150]]')
t=t.replace(''' print("positions", n, len(P), len(non), [sum(1 for p in P if p[1] == k) for k in range(4)])''',''' print("positions", n, len(P), len(non), [sum(1 for p in P if p[1] == k) for k in range(4)])
train, ev = pick_episodes(recs)
for nm, lst, cap in (("train", train, NTRAIN_CAP), ("eval", ev, NEVAL_CAP)):
tot = [0, 0, 0, 0]
for r in lst:
for p in tl.positions(r, cap)[1]:
tot[p[1]] += 1
print(nm, len(lst), "positions by class", tot, "subs", sum(1 for r in lst for a, b in zip(tl.positions(r, cap)[3], r["ids"]) if a != b))''')
open("think_job.py","w").write(t)
EOF
grep -n "ids2\|non\[" think_job.py | head -20; cd .. && scp -q code/thinklib.py code/think_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; cat scratch/logs/hold1.log; cd code && $PY think_job.py --dry 2>&1 | tail -4'output
80: n, P, non, ids2 = tl.positions(recs[0], 9000) (13 lines)
80: n, P, non, ids2 = tl.positions(recs[0], 9000)
127: n, P, non, ids2 = tl.positions(rec, NTRAIN_CAP)
135: sel += [(p, 4, 0) for p in non[:80]]
140: r = m.forward(ids2, out_pos=[n - 1], capture=("resid_post", "mlp_in", "mlp_act", "mlp_raw"),
218: n, P, non, ids2 = tl.positions(rec, NEVAL_CAP)
235: rows += [(p, 4, 0, ids2[p + 1], -1) for p in non[:150]]
247: r = m.forward(ids2, out_pos=[n - 1], capture=("mlp_in",), cap_layers=tl.GL,
255: r = m.forward(ids2, out_pos=[n - 1], stop_after=59)
Thu Nov 12 08:45:27 PM EST 2026
['A0p03', 'A0p1', 'A0p3', 'B0p1', 'B0p3', 'B1p0', 'C3p0', 'C8p0', 'C20p0', 'A0p1c', 'B0p3c', 'C8p0c']
positions 9000 956 5210 [7, 458, 408, 83]
train 22 positions by class [151, 10341, 11538, 7564] subs 123
eval 8 positions by class [62, 3899, 3771, 3898] subs 48tool
Bash: cd code && sed -i 's/NTRAIN_CAP, NEVAL_CAP = 9000, 8000/NTRAIN_CAP, NEVAL_CAP = 11000, 8000/' think_job.py && grep -n "NTRAIN_CAP, NEVAL_CAP =" think_job.py && cd .. && scp -q code/think_job.py code/gpu_job.sh wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date; cat scratch/logs/hold1.log; ls scratch code; ps aux | grep -E "flock" | grep -v grep | cut -c1-200; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader'
output
26:NTRAIN_CAP, NEVAL_CAP = 11000, 8000 (21 lines)
26:NTRAIN_CAP, NEVAL_CAP = 11000, 8000 Thu Nov 12 08:45:39 PM EST 2026 code: __pycache__ fail_stats.py gpu_hold.sh gpu_job.sh inspect_probe.py req_stats.py think_job.py thinklib.py scratch: job1 logs keith 215046 0.0 0.0 8372 2168 ? S 20:01 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job2.sh keith 270626 0.0 0.0 8372 2172 ? S 20:24 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessB.sh 1620 keith 306309 0.0 0.0 8372 2168 ? S 20:39 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh 0, 86484 MiB 1, 90512 MiB [This command modified 1 file you've previously read: code\think_job.py. Call Read before editing.]
tool
Bash: cd code && python - <<'EOF' (65 lines)
Bash: cd code && python - <<'EOF'
t=open("think_job.py").read()
t=t.replace('''def tl_adapter(path):
import g31
return g31.Adapter(path, "cuda:0")
''','''DEV = "cuda:0"
def tl_adapter(path):
import g31
return g31.Adapter(path, DEV)
class Mock:
"""Shape-faithful stand-in for g31.G31 (CPU, random numbers) to test this script's plumbing without the GPU."""
def __init__(self):
self.dev = torch.device("cpu")
self.emb = torch.randn(300, tl.D).to(torch.bfloat16)
self.final_norm = torch.rand(tl.D).to(torch.bfloat16)
self.L = {L: dict(post_feedforward_layernorm=torch.randn(tl.D).to(torch.bfloat16),
layer_scalar=torch.tensor([0.7]).to(torch.bfloat16)) for L in range(60)}
self.e = EditSet()
def set_adapter(self, a):
self.ad = a
def base_weight(self, l, mod):
return self.e.W(l, mod).to(torch.bfloat16)
def rms(self, x, w=None):
return x
def logprobs_from_final(self, hp):
return torch.log_softmax(torch.randn(hp.shape[0], 262144), -1)
def forward(self, ids, out_pos=None, capture=None, cap_layers=None, cap_pos=None, stop_after=None):
n = len(ids)
dims = dict(resid_post=tl.D, mlp_in=tl.D, mlp_act=21504, mlp_raw=tl.D)
caps = {}
for c in (capture or ()):
x = torch.randn(len(cap_layers), len(cap_pos), dims[c])
if c == "mlp_in":
x += (torch.tensor(cap_pos)[None, :, None] % 7 == 0).float() * 0.3 + 1.0
caps[c] = x
return dict(logprobs=None, caps=caps, resid=torch.randn(n, tl.D).to(torch.bfloat16))
''')
t=t.replace(''' m = g31.G31()
log("model loaded")''',''' global DEV, DEADLINE_CAPTURE
if "--mock" in sys.argv:
DEV = "cpu"
m = Mock()
train, ev = train[:5], ev[:2]
GRID[:] = GRID[:1] + GRID[3:4] + GRID[6:7]
CTRL[:] = CTRL[:1]
else:
m = g31.G31()
log("model loaded")''')
t=t.replace('''S = A_DIR + "/scratch/job1"
STATS = A_DIR + "/code/data/think_stats.pt"''','''MOCK = "--mock" in sys.argv
S = A_DIR + ("/scratch/mock" if MOCK else "/scratch/job1")
STATS = A_DIR + ("/scratch/mock/think_stats.pt" if MOCK else "/code/data/think_stats.pt")''')
open("think_job.py","w").write(t)
EOF
cd .. && scp -q code/think_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; cat scratch/logs/hold1.log; cd code && (time $PY think_job.py --mock) 2>&1 | cut -c1-420 | tail -45'output
Thu Nov 12 08:46:12 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 08:46:12 PM EST 2026
[ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-']
[ 0.1] model loaded
[ 2.4] captured 0 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]
[ 4.8] captured 1 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]
[ 7.1] captured 2 Peter-van-Tol__pydantic-shapel n 11000 pos 346 cls [6, 80, 60, 120, 80]
[ 9.5] captured 3 [task] n 11000 pos 351 cls [11, 80, 60, 120, 80]
[ 11.8] captured 4 [task] n 11000 pos 347 cls [7, 80, 60, 120, 80]
[ 12.4] gate 30 j 20774 auc_z 0.974 auc_g 0.998 cbar 76.62 csd 7.84 {'E': '0.44/0.45', 'early': '0.03/0.00', 'mid': '0.36/0.44', 'late': '0.98/4.22', 'non': '0.03/0.00'}
[ 12.9] gate 32 j 2092 auc_z 0.967 auc_g 0.998 cbar 76.60 csd 7.84 {'E': '0.32/0.51', 'early': '0.02/-0.01', 'mid': '0.36/0.43', 'late': '0.97/3.70', 'non': '0.01/-0.01'}
[ 13.6] gate 34 j 16432 auc_z 0.972 auc_g 0.998 cbar 76.55 csd 7.83 {'E': '0.29/0.22', 'early': '0.02/-0.00', 'mid': '0.37/0.41', 'late': '0.98/3.91', 'non': '0.03/0.00'}
[ 14.2] gate 36 j 20739 auc_z 0.977 auc_g 0.997 cbar 76.57 csd 7.88 {'E': '0.44/0.47', 'early': '0.03/0.01', 'mid': '0.37/0.43', 'late': '0.97/4.08', 'non': '0.03/0.00'}
[ 14.9] gate 38 j 12982 auc_z 0.965 auc_g 0.998 cbar 76.60 csd 7.87 {'E': '0.35/0.37', 'early': '0.01/0.00', 'mid': '0.39/0.45', 'late': '0.98/3.77', 'non': '0.02/-0.01'}
[ 15.5] gate 40 j 11759 auc_z 0.974 auc_g 0.998 cbar 76.59 csd 7.87 {'E': '0.56/1.00', 'early': '0.03/0.00', 'mid': '0.38/0.57', 'late': '0.98/4.23', 'non': '0.01/-0.00'}
[ 16.2] gate 42 j 19707 auc_z 0.963 auc_g 0.998 cbar 76.62 csd 7.87 {'E': '0.35/0.40', 'early': '0.03/-0.01', 'mid': '0.40/0.55', 'late': '0.98/3.90', 'non': '0.03/-0.01'}
[ 16.8] gate 44 j 5841 auc_z 0.959 auc_g 0.998 cbar 76.57 csd 7.85 {'E': '0.24/0.22', 'early': '0.03/0.00', 'mid': '0.40/0.44', 'late': '0.98/3.69', 'non': '0.02/-0.00'}
[ 17.4] gate 46 j 18774 auc_z 0.970 auc_g 0.998 cbar 76.57 csd 7.84 {'E': '0.32/0.56', 'early': '0.02/0.01', 'mid': '0.36/0.41', 'late': '0.98/4.02', 'non': '0.01/-0.01'}
[ 18.0] gate 48 j 20166 auc_z 0.967 auc_g 0.997 cbar 76.57 csd 7.85 {'E': '0.21/0.16', 'early': '0.02/-0.00', 'mid': '0.32/0.37', 'late': '0.95/3.64', 'non': '0.02/-0.01'}
[ 18.6] gate 54 j 19938 auc_z 0.971 auc_g 0.997 cbar 76.60 csd 7.85 {'E': '0.32/0.42', 'early': '0.03/0.00', 'mid': '0.37/0.50', 'late': '0.98/4.00', 'non': '0.03/-0.00'}
[ 19.3] gate 56 j 4628 auc_z 0.972 auc_g 0.998 cbar 76.61 csd 7.83 {'E': '0.44/0.62', 'early': '0.03/-0.01', 'mid': '0.44/0.54', 'late': '0.98/4.11', 'non': '0.02/0.01'}
[... 16 lines cut ...]
[ 20.0] grid built
[ 36.4] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 5
[ 44.6] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 5
[ 44.7] done
real 0m45.883s
user 2m25.438s
sys 1m27.695stool
Bash: cat > code/build_think.py <<'EOF' (93 lines, middle cut at capture)
Bash: cat > code/build_think.py <<'EOF'
"""build_think.py OUT_DIR FAM:DOSE[,FAM:DOSE...] [--ctrl] [--stats FILE]
Builds an "end the thought" adapter (families A, B, C of thinklib.py) from the saved activation statistics
code/data/think_stats.pt (written by think_job.py in one GPU-0 hold). --ctrl builds the matched control: same code
path, the written unit direction has every coordinate's sign flipped at random (seed 1000 + layer)."""
import json
import os
import sys
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools")
sys.path.insert(0, A_DIR + "/code")
import torch # noqa: E402
import thinklib as tl # noqa: E402
from edits import EditSet # noqa: E402
out, spec = sys.argv[1], sys.argv[2]
ctrl = "--ctrl" in sys.argv
stats = sys.argv[sys.argv.index("--stats") + 1] if "--stats" in sys.argv else A_DIR + "/code/data/think_stats.pt"
st = torch.load(stats)
e = EditSet()
note = dict(spec=spec, ctrl=ctrl, stats=stats, families={})
for part in spec.split(","):
fam, dose = part.split(":")
info = tl.build(e, st, fam, float(dose), ctrl)
note["families"][part] = {str(k): {a: (round(b, 5) if isinstance(b, float) else b) for a, b in v.items()}
for k, v in info.items()}
rep = e.save(out, note=note)
rel = {k: round(v.get("rel_fro", 0), 5) for k, v in rep["modules"].items()}
print("saved", out, "r", rep["r"], "modules", len(rel), "max rel_fro", max(rel.values()))
EOF
cat > code/think_report.py <<'EOF'
"""think_report.py [eval.pt]: table of the teacher-forced evaluation written by think_job.py (CPU)."""
import sys
import torch
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
sys.path.insert(0, A_DIR + "/code")
import thinklib as tl
res = torch.load(sys.argv[1] if len(sys.argv) > 1 else A_DIR + "/scratch/job1/eval.pt")
st = None
eps = res["episodes"]
print("episodes", len(eps), [e["instance"][:18] for e in eps], "arms per episode", [len(e["arms"]) for e in eps])
arms = [a for a in res["arms"] if all(a in e["arms"] for e in eps)]
if len(arms) < len(res["arms"]):
print("arms missing in the last episode (deadline):", [a for a in res["arms"] if a not in arms])
arms2 = [a for a in res["arms"] if all(a in e["arms"] for e in eps[:-1])]
else:
arms2 = arms
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def explen(epl, arm):
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out = {"short": [0.0, 0.0, 0], "long": [0.0, 0.0, 0]}
for e in epl:
rows = e["rows"]
h = e["arms"][arm]["lp101"].double().exp()
for ci in rows[:, 4].unique().tolist():
if ci < 0:
continue
mk = (rows[:, 4] == ci) & (rows[:, 1] <= 3)
d = rows[mk, 2]
o = d.argsort()
hh = h[mk][o]
n = len(hh)
if n < 3:
continue
S = torch.cumprod(1 - hh, 0)
S = torch.cat([torch.ones(1, dtype=S.dtype), S[:-1]])
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return out
for label, al, epl in (("all episodes", arms, eps), ("all but last episode", arms2, eps[:-1])):
if label != "all episodes" and al == arms:
continue
print("==", label, len(epl))
rows = torch.cat([e["rows"] for e in epl])
cls = rows[:, 1]
b101, bnx, btop = cat("lp101", "base", epl), cat("lpnext", "base", epl), cat("top", "base", epl)
be = explen(epl, "base")
print("n by class", {n: int((cls == k).sum()) for k, n in enumerate(tl.CLS + ["think_pt", "tool_pt"])})
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be["short"][0], be["short"][1], be["short"][2], be["long"][0], be["long"][1], be["long"][2]))
print("base mean lp101: early %.2f mid %.2f late %.2f E %.2f non %.2f" % tuple(float(b101[cls == k].mean()) for k in (1, 2, 3, 0, 4)))
hdr = "arm | dlp101 early mid lat
[... 2231 characters cut ...]output
Thu Nov 12 08:47:38 PM EST 2026 (22 lines, middle cut at capture)
Thu Nov 12 08:47:38 PM EST 2026
n by class {'E': 8, 'early': 633, 'mid': 1072, 'late': 3305, 'non': 300, 'think_pt': 8, 'tool_pt': 8}
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base mean lp101: early -12.98 mid -12.94 late -12.97 E -12.71 non -13.01
arm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non
A0p03 | 0.03 -0.01 0.01 -0.57 | 1.00 1.00 | -0.02 -0.02 -0.08 0.05 0.07 | 0.00 0.00 0.00 0.00 | 0.0000 (base 0.0000)
B0p1 | 0.03 -0.11 0.00 -0.40 | 1.00 1.00 | -0.02 0.03 -0.07 0.21 0.12 | 0.00 0.00 0.00 0.00 | 0.0000 (base 0.0000)
C3p0 | 0.01 -0.06 0.00 0.06 | 1.00 1.00 | -0.00 0.01 0.00 0.35 -0.17 | 0.00 0.00 0.00 0.00 | 0.0000 (base 0.0000)
A0p1c | 0.00 -0.02 0.01 0.40 | 1.00 1.00 | -0.03 0.02 -0.01 0.02 0.03 | 0.00 0.00 0.00 0.00 | 0.0000 (base 0.0000)
== held-out gate pre-activation > 0 by class (and AUC late vs early+non), per gate layer
30 auc 0.494 {'E': '0.62', 'early': '0.38', 'mid': '0.39', 'late': '0.39', 'non': '0.40', 'think_pt': '0.25', 'tool_pt': '0.38'} {'d3-50': '0.36', 'd50-100': '0.41', 'd100-200': '0.41', 'd200-400': '0.38', 'd400-800': '0.38', 'd800-1600': '0.39', 'd1600-5000': '0.39'}
32 auc 0.507 {'E': '0.62', 'early': '0.35', 'mid': '0.39', 'late': '0.37', 'non': '0.36', 'think_pt': '0.38', 'tool_pt': '0.38'} {'d3-50': '0.36', 'd50-100': '0.34', 'd100-200': '0.37', 'd200-400': '0.40', 'd400-800': '0.39', 'd800-1600': '0.35', 'd1600-5000': '0.36'}
34 auc 0.501 {'E': '0.50', 'early': '0.36', 'mid': '0.36', 'late': '0.37', 'non': '0.39', 'think_pt': '0.62', 'tool_pt': '0.62'} {'d3-50': '0.34', 'd50-100': '0.38', 'd100-200': '0.37', 'd200-400': '0.36', 'd400-800': '0.38', 'd800-1600': '0.34', 'd1600-5000': '0.39'}
36 auc 0.512 {'E': '0.50', 'early': '0.38', 'mid': '0.38', 'late': '0.39', 'non': '0.35', 'think_pt': '0.38', 'tool_pt': '0.62'} {'d3-50': '0.39', 'd50-100': '0.37', 'd100-200': '0.39', 'd200-400': '0.37', 'd400-800': '0.37', 'd800-1600': '0.39', 'd1600-5000': '0.40'}
38 auc 0.494 {'E': '0.50', 'early': '0.35', 'mid': '0.37', 'late': '0.35', 'non': '0.40', 'think_pt': '0.38', 'tool_pt': '0.25'} {'d3-50': '0.34', 'd50-100': '0.35', 'd100-200': '0.37', 'd200-400': '0.37', 'd400-800': '0.35', 'd800-1600': '0.36', 'd1600-5000': '0.36'}
40 auc 0.516 {'E': '0.38', 'early': '0.34', 'mid': '0.40', 'late': '0.38', 'non': '0.42', 'think_pt': '0.38', 'tool_pt': '0.12'} {'d3-50': '0.35', 'd50-100': '0.32', 'd100-200': '0.39', 'd200-400': '0.41', 'd400-800': '0.38', 'd800-1600': '0.37', 'd1600-5000': '0.41'}
42 auc 0.513 {'E': '0.00', 'early': '0.39', 'mid': '0.41', 'late': '0.43', 'non': '0.43', 'think_pt': '0.38', 'tool_pt': '0.62'} {'d3-50': '0.39', 'd50-100': '0.39', 'd100-200': '0.44', 'd200-400': '0.39', 'd400-800': '0.42', 'd800-1600': '0.42', 'd1600-5000': '0.45'}
44 auc 0.488 {'E': '0.12', 'early': '0.36', 'mid': '0.39', 'late': '0.37', 'non': '0.41', 'think_pt': '0.38', 'tool_pt': '0.25'} {'d3-50': '0.36', 'd50-100': '0.37', 'd100-200': '0.38', 'd200-400': '0.40', 'd400-800': '0.36', 'd800-1600': '0.37', 'd1600-5000': '0.37'}
46 auc 0.501 {'E': '0.38', 'early': '0.38', 'mid': '0.38', 'late': '0.39', 'non': '0.39', 'think_pt': '0.25', 'tool_pt': '0.38'} {'d3-50': '0.39', 'd50-100': '0.37', 'd100-200': '0.39', 'd200-400': '0.37', 'd400-800': '0.37', 'd800-1600': '0.39', 'd1600-5000': '0.42'}
48 auc 0.510 {'E': '0.25', 'early': '0.33', 'mid': '0.34', 'late': '0.35', 'non': '0.35', 'think_pt': '0.62', 'tool_pt': '0.25'} {'d3-50': '0.32', 'd50-100': '0.35', 'd100-200': '0.31', 'd200-400': '0.36', 'd400-800': '0.35', 'd800-1600': '0.35', 'd1600-5000': '0.33'}
54 auc 0.511 {'E': '0.12', 'early': '0.37', 'mid': '0.38', 'late': '0.38', 'non': '0.35', 'think_pt': '0.38', 'tool_pt': '0.50'} {'d3-50': '0.36', 'd50-100': '0.38', 'd100-200': '0.37', 'd200-400': '0.38', 'd400-800': '0.38'
[... 728 characters cut ...]tool
Bash: cat > code/cf_budget.py <<'EOF'
[line removed by the dashboard scrubber]
import json,os,collections,statistics as st,sys
sys.path.insert(0,"/home/keith/gemma-v2/wsp31/tools")
R="/home/keith/gemma-v2/results/wsp31"
agg=collections.defaultdict(list)
ex=[]
for arm in ("base","aa","zero"):
d=R+"/batches/b001/"+arm+"/rebench/shard0_gpu0"
for l in open(d+"/task_results.jsonl"):
r=json.loads(l)
p=r.get("request_log_path") or ""
if not os.path.exists(p): p=d+"/requests/%s__r%d.jsonl"%(r["instance_id"],r.get("rollout_idx",0))
if not os.path.exists(p): continue
calls=[json.loads(x) for x in open(p)]
th=[]; tot=0
for c in calls:
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tot+=len(ids); t=ids.index(101) if 101 in ids else len(ids); th.append(t)
if t>2500 and len(ex)<3: ex.append((r["instance_id"],r["resolved"],t,c["response_message"]))
p1=r.get("phase1_seconds") or 0
cls="solved" if r["resolved"] else ("limit" if p1>=420 else "wrong")
agg[cls].append(dict(tot=tot,th=th,p1=p1))
for cls,v in agg.items():
n=len(v); tot=sum(e["tot"] for e in v); th=sum(sum(e["th"]) for e in v)
deep=sum(sum(max(0,t-400) for t in e["th"]) for e in v)
deep8=sum(sum(max(0,t-800) for t in e["th"]) for e in v)
nl=sum(sum(1 for t in e["th"] if t>=400) for e in v); nc=sum(len(e["th"]) for e in v)
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for iid,res,t,msg in ex[:2]:
txt=json.dumps(msg)[:200]
rc=(msg.get("reasoning_content") or msg.get("reasoning") or "")
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print(rc[:700].replace("\n"," | "))
print(" ... ")
print(rc[-900:].replace("\n"," | "))
EOF
scp -q code/cf_budget.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; cat scratch/logs/hold1.log; $PY code/cf_budget.py 2>&1 | cut -c1-1700'output
Thu Nov 12 08:47:58 PM EST 2026 [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] Now let's look at `irfft_grad` and `rfft_grad` again. | | ```python | def rfft_grad(get_args, irfft_fun, ans, x, *args, **kwargs): | axes, s, norm = get_args(x, *args, **kwargs) | vs = vspace(x) | gvs = vspace(ans) | check_no_repeated_axes(axes) | if s is None: | s = [vs.shape[i] for i in axes] | check_even_shape(s) | | # s is the full fft shape | # gs is the compressed shape | gs = list(s) | gs[-1] = gs[-1] // 2 + 1 | fac = make_rfft_factors(axes, gvs.shape, gs, s, norm) | | def vjp(g): | g = anp.conj(g / fac) | r = match_complex(x, truncate_pad((irfft_fun(g, *args, **kwargs)), vs.shape)) | return r | | return vjp | | | def irfft_grad(get_args, rfft_fun ... ] = s[axes[-1]] // 2 + 1`. | | Let's check `rfft_grad` too. | ```python | def rfft_grad(get_args, irfft_fun, ans, x, *args, **kwargs): | # ... | gs = list(s) | gs[-1] = gs[-1] // 2 + 1 | fac = make_rfft_factors(axes, gvs.shape, gs, s, norm) | ``` | Same bug here! `gs[-1]` is used instead of `gs[axes[-1]]`. | | Wait, let's look at `make_rfft_factors` again. | ```python | def make_rfft_factors(axes, resshape, facshape, normshape, norm): | # ... | fac = anp.zeros(resshape) | fac[...] = 2 | index = [slice(None)] * len(resshape) | if facshape[-1] <= resshape[axes[-1]]: | index[axes[-1]] = (0, facshape[-1] - 1) | else: | index[axes[-1]] = (0,) | fac[tuple(index)] = 1 | # ... | ``` | Here `facshape[-1]` is used. It should be `facshape[axes[-1]]`. | | So there are multiple places where `[-1]` is used instead of `[axes[-1]]`. | | Let's try to reproduce this with `axis=0` for a 2D array. | | [line removed by the dashboard scrubber] Still success. Let's try `rfft2` with `axes=(0, 1)` on a 3D array. | If `x` has shape (4, 6, 8), and `axes=(0, 1)`, then the output of `rfft2` should have shape (3, 6, 8). | Wait, `rfft2` compresses the last axis in `axes`. | If `axes=(0, 1)`, then axis 1 is compressed. | So the output shape should be (4, 4, 8). | Wait, `numpy.fft.rfft2` documentation: "The output is complex and has the same shape as the input, except for the last axis specified in axes, which is compressed". | So if `axes=(0, 1)`, axis 1 is compressed. | Input shape (4, 6, 8) -> Output shape (4, 4, 8). | | In `rfft_grad`: | `axes = (0, 1)`. `s = (4, 6, 8)`. | `gs = list(s) = [4, 6, 8]`. | `gs[-1] = gs[2] = 8 // 2 + 1 = 5`. | So `gs = [4, 6, 5]`. | Bu ... `. | If `s` is an integer, `list(s)` will raise `TypeError`. | | Wait, if `s` is an integer, it's the length of the transformed axis. | But `gs` should be the shape of the compressed array. | If `s` is an integer, `gs` should be `s // 2 + 1`. | | Let's see how `rfft_grad` handles `s`. | ```python | def rfft_grad(get_args, irfft_fun, ans, x, *args, **kwargs): | axes, s, norm = get_args(x, *args, **kwargs) | vs = vspace(x) | gvs = vspace(ans) | check_no_repeated_axes(axes) | if s is None: | s = [vs.shape[i] for i in axes] | check_even_shape(s) | | # s is the full fft shape | # gs is the compressed shape | gs = list(s) | gs[-1] = gs[-1] // 2 + 1 | ``` | If `s` is an integer, `list(s)` will fail here too! | | Wait, if `s` is an integer, `s = [vs.shape[i] for i in axes]` is skipped. | Then `gs = list(s)` is called. | This will definitely fail if `s` is an integer. | | Let's try to reproduce this. |
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date; cat scratch/logs/hold1.log; ls scratch; ps aux | grep -E "flock|job2|sessB|capture.py|causal.py|python" | grep -v grep | cut -c1-180 | head; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader; ls /home/keith/gemma-v2/wsp31/authors/; ls /home/keith/gemma-v2/wsp31/authors/*/queue 2>/dev/null | head'
output
Thu Nov 12 08:48:10 PM EST 2026 (20 lines)
Thu Nov 12 08:48:10 PM EST 2026 job1 logs mock root 2724 0.0 0.0 49444 30316 ? Ss 17:57 0:00 /usr/bin/python3 /usr/bin/networkd-dispatcher --run-startup-triggers root 2809 0.0 0.0 235864 43528 ? Ssl 17:57 0:09 /usr/bin/python3 /usr/local/sbin/wrx90-gpu-fan-controller.py root 3039 0.0 0.0 128720 33020 ? Ssl 17:57 0:00 /usr/bin/python3 /usr/share/unattended-upgrades/unattended-upgrade-shutdown --wait-for-signal root 3738 3.3 0.0 309296 77752 tty1 SNsl+ 17:57 5:46 /usr/bin/python3 /opt/wrx90-dash/dash.py keith 184764 2.7 1.1 20440536 2305360 ? Ssl 19:50 1:33 /home/keith/gemma-v2/tii-venv/bin/python -m vllm.entrypoints.openai.api_server --model /home/keith/gemma-v2/model keith 184766 0.0 0.0 39564 27488 ? Ss 19:50 0:00 python3 wsp31/queue/runner.py loop keith 184975 0.0 0.0 44436 16252 ? S 19:50 0:00 /home/keith/gemma-v2/tii-venv/bin/python -c from multiprocessing.resource_tracker import main;main(47) keith 186432 0.0 0.1 340896 314460 ? Ss 19:52 0:00 /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/run_arm.py --set wsp31_screen_v1 --served bas keith 186433 0.0 0.1 340956 314280 ? Ss 19:52 0:00 /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/run_arm.py --set wsp31_screen_v1 --served gem keith 186434 0.0 0.1 340896 314212 ? Ss 19:52 0:00 /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/run_arm.py --set wsp31_screen_v1 --served zer 0, 91772 MiB 1, 90512 MiB BRIEF.md claude turn_close.sh turn_open.sh
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors; cat turn_close.sh | head -40; cat claude/.turn_start; ps aux | grep -E "flock|sessB|job2" | grep -v grep | cut -c1-160; ls ../tools/logs | tail -5'
output
#!/bin/bash (28 lines)
#!/bin/bash
# turn_close.sh <author> <turn> <cli exit code>: report what the turn left behind. Kills nothing.
A=$1; N=$2; RC=$3; D=/home/keith/gemma-v2/wsp31/authors/$A
case $A in claude|gpt|gemini) ;; *) echo "unknown author $A"; exit 2 ;; esac
T0=$(cat $D/.turn_start 2>/dev/null || echo 0)
new=0; names=""
for r in $D/queue/*/READY; do
[ -f "$r" ] || continue
if [ "$(stat -c %Y "$r")" -ge "$T0" ]; then new=$((new+1)); names="$names $(basename $(dirname $r))"; fi
done
half=$(find $D/queue -mindepth 1 -maxdepth 1 -type d -newermt "@$T0" ! -exec test -e {}/READY \; -print 2>/dev/null | wc -l)
sz=$(du -sm $D 2>/dev/null | cut -f1)
echo "$(date '+%Y-%m-%d %H:%M:%S') close turn $N $A rc=$RC ready=$new unfinished=$half dir_mb=$sz:$names" | tee -a /home/keith/gemma-v2/wsp31/queue/turns.log
# GPU processes still alive on GPU 0 (for the supervisor to look at; nothing is killed here)
nvidia-smi --query-compute-apps=gpu_uuid,pid,used_memory --format=csv,noheader 2>/dev/null | head -8
1794533816
keith 215044 0.0 0.0 10136 3912 ? S 20:01 0:00 /bin/bash ./run_gpu.sh logs/job2.log ./job2.sh
keith 215046 0.0 0.0 8372 2168 ? S 20:01 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job2.sh
keith 270626 0.0 0.0 8372 2172 ? S 20:24 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessB.sh 1620
keith 302424 0.0 0.0 17652 7948 ? S 20:37 0:00 timeout 1740 ./job2.sh
keith 302425 0.0 0.0 10136 4008 ? S 20:37 0:00 /bin/bash ./job2.sh
keith 306309 0.0 0.0 8372 2168 ? S 20:39 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
keith 326637 0.0 0.0 10136 4068 ? Ss 20:47 0:00 bash -c cd /home/keith/gemma-v2/wsp31/tools && PY=/home/keith/gemma-v2/tii-venv/bin/python; e
server_18200_1112_195910.log
sessA.log
sessB.log
smoke.log
verify_dequant_ref.logtool
Bash: cd code && python - <<'EOF'
t=open("think_job.py").read()
t=t.replace('DEADLINE_CAPTURE, DEADLINE = 600, 1400','''DEADLINE_CAPTURE, DEADLINE = 600, 1400
TURN_END = 1794533816 + 90 * 60 # this turn is stopped then; leave 13 minutes after the job for the queue entries
if not ("--mock" in sys.argv or "--dry" in sys.argv):
DEADLINE = min(DEADLINE, TURN_END - 13 * 60 - T0)
DEADLINE_CAPTURE = min(DEADLINE_CAPTURE, 0.42 * DEADLINE)''')
t=t.replace('''GRID = [("A", 0.03), ("A", 0.1), ("A", 0.3), ("B", 0.1), ("B", 0.3), ("B", 1.0), ("C", 3.0), ("C", 8.0), ("C", 20.0)]
CTRL = [("A", 0.1), ("B", 0.3), ("C", 8.0)]''','''# evaluation order = priority (arms late in the list are the first to be dropped at the deadline)
GRID = [("B", 0.3), ("C", 8.0), ("A", 0.1), ("B", 1.0), ("C", 20.0), ("A", 0.3), ("B", 0.1), ("C", 3.0), ("A", 0.03)]
CTRL = [("B", 0.3), ("C", 8.0), ("A", 0.1)]''')
t=t.replace(''' GRID[:] = GRID[:1] + GRID[3:4] + GRID[6:7]''',''' GRID[:] = GRID[:3]''')
t=t.replace(''' log("train", len(train), "eval", len(ev),''',''' log("deadline", DEADLINE, "capture deadline", DEADLINE_CAPTURE)
if DEADLINE < 420:
log("not enough time left in the turn for a useful hold; releasing the lock")
return
log("train", len(train), "eval", len(ev),''')
open("think_job.py","w").write(t)
EOF
grep -n "DEADLINE" think_job.py | head -20; cd .. && scp -q code/think_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; cat scratch/logs/hold1.log; cd code; $PY -c "import ast,sys;ast.parse(open(\"think_job.py\").read())" && $PY think_job.py --mock 2>&1 | tail -3 | cut -c1-200; $PY $R/wsp31/tools/tok.py ids "Wait" 2>&1 | tail -3; $PY $R/wsp31/tools/tok.py ids " Wait" 2>&1 | tail -2; $PY $R/wsp31/tools/tok.py ids "
Wait" 2>&1 | tail -2'output
29:DEADLINE_CAPTURE, DEADLINE = 600, 1400 (19 lines)
29:DEADLINE_CAPTURE, DEADLINE = 600, 1400
32: DEADLINE = min(DEADLINE, TURN_END - 13 * 60 - T0)
33: DEADLINE_CAPTURE = min(DEADLINE_CAPTURE, 0.42 * DEADLINE)
130: if time.time() - T0 > DEADLINE_CAPTURE:
222: if time.time() - T0 > DEADLINE - 60:
247: if time.time() - T0 > DEADLINE:
330: log("deadline", DEADLINE, "capture deadline", DEADLINE_CAPTURE)
331: if DEADLINE < 420:
335: global DEV, DEADLINE_CAPTURE
Thu Nov 12 08:48:46 PM EST 2026
global DEV, DEADLINE_CAPTURE
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
SyntaxError: name 'DEADLINE_CAPTURE' is used prior to global declaration
[20470]
['Wait']
[29202]
[' Wait']
[107, 20470]
['\\n', 'Wait']tool
Bash: cd code && python - <<'EOF'
t=open("think_job.py").read()
t=t.replace(" global DEV, DEADLINE_CAPTURE\n","")
t=t.replace("def main():\n import g31\n","def main():\n global DEV\n import g31\n")
open("think_job.py","w").write(t)
import ast; ast.parse(t)
EOF
cd .. && scp -q code/think_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; cat scratch/logs/hold1.log; cd code; $PY think_job.py --mock 2>&1 | tail -4 | cut -c1-200; ls ../scratch/job1 2>/dev/null'output
Thu Nov 12 08:49:04 PM EST 2026 [ 19.0] grid built [ 35.8] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 5 [ 45.4] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 5 [ 45.4] done dry
tool
Bash: cat > code/wait_stats.py <<'EOF'
[line removed by the dashboard scrubber]
import json,os,collections
R="/home/keith/gemma-v2/results/wsp31"
cnt=collections.defaultdict(lambda:[0,0,0,0]) # cls -> [wait<400, tok<400, wait>=400, tok>=400]
per=collections.defaultdict(list)
for arm in ("base","aa","zero"):
d=R+"/batches/b001/"+arm+"/rebench/shard0_gpu0"
for l in open(d+"/task_results.jsonl"):
r=json.loads(l)
p=r.get("request_log_path") or ""
if not os.path.exists(p): p=d+"/requests/%s__r%d.jsonl"%(r["instance_id"],r.get("rollout_idx",0))
if not os.path.exists(p): continue
p1=r.get("phase1_seconds") or 0
cls="solved" if r["resolved"] else ("limit" if p1>=420 else "wrong")
w=0
for x in open(p):
[line removed by the dashboard scrubber]
t=ids.index(101) if 101 in ids else len(ids)
for o in range(t):
k=0 if o<400 else 2
cnt[cls][k+1]+=1
if ids[o] in (20470,29202): cnt[cls][k]+=1; w+=1
per[cls].append(w)
for cls,v in cnt.items():
[line removed by the dashboard scrubber]
EOF
scp -q code/wait_stats.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; cat scratch/logs/hold1.log; $PY code/wait_stats.py'output
Thu Nov 12 08:50:01 PM EST 2026 [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber]
tool
Write: C:\Users\Owner\wsp31_loop\work\claude\code\finalize.py
output
File created successfully at: C:\Users\Owner\wsp31_loop\work\claude\code\finalize.py (file state is current in your context — no need to Read it back)
tool
Bash: scp -q code/finalize.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date; cat scratch/logs/hold1.log; ls scratch/job1; tail -3 scratch/logs/job1.log 2>/dev/null; ps aux | grep -E "flock" | grep -v grep | cut -c1-140; cat /home/keith/gemma-v2/wsp31/queue/STATUS.md | sed -n 1,12p'
output
Thu Nov 12 08:50:50 PM EST 2026 (15 lines)
Thu Nov 12 08:50:50 PM EST 2026 dry keith 270626 0.0 0.0 8372 2172 ? S 20:24 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessB.sh 1620 keith 306309 0.0 0.0 8372 2168 ? S 20:39 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh # wsp31 queue status (2026-11-12 20:50:33 EST) Rule: not fixed yet (waits for the unedited baseline, ledger record 0001) ## Batches | batch | stage | gpu | state | arms | rows | note | |---|---|---|---|---|---|---| | b001 | s1 | 1 | running | base aa zero | 65 | | ## Edits
tool
Bash: cd code && python - <<'EOF'
t=open("build_think.py").read()
t=t.replace('else A_DIR + "/code/data/think_stats.pt"','else os.environ.get("THINK_STATS", A_DIR + "/code/data/think_stats.pt")')
open("build_think.py","w").write(t)
t=open("think_report.py").read()
t=t.replace('import sys\nimport torch','import os\nimport sys\nimport torch')
t=t.replace('else A_DIR + "/scratch/job1/eval.pt")','else os.environ.get("THINK_EVAL", A_DIR + "/scratch/job1/eval.pt"))')
open("think_report.py","w").write(t)
EOF
cd .. && scp -q code/build_think.py code/think_report.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; export THINK_STATS=$PWD/scratch/mock/think_stats.pt THINK_EVAL=$PWD/scratch/mock/eval.pt; echo "- test extra" > scratch/mock/extra.txt; $PY code/finalize.py zz-test B:0.3,C:8 -2 9 "test title" --arms B0p3,B0p3c,C8p0 --extra scratch/mock/extra.txt 2>&1 | tail -5; sed -n 1,12p queue/zz-test/record.md | cut -c1-150; sed -n 48,90p queue/zz-test/record.md | cut -c1-260; ls -la queue/zz-test queue/zz-test/edit; rm -rf queue/zz-test; ls queue; date; cat scratch/logs/hold1.log'output
saved queue/zz-test/edit r 1 modules 39 max rel_fro 0.07436 (29 lines, middle cut at capture)
saved queue/zz-test/edit r 1 modules 39 max rel_fro 0.07436 saved queue/zz-test/ctrl r 1 modules 39 max rel_fro 0.0743 OK edit: 39 modules changed (of 39 present), r=1, max |dW|_F/|W|_F 0.07436 at 34.down_proj OK ctrl: 39 modules changed (of 39 present), r=1, max |dW|_F/|W|_F 0.0743 at 34.down_proj wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/zz-test/record.md title: test title ## Hypothesis In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the 7.5-minute session limit, 10 of them with an empty patch and 9 of those without a single file edit. At about [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] "Wait, ..." restarts. Making long thoughts end sooner should return that wall-clock to tool calls, so that episodes which now time out without a patch get to edit, test and submit. Built as intended; approximations: bf16 storage of the factors (8 significant bits), the 5% `clipdiv` mask, and the bias-free threshold (the constant is read from the mean-input direction). [... 49 lines cut ...] queue/zz-test/edit: total 2080 drwxrwxr-x 2 keith keith 4096 Nov 12 20:51 . drwxrwxr-x 4 keith keith 4096 Nov 12 20:51 .. -rw-rw-r-- 1 keith keith 371 Nov 12 20:51 adapter_config.json -rw------- 1 keith keith 2107864 Nov 12 20:51 adapter_model.safetensors -rw-rw-r-- 1 keith keith 6078 Nov 12 20:51 edit.json Thu Nov 12 08:51:07 PM EST 2026
tool
Bash: cat > NOTES.md <<'EOF' # Notes of author claude (turn 0, written 12 Nov 2026 about 8:55 PM ET; DRAFT, updated at end of turn) ## What I learned (unedited b001, 61 episodes; code/fail_stats.py, req_stats.py, cf_budget.py) - 14 of 28 failures end at the 7.5-min session limit, 10 with an empty patch, 9 of those with no edit at all. [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] ## What I built (code/) - thinklib.py, think_job.py (one GPU hold: capture class means, fit gates, build dose grid, teacher-forced eval), think_report.py (table), build_think.py (final builder from code/data/think_stats.pt), finalize.py (queue entry). - gpu_hold.sh / gpu_job.sh: queue for the lock with `nohup flock $R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh &`. The lock queue was 45+ minutes at the start of this turn: get in line in the first minutes. - `think_job.py --mock` tests all plumbing on CPU in 45 s. Use it before any hold. ## State at the time of writing - Waiting for the GPU lock; no entry queued yet. See the end of this file for the final state. EOF scp -q NOTES.md wrx90:/home/keith/gemma-v2/wsp31/authors/claude/NOTES.md; ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date; cat scratch/logs/hold1.log; ls scratch; tail -2 /home/keith/gemma-v2/wsp31/tools/logs/sessB.log | cut -c1-200; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader'
output
Thu Nov 12 08:51:24 PM EST 2026 job1 logs mock === replay base + kernel (20:49:20, 1620 s left) 0, 50108 MiB 1, 90512 MiB
tool
Bash: cat > code/end_neurons.py <<'EOF' (57 lines)
Bash: cat > code/end_neurons.py <<'EOF'
"""end_neurons.py (CPU, weights only): MLP neurons of layers 50..59 whose write points along the direction that
raises the <channel|> logit, r = (E[101] - mean_vocab E) * final_norm_weight, after the layer's post-feedforward norm
weight. Saves code/data/end_neurons.pt: per layer the top K neurons by cosine, their down columns, and K control
neurons (random among the neurons with |cosine| below the layer median, each matched to a top neuron's column norm
rank: the nearest unused neuron in norm). Family N of thinklib.py scales these columns."""
import sys
import torch
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools")
import g31
from edits import EditSet
K = 16
LAYERS = list(range(50, 60))
e = EditSet()
[line removed by the dashboard scrubber]
tot = torch.zeros(emb.shape[1], dtype=torch.float64)
for s in range(0, emb.shape[0], 32768):
tot += emb[s:s + 32768].double().sum(0)
emean = (tot / emb.shape[0]).float()
e101 = emb[101].float()
w_final = e.norm(0, "pre_feedforward_layernorm") * 0 + e._file().get_tensor(g31.LM + "norm.weight").float()
r = (e101 - emean) * w_final
out = dict(K=K, layers=LAYERS, r=r, sel={})
g = torch.Generator().manual_seed(4242)
for L in LAYERS:
W = e.W(L, "down_proj", cache=False) # [5376, 21504]
wp = e.norm(L, "post_feedforward_layernorm").float()
rw = (r * wp).double()
s = (rw[None, :] @ W.double())[0] # direct logit-direction content of each column
cn = (W.double() * wp.double()[:, None]).norm(dim=0) # column norm after the post-norm weight
cos = (s / (cn * r.double().norm() + 1e-30)).float()
raw = W.double().norm(dim=0).float()
top = cos.topk(K).indices
pool = (cos.abs() < cos.abs().median()).nonzero()[:, 0]
pool = pool[torch.randperm(len(pool), generator=g)][:4000]
used, ctrl = set(top.tolist()), []
for j in top.tolist():
d = (raw[pool] - raw[j]).abs()
for k in d.argsort().tolist():
if int(pool[k]) not in used:
used.add(int(pool[k]))
ctrl.append(int(pool[k]))
break
ctrl = torch.tensor(ctrl)
out["sel"][L] = dict(top=top, ctrl=ctrl, cols=W[:, top].clone(), ctrl_cols=W[:, ctrl].clone(), cos=cos[top],
ctrl_cos=cos[ctrl], norm=raw[top], ctrl_norm=raw[ctrl])
z = (cos - cos.mean()) / cos.std()
print(L, "top cos", [round(float(x), 3) for x in cos[top][:8]], "z max %.1f" % float(z.max()), "cos sd %.4f" % float(cos.std()),
"bottom cos %.3f" % float(cos.min()), "norm top %.2f ctrl %.2f" % (float(raw[top].mean()), float(raw[ctrl].mean())), "top j", top[:4].tolist(), flush=True)
del W
import os
os.makedirs(A_DIR + "/code/data", exist_ok=True)
torch.save(out, A_DIR + "/code/data/end_neurons.pt")
print("saved")
EOF
scp -q code/end_neurons.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; (time $PY code/end_neurons.py) 2>&1 | cut -c1-330 | tail -16; cat scratch/logs/hold1.log'output
Thu Nov 12 08:52:12 PM EST 2026 (16 lines)
Thu Nov 12 08:52:12 PM EST 2026 50 top cos [0.014, 0.011, 0.011, 0.011, 0.011, 0.01, 0.01, 0.01] z max 4.8 cos sd 0.0029 bottom cos -0.011 norm top 0.52 ctrl 0.52 top j [14878, 16993, 10055, 16066] 51 top cos [0.016, 0.012, 0.011, 0.01, 0.01, 0.01, 0.01, 0.01] z max 5.6 cos sd 0.0029 bottom cos -0.012 norm top 0.47 ctrl 0.47 top j [7767, 9433, 16800, 11401] 52 top cos [0.014, 0.013, 0.012, 0.01, 0.01, 0.01, 0.01, 0.01] z max 4.7 cos sd 0.0029 bottom cos -0.012 norm top 0.50 ctrl 0.50 top j [1744, 18522, 5155, 9583] 53 top cos [0.012, 0.012, 0.011, 0.011, 0.01, 0.01, 0.01, 0.01] z max 4.3 cos sd 0.0029 bottom cos -0.013 norm top 0.48 ctrl 0.48 top j [3360, 9231, 18260, 6613] 54 top cos [0.011, 0.011, 0.011, 0.01, 0.01, 0.01, 0.01, 0.01] z max 3.9 cos sd 0.0029 bottom cos -0.011 norm top 0.53 ctrl 0.53 top j [4416, 6208, 10551, 12258] 55 top cos [0.014, 0.013, 0.012, 0.012, 0.011, 0.011, 0.011, 0.01] z max 4.9 cos sd 0.0029 bottom cos -0.013 norm top 0.46 ctrl 0.46 top j [12418, 11827, 549, 1721] 56 top cos [0.013, 0.012, 0.012, 0.011, 0.011, 0.01, 0.01, 0.01] z max 4.4 cos sd 0.0029 bottom cos -0.011 norm top 0.50 ctrl 0.50 top j [736, 19877, 8866, 1039] 57 top cos [0.047, 0.035, 0.011, 0.011, 0.011, 0.01, 0.01, 0.01] z max 16.2 cos sd 0.0029 bottom cos -0.012 norm top 0.47 ctrl 0.47 top j [4238, 18123, 17173, 9066] 58 top cos [0.031, 0.019, 0.017, 0.015, 0.014, 0.013, 0.013, 0.013] z max 10.4 cos sd 0.0030 bottom cos -0.034 norm top 0.43 ctrl 0.43 top j [1622, 15419, 17273, 2974] 59 top cos [0.037, 0.035, 0.03, 0.018, 0.017, 0.017, 0.016, 0.015] z max 11.0 cos sd 0.0034 bottom cos -0.019 norm top 0.50 ctrl 0.50 top j [14974, 6539, 3378, 18515] saved real 0m7.117s user 0m45.959s sys 0m31.476s
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; $PY $R/wsp31/tools/wv_neuron.py 57 4238 2>&1 | cut -c1-400 | head -30; $PY $R/wsp31/tools/wv_neuron.py 59 14974 2>&1 | cut -c1-400 | sed -n 1,12p; date; cat scratch/logs/hold1.log'
output
layer 57 neuron 4238 (local layer, layer_scalar 0.793) (23 lines)
layer 57 neuron 4238 (local layer, layer_scalar 0.793) |gate| 1.170 (pct 92) |up| 0.914 (pct 11) |down| 0.324 (pct 0) cos(gate, up) -0.225 write lens, promotes: <channel|>(+0.55) <(+0.31) )<(+0.23) !<(+0.23) :<(+0.21) ,<(+0.21) <(+0.20) ?<(+0.20) .<(+0.19) <ul>(+0.18) "."(+0.18) ()<(+0.18) write lens, suppresses: **:(-0.15) Mitt(-0.14) $[(-0.14) \u200e(-0.14) dato(-0.14) 就开始(-0.13) 泳(-0.13) itatea(-0.13) 总是(-0.13) \u200b(-0.13) kerül(-0.13) \\,(-0.13) gate read, embedding side (+): el(+615.65) ف(+571.43) 스(+569.63) س(+532.49) K(+527.18) चांग(+523.33) ä(+518.61) 問題(+491.09) AL(+477.11) ick(+472.34) oll(+466.94) टा(+455.98) gate read, embedding side (-): ्\u200dयादा(+683.45) ബരിമല(+622.32) \U000f0855(+613.95) 然而(+609.61) oniazid(+564.82) เธอ(+560.16) ূতন(+539.93) तरंज(+522.27) しかし(+521.74) बीयत(+518.07) elaborate(+512.79) तमंद(+507.96) up read, embedding side (+): ö(+343.34) ബ്രിട്ട(+313.66) 것(+312.97) så(+306.99) अंग(+299.44) 难以(+298.91) 땔(+297.19) র্কে(+297.04) 돈(+292.92) 놋(+291.81) larghezza(+289.34) skyrocketed(+289.06) up read, embedding side (-): 陴(+442.86) 掫(+434.66) মধ্যেই(+428.34) 沍(+411.88) 愊(+410.23) 腖(+402.10) 拃(+398.39) 飥(+390.00) 侓(+389.33) 蒈(+385.98) 淈(+377.66) ग्विजय(+376.74) nearest by write, same layer: 18123(+0.12) 18069(-0.11) 9669(+0.10) 9134(-0.09) 1038(+0.09) 9795(-0.09) 12755(-0.07) 14022(+0.07) nearest by gate read, same layer: 6348(+0.24) 17012(+0.23) 1144(-0.22) 9868(-0.21) 2159(-0.20) 9669(+0.20) 20103(+0.19) 15007(+0.18) nearest by up read, same layer: 2673(-0.29) 20103(-0.27) 7855(-0.26) 11783(-0.22) 7276(+0.21) 6831(+0.21) 6501(+0.21) 20677(-0.20) layer 59 neuron 14974 (global layer, layer_scalar 0.032) |gate| 1.231 (pct 93) |up| 1.232 (pct 95) |down| 0.622 (pct 98) cos(gate, up) +0.145 write lens, promotes: <channel|>(+3.02) หัว(+0.86) จิต(+0.85) >(+0.78) 思考(+0.77) ด(+0.75) 意识(+0.75) 省略(+0.73) Thinking(+0.73) END(+0.73) 消失(+0.72) punctuation(+0.71) write lens, suppresses: كل(-0.47) لأن(-0.43) طف(-0.42) ط(-0.41) ifah(-0.40) UnitPrice(-0.40) periodicity(-0.39) الس(-0.39) wh(-0.39) chính(-0.38) ều(-0.38) mez(-0.38) gate read, embedding side (+): ب(+50.39) ass(+44.32) ash(+44.00) las(+43.87) Does(+42.62) الأ(+42.48) ame(+40.09) shower(+40.08) ast(+39.96) remodeled(+39.70) ting(+39.14) ال(+38.96) gate read, embedding side (-): ইহাতে(+64.75) BlueprintName(+56.57) Balliye(+55.91) 可能会(+53.85) िफिशियल(+53.31) txtbtn(+53.27) घटस्फोट(+53.22) এগুলো(+52.15) ्युनिकेशन(+51.88) pcMove(+51.64) ić(+51.27) ości(+51.19) up read, embedding side (+): िल्\u200dली(+40.61) ’,(+35.36) ग्रीकल्चर(+35.01) ):["(+34.90) 라인(+34.88) olu(+34.28) निर्माताओं(+33.91) ospor(+33.45) phenylsulfanyl(+33.14) প্রতীক(+32.85) ное(+32.40) پ(+32.25) up read, embedding side (-): clockwise(+32.56) st(+32.04) 同时也(+31.77) гідно(+31.38) ert(+30.48) ick(+29.24) acaktır(+29.12) ہو۔(+28.90) 篒(+28.54) memang(+28.43) কথাটা(+28.38) க்கொண்ட(+27.48) nearest by write, same layer: 8984(-0.65) 11451(+0.63) 15965(+0.63) 8818(-0.61) 11754(+0.61) 21089(+0.59) 11605(-0.59) 3734(-0.57) nearest by gate read, same layer: 18515(+0.25) 9308(-0.22) 21444(-0.22) 17507(-0.22) 12361(-0.22) 3604(-0.22) 19502(+0.22) 13707(-0.22) nearest by up read, same layer: 11754(+0.11) 6539(+0.09) 11084(+0.08) 18376(-0.08) 19135(-0.08) 6601(-0.07) 6190(+0.07) 19672(-0.07) Thu Nov 12 08:52:43 PM EST 2026
tool
Bash: cat > code/end_neurons.py <<'EOF' (72 lines)
Bash: cat > code/end_neurons.py <<'EOF'
[line removed by the dashboard scrubber]
For neuron j of layer L the direct-path write lens is lens_j = E @ x_j, x_j = final_norm_w * post_ff_norm_w[L] *
down_proj[L][:, j]. z_j = (lens_j[101] - mean_vocab lens_j) / sd_vocab lens_j, computed exactly from the vocabulary
mean and covariance of the embedding matrix (no 262144 x 21504 product). "End" neurons: z >= ZMIN, at most K per
layer (largest z). "Anti" neurons: z <= -ZMIN. Controls: for each selected neuron the unused neuron of the same layer
with |z| < 1 nearest in raw column norm (pool of 4000 drawn with seed 4242).
Saves code/data/end_neurons.pt. Family N of thinklib.py scales the down columns of the end neurons."""
import os
import sys
import torch
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools")
import g31
from edits import EditSet
K, ZMIN = 16, 6.0
LAYERS = list(range(48, 60))
e = EditSet()
[line removed by the dashboard scrubber]
V, D = emb.shape
tot = torch.zeros(D, dtype=torch.float64)
S = torch.zeros(D, D, dtype=torch.float64)
for s in range(0, V, 16384):
c = emb[s:s + 16384].float()
tot += c.double().sum(0)
S += (c.T @ c).double()
emean = tot / V
C = (S / V - torch.outer(emean, emean)).float()
e101 = emb[101].double()
w_final = e._file().get_tensor(g31.LM + "norm.weight").double()
dirv = (e101 - emean)
out = dict(K=K, ZMIN=ZMIN, layers=LAYERS, sel={})
g = torch.Generator().manual_seed(4242)
for L in LAYERS:
W = e.W(L, "down_proj", cache=False)
wp = e.norm(L, "post_feedforward_layernorm").double()
X = (W.double() * (w_final * wp)[:, None]).float()
num = (dirv.float()[None, :] @ X)[0]
den = (X * (C @ X)).sum(0).clamp_min(1e-30).sqrt()
z = num / den
raw = W.double().norm(dim=0).float()
order = z.argsort(descending=True)
top = order[:K][z[order[:K]] >= ZMIN]
anti = order.flip(0)[:K]
anti = anti[z[anti] <= -ZMIN]
pool = (z.abs() < 1).nonzero()[:, 0]
pool = pool[torch.randperm(len(pool), generator=g)][:4000]
used = set(top.tolist()) | set(anti.tolist())
def match(sel):
res = []
for j in sel.tolist():
d = (raw[pool] - raw[j]).abs()
for k in d.argsort().tolist():
if int(pool[k]) not in used:
used.add(int(pool[k]))
res.append(int(pool[k]))
break
return torch.tensor(res, dtype=torch.long)
ctrl, actrl = match(top), match(anti)
out["sel"][L] = dict(top=top, anti=anti, ctrl=ctrl, actrl=actrl, cols=W[:, top].clone(), anti_cols=W[:, anti].clone(),
ctrl_cols=W[:, ctrl].clone(), actrl_cols=W[:, actrl].clone(), z=z[top], anti_z=z[anti],
norm=raw[top], ctrl_norm=raw[ctrl])
print(L, "n z>=6:", int((z >= 6).sum()), "n z<=-6:", int((z <= -6).sum()), "n |z|>=10:", int((z.abs() >= 10).sum()),
"top", [(int(j), round(float(z[j]), 1)) for j in order[:5]], "bottom", [(int(j), round(float(z[j]), 1)) for j in order.flip(0)[:4]],
"selected", len(top), len(anti), "norm top %.2f ctrl %.2f" % (float(raw[top].mean()) if len(top) else 0, float(raw[ctrl].mean()) if len(ctrl) else 0), flush=True)
del W, X
os.makedirs(A_DIR + "/code/data", exist_ok=True)
torch.save(out, A_DIR + "/code/data/end_neurons.pt")
print("saved; end neurons", sum(len(v["top"]) for v in out["sel"].values()), "anti", sum(len(v["anti"]) for v in out["sel"].values()))
EOF
scp -q code/end_neurons.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; (time $PY code/end_neurons.py) 2>&1 | cut -c1-360 | tail -18; cat scratch/logs/hold1.log'output
Thu Nov 12 08:53:27 PM EST 2026 (18 lines)
Thu Nov 12 08:53:27 PM EST 2026 48 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(10224, 4.1), (13183, 4.0), (4757, 4.0), (10141, 3.9), (9159, 3.6)] bottom [(3610, -3.8), (3305, -3.6), (8342, -3.3), (10146, -3.3)] selected 0 0 norm top 0.00 ctrl 0.00 49 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(20126, 4.3), (16293, 3.9), (4939, 3.8), (20360, 3.7), (3494, 3.7)] bottom [(8157, -4.3), (7793, -3.8), (9858, -3.8), (17093, -3.7)] selected 0 0 norm top 0.00 ctrl 0.00 50 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(14878, 4.6), (10055, 3.7), (10888, 3.7), (16993, 3.7), (16066, 3.6)] bottom [(3705, -3.6), (4544, -3.5), (10168, -3.5), (1868, -3.5)] selected 0 0 norm top 0.00 ctrl 0.00 51 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(7767, 5.6), (9433, 4.1), (2893, 3.7), (16800, 3.7), (11401, 3.6)] bottom [(21365, -4.3), (629, -4.0), (15290, -3.9), (4457, -3.9)] selected 0 0 norm top 0.00 ctrl 0.00 52 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(1744, 4.8), (18522, 4.4), (5155, 4.0), (9583, 3.6), (7057, 3.6)] bottom [(2358, -4.1), (20099, -3.9), (14235, -3.8), (16474, -3.8)] selected 0 0 norm top 0.00 ctrl 0.00 53 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(3360, 4.2), (9231, 4.1), (6613, 3.8), (18260, 3.7), (21026, 3.6)] bottom [(21058, -4.5), (13396, -3.7), (5525, -3.6), (17941, -3.6)] selected 0 0 norm top 0.00 ctrl 0.00 54 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(4416, 4.0), (6208, 3.9), (10551, 3.8), (12258, 3.6), (3406, 3.4)] bottom [(5310, -3.8), (12822, -3.7), (5121, -3.7), (11902, -3.6)] selected 0 0 norm top 0.00 ctrl 0.00 55 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(12418, 4.9), (11827, 4.4), (549, 4.2), (1721, 4.1), (7180, 3.8)] bottom [(1252, -4.7), (11561, -3.9), (11246, -3.9), (4694, -3.8)] selected 0 0 norm top 0.00 ctrl 0.00 56 n z>=6: 0 n z<=-6: 0 n |z|>=10: 0 top [(736, 4.5), (19877, 4.4), (8866, 4.1), (10405, 3.7), (1039, 3.7)] bottom [(2879, -4.0), (6774, -3.9), (1472, -3.8), (21390, -3.7)] selected 0 0 norm top 0.00 ctrl 0.00 57 n z>=6: 2 n z<=-6: 0 n |z|>=10: 2 top [(4238, 16.9), (18123, 11.8), (17173, 3.9), (3922, 3.8), (5729, 3.8)] bottom [(2071, -4.2), (5305, -4.0), (6549, -3.9), (2359, -3.7)] selected 2 0 norm top 0.36 ctrl 0.36 58 n z>=6: 2 n z<=-6: 4 n |z|>=10: 3 top [(1622, 10.9), (15419, 6.5), (17273, 5.9), (2974, 5.3), (17557, 5.0)] bottom [(9886, -11.5), (2198, -10.6), (17522, -9.2), (3459, -8.5)] selected 2 4 norm top 0.40 ctrl 0.40 59 n z>=6: 16 n z<=-6: 15 n |z|>=10: 4 top [(14974, 22.7), (6539, 18.6), (3378, 12.0), (18515, 11.2), (11754, 8.9)] bottom [(10983, -8.9), (987, -7.7), (17220, -7.5), (15385, -7.4)] selected 16 15 norm top 0.46 ctrl 0.46 saved; end neurons 20 anti 19 real 0m24.314s user 5m37.957s sys 0m45.550s
tool
Bash: cd code && python - <<'EOF' (71 lines, middle cut at capture)
Bash: cd code && python - <<'EOF'
s=open("thinklib.py").read()
s=s.replace(''' else:
raise ValueError(fam)
return info''',''' elif fam in ("N", "M"):
# weights-only families: scale the down columns of the model's own <channel|> neurons (code/end_neurons.py).
[line removed by the dashboard scrubber]
en = torch.load(A_DIR + "/code/data/end_neurons.pt")
for L, s in en["sel"].items():
if fam == "N":
js, cols, f = (s["ctrl"], s["ctrl_cols"], dose) if ctrl else (s["top"], s["cols"], dose)
else:
js, cols, f = (s["actrl"], s["actrl_cols"], -dose) if ctrl else (s["anti"], s["anti_cols"], -dose)
for k, j in enumerate(js.tolist()):
e.col_add(L, "down_proj", j, f * cols[:, k].float())
if len(js):
info[L] = dict(n=len(js), neurons=js.tolist(), rho=0.0)
else:
raise ValueError(fam)
return info''')
s=s.replace('''sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools")
LA = ''','''sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools")
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
LA = ''')
open("thinklib.py","w").write(s)
t=open("think_job.py").read()
t=t.replace('''GRID = [("B", 0.3), ("C", 8.0), ("A", 0.1), ("B", 1.0), ("C", 20.0), ("A", 0.3), ("B", 0.1), ("C", 3.0), ("A", 0.03)]
CTRL = [("B", 0.3), ("C", 8.0), ("A", 0.1)]''','''GRID = [("B", 0.3), ("C", 8.0), ("A", 0.1), ("N", 3.0), ("B", 1.0), ("C", 20.0), ("A", 0.3), ("N", 1.0), ("M", 0.75),
("B", 0.1), ("C", 3.0)]
CTRL = [("B", 0.3), ("C", 8.0), ("A", 0.1), ("N", 3.0)]
GRID_TAIL = [("A", 0.03), ("N", 8.0)]''')
t=t.replace(''' for fam, dose, ctrl in [(f, d, False) for f, d in GRID] + [(f, d, True) for f, d in CTRL]:
e = EditSet()
info = tl.build(e, st, fam, dose, ctrl)
nm = name_of(fam, dose, ctrl)
rep = e.save(f"{out}/{nm}", note=dict(family=fam, dose=dose, ctrl=ctrl))''',''' for fam, dose, ctrl in ([(f, d, False) for f, d in GRID] + [(f, d, True) for f, d in CTRL]
+ [(f, d, False) for f, d in GRID_TAIL]):
nm = name_of(fam, dose, ctrl)
try:
e = EditSet()
info = tl.build(e, st, fam, dose, ctrl)
rep = e.save(f"{out}/{nm}", note=dict(family=fam, dose=dose, ctrl=ctrl))
except Exception as ex: # one family failing must not cost the hold
log("BUILD FAILED", nm, repr(ex)[:300])
continue''')
t=t.replace(''' GRID[:] = GRID[:3]''',''' GRID[:] = GRID[:4]
GRID_TAIL[:] = []''')
t=t.replace(''' ads = {nm: tl_adapter(f"{addir}/{nm}") for nm in names}
arms = [("base", None)] + [(nm, ads[nm]) for nm in names]''',''' ads = {}
for nm in names:
try:
ads[nm] = tl_adapter(f"{addir}/{nm}")
except Exception as ex:
log("ADAPTER LOAD FAILED", nm, repr(ex)[:300])
arms = [("base", None)] + [(nm, ads[nm]) for nm in names if nm in ads]''')
open("think_job.py","w").write(t)
f=open("finalize.py").read()
f=f.replace('''}
recipe = ''',''' "N": """Family N (weights only, the model's own end-of-thought neurons): the down_proj columns of the MLP neurons of
[line removed by the dashboard scrubber]
(`code/end_neurons.py`, CPU, no activations): for neuron j, `x_j = final_norm_w * post_ff_norm_w * down[:, j]`,
`z_j = ((E[101] - mean_vocab E) . x_j) / sqrt(x_j^T Cov_vocab(E) x_j)` (the z-score of `<channel|>` in the neuron's
[line removed by the dashboard scrubber]
searched: 2 neurons in layer 57 (4238 z 16.9, 18123 z 11.8), 2 in layer 58 (1622 z 10.9, 15419 z 6.5), 16 in layer
59 (14974 z 22.7, 6539 z 18.6, 3378 z 12.0, 18515 z 11.2, ...); none in layers 48..56. Lists and columns:
`code/data/end_neurons.pt`. Control for this family: for each selected neuron, a neuron of the same layer with
`|z| < 1` nearest in column norm (pool of 4000, seed 4242), scaled by the same factor.""",
"M": """Family M (weights only): t
[... 735 characters cut ...]output
Thu Nov 12 08:54:27 PM EST 2026 (15 lines)
Thu Nov 12 08:54:27 PM EST 2026
[ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-']
[ 0.1] model loaded
[ 17.0] stats saved; n per class [34.0, 400.0, 300.0, 600.0, 400.0] A_proj {"E": {"mean": [0.20697154104709625, 0.12351810187101364, -0.014883400872349739, -0.07870633900165558, -0.14660318195819855, -0.9176135063171387, 0.01764
[ 17.0] built B0p3 r 1 max rel_fro 0.0692 rho ['0.279', '0.258', '0.240', '0.258', '0.274', '0.248', '0.265', '0.243', '0.254', '0.268']
[ 17.0] built C8p0 r 1 max rel_fro 0.0165 rho ['0.004', '0.004', '0.004']
[ 17.0] built A0p1 r 1 max rel_fro 0.0233 rho ['0.093', '0.086', '0.080', '0.086', '0.091', '0.083', '0.088', '0.081', '0.085', '0.089']
[ 17.0] built N3p0 r 16 max rel_fro 0.0750 rho ['0.000', '0.000', '0.000']
[ 17.0] built B0p3c r 1 max rel_fro 0.0692 rho ['0.279', '0.258', '0.240', '0.258', '0.274', '0.248', '0.265', '0.243', '0.254', '0.268']
[ 17.0] grid built
[ 34.4] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 6
[ 43.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 6
[ 43.5] done
17
Thu Nov 12 08:55:14 PM EST 2026tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; for i in $(seq 1 56); do [ -f scratch/hold_start ] && break; sleep 10; done; date; cat scratch/hold_start 2>/dev/null; tail -3 scratch/logs/job1.log 2>/dev/null | cut -c1-200; tail -2 /home/keith/gemma-v2/wsp31/tools/logs/sessB.log | cut -c1-160'
output
Thu Nov 12 09:04:48 PM EST 2026 24 points, layers 40..41 (2), resid_post (24, 2, 5376), mlp_act (24, 2, 21504) -> data/capture_test.pt (2 MB) === single-module + consist (21:03:47, 753 s left)
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; for i in $(seq 1 57); do [ -f scratch/hold_start ] && break; sleep 10; done; date; cat scratch/hold_start 2>/dev/null; sleep 20; tail -4 scratch/logs/job1.log 2>/dev/null | cut -c1-250; cat scratch/logs/hold1.log'
output
Thu Nov 12 09:14:28 PM EST 2026
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; for i in $(seq 1 40); do [ -f scratch/hold_start ] && break; sleep 6; done; date; cat scratch/hold_start 2>/dev/null; sleep 45; tail -5 scratch/logs/job1.log 2>/dev/null | cut -c1-250; cat scratch/logs/hold1.log; ps aux | grep flock | grep -v grep | cut -c1-150'
output
Thu Nov 12 09:15:53 PM EST 2026 Thu Nov 12 09:15:51 PM EST 2026 [ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-'] [ 2.3] model loaded [ 14.2] captured 0 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80] [ 35.9] captured 1 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80] [ 46.7] captured 2 Peter-van-Tol__pydantic-shapel n 11000 pos 346 cls [6, 80, 60, 120, 80] keith 306309 0.0 0.0 8372 12 ? S 20:39 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh keith 394515 0.0 0.0 8372 104 ? S 21:06 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job3.sh
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; for i in $(seq 1 50); do grep -q "grid built" scratch/logs/job1.log && break; sleep 8; done; date; grep -v "captured" scratch/logs/job1.log | cut -c1-420'
output
Thu Nov 12 09:20:04 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 09:20:04 PM EST 2026
[ 0.1] deadline 1400 capture deadline 588.0
[ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-']
[ 2.3] model loaded
[ 245.7] gate 30 j 14827 auc_z 0.888 auc_g 0.889 cbar 7.73 csd 1.47 {'E': '0.02/-0.01', 'early': '0.09/0.06', 'mid': '0.28/0.27', 'late': '0.60/0.95', 'non': '0.01/0.01'}
[ 245.8] gate 32 j 16798 auc_z 0.866 auc_g 0.865 cbar 4.09 csd 1.02 {'E': '0.00/-0.01', 'early': '0.09/0.05', 'mid': '0.28/0.24', 'late': '0.56/0.81', 'non': '0.01/-0.00'}
[ 245.8] gate 34 j 989 auc_z 0.895 auc_g 0.894 cbar 20.57 csd 4.58 {'E': '0.00/-0.01', 'early': '0.09/0.07', 'mid': '0.31/0.36', 'late': '0.62/1.21', 'non': '0.02/0.02'}
[ 245.9] gate 36 j 19635 auc_z 0.876 auc_g 0.874 cbar 6.47 csd 1.27 {'E': '0.00/-0.00', 'early': '0.10/0.07', 'mid': '0.29/0.31', 'late': '0.58/1.01', 'non': '0.00/-0.00'}
[ 245.9] gate 38 j 18261 auc_z 0.857 auc_g 0.850 cbar 6.40 csd 1.50 {'E': '0.00/-0.02', 'early': '0.12/0.05', 'mid': '0.27/0.23', 'late': '0.52/0.70', 'non': '0.00/0.00'}
[ 246.0] gate 40 j 7243 auc_z 0.850 auc_g 0.841 cbar 13.66 csd 2.52 {'E': '0.02/-0.02', 'early': '0.13/0.06', 'mid': '0.27/0.26', 'late': '0.52/0.75', 'non': '0.00/0.00'}
[ 246.1] gate 42 j 8281 auc_z 0.832 auc_g 0.818 cbar 21.35 csd 6.26 {'E': '0.00/-0.01', 'early': '0.16/0.07', 'mid': '0.27/0.19', 'late': '0.49/0.57', 'non': '0.00/0.00'}
[ 246.1] gate 44 j 16800 auc_z 0.820 auc_g 0.809 cbar 35.89 csd 8.28 {'E': '0.00/-0.01', 'early': '0.14/0.05', 'mid': '0.23/0.17', 'late': '0.45/0.51', 'non': '0.00/-0.00'}
[ 246.2] gate 46 j 16289 auc_z 0.835 auc_g 0.825 cbar 36.32 csd 7.39 {'E': '0.00/-0.01', 'early': '0.13/0.05', 'mid': '0.23/0.19', 'late': '0.46/0.61', 'non': '0.00/-0.00'}
[ 246.3] gate 48 j 18053 auc_z 0.820 auc_g 0.810 cbar 31.98 csd 7.18 {'E': '0.00/-0.00', 'early': '0.14/0.07', 'mid': '0.22/0.18', 'late': '0.44/0.54', 'non': '0.00/-0.00'}
[ 246.4] gate 54 j 16046 auc_z 0.765 auc_g 0.756 cbar 26.77 csd 5.87 {'E': '0.00/-0.01', 'early': '0.15/0.05', 'mid': '0.18/0.09', 'late': '0.35/0.27', 'non': '0.00/-0.00'}
[ 246.6] gate 56 j 7262 auc_z 0.777 auc_g 0.770 cbar 29.03 csd 7.05 {'E': '0.00/-0.01', 'early': '0.14/0.03', 'mid': '0.19/0.07', 'late': '0.35/0.24', 'non': '0.00/-0.00'}
[ 246.6] gate 58 j 1883 auc_z 0.769 auc_g 0.759 cbar 41.33 csd 5.60 {'E': '0.00/-0.01', 'early': '0.16/0.07', 'mid': '0.17/0.10', 'late': '0.28/0.22', 'non': '0.00/-0.00'}
[ 248.1] stats saved; n per class [197.0, 1760.0, 1304.0, 1637.0, 1760.0] A_proj {"E": {"mean": [2.8446428775787354, 1.1685497760772705, 10.992581367492676, 1.9841610193252563, 1.4817982912063599, 5.0086140632629395, 9.502419471740723, 15.319825172424316, 19.195873260498047, 23.214214324951172], "sd": [0.3666640818119049, 0.151480033993721, 1.0210658311843872, 0.26093870401382446, 0.17310023307800293, 0.666898787021
[ 248.1] d_end 30 norm 41.27 resid norm late 287.12
[ 248.1] d_end 32 norm 39.66 resid norm late 321.94
[... 18 lines cut ...]
[ 248.3] built C3p0 r 1 max rel_fro 0.0120 rho ['0.001', '0.001', '0.001']
[ 248.3] built B0p3c r 1 max rel_fro 0.3714 rho ['0.279', '0.354', '0.357', '0.336', '0.370', '0.278', '0.431', '0.511', '0.499', '0.525']
[ 248.3] built C8p0c r 1 max rel_fro 0.0120 rho ['0.004', '0.003', '0.001']
[ 248.3] built A0p1c r 1 max rel_fro 0.0066 rho ['0.093', '0.118', '0.119', '0.112', '0.123', '0.093', '0.144', '0.170', '0.166', '0.175']
[ 248.3] built N3p0c r 16 max rel_fro 0.0750 rho ['0.000', '0.000', '0.000']
[ 248.3] built A0p03 r 1 max rel_fro 0.0020 rho ['0.028', '0.035', '0.036', '0.034', '0.037', '0.028', '0.043', '0.051', '0.050', '0.052']
[ 248.3] built N8p0 r 16 max rel_fro 0.1999 rho ['0.000', '0.000', '0.000']
[ 248.3] grid builttool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; for i in $(seq 1 40); do grep -q "evaluated 1" scratch/logs/job1.log && break; sleep 8; done; date; grep "evaluated\|FAILED\|deadline inside" scratch/logs/job1.log | cut -c1-200; cp scratch/job1/eval.pt scratch/job1/eval_snap.pt; $PY code/think_report.py scratch/job1/eval_snap.pt 2>&1 | cut -c1-420 | head -60'
output
Thu Nov 12 09:24:16 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 09:24:16 PM EST 2026
[ 374.2] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 18
[ 499.9] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 18
episodes 2 ['PyCQA__flake8.cf15', 'Mimino666__langdet'] arms per episode [18, 18]
== all episodes 2
n by class {'E': 8, 'early': 633, 'mid': 1072, 'late': 3305, 'non': 300, 'think_pt': 8, 'tool_pt': 8}
[line removed by the dashboard scrubber]
base mean lp101: early -25.27 mid -26.47 late -24.94 E -1.29 non -20.25
arm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non
B0p3 | 0.64 2.57 6.76 -2.05 | 1.00 0.23 | -0.07 -0.98 0.03 -0.00 -0.02 | 0.97 0.77 1.00 1.00 | 0.0000 (base 0.0000)
C8p0 | 0.02 0.05 0.09 0.01 | 1.00 1.00 | 0.00 -0.00 -0.00 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0000 (base 0.0000)
A0p1 | 3.28 3.57 2.94 1.16 | 0.96 0.99 | -0.07 -0.04 0.05 -0.06 -0.07 | 0.95 0.96 1.00 1.00 | 0.0006 (base 0.0000)
N3p0 | -0.01 -0.34 0.20 0.51 | 1.00 1.00 | -0.00 -0.00 -0.02 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0000)
B1p0 | 0.24 2.35 5.96 -4.70 | 1.00 0.22 | -0.12 -1.15 0.03 0.00 -0.03 | 0.94 0.74 1.00 1.00 | 0.0001 (base 0.0000)
C20p0 | 0.03 0.09 0.23 0.01 | 1.00 1.00 | -0.00 -0.00 -0.00 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0000 (base 0.0000)
A0p3 | 22.34 24.10 22.07 0.91 | 0.08 0.00 | -7.58 -7.51 -11.59 -6.21 -13.23 | 0.21 0.23 0.00 0.00 | 0.3699 (base 0.0000)
N1p0 | -0.01 -0.13 0.08 0.23 | 1.00 1.00 | -0.00 -0.00 -0.01 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0000)
M0p75 | -0.16 -0.21 -0.16 -0.08 | 1.00 1.00 | -0.00 -0.00 -0.00 0.00 -0.00 | 1.00 1.00 1.00 1.00 | 0.0000 (base 0.0000)
B0p1 | 0.52 2.10 5.04 -0.75 | 1.00 0.26 | -0.01 -0.36 0.02 -0.01 -0.01 | 0.99 0.87 1.00 1.00 | 0.0000 (base 0.0000)
C3p0 | 0.01 0.01 0.03 0.00 | 1.00 1.00 | -0.00 -0.00 0.00 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0000 (base 0.0000)
[... 12 lines cut ...]
40 auc 0.823 {'E': '0.00', 'early': '0.09', 'mid': '0.23', 'late': '0.54', 'non': '0.00', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.09', 'd50-100': '0.08', 'd100-200': '0.09', 'd200-400': '0.33', 'd400-800': '0.53', 'd800-1600': '0.46', 'd1600-5000': '0.75'}
42 auc 0.787 {'E': '0.00', 'early': '0.10', 'mid': '0.23', 'late': '0.51', 'non': '0.00', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.12', 'd50-100': '0.09', 'd100-200': '0.09', 'd200-400': '0.33', 'd400-800': '0.49', 'd800-1600': '0.44', 'd1600-5000': '0.69'}
44 auc 0.770 {'E': '0.00', 'early': '0.08', 'mid': '0.21', 'late': '0.47', 'non': '0.00', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.09', 'd50-100': '0.07', 'd100-200': '0.08', 'd200-400': '0.31', 'd400-800': '0.46', 'd800-1600': '0.40', 'd1600-5000': '0.63'}
46 auc 0.786 {'E': '0.00', 'early': '0.07', 'mid': '0.22', 'late': '0.49', 'non': '0.00', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.07', 'd50-100': '0.08', 'd100-200': '0.08', 'd200-400': '0.32', 'd400-800': '0.48', 'd800-1600': '0.42', 'd1600-5000': '0.67'}
48 auc 0.765 {'E': '0.00', 'early': '0.08', 'mid': '0.20', 'late': '0.46', 'non': '0.00', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.07', 'd50-100': '0.08', 'd100-200': '0.07', 'd200-400': '0.30', 'd400-800': '0.43', 'd800-1600': '0.40', 'd1600-5000': '0.63'}
54 auc 0.706 {'E': '0.00', 'early': '0.09', 'mid': '0.16', 'late': '0.35', 'non': '0.00', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.11', 'd50-100': '0.07', 'd100-200': '0.06', 'd200-400': '0.24', 'd400-800': '0.36', 'd800-1600': '0.31', 'd1600-5000': '0.43'}
56 auc 0.714 {'E': '0.00', 'early': '0.09', 'mid': '0.14', 'late': '0.34', 'non':
[... 455 characters cut ...]tool
Bash: cat > code/think_greedy.py <<'EOF'
"""think_greedy.py [eval.pt]: greedy (temperature 0) reading of the teacher-forced evaluation: along each logged
[line removed by the dashboard scrubber]
import os, sys, torch, statistics as st
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
res = torch.load(sys.argv[1] if len(sys.argv) > 1 else os.environ.get("THINK_EVAL", A_DIR + "/scratch/job1/eval.pt"))
eps = res["episodes"]
arms = [a for a in res["arms"] if all(a in e["arms"] for e in eps)]
print("episodes", len(eps), "arms complete", len(arms), "of", len(res["arms"]))
[line removed by the dashboard scrubber]
for a in arms:
S = dict(short=[0, 0, 0, 0, 0], long=[0, 0, 0, 0, 0]); cuts = []; etop = [0, 0]; inner = [0, 0]
for e in eps:
rows = e["rows"]; top = e["arms"][a]["top"]
for ci in rows[:, 4].unique().tolist():
if ci < 0: continue
mk = (rows[:, 4] == ci) & (rows[:, 1] <= 3)
d = rows[mk, 2]; o = d.argsort(); t = top[mk][o]; c = rows[mk, 1][o]; d = d[o]
n = len(t)
if n < 3 or int(c[-1]) != 0: continue # need the logged end (E) as last position
[line removed by the dashboard scrubber]
hit = (t == 101).nonzero()
[line removed by the dashboard scrubber]
etop[1] += 1; etop[0] += int(t[-1] == 101)
inner[1] += n - 1; inner[0] += int((t[:-1] == 101).sum())
if len(hit) == 0:
[line removed by the dashboard scrubber]
else:
k = int(hit[0])
[line removed by the dashboard scrubber]
if k < n - 1:
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
f = lambda k: "%3d %5.2f %5.2f %5.2f" % (S[k][0], S[k][1] / max(S[k][0], 1), S[k][2] / max(S[k][0], 1), S[k][3] / max(S[k][4], 1))
print("%-8s | %s | %s %s | %.2f (%d) | %.4f" % (a, f("short"), f("long"), ("%6d" % st.median(cuts)) if cuts else " -", etop[0] / max(etop[1], 1), etop[1], inner[0] / max(inner[1], 1)))
EOF
scp -q code/think_greedy.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; date; grep "evaluated\|FAILED\|deadline inside" scratch/logs/job1.log | cut -c1-200; cp scratch/job1/eval.pt scratch/job1/eval_snap.pt; $PY code/think_greedy.py scratch/job1/eval_snap.pt 2>&1 | cut -c1-300'output
Thu Nov 12 09:25:12 PM EST 2026 (23 lines)
Thu Nov 12 09:25:12 PM EST 2026 [ 374.2] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 18 [ 499.9] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 18 episodes 2 arms complete 18 of 18 [line removed by the dashboard scrubber] base | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 B0p3 | 5 0.00 0.20 1.00 | 3 1.00 0.00 0.22 369 | 0.62 (8) | 0.0443 C8p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 A0p1 | 5 0.80 0.20 0.96 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0010 N3p0 | 5 0.20 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0004 B1p0 | 5 0.00 0.20 1.00 | 3 1.00 0.00 0.23 361 | 0.62 (8) | 0.0218 C20p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 A0p3 | 5 1.00 0.00 0.05 | 3 1.00 0.00 0.00 7 | 1.00 (8) | 0.4353 N1p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 M0p75 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 B0p1 | 5 0.00 0.20 1.00 | 3 1.00 0.00 0.27 369 | 0.62 (8) | 0.0283 C3p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 B0p3c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 C8p0c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 A0p1c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 N3p0c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002 A0p03 | 5 0.40 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0006 N8p0 | 5 0.20 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0004
tool
Bash: cat > code/think_refit.py <<'EOF' (64 lines, middle cut at capture)
Bash: cat > code/think_refit.py <<'EOF'
"""think_refit.py (CPU): refit the "deep in a long thought" gates from the raw MLP inputs saved by think_job.py with a
sharper gate (k = 12, so that positions below the threshold sit where gelu_tanh is ~0 instead of at its negative lobe)
and, for g3, a deeper positive class. Writes code/data/think_stats_g2.pt (positives depth >= 400, negatives depth < 100
or outside thoughts) and think_stats_g3.pt (positives depth >= 800, negatives depth < 200 or outside thoughts), same
neurons and same class means as think_stats.pt, and builds the stage-2 adapters into scratch/job2/ad."""
import os, sys, torch
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools"); sys.path.insert(0, A_DIR + "/code")
import thinklib as tl
from edits import EditSet
st = torch.load(A_DIR + "/code/data/think_stats.pt")
raw = torch.load(A_DIR + "/scratch/job1/raw_mlpin.pt")
cls, dep = raw["cls"], raw["depth"]
think = (cls >= 1) & (cls <= 3)
lab = {"g2": torch.where(cls == 3, 3, torch.where((cls == 1) | (cls == 4), 1, 2)),
"g3": torch.where(think & (dep >= 800), 3, torch.where((think & (dep < 200)) | (cls == 4), 1, 2))}
S = {}
[line removed by the dashboard scrubber]
s2 = dict(st); s2["gate"] = {}
for gi, L in enumerate(tl.GL):
if L not in tl.LA: continue
X = raw["X"][gi].float(); G = st["gate"][L]
gr, ur, fs = tl.fit_gate(X, lb, k=12.0)
greal = G["gate_old"] + (gr - G["gate_old"]).to(torch.bfloat16).float()
ureal = G["up_old"] + (ur - G["up_old"]).to(torch.bfloat16).float()
g = X @ greal; a = tl.gelu_tanh(g) * (X @ ureal)
gbar = max(float(a[lb == 3].mean()), 0.5)
s2["gate"][L] = dict(j=G["j"], gate_row=gr, up_row=ur, gate_old=G["gate_old"], up_old=G["up_old"], down_old=G["down_old"], gbar=gbar, fit=fs)
fire = lambda m: "%.2f" % float((g[m] > 0).float().mean())
[line removed by the dashboard scrubber]
"400-800", fire(think & (dep >= 400) & (dep < 800)), ">=800", fire(think & (dep >= 800)), "non", fire(cls == 4), "act E %.3f" % float(a[cls == 0].mean()), flush=True)
[line removed by the dashboard scrubber]
S["g1"] = st
ARMS = [("G2B0p1", [("B", 0.1, "g2")], False), ("G3B0p1", [("B", 0.1, "g3")], False), ("G3B0p3", [("B", 0.3, "g3")], False),
("G2B0p1N8", [("B", 0.1, "g2"), ("N", 8.0, "g1")], False), ("B0p1N8", [("B", 0.1, "g1"), ("N", 8.0, "g1")], False),
("G3B0p1N8", [("B", 0.1, "g3"), ("N", 8.0, "g1")], False), ("G2B0p03", [("B", 0.03, "g2")], False),
("G2B0p1c", [("B", 0.1, "g2")], True), ("G3B0p1c", [("B", 0.1, "g3")], True), ("N24p0", [("N", 24.0, "g1")], False)]
out = A_DIR + "/scratch/job2/ad"; os.makedirs(out, exist_ok=True)
for nm, parts, ctrl in ARMS:
e = EditSet()
[line removed by the dashboard scrubber]
rep = e.save(f"{out}/{nm}", note=dict(parts=[list(p) for p in parts], ctrl=ctrl)); print("built", nm, rep["r"])
open(A_DIR + "/scratch/job2/arms.txt", "w").write("\n".join(a[0] for a in ARMS))
EOF
cat > code/think_job2.py <<'EOF'
"""think_job2.py: second stage inside the same GPU-0 hold: teacher-forced evaluation of the stage-2 adapters
(scratch/job2/ad, built on CPU by think_refit.py) on the first held-out episodes, same rows as stage 1."""
import json, os, sys, time, torch
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
sys.path.insert(0, A_DIR + "/code")
import think_job as tj
import thinklib as tl
import g31
hold_start = os.path.getmtime(A_DIR + "/scratch/hold_start")
tj.S = A_DIR + "/scratch/job2"
tj.DEADLINE = hold_start + 1560 - 45 - tj.T0
tj.log("stage 2; seconds available", tj.DEADLINE)
names = open(tj.S + "/arms.txt").read().split()
st = torch.load(A_DIR + "/code/data/think_stats_g2.pt")
st3 = torch.load(A_DIR + "/code/data/think_stats_g3.pt")
for L in tl.LC: # evaluate() computes gate pre-activations for every layer of tl.GL
st["gate"][L] = st3["gate"][tl.LA[tl.LC.index(L)]] # slots 54/56/5
[... 637 characters cut ...]output
Thu Nov 12 09:26:39 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 09:26:39 PM EST 2026 [ 374.2] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 18 [ 499.9] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 18 [ 623.6] evaluated 2 [task] rows 1949 arms 18 g2 30 auc 0.889 gbar 3.99 fire: E 0.02 d<100 0.09 100-400 0.28 400-800 0.59 >=800 0.61 non 0.01 act E 0.036 g2 32 auc 0.865 gbar 3.42 fire: E 0.00 d<100 0.09 100-400 0.28 400-800 0.55 >=800 0.59 non 0.01 act E -0.001 g2 34 auc 0.894 gbar 5.05 fire: E 0.00 d<100 0.09 100-400 0.31 400-800 0.60 >=800 0.64 non 0.02 act E -0.000 g2 36 auc 0.874 gbar 4.23 fire: E 0.00 d<100 0.10 100-400 0.29 400-800 0.59 >=800 0.56 non 0.00 act E -0.000 g2 38 auc 0.850 gbar 2.97 fire: E 0.00 d<100 0.12 100-400 0.27 400-800 0.54 >=800 0.48 non 0.00 act E -0.001 g2 40 auc 0.841 gbar 3.17 fire: E 0.02 d<100 0.13 100-400 0.27 400-800 0.55 >=800 0.48 non 0.00 act E 0.019 g2 42 auc 0.818 gbar 2.46 fire: E 0.00 d<100 0.16 100-400 0.27 400-800 0.51 >=800 0.47 non 0.00 act E -0.000 g2 44 auc 0.809 gbar 2.23 fire: E 0.00 d<100 0.14 100-400 0.23 400-800 0.47 >=800 0.41 non 0.00 act E -0.000 g2 46 auc 0.825 gbar 2.63 fire: E 0.00 d<100 0.13 100-400 0.23 400-800 0.49 >=800 0.42 non 0.00 act E -0.000 g2 48 auc 0.810 gbar 2.34 fire: E 0.00 d<100 0.14 100-400 0.22 400-800 0.46 >=800 0.40 non 0.00 act E -0.000 g3 30 auc 0.904 gbar 5.20 fire: E 0.00 d<100 0.06 100-400 0.21 400-800 0.34 >=800 0.71 non 0.02 act E -0.001 g3 32 auc 0.886 gbar 4.28 fire: E 0.00 d<100 0.06 100-400 0.19 400-800 0.31 >=800 0.67 non 0.03 act E 0.000 g3 34 auc 0.913 gbar 6.83 fire: E 0.00 d<100 0.05 100-400 0.21 400-800 0.36 >=800 0.75 non 0.02 act E 0.000 g3 36 auc 0.904 gbar 5.69 fire: E 0.00 d<100 0.06 100-400 0.23 400-800 0.41 >=800 0.72 non 0.02 act E 0.000 g3 38 auc 0.898 gbar 5.15 fire: E 0.00 d<100 0.08 100-400 0.23 400-800 0.45 >=800 0.63 non 0.00 act E 0.000 g3 40 auc 0.905 gbar 6.34 fire: E 0.00 d<100 0.08 100-400 0.22 400-800 0.42 >=800 0.67 non 0.00 act E 0.000 [... 7 lines cut ...] built G2B0p1N8 16 built B0p1N8 16 built G3B0p1N8 16 built G2B0p03 1 built G2B0p1c 1 built G3B0p1c 1 built N24p0 16 Thu Nov 12 09:26:41 PM EST 2026
tool
Bash: cd code && python - <<'EOF'
t=open("think_job2.py").read()
t=t.replace('''st3 = torch.load(A_DIR + "/code/data/think_stats_g3.pt")
for L in tl.LC: # evaluate() computes gate pre-activations for every layer of tl.GL
st["gate"][L] = st3["gate"][tl.LA[tl.LC.index(L)]] # slots 54/56/58 carry the g3 gates of layers 30/32/34
''','''st1 = torch.load(A_DIR + "/code/data/think_stats.pt")
for L in tl.LC: # evaluate() computes gate pre-activations for every layer of tl.GL
st["gate"][L] = st1["gate"][L] # layers 54/56/58: stage-1 gates (not used by any stage-2 arm)
''')
open("think_job2.py","w").write(t)
EOF
cd .. && scp -q code/think_job2.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; mkdir -p scratch/t; printf "#!/bin/bash\nsleep 3 > /dev/null 2>&1\n" > scratch/t/t.sh; (bash scratch/t/t.sh > scratch/t/out.txt 2>&1 &); sleep 1; echo "echo appended-ran" >> scratch/t/t.sh; sleep 3; cat scratch/t/out.txt; rm -rf scratch/t; $PY -c "import ast;ast.parse(open(\"code/think_job2.py\").read())"; date; grep "evaluated" scratch/logs/job1.log | tail -1 | cut -c1-80; cat code/gpu_job.sh | tail -2; pgrep -f "think_job.py" -a | cut -c1-100'output
appended-ran Thu Nov 12 09:27:04 PM EST 2026 [ 623.6] evaluated 2 [task] rows 1949 arms 18 export WSP31_WORK=$PWD/scratch CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 $PY code/think_job.py > scratch/logs/job1.log 2>&1 423047 /home/keith/gemma-v2/tii-venv/bin/python code/think_job.py 478005 bash -c cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; mkdir -p
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; echo "\$PY code/think_job2.py 5 > scratch/logs/job2.log 2>&1" >> code/gpu_job.sh; tail -2 code/gpu_job.sh; for i in $(seq 1 100); do grep -q "evaluated 4" scratch/logs/job1.log && break; sleep 4; done; date; cp scratch/job1/eval.pt scratch/job1/eval1.pt; pkill -f "code/think_job.py" ; sleep 12; date; tail -2 scratch/logs/job1.log | cut -c1-100; cat scratch/logs/job2.log | cut -c1-200; cat scratch/logs/hold1.log; ls scratch/hold_end 2>/dev/null'
output
Exit code 255 $PY code/think_job.py > scratch/logs/job1.log 2>&1 $PY code/think_job2.py 5 > scratch/logs/job2.log 2>&1 Thu Nov 12 09:30:29 PM EST 2026
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date; tail -2 scratch/logs/job1.log | cut -c1-100; cat scratch/logs/job2.log | cut -c1-200; cat scratch/logs/hold1.log; ls scratch/hold_end 2>/dev/null; ls -la scratch/job1/ | head; pgrep -f "think_job" -a | cut -c1-90'
output
Thu Nov 12 09:30:34 PM EST 2026 (16 lines)
Thu Nov 12 09:30:34 PM EST 2026 [ 749.2] evaluated 3 [task] rows 1504 arms 18 [ 874.6] evaluated 4 HIPS__autograd.ac044f0d.lm_rew rows 991 arms 18 [ 0.0] stage 2; seconds available 635.9578437805176 Terminated $PY code/think_job.py > scratch/logs/job1.log 2>&1 total 918400 drwxrwxr-x 4 keith keith 4096 Nov 12 21:30 . drwxrwxr-x 6 keith keith 4096 Nov 12 21:27 .. drwxrwxr-x 19 keith keith 4096 Nov 12 21:20 ad drwxrwxr-x 19 keith keith 4096 Nov 12 20:55 dry -rw-rw-r-- 1 keith keith 3780149 Nov 12 21:30 eval.pt -rw-rw-r-- 1 keith keith 3780149 Nov 12 21:30 eval1.pt -rw-rw-r-- 1 keith keith 2055869 Nov 12 21:25 eval_snap.pt -rw-rw-r-- 1 keith keith 930790769 Nov 12 21:19 raw_mlpin.pt 487355 /home/keith/gemma-v2/tii-venv/bin/python code/think_job2.py 5 487540 bash -c cd /home/keith/gemma-v2/wsp31/authors/claude; date; tail -2 scratch/logs/jo
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; $PY code/think_report.py scratch/job1/eval1.pt 2>&1 | cut -c1-330 | head -26; $PY code/think_greedy.py scratch/job1/eval1.pt 2>&1 | cut -c1-300; date; tail -2 scratch/logs/job2.log | cut -c1-120'
output
episodes 5 ['PyCQA__flake8.cf15', 'Mimino666__langdet', '[task]', '[task]', 'HIPS__autograd.ac0'] arms per episode [18, 18, 18, 18, 18] (29 lines, middle cut at capture)
episodes 5 ['PyCQA__flake8.cf15', 'Mimino666__langdet', '[task]', '[task]', 'HIPS__autograd.ac0'] arms per episode [18, 18, 18, 18, 18]
== all episodes 5
n by class {'E': 29, 'early': 2051, 'mid': 3020, 'late': 3870, 'non': 750, 'think_pt': 29, 'tool_pt': 29}
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base mean lp101: early -24.99 mid -25.83 late -24.72 E -0.79 non -20.54
arm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non
B0p3 | 0.76 2.71 6.85 -1.25 | 0.82 0.27 | -0.08 -0.98 0.01 0.00 -0.01 | 0.96 0.77 1.00 1.00 | 0.0001 (base 0.0001)
C8p0 | 0.02 0.05 0.10 -0.00 | 1.00 1.00 | 0.00 -0.00 -0.00 -0.00 -0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0001)
A0p1 | 3.28 3.26 2.82 0.59 | 0.87 0.97 | -0.07 -0.04 -0.06 -0.04 -0.02 | 0.94 0.95 1.00 1.00 | 0.0010 (base 0.0001)
N3p0 | -0.03 -0.08 0.20 0.28 | 0.98 0.96 | 0.00 -0.00 -0.01 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0002 (base 0.0001)
B1p0 | 0.61 2.65 6.04 -3.75 | 0.81 0.27 | -0.14 -1.16 0.00 0.01 -0.01 | 0.94 0.74 1.00 1.00 | 0.0001 (base 0.0001)
C20p0 | 0.05 0.10 0.24 0.00 | 1.00 1.00 | -0.00 -0.00 -0.00 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0001)
A0p3 | 22.04 23.02 21.68 0.31 | 0.08 0.01 | -7.30 -7.20 -11.97 -6.05 -12.60 | 0.21 0.24 0.00 0.00 | 0.3461 (base 0.0001)
N1p0 | -0.01 -0.02 0.08 0.14 | 1.00 1.00 | 0.00 -0.00 -0.00 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0001)
M0p75 | -0.17 -0.18 -0.15 -0.02 | 1.00 1.00 | -0.00 -0.00 -0.00 0.00 -0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0001)
B0p1 | 0.47 1.88 4.99 -0.33 | 0.93 0.34 | -0.02 -0.37 0.01 -0.00 -0.00 | 0.98 0.87 1.00 1.00 | 0.0001 (base 0.0001)
C3p0 | 0.01 0.02 0.04 -0.00 | 1.00 1.00 | 0.00 -0.00 -0.00 -0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0001)
B0p3c | 0.10 0.33 1.28 -0.02 | 1.00 1.00 | -0.02 -0.20 0.00 -0.00 -0.00 | 0.98 0.91 1.00 1.00 | 0.0001 (base 0.0001)
C8p0c | 0.01 0.01 0.01 -0.00 | 1.00 1.00 | -0.00 -0.00 -0.00 0.00 0.00 | 1.00 1.00 1.00 1.00 | 0.0001 (base 0.0001)
A0p1c | 0.32 0.38 0.32 -0.13 | 1.00 1.00 | -0.00 -0.01 -0.08 -0.00 0.00 | 0.99 0.98 1.00 1.00 | 0.0001 (base 0.0001)
[... 20 lines cut ...]
B0p3c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001
C8p0c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001
A0p1c | 23 0.00 0.17 1.00 | 6 0.17 0.17 0.99 1284 | 0.79 (29) | 0.0001
N3p0c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001
A0p03 | 23 0.17 0.09 1.00 | 6 0.17 0.17 0.99 1284 | 0.90 (29) | 0.0006
N8p0 | 23 0.22 0.09 0.95 | 6 0.33 0.00 0.90 701 | 0.93 (29) | 0.0008
Thu Nov 12 09:30:40 PM EST 2026
[ 0.0] stage 2; seconds available 635.9578437805176tool
Bash: cd code && python - <<'EOF' (42 lines)
Bash: cd code && python - <<'EOF'
f=open("finalize.py").read()
f=f.replace('''extra = open(sys.argv[sys.argv.index("--extra") + 1]).read() if "--extra" in sys.argv else ""''','''extra = open(sys.argv[sys.argv.index("--extra") + 1]).read() if "--extra" in sys.argv else ""
gv = sys.argv[sys.argv.index("--gate") + 1] if "--gate" in sys.argv else "g1" # g1 (stage 1), g2 or g3 (think_refit.py)
evf = A + ("/scratch/job1/eval1.pt" if gv == "g1" and "--eval2" not in sys.argv else "/scratch/job2/eval.pt")
sopt = "" if gv == "g1" else f" --stats code/data/think_stats_{gv}.pt"''')
f=f.replace('''$PY code/build_think.py queue/{slug}/edit {spec}
$PY code/build_think.py queue/{slug}/ctrl {spec} --ctrl''','''$PY code/build_think.py queue/{slug}/edit {spec}{sopt}
$PY code/build_think.py queue/{slug}/ctrl {spec} --ctrl{sopt}''')
f=f.replace('''rep = subprocess.run([PY, A + "/code/think_report.py"], capture_output=True, text=True).stdout.splitlines()''','''rep = subprocess.run([PY, A + "/code/think_report.py", evf], capture_output=True, text=True).stdout.splitlines()
gre = subprocess.run([PY, A + "/code/think_greedy.py", evf], capture_output=True, text=True).stdout.splitlines()
grows = [l for l in gre if l.startswith(("arm ", "base ")) or any(l.startswith(a + " ") for a in arms)]
if gv != "g1":
gate_note = {"g2": "Gate variant g2 (`code/think_refit.py`, CPU refit from the saved MLP inputs): sharpness 12 instead of 3 (positions below the threshold then sit where gelu_tanh is 0, not at its negative lobe, which in the first version lowered p(`<channel|>`) at the true ends of thoughts); classes as above.",
"g3": "Gate variant g3 (`code/think_refit.py`, CPU refit from the saved MLP inputs): sharpness 12 instead of 3, positives = thought positions at depth >= 800, negatives = thought positions at depth < 200 and positions outside thoughts (threshold = 95% quantile of the negatives). Training-set firing rate, layers 30..48: depth < 100 0.05-0.09, 100..399 0.17-0.23, 400..799 0.28-0.45, >= 800 0.57-0.75, outside thoughts 0.00-0.03, at thought ends 0.00; AUC 0.88-0.91."}[gv]
else:
gate_note = ""''')
f=f.replace('''if "B" in spec or "C" in spec:''','''if ("B" in spec or "C" in spec) and gv in ("g1", "g2"):''')
f=f.replace('''recipe = "\\n\\n".join(fam[p.split(":")[0]] for p in spec.split(","))''','''recipe = "\\n\\n".join(fam[p.split(":")[0]] for p in spec.split(",")) + ("\\n\\n" + gate_note if gate_note else "")''')
f=f.replace('''```
{nl.join(base)}
{nl.join(hdr)}
{nl.join(urows)}
```''','''```
{nl.join(base)}
{nl.join(hdr)}
{nl.join(urows)}
```
Greedy reading of the same passes (the harness decodes at temperature 0): along each logged thought, the first
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(the unedited forward pass itself reproduces only this share of the logged ends, see `base`).
```
{nl.join(grows)}
```''')
open("finalize.py","w").write(f)
import ast; ast.parse(f)
EOF
cd .. && scp -q code/finalize.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date; grep "evaluated\|FAILED\|deadline\|done" scratch/logs/job2.log | cut -c1-120'output
Thu Nov 12 09:31:10 PM EST 2026
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; for i in $(seq 1 60); do grep -q "evaluated 2" scratch/logs/job2.log && break; sleep 5; done; date; grep "evaluated\|FAILED\|deadline\|done\|Error\|error" scratch/logs/job2.log | cut -c1-160; cp scratch/job2/eval.pt scratch/job2/snap.pt; $PY code/think_report.py scratch/job2/snap.pt 2>&1 | cut -c1-330 | sed -n 2,17p; $PY code/think_greedy.py scratch/job2/snap.pt 2>&1 | cut -c1-300'
output
Thu Nov 12 09:34:26 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 09:34:26 PM EST 2026
[ 80.5] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 11
[ 157.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 11
[ 233.1] evaluated 2 [task] rows 1949 arms 11
== all episodes 3
n by class {'E': 19, 'early': 1240, 'mid': 1894, 'late': 3642, 'non': 450, 'think_pt': 19, 'tool_pt': 19}
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base mean lp101: early -24.90 mid -25.66 late -24.81 E -0.82 non -22.33
arm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non
G2B0p1 | 0.65 2.42 5.19 -0.25 | 0.91 0.26 | -0.02 -0.37 0.01 -0.00 -0.01 | 0.98 0.87 1.00 1.00 | 0.0000 (base 0.0000)
G3B0p1 | 0.10 0.63 2.12 -0.08 | 1.00 0.63 | -0.00 -0.06 0.00 -0.00 -0.00 | 0.99 0.95 1.00 1.00 | 0.0000 (base 0.0000)
G3B0p3 | 0.20 1.22 4.26 -0.22 | 1.00 0.47 | -0.01 -0.24 0.01 -0.00 -0.00 | 0.99 0.90 1.00 1.00 | 0.0000 (base 0.0000)
G2B0p1N8 | 1.22 2.79 6.50 0.01 | 0.73 0.19 | -0.02 -0.59 -0.02 -0.01 -0.00 | 0.98 0.82 1.00 1.00 | 0.0006 (base 0.0000)
B0p1N8 | 1.09 2.72 6.40 -0.21 | 0.74 0.19 | -0.02 -0.60 -0.03 -0.02 -0.00 | 0.97 0.82 1.00 1.00 | 0.0006 (base 0.0000)
G3B0p1N8 | 0.53 0.80 3.15 0.51 | 0.92 0.35 | 0.00 -0.07 -0.04 -0.01 -0.00 | 0.99 0.95 1.00 1.00 | 0.0007 (base 0.0000)
G2B0p03 | 0.15 0.73 1.60 -0.01 | 1.00 0.82 | -0.00 -0.04 0.00 0.00 -0.00 | 0.99 0.96 1.00 1.00 | 0.0000 (base 0.0000)
G2B0p1c | 0.05 0.14 0.48 0.02 | 1.00 1.00 | -0.00 -0.04 0.00 -0.01 -0.00 | 0.99 0.96 1.00 1.00 | 0.0000 (base 0.0000)
G3B0p1c | 0.00 0.06 0.26 -0.00 | 1.00 1.00 | -0.00 -0.01 -0.00 -0.00 0.00 | 1.00 0.98 1.00 1.00 | 0.0000 (base 0.0000)
N24p0 | 2.31 1.98 2.31 0.66 | 0.77 0.33 | -0.01 -0.01 -0.19 -0.06 -0.03 | 0.98 0.99 1.00 1.00 | 0.0057 (base 0.0000)
== held-out gate pre-activation > 0 by class (and AUC late vs early+non), per gate layer
[... 5 lines cut ...]
G3B0p3 | 15 0.00 0.07 1.00 | 4 0.50 0.25 0.44 317 | 0.79 (19) | 0.0037
G2B0p1N8 | 15 0.33 0.07 0.73 | 4 1.00 0.00 0.19 209 | 0.79 (19) | 0.0717
B0p1N8 | 15 0.40 0.07 0.73 | 4 1.00 0.00 0.19 209 | 0.79 (19) | 0.0704
G3B0p1N8 | 15 0.27 0.07 0.91 | 4 0.75 0.00 0.45 340 | 0.84 (19) | 0.0047
G2B0p03 | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001
G2B0p1c | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001
G3B0p1c | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001
N24p0 | 15 0.47 0.00 0.78 | 4 0.75 0.00 0.32 63 | 0.95 (19) | 0.0031tool
Bash: mkdir -p extras && cat > extras/common_tail.txt <<'EOF' (61 lines, middle cut at capture)
Bash: mkdir -p extras && cat > extras/common_tail.txt <<'EOF'
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gain between -4 and +4 (identical unedited arms already differ by a few tasks).
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transfer to free-running episodes (the model re-opens the thought it was made to close); or (b) thinking falls by
20% or more and the limit rate falls, but the edit does not gain tasks: long thoughts are then a symptom of hard
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this direction. A rise of episodes that stop at the 30-tool-call cap above 15% would mean the saved time went into
more calls and not into finishing.
Caveats written before the run: the pre-test has only 5 held-out episodes (6 long thoughts); the lane's forward pass
reproduces only 83% of the logged thought ends on the unedited model (agreement with the server NOT VERIFIED YET);
nothing was generated under the edit, so the first tool call after a thought that was cut short is untested.
Unedited reference (b001, 61 episodes when read): solved 54%, at the session limit 23%, empty patch 16%, thinking
[line removed by the dashboard scrubber]
27% in limit episodes.
EOF
cat > extras/e1.txt <<'EOF'
Behaviour that should move: thoughts are ended at a sentence boundary once they are deep (the cut falls at depth
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
- episodes ending at the 7.5-minute limit: from 23% to 10-17%; empty patches from 16% to 8-13%;
- tool calls per episode: up by 1 to 4; first file edit about as early as now in call number, earlier in seconds.
EOF
cat > extras/e2.txt <<'EOF'
Behaviour that should move: every thought ends at the first place where the model itself already gives `<channel|>`
some weight; short and long thoughts both get shorter (pre-test: 47% of short thoughts cut before their logged end with
[line removed by the dashboard scrubber]
(`E top=101` 0.95 against 0.84 unedited). This is the "think briefly" kind of edit that was neutral to harmful on E4B;
it is queued because here the session limit binds, because it is built from weights alone (the model's own 20
end-of-thought neurons, found by their write lens), and because its collateral in the pre-test is the smallest of all
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- episodes ending at the 7.5-minute limit: from 23% to 6-15%; empty patches from 16% to 6-12%;
- tool calls per episode: up by 2 to 5; episodes at the 30-call cap up from 7% to at most 15%;
- risk I can name: p(`<channel|>`) outside thoughts rises from 0.0000 to 0.006 on average; a stray `<channel|>` inside a
tool call would show up as malformed calls (predicted: under 2% of calls).
EOF
cat > extras/e3.txt <<'EOF'
Behaviour that should move: the deep-thought gate (family B, dose 0.1, gate g3) ends long thoughts, and the model's own
end-of-thought neurons at 9 times their write (family N, dose 8) keep natural ends decisive, which the gate alone
weakens slightly (`E top=101` 0.79 for the gate alone, 0.84 unedited, 0.84 for the combination). Pre-test of the
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- episodes ending at the 7.5-minute limit: from 23% to 10-17%; empty patches from 16% to 8-13%;
- tool calls per episode: up by 1 to 4.
EOF
cd code && python - <<'EOF'
t=open("think_greedy.py").read()
t=t.replace(''' f = lambda k:''',''' nn = [0, 0, 0]
for e in eps:
rows = e["rows"]; top = e["arms"][a]["top"]; btop = e["arms"]["base"]["top"]
mk = rows[:, 1] >= 4
nn[0] += int(mk.sum()); nn[1] += int((top[mk] == 101).sum()); nn[2] += int((top[mk] == btop[mk]).sum())
extra = " | outside thoughts (non, think, tool points): n %d, top=101 %d, top unchanged %.3f" % (nn[0], nn[1], nn[2] / max(nn[0], 1))
f = lambda k:''')
t=t.replace('''inner[0] / max(inner[1], 1)))''','''inner[0] / max(inner[1], 1)) + extra)''')
open("think_gree
[... 631 characters cut ...]output
Thu Nov 12 09:35:22 PM EST 2026 (21 lines)
Thu Nov 12 09:35:22 PM EST 2026 [ 80.5] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 11 [ 157.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 11 [ 233.1] evaluated 2 [task] rows 1949 arms 11 episodes 3 arms complete 11 of 11 [line removed by the dashboard scrubber] base | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 1.000 G2B0p1 | 15 0.13 0.13 0.89 | 4 1.00 0.00 0.29 327 | 0.74 (19) | 0.0226 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.996 G3B0p1 | 15 0.00 0.07 1.00 | 4 0.50 0.25 0.59 714 | 0.79 (19) | 0.0003 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.990 G3B0p3 | 15 0.00 0.07 1.00 | 4 0.50 0.25 0.44 317 | 0.79 (19) | 0.0037 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.990 G2B0p1N8 | 15 0.33 0.07 0.73 | 4 1.00 0.00 0.19 209 | 0.79 (19) | 0.0717 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.980 B0p1N8 | 15 0.40 0.07 0.73 | 4 1.00 0.00 0.19 209 | 0.79 (19) | 0.0704 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.980 G3B0p1N8 | 15 0.27 0.07 0.91 | 4 0.75 0.00 0.45 340 | 0.84 (19) | 0.0047 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.980 G2B0p03 | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.992 G2B0p1c | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.990 G3B0p1c | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001 | outside thoughts (non, think, tool points): n 488, top=101 0, top unchanged 0.990 N24p0 | 15 0.47 0.00 0.78 | 4 0.75 0.00 0.32 63 | 0.95 (19) | 0.0031 | outside thoughts (non, think, tool points): n 488, top=101 1, top unchanged 0.934 base | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 1.000 A0p1 | 23 0.48 0.09 0.81 | 6 0.33 0.17 0.99 906 | 0.90 (29) | 0.0016 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.896 N3p0 | 23 0.09 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.86 (29) | 0.0003 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.993 N8p0 | 23 0.22 0.09 0.95 | 6 0.33 0.00 0.90 701 | 0.93 (29) | 0.0008 | outside thoughts (non, think, tool points): n 808, top=101 2, top unchanged 0.980
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; for i in $(seq 1 40); do [ -f scratch/hold_end ] && break; sleep 4; done; date; cat scratch/hold_end; grep "evaluated\|FAILED\|deadline\|done" scratch/logs/job2.log | cut -c1-100; cat scratch/logs/hold1.log; nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader; pgrep -f think_job -a | cut -c1-80; $PY code/think_report.py scratch/job2/eval.pt 2>&1 | cut -c1-330 | sed -n 2,17p; $PY code/think_greedy.py scratch/job2/eval.pt 2>&1 | cut -c1-420'
output
Thu Nov 12 09:36:58 PM EST 2026 (29 lines, middle cut at capture)
Thu Nov 12 09:36:58 PM EST 2026
Thu Nov 12 09:36:58 PM EST 2026
[ 80.5] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 11
[ 157.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 11
[ 233.1] evaluated 2 [task] rows 1949 arms 11
[ 310.1] evaluated 3 [task] rows 1504 arms 11
[ 386.9] evaluated 4 HIPS__autograd.ac044f0d.lm_rew rows 991 arms 11
[ 386.9] stage 2 done
Terminated $PY code/think_job.py > scratch/logs/job1.log 2>&1
184976, 90492 MiB
503061 bash -c cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma
== all episodes 5
n by class {'E': 29, 'early': 2051, 'mid': 3020, 'late': 3870, 'non': 750, 'think_pt': 29, 'tool_pt': 29}
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base mean lp101: early -24.99 mid -25.83 late -24.72 E -0.79 non -20.54
arm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non
G2B0p1 | 0.57 1.96 5.10 -0.18 | 0.93 0.33 | -0.02 -0.36 0.00 -0.00 -0.00 | 0.98 0.87 1.00 1.00 | 0.0001 (base 0.0001)
G3B0p1 | 0.11 0.47 2.04 -0.06 | 1.00 0.69 | -0.00 -0.06 -0.00 -0.00 -0.00 | 1.00 0.95 1.00 1.00 | 0.0001 (base 0.0001)
G3B0p3 | 0.22 0.92 4.10 -0.15 | 1.00 0.55 | -0.01 -0.23 0.00 -0.00 -0.00 | 0.99 0.90 1.00 1.00 | 0.0001 (base 0.0001)
G2B0p1N8 | 0.85 2.25 6.37 0.03 | 0.76 0.21 | -0.02 -0.57 -0.03 -0.02 0.00 | 0.97 0.82 1.00 1.00 | 0.0017 (base 0.0001)
[... 12 lines cut ...]
G3B0p3 | 23 0.00 0.13 1.00 | 6 0.33 0.17 0.53 317 | 0.79 (29) | 0.0028 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.990
G2B0p1N8 | 23 0.35 0.09 0.82 | 6 1.00 0.00 0.21 148 | 0.83 (29) | 0.0568 | outside thoughts (non, think, tool points): n 808, top=101 2, top unchanged 0.978
B0p1N8 | 23 0.39 0.09 0.81 | 6 1.00 0.00 0.21 148 | 0.79 (29) | 0.0557 | outside thoughts (non, think, tool points): n 808, top=101 2, top unchanged 0.978
G3B0p1N8 | 23 0.22 0.09 0.95 | 6 0.67 0.00 0.48 245 | 0.86 (29) | 0.0038 | outside thoughts (non, think, tool points): n 808, top=101 1, top unchanged 0.976
G2B0p03 | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.991
G2B0p1c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.989
G3B0p1c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.991
N24p0 | 23 0.39 0.04 0.86 | 6 0.83 0.00 0.34 98 | 0.93 (29) | 0.0030 | outside thoughts (non, think, tool points): n 808, top=101 8, top unchanged 0.918tool
Bash: cat > code/non_split.py <<'EOF'
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
import json, sys, torch
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
recs = {}
for l in open("/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl"):
r = json.loads(l); recs.setdefault(r["instance"], []).append(r)
for f in sys.argv[1:]:
res = torch.load(f); eps = res["episodes"]
arms = [a for a in res["arms"] if all(a in e["arms"] for e in eps)]
for a in arms:
if a == "base": continue
c = {"gen": [0, 0, 0], "read": [0, 0, 0]}
for e in eps:
cand = [r for r in recs[e["instance"]] if len(r["ids"]) >= e["n"]] or recs[e["instance"]]
[line removed by the dashboard scrubber]
spans = [(k["start"], k["start"] + len(k["comp"])) for k in rec["calls"]]
rows = e["rows"]; top = e["arms"][a]["top"]; bt = e["arms"]["base"]["top"]
for i in (rows[:, 1] == 4).nonzero()[:, 0].tolist():
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print("%-9s generated: n %d top=101 %d changed %d | read-only: n %d top=101 %d changed %d" % (a, *c["gen"], *c["read"]))
EOF
scp -q code/non_split.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; cat code/extras/e1.txt code/extras/common_tail.txt > scratch/x1.txt; cat code/extras/e3.txt code/extras/common_tail.txt > scratch/x3.txt; $PY code/finalize.py deep-thought-gate-g3-b03 B:0.3 -4 9 "Deep-thought gate: one neuron per layer 30-48 detects depth >= 800 inside a thought and writes the end-of-thought state (dose 0.3)" --gate g3 --arms G3B0p3,G3B0p1,G3B0p1c --extra scratch/x1.txt 2>&1 | tail -5; $PY code/finalize.py deep-gate-b01-end-neurons-x9 B:0.1,N:8 -5 9 "Deep-thought gate (dose 0.1) plus the model s own end-of-thought neurons at 9 times their write" --gate g3 --arms G3B0p1N8,G3B0p1,G3B0p1c --extra scratch/x3.txt 2>&1 | tail -5; date; $PY code/non_split.py scratch/job2/eval.pt scratch/job1/eval1.pt 2>&1 | grep "^N\|^G3\|^B0p1 \|^A0p1 \|Trace\|Error" | cut -c1-200'output
saved queue/deep-thought-gate-g3-b03/edit r 1 modules 30 max rel_fro 0.10156 (22 lines)
saved queue/deep-thought-gate-g3-b03/edit r 1 modules 30 max rel_fro 0.10156 saved queue/deep-thought-gate-g3-b03/ctrl r 1 modules 30 max rel_fro 0.10156 OK edit: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj OK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/record.md saved queue/deep-gate-b01-end-neurons-x9/edit r 16 modules 33 max rel_fro 0.1999 saved queue/deep-gate-b01-end-neurons-x9/ctrl r 16 modules 33 max rel_fro 0.19988 OK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj OK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/record.md Thu Nov 12 09:37:32 PM EST 2026 G3B0p1 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6 G3B0p3 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 8 G3B0p1N8 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 1 changed 19 G3B0p1c generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 7 N24p0 generated: n 152 top=101 0 changed 1 | read-only: n 598 top=101 8 changed 65 A0p1 generated: n 152 top=101 0 changed 2 | read-only: n 598 top=101 0 changed 82 N3p0 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6 N1p0 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6 B0p1 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 8 N3p0c generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6 N8p0 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 2 changed 16
tool
Bash: cd extras && python - <<'EOF'
t=open("e2.txt").read()
i=t.index("- risk I can name")
t=t[:i]+'''- risk I can name: p(`<channel|>`) outside thoughts rises from 0.0001 to 0.011 on average. Split by
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
and is `<channel|>` at 8. A stray `<channel|>` inside a tool call would show up as malformed calls (predicted: under
2% of calls).
[line removed by the dashboard scrubber]
`E top=101`): x2 1.00 / 0.99 / 0.83; x4 1.00 / 0.99 / 0.86; x9 0.95 / 0.90 / 0.93; x25 (this entry) 0.86 / 0.34 /
0.93; norm-matched control neurons x4: 1.00 / 0.99 / 0.83 (= unedited). The control of this entry (x25 on the
control neurons) was not pre-tested.
'''
open("e2.txt","w").write(t)
EOF
[line removed by the dashboard scrubber]output
saved queue/end-neurons-x25/edit r 16 modules 3 max rel_fro 0.59969 (29 lines, middle cut at capture)
saved queue/end-neurons-x25/edit r 16 modules 3 max rel_fro 0.59969 saved queue/end-neurons-x25/ctrl r 16 modules 3 max rel_fro 0.59963 OK edit: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5997 at 59.down_proj OK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/record.md Thu Nov 12 09:37:52 PM EST 2026 Statistics (`code/data/think_stats.pt`, written by `code/think_job.py` in one GPU-0 hold with `tools/g31.py`, the tools lane's forward pass, whose agreement with the served model is NOT VERIFIED YET in TOOLS.md): one rollout per instance of `tools/data/probe_screen.jsonl` (unedited run s1_base), every fourth instance held out for the pre-test, [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] Family B (gated, state side), layers 30, 32, ..., 48, one neuron per layer replaced outright (`set`): the neuron `j_L` with the smallest mean |activation| over all sampled positions. Gate row = `(3 / sigma) (w - (theta / cbar) m)`: `w` = unit diagonal-LDA probe on `mlp_in[L]` (late thought positions, depth >= 400, against early thought positions, depth < 100, and positions outside thoughts), `theta` = 95% quantile of the probe on the negatives, `sigma` = sd on the positives, `m` = unit mean `mlp_in` (the model has no bias; `m . x` is `cbar` at every position). Up row = `m / cbar` (reads 1). Down column = `clipdiv(d_L / |d_L|, w_post_L) * dose * |d_L| * rms_y_L / (gbar_L * [... 75 lines cut ...] more calls and not into finishing. Caveats written before the run: the pre-test has only 5 held-out episodes (6 long thoughts); the lane's forward pass reproduces only 83% of the logged thought ends on the unedited model (agreement with the server NOT VERIFIED YET); nothing was generated under the edit, so the first tool call after a thought that was cut short is untested. Unedited reference (b001, 61 episodes when read): solved 54%, at the session limit 23%, empty patch 16%, thinking [line removed by the dashboard scrubber] 27% in limit episodes.
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; grep -n "^recipe\|gate_note" code/finalize.py | cut -c1-160; $PY - <<EOF
import json,os
R="/home/keith/gemma-v2/results/wsp31"
n=0;k8=0;k4=0;ep=0;lim=0;emp=0;cap=0;sol=0
for arm in ("base","aa","zero"):
d=R+"/batches/b001/"+arm+"/rebench/shard0_gpu0"
for l in open(d+"/task_results.jsonl"):
r=json.loads(l); p=r.get("request_log_path") or ""
if not os.path.exists(p): continue
ep+=1; sol+=bool(r["resolved"]); lim+=(r.get("phase1_seconds") or 0)>=420; emp+=not r["patch_chars"]; cap+=r["tool_calls"]>=30
for x in open(p):
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t=ids.index(101) if 101 in ids else len(ids); n+=1; k8+=t>=800; k4+=t>=400
print("episodes",ep,"solved",sol,"limit",lim,"empty",emp,"cap30",cap,"calls",n,"thought>=800: %.3f"%(k8/n),">=400: %.3f"%(k4/n))
EOF'output
38: gate_note = {"g2": "Gate variant g2 (`code/think_refit.py`, CPU refit from the saved MLP inputs): sharpness 12 instead of 3 (positions below the threshol
41: gate_note = ""
94:recipe = "\n\n".join(fam[p.split(":")[0]] for p in spec.split(","))
episodes 110 solved 48 limit 39 empty 27 cap30 19 calls 2496 thought>=800: 0.086 >=400: 0.176tool
Bash: cd code && python - <<'EOF'
f=open("finalize.py").read()
old='recipe = "\\n\\n".join(fam[p.split(":")[0]] for p in spec.split(","))'
assert old in f, "recipe line"
f=f.replace(old, old+' + (("\\n\\n" + gate_note + " This entry uses `code/data/think_stats_" + gv + ".pt`: the class means and neurons of `think_stats.pt` with these gates in place of the first version described under Family B.") if gate_note else "")')
old2='''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the
7.5-minute session limit, 10 of them with an empty patch and 9 of those without a single file edit.'''
assert old2 in f
f=f.replace(old2,'''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the
7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110
episodes, 9:40 PM ET: solved 44%, at the limit 35%, empty patch 25%).''')
f=f.replace("here it ends 23% of unedited episodes","here it ends 23% to 35% of unedited episodes")
open("finalize.py","w").write(f)
import ast; ast.parse(f)
c=open("../extras/common_tail.txt").read()
i=c.index("Unedited reference")
[line removed by the dashboard scrubber]
(solved 4,637, limit 9,149). At 110 episodes (9:40 PM ET): solved 44%, at the session limit 35%, empty patch 25%, at
[line removed by the dashboard scrubber]
above are relative to whatever the unedited arm of the same batch shows.
'''
c=c.replace("A rise of episodes that stop at the 30-tool-call cap above 15% would mean","A rise of episodes that stop at the 30-tool-call cap by more than 8 points over the unedited arm would mean")
open("../extras/common_tail.txt","w").write(c)
for n in ("e1","e2","e3"):
t=open(f"../extras/{n}.txt").read()
t=t.replace("0 of 15 cut","0 of 23 cut").replace("from about 8% to under 3%","from 8.6% to under 4%")
t=t.replace("from 23% to 10-17%; empty patches from 16% to 8-13%","down by a quarter to a half of the unedited arm's rate (35% at 110 episodes: to 18-27%); empty patches down in the same proportion (25%: to 13-20%)")
t=t.replace("from 23% to 6-15%; empty patches from 16% to 6-12%","down by a third to two thirds of the unedited arm's rate (35% at 110 episodes: to 12-24%); empty patches down in the same proportion (25%: to 9-17%)")
t=t.replace("episodes at the 30-call cap up from 7% to at most 15%","episodes at the 30-call cap up by at most 8 points (unedited 17%)")
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
open(f"../extras/{n}.txt","w").write(t)
print(n, "23%" in t, "16%" in t)
EOF
[line removed by the dashboard scrubber]output
Traceback (most recent call last): (29 lines, middle cut at capture)
Traceback (most recent call last): File "<stdin>", line 3, in <module> AssertionError: recipe line OK edit: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj OK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/record.md OK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj OK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/record.md OK edit: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5997 at 59.down_proj OK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/record.md Thu Nov 12 09:39:00 PM EST 2026 ==> queue/deep-gate-b01-end-neurons-x9/record.md <== title: Deep-thought gate (dose 0.1) plus the model s own end-of-thought neurons at 9 times their write ==> queue/deep-thought-gate-g3-b03/record.md <== title: Deep-thought gate: one neuron per layer 30-48 detects depth >= 800 inside a thought and writes the end-of-thought state (dose 0.3) ==> queue/end-neurons-x25/record.md <== [... 27 lines cut ...] control neurons) was not pre-tested. [line removed by the dashboard scrubber] gain between -4 and +4 (identical unedited arms already differ by a few tasks). [line removed by the dashboard scrubber] transfer to free-running episodes (the model re-opens the thought it was made to close); or (b) thinking falls by 20% or more and the limit rate falls, but the edit does not gain tasks: long thoughts are then a symptom of hard [line removed by the dashboard scrubber]
tool
Bash: cd code && python - <<'EOF'
f=open("finalize.py").read()
for l in f.splitlines():
if l.startswith("recipe"): print(repr(l))
old='recipe = "\\n\\n".join(fam[p.split(":")[0]] for p in spec.split(","))'
print(repr(old), old in f)
EOFoutput
'recipe = "\\n\\n".join(fam[p.split(":")[0]] for p in spec.split(","))'
'recipe = "\n\n".join(fam[p.split(":")[0]] for p in spec.split(","))' Falsetool
Bash: python - <<'EOF'
BS=chr(92)
f=open("finalize.py").read()
lines=f.split(chr(10))
for i,l in enumerate(lines):
if l.startswith("recipe = "):
nn='"'+BS+'n'+BS+'n"'
lines[i]=l+' + (('+nn+' + gate_note + " This entry uses `code/data/think_stats_" + gv + ".pt`: the class means and neurons of `think_stats.pt` with these gates in place of the first version described under Family B.") if gate_note else "")'
print(lines[i][:200])
f=chr(10).join(lines)
old2='''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the
7.5-minute session limit, 10 of them with an empty patch and 9 of those without a single file edit.'''
assert old2 in f
f=f.replace(old2,'''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the
7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110
episodes, 9:40 PM ET: solved 44%, at the limit 35%, empty patch 25%).''')
f=f.replace("here it ends 23% of unedited episodes","here it ends 23% to 35% of unedited episodes")
open("finalize.py","w",newline=chr(10)).write(f)
import ast; ast.parse(f)
c=open("../extras/common_tail.txt").read()
i=c.index("Unedited reference")
[line removed by the dashboard scrubber]
(solved 4,637, limit 9,149). At 110 episodes (9:40 PM ET): solved 44%, at the session limit 35%, empty patch 25%, at
[line removed by the dashboard scrubber]
above are relative to whatever the unedited arm of the same batch shows.
'''
c=c.replace("A rise of episodes that stop at the 30-tool-call cap above 15% would mean","A rise of episodes that stop at the 30-tool-call cap by more than 8 points over the unedited arm would mean")
open("../extras/common_tail.txt","w",newline=chr(10)).write(c)
for n in ("e1","e2","e3"):
t=open(f"../extras/{n}.txt").read()
t=t.replace("0 of 15 cut","0 of 23 cut").replace("from about 8% to under 3%","from 8.6% to under 4%")
t=t.replace("from 23% to 10-17%; empty patches from 16% to 8-13%","down by a quarter to a half of the unedited arm's rate (35% at 110 episodes: to 18-27%); empty patches down in the same proportion (25%: to 13-20%)")
t=t.replace("from 23% to 6-15%; empty patches from 16% to 6-12%","down by a third to two thirds of the unedited arm's rate (35% at 110 episodes: to 12-24%); empty patches down in the same proportion (25%: to 9-17%)")
t=t.replace("episodes at the 30-call cap up from 7% to at most 15%","episodes at the 30-call cap up by at most 8 points (unedited 17%)")
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[line removed by the dashboard scrubber]
open(f"../extras/{n}.txt","w",newline=chr(10)).write(t)
print(n, "from 23%" in t, "0.84" in t, "78%" in t)
EOF
[line removed by the dashboard scrubber]output
recipe = "\n\n".join(fam[p.split(":")[0]] for p in spec.split(",")) + (("\n\n" + gate_note + " This entry uses `code/data/think_stats_" + gv + ".pt`: the class (31 lines)
recipe = "\n\n".join(fam[p.split(":")[0]] for p in spec.split(",")) + (("\n\n" + gate_note + " This entry uses `code/data/think_stats_" + gv + ".pt`: the class means and neurons of `think_stats.pt` wi
e1 False False False
e2 False False False
e3 False False False
OK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj
wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/record.md
OK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj
wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/record.md
OK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj
wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/record.md
Thu Nov 12 09:39:50 PM EST 2026
queue/deep-gate-b01-end-neurons-x9/record.md:1
queue/deep-thought-gate-g3-b03/record.md:1
queue/end-neurons-x25/record.md:0
queue/deep-gate-b01-end-neurons-x9/record.md:6:7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file
[line removed by the dashboard scrubber]
queue/deep-gate-b01-end-neurons-x9/record.md:113:- episodes ending at the 7.5-minute limit: down by a quarter to a half of the unedited arm's rate (35
queue/deep-thought-gate-g3-b03/record.md:6:7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit
queue/deep-thought-gate-g3-b03/record.md:102:- episodes ending at the 7.5-minute limit: down by a quarter to a half of the unedited arm's rate (35% at
queue/end-neurons-x25/record.md:6:7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110
[line removed by the dashboard scrubber]
queue/end-neurons-x25/record.md:96:(`E top=101` 0.93 against 0.83 unedited). This is the "think briefly" kind of edit that was neutral to harmful on E
queue/end-neurons-x25/record.md:101:- episodes ending at the 7.5-minute limit: down by a third to two thirds of the unedited arm's rate (35% at 110 ep
#!/bin/bash
# rebuilds edit/ and ctrl/ of deep-thought-gate-g3-b03 (CPU, seconds). Needs code/data/think_stats.pt (written by code/think_job.py
# in one GPU-0 hold: `flock $R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh`, which runs code/gpu_job.sh).
. /home/keith/gemma-v2/env.sh
cd /home/keith/gemma-v2/wsp31/authors/claude
$PY code/build_think.py queue/deep-thought-gate-g3-b03/edit B:0.3 --stats code/data/think_stats_g3.pt
$PY code/build_think.py queue/deep-thought-gate-g3-b03/ctrl B:0.3 --ctrl --stats code/data/think_stats_g3.pt
$PY $R/wsp31/tools/check_adapter.py queue/deep-thought-gate-g3-b03/edit && $PY $R/wsp31/tools/check_adapter.py queue/deep-thought-gate-g3-b03/ctrltool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; $PY - <<EOF
from safetensors.torch import load_file
import torch
ok=True
for slug,arm in (("deep-thought-gate-g3-b03","G3B0p3"),("deep-gate-b01-end-neurons-x9","G3B0p1N8"),("end-neurons-x25","N24p0")):
a=load_file(f"queue/{slug}/edit/adapter_model.safetensors"); b=load_file(f"scratch/job2/ad/{arm}/adapter_model.safetensors")
[line removed by the dashboard scrubber]
c=load_file(f"queue/{slug}/ctrl/adapter_model.safetensors")
diff = sum(0 if torch.equal(a[k],c[k]) else 1 for k in a if k in c)
[line removed by the dashboard scrubber]
ok &= same
print("ALL OK" if ok else "MISMATCH")
EOF
for s in deep-thought-gate-g3-b03 deep-gate-b01-end-neurons-x9; do sed -i "s|^# in one GPU-0 hold: .*|# in one GPU-0 hold: \`flock \$R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\`, which runs code/gpu_job.sh) and\n# code/data/think_stats_g3.pt (code/think_refit.py, CPU, from scratch/job1/raw_mlpin.pt saved by the same hold).|" queue/$s/build.sh; done; sed -n 1,5p queue/deep-thought-gate-g3-b03/build.sh; ls -la queue/*/ | head -40; du -sh queue scratch code'output
[line removed by the dashboard scrubber] (29 lines, middle cut at capture)
[line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] ALL OK #!/bin/bash # rebuilds edit/ and ctrl/ of deep-thought-gate-g3-b03 (CPU, seconds). Needs code/data/think_stats.pt (written by code/think_job.py # in one GPU-0 hold: `flock $R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh`, which runs code/gpu_job.sh) and # code/data/think_stats_g3.pt (code/think_refit.py, CPU, from scratch/job1/raw_mlpin.pt saved by the same hold). . /home/keith/gemma-v2/env.sh queue/deep-gate-b01-end-neurons-x9/: total 32 drwxrwxr-x 4 keith keith 4096 Nov 12 21:40 . drwxrwxr-x 5 keith keith 4096 Nov 12 21:37 .. -rwxr-xr-x 1 keith keith 829 Nov 12 21:40 build.sh drwxrwxr-x 2 keith keith 4096 Nov 12 21:39 ctrl drwxrwxr-x 2 keith keith 4096 Nov 12 21:39 edit -rw-rw-r-- 1 keith keith 11563 Nov 12 21:39 record.md queue/deep-thought-gate-g3-b03/: total 32 [... 10 lines cut ...] drwxrwxr-x 5 keith keith 4096 Nov 12 21:37 .. -rwxr-xr-x 1 keith keith 566 Nov 12 21:39 build.sh drwxrwxr-x 2 keith keith 4096 Nov 12 21:39 ctrl drwxrwxr-x 2 keith keith 4096 Nov 12 21:39 edit -rw-rw-r-- 1 keith keith 10655 Nov 12 21:39 record.md 63M queue 1.4G scratch 159M code
tool
Bash: cat > code/fix_records.py <<'EOF'
import sys
A = "/home/keith/gemma-v2/wsp31/authors/claude/queue/"
def paras(p): return open(p).read().split("\n\n")
# end-neurons-x25: weights-only entry
p = A + "end-neurons-x25/record.md"; P = paras(p)
for i, x in enumerate(P):
if x.startswith("Statistics (`code/data/think_stats.pt`"):
P[i] = ("No activation statistics enter this edit: it is built from the checkpoint alone (`code/end_neurons.py`, CPU).\n"
"The pre-test below ran in one GPU-0 hold with `tools/g31.py` (the tools lane's forward pass, whose agreement with\n"
"the served model is NOT VERIFIED YET in TOOLS.md) on 5 held-out episodes of `tools/data/probe_screen.jsonl`\n"
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
if x.startswith("Control (`ctrl/`): same code path and statistics"):
P[i] = ("Control (`ctrl/`): same code path (`build_think.py N:24 --ctrl`): the same factor 25 applied to the down columns of\n"
"20 other neurons, one per selected neuron, same layer, `|z| < 1` for `<channel|>`, nearest in column norm. Same\n"
"layers, modules, rank (16) and |dW|_F (0.5996 against 0.5997 of |W|_F at 59.down_proj). It is the right control for\n"
"\"these neurons, not any 20 late-layer neurons made 25 times louder\".")
if x.startswith("Built as intended; approximations"):
P[i] = "Built as intended; the only approximation is bf16 storage of the added columns (8 significant bits)."
open(p, "w").write("\n\n".join(P))
p = A + "deep-gate-b01-end-neurons-x9/record.md"; P = paras(p)
for i, x in enumerate(P):
if x.startswith("Control (`ctrl/`): same code path and statistics"):
P[i] = x + ("\nFor the family N part the control scales, by the same factor 9, 20 other neurons (one per selected neuron, same layer,\n"
"`|z| < 1`, nearest in column norm). The control of the combination was not pre-tested as one adapter; its two parts\n"
"were (rows `G3B0p1c` below; family N control at factor 4 in the first pass: no change from unedited).")
open(p, "w").write("\n\n".join(P))
p = A + "deep-thought-gate-g3-b03/record.md"; P = paras(p)
for i, x in enumerate(P):
if x.startswith("Control (`ctrl/`): same code path and statistics"):
P[i] = x + ("\nThe control at this dose was not pre-tested; the control at dose 0.1 with the same gates (row `G3B0p1c` below) and the\n"
"control at dose 0.3 with the first gates left every thought uncut.")
open(p, "w").write("\n\n".join(P))
print("fixed")
EOF
scp -q code/fix_records.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; $PY code/fix_records.py; sed -n 1,48p queue/end-neurons-x25/record.md | cut -c1-200; echo ....; sed -n 20,52p queue/deep-gate-b01-end-neurons-x9/record.md | cut -c1-200; date'output
fixed (29 lines, middle cut at capture)
fixed [line removed by the dashboard scrubber] ## Hypothesis In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the 7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110 episodes, 9:40 PM ET: solved 44%, at the limit 35%, empty patch 25%). At about [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] "Wait, ..." restarts. Making long thoughts end sooner should return that wall-clock to tool calls, so that episodes which now time out without a patch get to edit, test and submit. Different from the E4B "think briefly" edits (28 / 28 / 25 vs 29, PRIOR_E4B.md): there the session limit was not reported as binding; here it ends 23% to 35% of unedited episodes, and the edit is built from the state at the logged ends of thoughts (and, in families B and C, acts only deep inside a long thought), not from a short-vs-long contrast. ## Exact recipe [... 56 lines cut ...] `|z| < 1` nearest in column norm (pool of 4000, seed 4242), scaled by the same factor. Gate variant g3 (`code/think_refit.py`, CPU refit from the saved MLP inputs): sharpness 12 instead of 3, positives = thought positions at depth >= 800, negatives = thought positions at depth < 200 and Control (`ctrl/`): same code path and statistics; the written unit direction has the sign of every coordinate flipped at random (seed 1000 + layer) before `clipdiv`. Per-coordinate magnitudes are unchanged, so the control has the same norm in the stream, in the raw MLP output and in |dW|_F, the same read vector / gate, and a write direction Thu Nov 12 09:40:26 PM EST 2026
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; for s in deep-thought-gate-g3-b03 deep-gate-b01-end-neurons-x9 end-neurons-x25; do $PY $R/wsp31/tools/check_adapter.py queue/$s/edit && $PY $R/wsp31/tools/check_adapter.py queue/$s/ctrl && grep -q "^predicted_gain_120: " queue/$s/record.md && head -1 queue/$s/record.md | grep -q "^title: " && for h in "## Hypothesis" "## Exact recipe" "## Prediction (written before the run)"; do grep -qF "$h" queue/$s/record.md || echo MISSING $h; done && touch queue/$s/READY && echo READY $s; done; date; ls queue/*/READY'
output
OK edit: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj (13 lines)
OK edit: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj OK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj READY deep-thought-gate-g3-b03 OK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj OK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj READY deep-gate-b01-end-neurons-x9 OK edit: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5997 at 59.down_proj OK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj READY end-neurons-x25 Thu Nov 12 09:40:42 PM EST 2026 queue/deep-gate-b01-end-neurons-x9/READY queue/deep-thought-gate-g3-b03/READY queue/end-neurons-x25/READY
tool
Bash: cat > NOTES.md <<'EOF' (50 lines)
Bash: cat > NOTES.md <<'EOF'
# Notes of author claude (rewritten end of turn 0, 12 Nov 2026 9:45 PM ET)
## What the unedited model does wrong (b001; code/fail_stats.py, req_stats.py, cf_budget.py)
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
- Unedited log p(<channel|>) inside thoughts is about -25: thoughts end only where the model decides; hazard-style
"expected length" is useless at temperature 0. Use the greedy table (code/think_greedy.py).
## Queued this turn (all READY; edits are bit-identical to the pre-tested adapters)
1. deep-thought-gate-g3-b03: one "set" neuron per layer 30..48 (even) detects depth >= 800 in a thought (diag-LDA
gate, AUC 0.88-0.91) and writes d_end = mean resid(thought end) - mean resid(other thought positions), dose 0.3.
Pre-test (5 held-out episodes, 23 short / 6 long thoughts): short kept 1.00, long kept 0.53. Predicted -4..9.
2. end-neurons-x25: weights only. 20 MLP neurons in layers 57-59 whose write lens is <channel|> (z >= 6;
code/end_neurons.py), down columns x25. Short kept 0.86, long 0.34, logged ends more decisive. Predicted -8..9.
3. deep-gate-b01-end-neurons-x9: gate at dose 0.1 plus the same neurons x9. Short 0.95, long 0.48. Predicted -5..9.
Controls: sign-flipped write direction (gate) / norm-matched other neurons (N). Controls cut nothing in the pre-test.
## Measured nulls and traps (do not repeat)
- Family C (gated write of the <channel|> unembedding direction in layers 54-58): no effect at my doses (3-20 "nats").
- Always-on Concept Country form (family A): 0.1 shortens only short thoughts (kept 0.81) and leaves long ones; 0.3
wrecks the model (tool top-1 agreement 0). No usable window for long thoughts.
- First gate (sharpness 3) leaked through the negative lobe of gelu and lowered p(<channel|>) at true thought ends.
Use sharpness 12 (think_refit.py). "Anti" neurons scaled to 0.25 lowered p(<channel|>): wrong sign, dropped.
- g31 forward reproduces only 83% of logged thought ends on the unedited model (forward vs server still unverified).
[line removed by the dashboard scrubber]
- Git Bash heredocs halve backslashes: do not patch files with "\\n" inside `python - <<EOF`.
- Never `pkill -f` a pattern that is in your own ssh command line.
## Code (code/)
thinklib.py (families A B C N M, positions), think_job.py (capture + grid + eval; `--mock` tests plumbing on CPU in
45 s), think_refit.py, think_job2.py (second stage in the same hold), think_report.py, think_greedy.py, non_split.py,
build_think.py, finalize.py (writes a queue entry), end_neurons.py. Data: code/data/think_stats{,_g2,_g3}.pt,
end_neurons.pt. Lock queue was 37 minutes: run `nohup flock $R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh &`
in the first minutes; gpu_job.sh is read when the lock arrives, and lines appended to it run in the same hold.
## Next
[line removed by the dashboard scrubber]
thought-length hypothesis is dead; go to what limit episodes do instead of editing (9 of 10 never edit).
- If it works: gate on depth >= 1500, dose series, and check the first tool call after a cut thought with fastprobe /
generation (not done: no served probe this turn).
- Untested ideas: gate "N tool calls and no edit yet" writing an edit-now direction; read_file loops in limit episodes.
## Missing tools that would have changed the plan
Served generation under an adapter (to see what follows a cut thought); likelihood of successful trajectories;
verified forward-vs-server agreement.
EOF
wc -w NOTES.md; scp -q NOTES.md wrx90:/home/keith/gemma-v2/wsp31/authors/claude/NOTES.md; ssh -o BatchMode=yes wrx90 'date; ls /home/keith/gemma-v2/wsp31/authors/claude/queue/*/; cat /home/keith/gemma-v2/wsp31/queue/STATUS.md | sed -n 10,20p'output
626 NOTES.md (29 lines)
626 NOTES.md Thu Nov 12 09:41:05 PM EST 2026 /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/: READY build.sh ctrl edit record.md /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/: READY build.sh ctrl edit record.md /home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/: READY build.sh ctrl edit record.md ## Edits | ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict | |---|---|---|---|---|---|---|---|---| Waiting for stage 1: 0. Waiting for stage 2: 0.
tool
Bash: cat > extras/e4.txt <<'EOF'
Behaviour that should move: this is the strong end of the gated family. The gate is the g2 version (fitted on depth
>= 400, so it starts to fire from depth 100 to 400) at dose 0.1, plus the model's own end-of-thought neurons at 9
times their write. Pre-test of this adapter: every one of the 6 long thoughts is cut (median depth 148, 21% of their
[line removed by the dashboard scrubber]
as decisive as unedited (`E top=101` 0.83). With entries deep-thought-gate-g3-b03 (long 0.53, short 1.00) and
deep-gate-b01-end-neurons-x9 (long 0.48, short 0.95) it gives three doses of "how much thinking is cut" by the same
mechanism.
[line removed by the dashboard scrubber]
- episodes ending at the 7.5-minute limit: down by a third to two thirds of the unedited arm's rate (35% at 110
episodes: to 12-24%); empty patches down in the same proportion (25%: to 9-17%);
- tool calls per episode: up by 2 to 5; episodes at the 30-call cap up by at most 8 points (unedited 17%);
- if cutting thoughts is good at all, this entry should gain at least as much as the two weaker doses; if it gains
less than they do, the cut has started to remove thinking the tasks need (collateral inside deep thoughts is the
[line removed by the dashboard scrubber]
of which is the intended early end).
EOF
scp -q extras/e4.txt wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/extras/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; cat code/extras/e4.txt code/extras/common_tail.txt > scratch/x4.txt; $PY code/finalize.py early-gate-b01-end-neurons-x9 B:0.1,N:8 -7 9 "Early deep-thought gate (fitted on depth >= 400, dose 0.1) plus own end-of-thought neurons x9: the strong dose of the gated family" --gate g2 --arms G2B0p1N8,G2B0p1,G2B0p1c --extra scratch/x4.txt 2>&1 | tail -3; $PY - <<EOF
from safetensors.torch import load_file
import torch
A="/home/keith/gemma-v2/wsp31/authors/claude/"
a=load_file(A+"queue/early-gate-b01-end-neurons-x9/edit/adapter_model.safetensors"); b=load_file(A+"scratch/job2/ad/G2B0p1N8/adapter_model.safetensors")
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p=A+"queue/early-gate-b01-end-neurons-x9/record.md"; P=open(p).read().split("\n\n")
for i,x in enumerate(P):
if x.startswith("Control (\`ctrl/\`): same code path and statistics"):
P[i]=x+("\nFor the family N part the control scales, by the same factor 9, 20 other neurons (one per selected neuron, same layer,\n\`|z| < 1\`, nearest in column norm). The control of the combination was not pre-tested as one adapter; its two parts\nwere (row \`G2B0p1c\` below; family N control at factor 4 in the first pass: no change from unedited).")
open(p,"w").write("\n\n".join(P))
EOF
$PY code/non_split.py scratch/job2/eval.pt | grep "^G2B0p1N8\|^G2B0p1 " ; sed -i "s|^# in one GPU-0 hold: .*|# in one GPU-0 hold: \`flock \$R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\`, which runs code/gpu_job.sh) and\n# code/data/think_stats_g2.pt (code/think_refit.py, CPU, from scratch/job1/raw_mlpin.pt saved by the same hold).|" queue/early-gate-b01-end-neurons-x9/build.sh; grep -n "Gate variant g2\|not pre-tested as one\|^G2B0p1\|^30 auc\|^48 auc" queue/early-gate-b01-end-neurons-x9/record.md | cut -c1-330; date'output
OK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj (17 lines)
OK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj
OK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj
wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/early-gate-b01-end-neurons-x9/record.md
identical to pre-tested: True
G2B0p1 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 4
G2B0p1N8 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 2 changed 18
48:Gate variant g2 (`code/think_refit.py`, CPU refit from the saved MLP inputs): sharpness 12 instead of 3 (positions below the threshold then sit where gelu_tanh is 0, not at its negative lobe, which in the first version lowered p(`<channel|>`) at the true ends of thoughts); classes as above. This entry uses `code/data/think_st
56:`|z| < 1`, nearest in column norm). The control of the combination was not pre-tested as one adapter; its two parts
85:G2B0p1 | 0.57 1.96 5.10 -0.18 | 0.93 0.33 | -0.02 -0.36 0.00 -0.00 -0.00 | 0.98 0.87 1.00 1.00 | 0.0001 (base 0.0001)
86:G2B0p1N8 | 0.85 2.25 6.37 0.03 | 0.76 0.21 | -0.02 -0.57 -0.03 -0.02 0.00 | 0.97 0.82 1.00 1.00 | 0.0017 (base 0.0001)
87:G2B0p1c | 0.05 0.12 0.46 0.01 | 1.00 1.00 | -0.00 -0.04 0.00 -0.01 -0.00 | 0.99 0.96 1.00 1.00 | 0.0001 (base 0.0001)
99:G2B0p1 | 23 0.13 0.17 0.93 | 6 0.67 0.00 0.40 327 | 0.76 (29) | 0.0172 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.995
100:G2B0p1N8 | 23 0.35 0.09 0.82 | 6 1.00 0.00 0.21 148 | 0.83 (29) | 0.0568 | outside thoughts (non, think, tool points): n 808, top=101 2, top unchanged 0.978
101:G2B0p1c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001 | outside thoughts (non, think, tool points): n 808, top=101 0, top unchanged 0.989
106:30 auc 0.884 {'E': '0.07', 'early': '0.11', 'mid': '0.34', 'late': '0.69', 'non': '0.03', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.05', 'd50-100': '0.19', 'd100-200': '0.31', 'd200-400': '0.37', 'd400-800': '0.66', 'd800-1600': '0.67', 'd1600-5000': '0.83'}
115:48 auc 0.744 {'E': '0.00', 'early': '0.14', 'mid': '0.25', 'late': '0.47', 'non': '0.01', 'think_pt': '0.00', 'tool_pt': '0.00'} {'d3-50': '0.12', 'd50-100': '0.15', 'd100-200': '0.22', 'd200-400': '0.27', 'd400-800': '0.47', 'd800-1600': '0.40', 'd1600-5000': '0.63'}
Thu Nov 12 09:41:36 PM EST 2026tool
Bash: python - <<'EOF'
t=open("NOTES.md").read()
t=t.replace("# Notes of author claude (rewritten end of turn 0, 12 Nov 2026 9:45 PM ET)","# Notes of author claude (rewritten end of turn 0, 12 Nov 2026 9:45 PM ET)")
t=t.replace('''Controls: sign-flipped write direction (gate) / norm-matched other neurons (N). Controls cut nothing in the pre-test.''','''4. early-gate-b01-end-neurons-x9: gate fitted on depth >= 400 (g2), dose 0.1, plus neurons x9: the strong dose.
Short 0.82, long 0.21. Predicted -7..9.
Controls: sign-flipped write direction (gate) / norm-matched other neurons (N). Controls cut nothing in the pre-test.''')
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''')
t=t.replace('''- Git Bash heredocs halve backslashes: do not patch files with "\\\\n" inside `python - <<EOF`.
- Never `pkill -f` a pattern that is in your own ssh command line.
''','''- Local heredocs halve backslashes; `pkill -f` matches your own ssh command line.
''')
t=t.replace('''thinklib.py (families A B C N M, positions), think_job.py (capture + grid + eval; `--mock` tests plumbing on CPU in
45 s), think_refit.py, think_job2.py (second stage in the same hold), think_report.py, think_greedy.py, non_split.py,
build_think.py, finalize.py (writes a queue entry), end_neurons.py.''','''thinklib.py (families A B C N M), think_job.py (capture, grid, eval; `--mock` tests it on CPU in 45 s),
think_refit.py, think_job2.py, think_report.py, think_greedy.py, non_split.py, build_think.py, finalize.py (writes a
queue entry), end_neurons.py.''')
t=t.replace('''- Untested ideas: gate "N tool calls and no edit yet" writing an edit-now direction; read_file loops in limit episodes.
''','''- Untested: a gate "many calls, no edit yet" that writes an edit-now direction.
''')
t=t.replace('''Served generation under an adapter (to see what follows a cut thought); likelihood of successful trajectories;
verified forward-vs-server agreement.''','''Served generation under an adapter (what follows a cut thought); verified forward-vs-server agreement.''')
open("NOTES.md","w",newline=chr(10)).write(t)
print(len(t.split()))
EOF
grep -n "heredoc\|early-gate" NOTES.md | cut -c1-120; scp -q NOTES.md wrx90:/home/keith/gemma-v2/wsp31/authors/claude/NOTES.md; ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; s=early-gate-b01-end-neurons-x9; $PY $R/wsp31/tools/check_adapter.py queue/$s/edit && $PY $R/wsp31/tools/check_adapter.py queue/$s/ctrl && grep -q "^predicted_gain_120: " queue/$s/record.md && head -1 queue/$s/record.md | grep -v -q "|" && touch queue/$s/READY && echo READY $s; rm -rf scratch/mock scratch/job1/dry scratch/x?.txt; du -sh scratch; wc -w NOTES.md; ls queue/*/READY; date; nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader; pgrep -u keith -f "think_job|gpu_hold" -a | grep -v pgrep | cut -c1-80'output
611 (15 lines)
611 18:4. early-gate-b01-end-neurons-x9: gate fitted on depth >= 400 (g2), dose 0.1, plus neurons x9: the strong dose. 30:- Local heredocs halve backslashes; `pkill -f` matches your own ssh command line. OK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj OK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj READY early-gate-b01-end-neurons-x9 1017M scratch 611 NOTES.md queue/deep-gate-b01-end-neurons-x9/READY queue/deep-thought-gate-g3-b03/READY queue/early-gate-b01-end-neurons-x9/READY queue/end-neurons-x25/READY Thu Nov 12 09:41:53 PM EST 2026 505422, 71764 MiB 184976, 90492 MiB
tool
Bash: python - <<'EOF'
t=open("NOTES.md").read()
t=t.replace("- g31 forward reproduces only 83% of logged thought ends on the unedited model (forward vs server still unverified).","- g31 forward reproduces only 83% of logged thought ends on the unedited model.")
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t=t.replace("- If it works: gate on depth >= 1500, dose series, and check the first tool call after a cut thought with fastprobe /\n generation (not done: no served probe this turn).","- If it works: gate on depth >= 1500, dose series, and check the first tool call after a cut thought (not done:\n no served probe this turn).")
t=t.replace(" thought-length hypothesis is dead; go to what limit episodes do instead of editing (9 of 10 never edit)."," thought-length hypothesis is dead; study what limit episodes do instead of editing (8 of 10 never edit).")
open("NOTES.md","w",newline=chr(10)).write(t)
print(len(t.split()))
EOF
scp -q NOTES.md wrx90:/home/keith/gemma-v2/wsp31/authors/claude/NOTES.md; ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; wc -w NOTES.md; ls queue/*/REJECTED 2>/dev/null; sed -n "/^## Edits/,\$p" /home/keith/gemma-v2/wsp31/queue/STATUS.md | cut -c1-200; date'output
598 598 NOTES.md ## Edits | ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict | |---|---|---|---|---|---|---|---|---| | 0007 | claude | deep-gate-b01-end-neurons-x9 | queued_s1 | -5 to +9 | | | | - | | 0008 | claude | deep-thought-gate-g3-b03 | queued_s1 | -4 to +9 | | | | - | | 0009 | claude | end-neurons-x25 | queued_s1 | -8 to +9 | | | | - | Waiting for stage 1: 3. Waiting for stage 2: 0. Thu Nov 12 09:42:02 PM EST 2026
tool
Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; sleep 45; ls queue/*/REJECTED 2>/dev/null; sed -n "/^## Edits/,\$p" /home/keith/gemma-v2/wsp31/queue/STATUS.md | cut -c1-140; date'
output
## Edits | ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict | |---|---|---|---|---|---|---|---|---| | 0007 | claude | deep-gate-b01-end-neurons-x9 | queued_s1 | -5 to +9 | | | | - | | 0008 | claude | deep-thought-gate-g3-b03 | queued_s1 | -4 to +9 | | | | - | | 0009 | claude | end-neurons-x25 | queued_s1 | -8 to +9 | | | | - | | 0010 | claude | early-gate-b01-end-neurons-x9 | queued_s1 | -7 to +9 | | | | - | Waiting for stage 1: 4. Waiting for stage 2: 0. Thu Nov 12 09:42:51 PM EST 2026
assistant
Four edits are queued and accepted by the runner (ledger 0007–0010, all `queued_s1`, none rejected); `NOTES.md` is current. [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] [line removed by the dashboard scrubber] 4. **`deep-gate-b01-end-neurons-x9`** (−5 to +9) and **`early-gate-b01-end-neurons-x9`** (−7 to +9): the gate at dose 0.1 plus the same neurons ×9, as the medium and strong doses (long thoughts keep 48% and 21%). Together with entry 2 they give three doses of one mechanism. [line removed by the dashboard scrubber] Caveats on the evidence: - The pre-test is teacher-forced on only 5 held-out episodes (23 short, 6 long thoughts). - The forward pass I used reproduces only 83% of the logged thought ends on the unedited model, and its agreement with the server is still unverified. - Nothing was generated under any edit, so the first tool call after a cut thought is untested. - The controls at the queued doses were not all pre-tested: entries 2 and 3 have no control row at their dose, and the two combination controls were only tested as separate parts. The control arms that were run left every thought uncut. Two families I tried had no usable effect and are not queued: a gated write of the end-of-thought unembedding direction (no effect at any dose), and the always-on Concept Country form (shortens only short thoughts at 0.1, wrecks the model at 0.3). I used one GPU hold of about 21 minutes. I ended its first stage early and chained a second evaluation into the same hold by appending to the running job script; it stayed inside the lock and the time limit.
assistant
Final message of the turn:
[claude-code:unrecognized_model] {"model":"claude-fable-5-2","query_source":"sdk"}
Four edits are queued and accepted by the runner (ledger 0007–0010, all `queued_s1`, none rejected); `NOTES.md` is current.
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[line removed by the dashboard scrubber]
4. **`deep-gate-b01-end-neurons-x9`** (−5 to +9) and **`early-gate-b01-end-neurons-x9`** (−7 to +9): the gate at dose 0.1 plus the same neurons ×9, as the medium and strong doses (long thoughts keep 48% and 21%). Together with entry 2 they give three doses of one mechanism.
[line removed by the dashboard scrubber]
Caveats on the evidence:
- The pre-test is teacher-forced on only 5 held-out episodes (23 short, 6 long thoughts).
- The forward pass I used reproduces only 83% of the logged thought ends on the unedited model, and its agreement with the server is still unverified.
- Nothing was generated under any edit, so the first tool call after a cut thought is untested.
- The controls at the queued doses were not all pre-tested: entries 2 and 3 have no control row at their dose, and the two combination controls were only tested as separate parts. The control arms that were run left every thought uncut.
Two families I tried had no usable effect and are not queued: a gated write of the end-of-thought unembedding direction (no effect at any dose), and the always-on Concept Country form (shortens only short thoughts at 0.1, wrecks the model at 0.3).
I used one GPU hold of about 21 minutes. I ended its first stage early and chained a second evaluation into the same hold by appending to the running job script; it stayed inside the lock and the time limit.End of trace · 135 steps · last chunk 29 min ago · seq 0