{"chunks":1,"id":"t000","last_seq":0,"rows":[{"i":0,"kind":"user","long":false,"text":"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."},{"i":1,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/BRIEF.md'"},{"i":2,"kind":"output","long":true,"text":"# wsp31 author brief (read fully at the start of every turn)\n\nYou are one of three authors (claude, gpt, gemini) taking turns. Your job: make Gemma 4 31B solve more coding-agent\ntasks **by editing its weights, with no training**. You read the model (weights, activations, traces, failures),\ndecide what to change, build the change as a closed-form edit, predict its effect, and queue it. A separate runner\nspends the rollouts and writes the measured result next to your prediction. You are scored on tasks gained over the\nunedited model in the same batch, on the margin over your matched control, and on how well your predictions held.\n\n**Be bold.** Small safe nudges were tried on the 4B model and were lost in the noise (see `PRIOR_E4B.md`). A queue\nslot is cheap; a third variation of a null result is a wasted slot. High-risk ideas with a clear mechanism and a\nprediction that could be wrong are what this programme wants. Say what would prove you wrong.\n\n## Where you work\n\n- Everything happens on host `wrx90`, reached with `ssh -o BatchMode=yes wrx90 ''`. Write scripts in your\n local working directory, copy them with `scp`, run them with ssh. Keep ssh output small (`tail`, `cut`, `head`).\n- Root: `/home/keith/gemma-v2` (below: `$R`). Python: `$R/tii-venv/bin/python` (torch, transformers, vLLM 0.19.1,\n peft, safetensors; no scipy). `. $R/env.sh` sets `R` and `PY`.\n- **You write only under `$R/wsp31/authors//`.** Everything else is read-only for you.\n - `queue//` one directory per proposed edit (format below)\n[... 101 lines cut ...]\n\n- Stage 1: your edit and the unedited model on the same server, 120 fixed screening tasks, one rollout each.\n- Stage 2 (only if stage 1 clears the bar in `STATUS.md`): the other 240 screening tasks, with your control.\n- Then confirmation on tasks you do not see, then one pre-registered held-out run. A changed edit is a new edit.\n- A combination of edits is a new queue entry with its own record, prediction and control.\n\nSingle-run agent evaluations are noisy by several tasks. An edit worth having shows up as ten or more tasks.\nAim there."},{"i":3,"kind":"tool","long":false,"text":"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'"},{"i":4,"kind":"output","long":true,"text":"Thu Nov 12 08:37:13 PM EST 2026\n-rw-rw-r-- 1 keith keith 63 Nov 12 20:36 wsp31/authors/claude/NOTES.md\n\nwsp31/authors/claude/:\ntotal 28\ndrwxrwxr-x 5 keith keith 4096 Nov 12 20:36 .\ndrwxrwxr-x 3 keith keith 4096 Nov 12 20:36 ..\n-rw-rw-r-- 1 keith keith 11 Nov 12 20:36 .turn_start\n-rw-rw-r-- 1 keith keith 63 Nov 12 20:36 NOTES.md\ndrwxrwxr-x 2 keith keith 4096 Nov 12 20:36 code\ndrwxrwxr-x 2 keith keith 4096 Nov 12 20:36 queue\ndrwxrwxr-x 2 keith keith 4096 Nov 12 20:36 scratch\n\nwsp31/authors/claude/code:\ntotal 8\ndrwxrwxr-x 2 keith keith 4096 Nov 12 20:36 .\ndrwxrwxr-x 5 keith keith 4096 Nov 12 20:36 ..\n\nwsp31/authors/claude/queue:\ntotal 8\n[... 20 lines cut ...]\n| b001 | s1 | 1 | running | base aa zero | 49 | |\n\n## Edits\n\n| ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict |\n|---|---|---|---|---|---|---|---|---|\n\nWaiting for stage 1: 0. Waiting for stage 2: 0."},{"i":5,"kind":"tool","long":false,"text":"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'"},{"i":6,"kind":"output","long":false,"text":"# Ledger index (generated by tools/ledger.py index; do not edit by hand)\n\nRead this before proposing anything. A row marked `null` or `harmful` is not to be retried unless the new\nrecord says what is different and why that difference should matter.\n\n| id | date | author | track | title | set / batch | status | verdict | result |\n|---|---|---|---|---|---|---|---|---|\n| [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 |\n| [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 |\n| [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 | - | - |\ntotal 28\ndrwxr-xr-x 2 keith keith 4096 Nov 12 19:50 .\ndrwxr-xr-x 6 keith keith 4096 Nov 12 19:42 ..\n-rw-r--r-- 1 keith keith 3816 Nov 12 19:50 0001-claude-unedited-baseline-noise-floor.md\n-rw-r--r-- 1 keith keith 2184 Nov 12 19:25 0002-claude-state-of-the-art-survey.md\n-rw-r--r-- 1 keith keith 4166 Nov 12 19:59 0003-claude-identical-arms-batch-noise-floor.md\n-rw-r--r-- 1 keith keith 1119 Nov 12 19:24 TEMPLATE.md\n 4311 tii-wsp31-doc/research/wsp31/PRIOR_E4B.md\n20376 wsp31/tools/TOOLS.md\n 9736 wsp31/tools/ARCH.md\n 2931 wsp31/interp/REPORT.md\n57903 tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md\n95257 total"},{"i":7,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; cat wsp31/tools/TOOLS.md; echo; echo ======ARCH; cat wsp31/tools/ARCH.md'"},{"i":8,"kind":"output","long":true,"text":"\nOutput 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\n\nPreview (first 2KB):\n# wsp31 tools for edit authors (interim version, Thu 12 Nov 2026, 8:36 PM ET)\n\nEverything here lives in `$R/wsp31/tools` (below `$T`; `$R = /home/keith/gemma-v2`). Each entry ends with\n**Verified by** (a measurement that was made) or **NOT VERIFIED YET** (written, first real run still queued; use it,\nbut do not build a claim on its numbers alone). The file is updated as runs finish; architecture facts are in\n`$T/ARCH.md` (read \"Consequences for weight edits\" there before designing an edit).\n\nStart every shell with:\n\n . /home/keith/gemma-v2/env.sh; T=$R/wsp31/tools\n export WSP31_WORK=$R/wsp31/authors//scratch # caches and server logs of the tools go here, not into $T\n\nDo not write into `$T`. Run the tools by absolute path from your own directory.\n\n## 1. Edit format (what `edit/` and `ctrl/` must be)\n\n[... 6 lines cut ...]\n `A` is `[r, in]`, `B` is `[out, r]`, dtype bf16, one `r` for the whole adapter, **`r` at most 32**,\n `lora_alpha = 2 r` (so the applied change is `dW = 2 * B @ A`; the builders below take care of the factor).\n- Reachable modules: `q_proj k_proj v_proj o_proj` (in `self_attn`) and `gate_proj up_proj down_proj` (in `mlp`) of\n language-model layers 0..59. Shapes per layer are in `ARCH.md`. An adapter may be sparse (only the modules it\n changes) and may touch any number of modules, each with rank up to 32. Entry size limit of the runner: 4 GB.\n- The ten global-attention layers (5, 11, 17, ..., 59) have no `v_proj` matrix (the server uses `k_proj` for both\n...\n"},{"i":9,"kind":"tool","long":false,"text":"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'"},{"i":10,"kind":"output","long":true,"text":"# What was already tried on Gemma 4 E4B (paper track, 4-12 Nov 2026)\n\nThese are training-free weight edits on the small model, written by Claude, GPT and Gemini, measured on 170\n[line removed by the dashboard scrubber]\nonly for 31B) and on 130 held-out tasks. Sources: the private paper repository (`gemma-weight-programming`), its\nexperiment log and result files. Counts are tasks solved of 170 unless stated; \"vs\" is the same-batch unedited arm.\n\nNone of these results transfers to 31B automatically. They are listed so that a 31B attempt of the same kind is made\non purpose, with a reason, and not by accident.\n\n## Method that was used\n\nA frontier LLM diagnosed failures from logged agent decisions, located a direction in the residual stream (difference\nof means between decision classes), compiled it into a closed-form MLP edit (always-on, or gated by a \"quiet neuron\"\nthat fires only under a condition such as \"no file edited yet\"), wrote a prediction, and tested against a\nnorm-matched random control.\n\n## What moved behaviour\n\n- Edits control their targeted decision with a dose-response in replay; matched random controls do not. Submit rate\n[... 136 lines cut ...]\n[line removed by the dashboard scrubber]\n\nThe first two predictions were not tested: no pass finished.\n\n[line removed by the dashboard scrubber]\nsweep on the other GPU (same model, same flags; `results/wsp31/throughput/bench/base_sweep_c{1,4,8,16}.json`)\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]"},{"i":11,"kind":"tool","long":false,"text":"Read: C:\\Users\\Owner\\.claude\\projects\\C--Users-Owner-wsp31-loop-work-claude\\40ddf36c-9d45-4e24-b4ca-9ee6d669f9bf\\tool-results\\b6f6fdby7.txt"},{"i":12,"kind":"output","long":true,"text":"1\t# wsp31 tools for edit authors (interim version, Thu 12 Nov 2026, 8:36 PM ET)\n2\t\n3\tEverything here lives in `$R/wsp31/tools` (below `$T`; `$R = /home/keith/gemma-v2`). Each entry ends with\n4\t**Verified by** (a measurement that was made) or **NOT VERIFIED YET** (written, first real run still queued; use it,\n5\tbut do not build a claim on its numbers alone). The file is updated as runs finish; architecture facts are in\n6\t`$T/ARCH.md` (read \"Consequences for weight edits\" there before designing an edit).\n7\t\n8\tStart every shell with:\n9\t\n10\t . /home/keith/gemma-v2/env.sh; T=$R/wsp31/tools\n11\t export WSP31_WORK=$R/wsp31/authors//scratch # caches and server logs of the tools go here, not into $T\n12\t\n13\tDo not write into `$T`. Run the tools by absolute path from your own directory.\n14\t\n15\t## 1. Edit format (what `edit/` and `ctrl/` must be)\n16\t\n17\tA PEFT LoRA adapter directory: `adapter_config.json` + `adapter_model.safetensors` (+ optional `edit.json` note).\n18\tThe runner serves it with vLLM (`$R/serve_31b.sh`, `--max-lora-rank 32`) on top of the unedited 4-bit checkpoint, so\n19\tthe model computes `y = W x + 2 * B (A x)` in bf16 for every module the adapter names; nothing is merged or\n20\tre-quantised.\n[... 368 lines cut ...]\n389\t\n[line removed by the dashboard scrubber]\n391\t|---|---|---|\n392\t| `turn` | newline after `<|turn>model` | think first (`<|channel>`) or call a tool at once |\n393\t| `step` | `` that closes a tool result | think, call another tool, or answer in text |\n394\t| `think` | `` that closes a thought | call a tool or write text |\n[line removed by the dashboard scrubber]\n396\t"},{"i":13,"kind":"tool","long":false,"text":"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 \"^#\" $F | head -60'"},{"i":14,"kind":"output","long":false,"text":"1:# Training-free editing of LLM weights: a survey to November 2026, aimed at raising a Gemma 4 31B coding agent's solve rate\n7:## A. Executive summary\n26:## B. Annotated bibliography by thread\n28:### Thread 1: LLM- or agent-authored weight edits, WSP, self-modification, hypernetworks\n52:### Thread 2: Baking activation steering into weights\n85:### Thread 3: Knowledge and model editing\n98:### Thread 4: Task arithmetic, merging, training-free transfer\n121:### Thread 5: Weight-space interpretability and interpretability agents\n148:### Thread 6: Training-free gains on agentic, coding and tool-use tasks\n162:### Thread 7: Tooling (see section D for recommendations)\n175:### Thread 8: Evaluation practice\n190:## C. Ranked edit ideas for raising a Gemma 4 31B coding agent's held-out solve rate (no training)\n224:## D. Tooling recommendations for the LLM-author harness\n241:## E. Gaps, open questions, and inaccessible sources"},{"i":15,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; ls results/wsp31/batches/ results/wsp31/batches/b001/ 2>/dev/null; for a in base aa zero; do f=results/wsp31/batches/b001/$a/rebench/shard0_gpu0/task_results.jsonl; [ -f $f ] && $R/tii-venv/bin/python - </dev/null || /home/keith/gemma-v2/tii-venv/bin/python -c \"\nimport json,sys\nrows=[json.loads(l) for l in open(\\\"$f\\\")]\nprint(\\\"$a\\\",len(rows),sum(bool(r.get(\\\"resolved\\\")) for r in rows))\n[line removed by the dashboard scrubber]\n\"\nEOF\ndone; nvidia-smi --query-gpu=index,memory.used,utilization.gpu --format=csv,noheader; ls wsp31/tools/ wsp31/tools/data | head -60'"},{"i":16,"kind":"output","long":true,"text":"results/wsp31/batches/:\nb001\n\nresults/wsp31/batches/b001/:\naa\naa.launch.log\nbase\nbase.launch.log\nserver.log\nzero\nzero.launch.log\nbase 16 9\n[line removed by the dashboard scrubber]\naa 18 11\n[line removed by the dashboard scrubber]\nzero 16 9\n[line removed by the dashboard scrubber]\n0, 72232 MiB, 100 %\n1, 90512 MiB, 100 %\nwsp31/tools/:\n[... 51 lines cut ...]\n\nwsp31/tools/data:\nfro_norms.json\nfro_norms_fp32sum.json\nprobe_31b_old.jsonl\nprobe_screen.jsonl\nrequant_cost.json\nsessA_log.json"},{"i":17,"kind":"tool","long":false,"text":"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'"},{"i":18,"kind":"output","long":true,"text":"## A. Executive summary\n\n[line removed by the dashboard scrubber]\n2. **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 \u22120.35, 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 \u22120.35 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.\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n5. **Nobody has yet shown an LLM-authored, baked-in weight edit that raises held-out agentic coding solve rate at \u22659B 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.\n6. **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\u20136.0 pp, with SD above 1.5 pp even at temperature 0. Your +2 to +5 of 170 (1.2\u20132.9 pp) is inside that band. Multi-run paired designs are mandatory.\n7. **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.\n8. **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.\n9. **Training-free capability transfer between checkpoints is real but needs a donor.** Fine-tuning transfer gave Llama 3.0 \u2192 3.1 +46.9% IFEval and +15.7% LiveCodeBench. Param\u0394 recovers about 95\u201398% of post-training. Reasoning vectors (GRPO \u2212 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.\n10. **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.\n11. **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\u201327B, 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,\n[... 2037 characters cut ...]"},{"i":19,"kind":"tool","long":false,"text":"Bash: mkdir -p code && cat > code/fail_stats.py <<'EOF'\nimport json,sys,glob,collections,statistics as st\nR=\"/home/keith/gemma-v2/results/wsp31\"\nruns=sys.argv[1:] or [R+\"/batches/b001/base\",R+\"/batches/b001/aa\",R+\"/batches/b001/zero\"]\nfor run in runs:\n rows=[json.loads(l) for l in open(run+\"/rebench/shard0_gpu0/task_results.jsonl\")]\n n=len(rows); s=sum(bool(r[\"resolved\"]) for r in rows)\n print(run.split(\"/\")[-1],\"n\",n,\"solved\",s)\n for lab,sel in ((\"solved\",[r for r in rows if r[\"resolved\"]]),(\"failed\",[r for r in rows if not r[\"resolved\"]])):\n if not sel: continue\n 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)),\n \"tools med\",st.median(r[\"tool_calls\"] for r in sel),\"llm med\",st.median(r[\"total_llm_calls\"] for r in sel),\n \"empty patch\",sum(1 for r in sel if not r[\"patch_chars\"]),\n \"tools>=30\",sum(1 for r in sel if r[\"tool_calls\"]>=30),\n \"p1>=440\",sum(1 for r in sel if (r.get(\"phase1_seconds\") or 0)>=440),\n \"err\",sum(1 for r in sel if r.get(\"error\") or r.get(\"agent_error\")))\n for r in rows:\n if not r[\"resolved\"]:\n g=r.get(\"grade\"); g=json.dumps(g)[:150] if g else \"\"\n 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)\nEOF\nscp -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'"},{"i":20,"kind":"output","long":true,"text":"base n 16 solved 9\n solved dur med 222 p1 med 220 tools med 20 llm med 22 empty patch 0 tools>=30 1 p1>=440 1 err 1\n failed dur med 454 p1 med 454 tools med 22 llm med 22 empty patch 4 tools>=30 0 p1>=440 5 err 7\n 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\n 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}\n 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\n 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}\n 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}\n 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}\n 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_\naa n 18 solved 11\n solved dur med 197 p1 med 196 tools med 17 llm med 20 empty patch 0 tools>=30 1 p1>=440 0 err 0\n failed dur med 454 p1 med 454 tools med 19 llm med 21 empty patch 4 tools>=30 1 p1>=440 4 err 7\n 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}\n 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\n 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\n 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}\n 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}\n 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_\n 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}\nzero n 16 solved 9\n solved dur med 250 p1 med 248 tools med 16 llm med 18 empty patch 0 tools>=30 0 p1>=440 0 err 0\n failed dur med 424 p1 med 422 tools med 21 llm med 25 empty patch 3 tools>=30 1 p1>=440 3 err 7\n F [task] dur 150 p1 147 \n[... 1699 characters cut ...]"},{"i":21,"kind":"tool","long":false,"text":"Bash: cat > code/req_stats.py <<'EOF'\nimport json,sys,glob,collections,statistics as st,os\nR=\"/home/keith/gemma-v2/results/wsp31\"\nruns=[R+\"/batches/b001/\"+a for a in (\"base\",\"aa\",\"zero\")]\nagg=collections.defaultdict(list)\nfirst=True\nfor run in runs:\n d=run+\"/rebench/shard0_gpu0\"\n for l in open(d+\"/task_results.jsonl\"):\n r=json.loads(l)\n p=r.get(\"request_log_path\")\n if not p or not os.path.exists(p):\n p=d+\"/requests/%s__r%d.jsonl\"%(r[\"instance_id\"],r.get(\"rollout_idx\",0))\n if not os.path.exists(p): continue\n calls=[json.loads(x) for x in open(p)]\n if first:\n first=False\n[line removed by the dashboard scrubber]\n tot=0;think=0;tools=[];plen=[];fin=collections.Counter();firstedit=None;maxthink=0;thinks=[]\n for i,c in enumerate(calls):\n[line removed by the dashboard scrubber]\n tot+=len(ids)\n t=ids.index(101) if 101 in ids else len(ids)\n think+=t; thinks.append(t); maxthink=max(maxthink,t)\n[line removed by the dashboard scrubber]\n fin[c.get(\"finish_reason\")]+=1\n tcs=(c.get(\"response_message\") or {}).get(\"tool_calls\") or []\n for tc in tcs:\n nm=tc[\"function\"][\"name\"]; tools.append(nm)\n if firstedit is None and nm.startswith((\"edit\",\"write\")): firstedit=i\n 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\"))\n 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)))\nfor cls,v in agg.items():\n print(cls,\"n\",len(v))\n for k in (\"calls\",\"tot\",\"think\",\"maxthink\",\"maxp\",\"dur\",\"len_fin\"):\n xs=[e[k] for e in v]; print(\" \",k,\"med\",st.median(xs),\"mean\",round(st.mean(xs)),\"max\",max(xs))\n fe=[e[\"firstedit\"] for e in v]; print(\" firstedit\",fe)\n tc=collections.Counter()\n for e in v: tc.update(e[\"tools\"])\n print(\" tools\",dict(tc))\n fc=collections.Counter()\n for e in v: fc.update(e[\"fin\"])\n print(\" finish\",dict(fc))\n if cls!=\"solved\":\n for e in v: print(\" \",e[\"id\"][:30],\"calls\",e[\"calls\"],\"tot\",e[\"tot\"],\"thinks\",e[\"thinks\"])\nEOF\nscp -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'"},{"i":22,"kind":"output","long":true,"text":"[line removed by the dashboard scrubber]\nsolved n 29\n calls med 20 mean 22 max 35\n tot med 5561 mean 6397 max 12032\n think med 3662 mean 4278 max 8834\n maxthink med 1059 mean 1224 max 3126\n maxp med 14563 mean 14664 max 17834\n dur med 238.0 mean 251 max 457.5\n len_fin med 0 mean 0 max 0\n 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]\n tools {'run_command': 380, 'read_file': 152, 'edit_file': 66, 'submit_patch': 29, 'get_status': 1}\n finish {'STOP': 630}\nwrong n 8\n calls med 22.0 mean 22 max 32\n tot med 4275.5 mean 5280 max 9594\n think med 2597.5 mean 3658 max 8440\n maxthink med 729.5 mean 874 max 1909\n maxp med 12199.5 mean 11998 max 15723\n dur med 146.645 mean 202 max 382.51\n len_fin med 0.0 mean 0 max 0\n[... 38 lines cut ...]\n dur med 464.91 mean 467 max 512.06\n len_fin med 0 mean 0 max 0\n firstedit [4, 4, 12]\n tools {'run_command': 32, 'read_file': 32, 'write_file': 3, 'edit_file': 3, 'submit_patch': 1}\n finish {'STOP': 71}\n 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]\n 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]\n 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]"},{"i":23,"kind":"tool","long":false,"text":"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'"},{"i":24,"kind":"output","long":true,"text":" 381 tools/g31.py\n 197 tools/edits.py\n 157 tools/causal.py\n 135 tools/probeset.py\n 52 tools/gpu0_session.sh\n 922 total\n26:class Arch:\n29: def __init__(self, model_dir=MODEL_DIR):\n46: def is_global(self, l):\n49: def geom(self, l):\n61: def shape(self, l, mod):\n69:def tensor_name(l, mod):\n74:def read_header(model_dir=MODEL_DIR):\n83:def dequant(packed, scale, group=32):\n99:def gelu_tanh(x):\n103:class Adapter:\n106: def __init__(self, path, device=\"cuda:0\"):\n122:class G31:\n130: def __init__(self, device=\"cuda:0\", model_dir=MODEL_DIR, mirror=True, cache_dense=False, layers=None):\n162: def base_weight(self, l, mod):\n[... 27 lines cut ...]\nkeith 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\nkeith 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\nroot 304402 0.0 0.0 18740 12648 ? Ss 20:38 0:00 sshd-session: keith [priv]\nkeith 304458 0.0 0.0 18900 7836 ? S 20:38 0:00 sshd-session: keith@notty\nroot 305686 0.0 0.0 18748 12652 ? Ss 20:38 0:00 sshd-session: keith [priv]\nkeith 305727 0.0 0.0 18908 7900 ? S 20:38 0:00 sshd-session: keith@notty\n302522, 71406 MiB\n184976, 90492 MiB"},{"i":25,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; cat > code/gpu_hold.sh <<\"EOF\"\n#!/bin/bash\n# runs under the GPU0 lock; executes code/gpu_job.sh as it exists at acquisition time\ncd /home/keith/gemma-v2/wsp31/authors/claude\ndate > scratch/hold_start\n[ -f code/gpu_job.sh ] || { echo \"no job\"; exit 3; }\nbash code/gpu_job.sh\ndate > scratch/hold_end\nEOF\nchmod +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 &\nsleep 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'"},{"i":26,"kind":"output","long":true,"text":"queued\nclass Adapter:\n \"\"\"A PEFT LoRA adapter in the arena format, as {(layer, mod): (A [r, in] bf16, B*alpha/r [out, r] bf16)}.\"\"\"\n\n def __init__(self, path, device=\"cuda:0\"):\n from safetensors.torch import load_file\n cfg = json.load(open(os.path.join(path, \"adapter_config.json\")))\n self.scale = cfg[\"lora_alpha\"] / cfg[\"r\"]\n sd = load_file(os.path.join(path, \"adapter_model.safetensors\"))\n self.mods = {}\n for k in sd:\n if not k.endswith(\"lora_A.weight\"):\n continue\n parts = k.split(\".\")\n l, mod = int(parts[parts.index(\"layers\") + 1]), parts[-3]\n a = sd[k].to(device=device, dtype=BF)\n b = (sd[k.replace(\"lora_A\", \"lora_B\")].float() * self.scale).to(device=device, dtype=BF)\n self.mods[(l, mod)] = (a, b)\n\n\n[... 93 lines cut ...]\n logits = torch.tanh(logits / cap) * cap\n return torch.log_softmax(logits, dim=-1)\n\n def unembed_f32(self, hp):\n out = []\n for s in range(0, self.emb.shape[0], 32768):\n out.append(F.linear(hp.float(), self.emb[s:s + 32768].float()))\n return torch.cat(out, dim=-1)"},{"i":27,"kind":"tool","long":false,"text":"Bash: cat > code/inspect_probe.py <<'EOF'\nimport json,sys\nsys.path.insert(0,\"/home/keith/gemma-v2/wsp31/tools\")\nrecs=[json.loads(l) for l in open(\"/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl\")]\n[line removed by the dashboard scrubber]\nimport collections\ntot=0\nfor r in recs[:6]:\n ids=r[\"ids\"]; calls=r[\"calls\"]\n print(r[\"instance\"][:30], r[\"resolved\"], \"len\",len(ids),\"calls\",len(calls),\"points\",len(r[\"points\"]))\n ok=0;k=0\n for c in calls[:40]:\n s=c[\"start\"]; comp=c[\"comp\"]\n m=sum(1 for a,b in zip(ids[s:s+len(comp)],comp) if a==b)\n e=comp.index(101) if 101 in comp else -1\n[line removed by the dashboard scrubber]\n k+=1\n if k>6: break\n# thought span stats in ids\nn100=n101=0\ndepths=[]\nfor r in recs:\n ids=r[\"ids\"]; d=-1\n for i,t in enumerate(ids):\n if t==100: d=0\n elif t==101:\n if d>=0: depths.append(d)\n d=-1\n elif d>=0: d+=1\nimport statistics as st\nprint(\"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))\nprint(\"episode lens\",sorted(len(r[\"ids\"]) for r in recs)[::6])\nEOF\nscp -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'"},{"i":28,"kind":"output","long":true,"text":"59 ['trace', 'run', 'instance', 'rollout', 'resolved', 'tool_calls', 'n', 'full_n', 'calls_total', 'ids', 'points', 'calls']\n[task] False len 10401 calls 9 points 27\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[task] False len 10400 calls 9 points 27\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[task] True len 8825 calls 17 points 51\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[... 82 lines cut ...]\n[line removed by the dashboard scrubber]\n f = self._file()\n w = g31.dequant(f.get_tensor(n + \".weight_packed\"), f.get_tensor(n + \".weight_scale\")).float()\n if cache:\n if len(self._w) >= 6:\n self._w.pop(next(iter(self._w)))\n[line removed by the dashboard scrubber]\n return w"},{"i":29,"kind":"tool","long":false,"text":"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'"},{"i":30,"kind":"output","long":true,"text":"Thu Nov 12 08:41:28 PM EST 2026\nlogs\n assert U.shape[1] == V.shape[0], (U.shape, V.shape)\n self.terms.setdefault((l, mod), []).append((U.clone(), V.clone()))\n\n def row_add(self, l, mod, j, delta):\n \"\"\"dW[l, mod][j, :] += delta.\"\"\"\n o, i = self._check(l, mod)\n assert 0 <= j < o\n d = torch.as_tensor(delta, dtype=torch.float32).reshape(i)\n r = self.rows.setdefault((l, mod), {})\n r[j] = r.get(j, 0) + d\n\n def col_add(self, l, mod, j, delta):\n \"\"\"dW[l, mod][:, j] += delta.\"\"\"\n o, i = self._check(l, mod)\n assert 0 <= j < i\n d = torch.as_tensor(delta, dtype=torch.float32).reshape(o)\n c = self.cols.setdefault((l, mod), {})\n c[j] = c.get(j, 0) + d\n[... 126 lines cut ...]\n d[\"k_norm\"] = g(p + \"self_attn.k_norm.weight\")\n for mod in MODS:\n if mod == \"v_proj\" and not self.arch.geom(l)[\"has_v\"]:\n continue\n n = tensor_name(l, mod)\n d[mod] = (g(n + \".weight_packed\"), g(n + \".weight_scale\"))\n self.L[l] = d\n self._rope = {}"},{"i":31,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\code\\thinklib.py"},{"i":32,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\code\\thinklib.py (file state is current in your context \u2014 no need to Read it back)"},{"i":33,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":34,"kind":"output","long":false,"text":"Thu Nov 12 08:44:03 PM EST 2026\nexport TII_MARLIN_SO=/home/keith/gemma-v2/tii-dev/v2/tii/engine/tii_marlin/build_k128n64L128t3/tii_marlin_k128n64L128t3.so\nR=/home/keith/gemma-v2; PY=$R/tii-venv/bin/python; S=$R/results/steer\nlogs\n88: uu = u.to(x.device, x.dtype)\n103: m = m or g31.G31(device=a.device)\n112: m.set_adapter(g31.Adapter(a.adapter, a.device))\n118: s = plib.summarise(lb.to(a.device), le.to(a.device), pts, wlib.tok(), wlib.name)\n140: ap.add_argument(\"--device\", default=\"cuda:0\")\n total used free shared buff/cache available\nMem: 183 33 94 0 58 150"},{"i":35,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\code\\think_job.py"},{"i":36,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\code\\think_job.py (file state is current in your context \u2014 no need to Read it back)"},{"i":37,"kind":"tool","long":false,"text":"Bash: cat > code/gpu_job.sh <<'EOF'\n#!/bin/bash\n# GPU-0 job of author claude (run only through code/gpu_hold.sh under the gpu0 lock)\n. /home/keith/gemma-v2/env.sh\ncd /home/keith/gemma-v2/wsp31/authors/claude\nexport WSP31_WORK=$PWD/scratch CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0\n$PY code/think_job.py > scratch/logs/job1.log 2>&1\nEOF\nscp -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'"},{"i":38,"kind":"output","long":false,"text":"Thu Nov 12 08:44:58 PM EST 2026\n30 1.0 1.0\n32 1.0 1.0\n[ 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']\n[ 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']\n[ 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']\n[ 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']\n[ 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']\n[ 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']\n[ 0.4] built C3p0 r 1 max rel_fro 0.0204 rho ['0.000', '0.000', '0.000']\n[ 0.4] built C8p0 r 1 max rel_fro 0.0204 rho ['0.000', '0.000', '0.000']\n[ 0.4] built C20p0 r 1 max rel_fro 0.0204 rho ['0.001', '0.001', '0.001']\n[ 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']\n[ 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']\n[ 0.4] built C8p0c r 1 max rel_fro 0.0204 rho ['0.000', '0.000', '0.000']\n['A0p03', 'A0p1', 'A0p3', 'B0p1', 'B0p3', 'B1p0', 'C3p0', 'C8p0', 'C20p0', 'A0p1c', 'B0p3c', 'C8p0c']\npositions 9000 194 5210 [1, 97, 96, 0]\n\nreal\t0m1.302s\nuser\t0m7.088s\nsys\t0m2.380s"},{"i":39,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nimport re\ns=open(\"thinklib.py\").read()\ns=s.replace(''' \"\"\"Thought positions of one probe-set episode. Returns n, P [(pos, cls, depth, next, call)], non [pos].\"\"\"\n ids = rec[\"ids\"]\n n = min(len(ids), nmax)\n P = []''',''' \"\"\"Thought positions of one probe-set episode. Returns n, P [(pos, cls, depth, next, call)], non [pos] and\n[line removed by the dashboard scrubber]\n history re-rendering adds a newline before ), so the context of every E position is the logged one.\"\"\"\n ids = list(rec[\"ids\"])\n n = min(len(ids), nmax)\n P = []''')\ns=s.replace(''' if s + e + 1 > n or ids[s:s + e] != comp[:e]:\n continue\n''',''' if e < 2 or s + e + 1 > n or ids[s:s + e - 1] != comp[:e - 1]:\n continue\n ids[s + e - 1] = comp[e - 1]\n''')\ns=s.replace(\" return n, P, non\\n\",\" return n, P, non, ids[:n]\\n\")\nopen(\"thinklib.py\",\"w\").write(s)\nt=open(\"think_job.py\").read()\nt=t.replace('n, P, non = tl.positions(recs[0], 9000)','n, P, non, ids2 = tl.positions(recs[0], 9000)')\nt=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)')\nt=t.replace('n, P, non = tl.positions(rec, NTRAIN_CAP)','n, P, non, ids2 = tl.positions(rec, NTRAIN_CAP)')\nt=t.replace('n, P, non = tl.positions(rec, NEVAL_CAP)','n, P, non, ids2 = tl.positions(rec, NEVAL_CAP)')\nassert t.count('rec[\"ids\"][:n]')==3\nt=t.replace('rec[\"ids\"][:n]','ids2')\nt=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]]')\nt=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)])\n train, ev = pick_episodes(recs)\n for nm, lst, cap in ((\"train\", train, NTRAIN_CAP), (\"eval\", ev, NEVAL_CAP)):\n tot = [0, 0, 0, 0]\n for r in lst:\n for p in tl.positions(r, cap)[1]:\n tot[p[1]] += 1\n 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))''')\nopen(\"think_job.py\",\"w\").write(t)\nEOF\ngrep -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'"},{"i":40,"kind":"output","long":false,"text":"80: n, P, non, ids2 = tl.positions(recs[0], 9000)\n127: n, P, non, ids2 = tl.positions(rec, NTRAIN_CAP)\n135: sel += [(p, 4, 0) for p in non[:80]]\n140: r = m.forward(ids2, out_pos=[n - 1], capture=(\"resid_post\", \"mlp_in\", \"mlp_act\", \"mlp_raw\"),\n218: n, P, non, ids2 = tl.positions(rec, NEVAL_CAP)\n235: rows += [(p, 4, 0, ids2[p + 1], -1) for p in non[:150]]\n247: r = m.forward(ids2, out_pos=[n - 1], capture=(\"mlp_in\",), cap_layers=tl.GL,\n255: r = m.forward(ids2, out_pos=[n - 1], stop_after=59)\nThu Nov 12 08:45:27 PM EST 2026\n['A0p03', 'A0p1', 'A0p3', 'B0p1', 'B0p3', 'B1p0', 'C3p0', 'C8p0', 'C20p0', 'A0p1c', 'B0p3c', 'C8p0c']\npositions 9000 956 5210 [7, 458, 408, 83]\ntrain 22 positions by class [151, 10341, 11538, 7564] subs 123\neval 8 positions by class [62, 3899, 3771, 3898] subs 48"},{"i":41,"kind":"tool","long":false,"text":"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'"},{"i":42,"kind":"output","long":false,"text":"26:NTRAIN_CAP, NEVAL_CAP = 11000, 8000\nThu Nov 12 08:45:39 PM EST 2026\ncode:\n__pycache__\nfail_stats.py\ngpu_hold.sh\ngpu_job.sh\ninspect_probe.py\nreq_stats.py\nthink_job.py\nthinklib.py\n\nscratch:\njob1\nlogs\nkeith 215046 0.0 0.0 8372 2168 ? S 20:01 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job2.sh\nkeith 270626 0.0 0.0 8372 2172 ? S 20:24 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessB.sh 1620\nkeith 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\n0, 86484 MiB\n1, 90512 MiB\n[This command modified 1 file you've previously read: code\\think_job.py. Call Read before editing.]"},{"i":43,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nt=open(\"think_job.py\").read()\nt=t.replace('''def tl_adapter(path):\n import g31\n return g31.Adapter(path, \"cuda:0\")\n''','''DEV = \"cuda:0\"\n\n\ndef tl_adapter(path):\n import g31\n return g31.Adapter(path, DEV)\n\n\nclass Mock:\n \"\"\"Shape-faithful stand-in for g31.G31 (CPU, random numbers) to test this script's plumbing without the GPU.\"\"\"\n\n def __init__(self):\n self.dev = torch.device(\"cpu\")\n self.emb = torch.randn(300, tl.D).to(torch.bfloat16)\n self.final_norm = torch.rand(tl.D).to(torch.bfloat16)\n self.L = {L: dict(post_feedforward_layernorm=torch.randn(tl.D).to(torch.bfloat16),\n layer_scalar=torch.tensor([0.7]).to(torch.bfloat16)) for L in range(60)}\n self.e = EditSet()\n\n def set_adapter(self, a):\n self.ad = a\n\n def base_weight(self, l, mod):\n return self.e.W(l, mod).to(torch.bfloat16)\n\n def rms(self, x, w=None):\n return x\n\n def logprobs_from_final(self, hp):\n return torch.log_softmax(torch.randn(hp.shape[0], 262144), -1)\n\n def forward(self, ids, out_pos=None, capture=None, cap_layers=None, cap_pos=None, stop_after=None):\n n = len(ids)\n dims = dict(resid_post=tl.D, mlp_in=tl.D, mlp_act=21504, mlp_raw=tl.D)\n caps = {}\n for c in (capture or ()):\n x = torch.randn(len(cap_layers), len(cap_pos), dims[c])\n if c == \"mlp_in\":\n x += (torch.tensor(cap_pos)[None, :, None] % 7 == 0).float() * 0.3 + 1.0\n caps[c] = x\n return dict(logprobs=None, caps=caps, resid=torch.randn(n, tl.D).to(torch.bfloat16))\n''')\nt=t.replace(''' m = g31.G31()\n log(\"model loaded\")''',''' global DEV, DEADLINE_CAPTURE\n if \"--mock\" in sys.argv:\n DEV = \"cpu\"\n m = Mock()\n train, ev = train[:5], ev[:2]\n GRID[:] = GRID[:1] + GRID[3:4] + GRID[6:7]\n CTRL[:] = CTRL[:1]\n else:\n m = g31.G31()\n log(\"model loaded\")''')\nt=t.replace('''S = A_DIR + \"/scratch/job1\"\nSTATS = A_DIR + \"/code/data/think_stats.pt\"''','''MOCK = \"--mock\" in sys.argv\nS = A_DIR + (\"/scratch/mock\" if MOCK else \"/scratch/job1\")\nSTATS = A_DIR + (\"/scratch/mock/think_stats.pt\" if MOCK else \"/code/data/think_stats.pt\")''')\nopen(\"think_job.py\",\"w\").write(t)\nEOF\ncd .. && 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'"},{"i":44,"kind":"output","long":true,"text":"Thu Nov 12 08:46:12 PM EST 2026\n[ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-']\n[ 0.1] model loaded\n[ 2.4] captured 0 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]\n[ 4.8] captured 1 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]\n[ 7.1] captured 2 Peter-van-Tol__pydantic-shapel n 11000 pos 346 cls [6, 80, 60, 120, 80]\n[ 9.5] captured 3 [task] n 11000 pos 351 cls [11, 80, 60, 120, 80]\n[ 11.8] captured 4 [task] n 11000 pos 347 cls [7, 80, 60, 120, 80]\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[... 16 lines cut ...]\n[ 20.0] grid built\n[ 36.4] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 5\n[ 44.6] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 5\n[ 44.7] done\n\nreal\t0m45.883s\nuser\t2m25.438s\nsys\t1m27.695s"},{"i":45,"kind":"tool","long":true,"text":"Bash: cat > code/build_think.py <<'EOF'\n\"\"\"build_think.py OUT_DIR FAM:DOSE[,FAM:DOSE...] [--ctrl] [--stats FILE]\nBuilds an \"end the thought\" adapter (families A, B, C of thinklib.py) from the saved activation statistics\ncode/data/think_stats.pt (written by think_job.py in one GPU-0 hold). --ctrl builds the matched control: same code\npath, the written unit direction has every coordinate's sign flipped at random (seed 1000 + layer).\"\"\"\nimport json\nimport os\nimport sys\n\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\")\nsys.path.insert(0, A_DIR + \"/code\")\nimport torch # noqa: E402\nimport thinklib as tl # noqa: E402\nfrom edits import EditSet # noqa: E402\n\nout, spec = sys.argv[1], sys.argv[2]\nctrl = \"--ctrl\" in sys.argv\nstats = sys.argv[sys.argv.index(\"--stats\") + 1] if \"--stats\" in sys.argv else A_DIR + \"/code/data/think_stats.pt\"\nst = torch.load(stats)\ne = EditSet()\nnote = dict(spec=spec, ctrl=ctrl, stats=stats, families={})\nfor part in spec.split(\",\"):\n fam, dose = part.split(\":\")\n info = tl.build(e, st, fam, float(dose), ctrl)\n note[\"families\"][part] = {str(k): {a: (round(b, 5) if isinstance(b, float) else b) for a, b in v.items()}\n for k, v in info.items()}\nrep = e.save(out, note=note)\nrel = {k: round(v.get(\"rel_fro\", 0), 5) for k, v in rep[\"modules\"].items()}\nprint(\"saved\", out, \"r\", rep[\"r\"], \"modules\", len(rel), \"max rel_fro\", max(rel.values()))\nEOF\ncat > code/think_report.py <<'EOF'\n\"\"\"think_report.py [eval.pt]: table of the teacher-forced evaluation written by think_job.py (CPU).\"\"\"\nimport sys\nimport torch\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, A_DIR + \"/code\")\nimport thinklib as tl\nres = torch.load(sys.argv[1] if len(sys.argv) > 1 else A_DIR + \"/scratch/job1/eval.pt\")\nst = None\neps = res[\"episodes\"]\nprint(\"episodes\", len(eps), [e[\"instance\"][:18] for e in eps], \"arms per episode\", [len(e[\"arms\"]) for e in eps])\narms = [a for a in res[\"arms\"] if all(a in e[\"arms\"] for e in eps)]\nif len(arms) < len(res[\"arms\"]):\n print(\"arms missing in the last episode (deadline):\", [a for a in res[\"arms\"] if a not in arms])\n arms2 = [a for a in res[\"arms\"] if all(a in e[\"arms\"] for e in eps[:-1])]\nelse:\n arms2 = arms\n\n\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n\ndef explen(epl, arm):\n[line removed by the dashboard scrubber]\n out = {\"short\": [0.0, 0.0, 0], \"long\": [0.0, 0.0, 0]}\n for e in epl:\n rows = e[\"rows\"]\n h = e[\"arms\"][arm][\"lp101\"].double().exp()\n for ci in rows[:, 4].unique().tolist():\n if ci < 0:\n continue\n mk = (rows[:, 4] == ci) & (rows[:, 1] <= 3)\n d = rows[mk, 2]\n o = d.argsort()\n hh = h[mk][o]\n n = len(hh)\n if n < 3:\n continue\n S = torch.cumprod(1 - hh, 0)\n S = torch.cat([torch.ones(1, dtype=S.dtype), S[:-1]])\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n return out\n\n\nfor label, al, epl in ((\"all episodes\", arms, eps), (\"all but last episode\", arms2, eps[:-1])):\n if label != \"all episodes\" and al == arms:\n continue\n print(\"==\", label, len(epl))\n rows = torch.cat([e[\"rows\"] for e in epl])\n cls = rows[:, 1]\n b101, bnx, btop = cat(\"lp101\", \"base\", epl), cat(\"lpnext\", \"base\", epl), cat(\"top\", \"base\", epl)\n be = explen(epl, \"base\")\n print(\"n by class\", {n: int((cls == k).sum()) for k, n in enumerate(tl.CLS + [\"think_pt\", \"tool_pt\"])})\n[line removed by the dashboard scrubber]\n be[\"short\"][0], be[\"short\"][1], be[\"short\"][2], be[\"long\"][0], be[\"long\"][1], be[\"long\"][2]))\n 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)))\n hdr = \"arm | dlp101 early mid lat\n[... 2231 characters cut ...]"},{"i":46,"kind":"output","long":true,"text":"Thu Nov 12 08:47:38 PM EST 2026\nn by class {'E': 8, 'early': 633, 'mid': 1072, 'late': 3305, 'non': 300, 'think_pt': 8, 'tool_pt': 8}\n[line removed by the dashboard scrubber]\nbase mean lp101: early -12.98 mid -12.94 late -12.97 E -12.71 non -13.01\narm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non\nA0p03 | 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)\nB0p1 | 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)\nC3p0 | 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)\nA0p1c | 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)\n== held-out gate pre-activation > 0 by class (and AUC late vs early+non), per gate layer\n30 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'}\n32 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'}\n34 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'}\n36 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'}\n38 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'}\n40 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'}\n42 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'}\n44 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'}\n46 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'}\n48 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'}\n54 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'\n[... 728 characters cut ...]"},{"i":47,"kind":"tool","long":false,"text":"Bash: cat > code/cf_budget.py <<'EOF'\n[line removed by the dashboard scrubber]\nimport json,os,collections,statistics as st,sys\nsys.path.insert(0,\"/home/keith/gemma-v2/wsp31/tools\")\nR=\"/home/keith/gemma-v2/results/wsp31\"\nagg=collections.defaultdict(list)\nex=[]\nfor arm in (\"base\",\"aa\",\"zero\"):\n d=R+\"/batches/b001/\"+arm+\"/rebench/shard0_gpu0\"\n for l in open(d+\"/task_results.jsonl\"):\n r=json.loads(l)\n p=r.get(\"request_log_path\") or \"\"\n if not os.path.exists(p): p=d+\"/requests/%s__r%d.jsonl\"%(r[\"instance_id\"],r.get(\"rollout_idx\",0))\n if not os.path.exists(p): continue\n calls=[json.loads(x) for x in open(p)]\n th=[]; tot=0\n for c in calls:\n[line removed by the dashboard scrubber]\n tot+=len(ids); t=ids.index(101) if 101 in ids else len(ids); th.append(t)\n if t>2500 and len(ex)<3: ex.append((r[\"instance_id\"],r[\"resolved\"],t,c[\"response_message\"]))\n p1=r.get(\"phase1_seconds\") or 0\n cls=\"solved\" if r[\"resolved\"] else (\"limit\" if p1>=420 else \"wrong\")\n agg[cls].append(dict(tot=tot,th=th,p1=p1))\nfor cls,v in agg.items():\n n=len(v); tot=sum(e[\"tot\"] for e in v); th=sum(sum(e[\"th\"]) for e in v)\n deep=sum(sum(max(0,t-400) for t in e[\"th\"]) for e in v)\n deep8=sum(sum(max(0,t-800) for t in e[\"th\"]) for e in v)\n 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)\n[line removed by the dashboard scrubber]\nfor iid,res,t,msg in ex[:2]:\n txt=json.dumps(msg)[:200]\n rc=(msg.get(\"reasoning_content\") or msg.get(\"reasoning\") or \"\")\n[line removed by the dashboard scrubber]\n print(rc[:700].replace(\"\\n\",\" | \"))\n print(\" ... \")\n print(rc[-900:].replace(\"\\n\",\" | \"))\nEOF\nscp -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'"},{"i":48,"kind":"output","long":false,"text":"Thu Nov 12 08:47:58 PM EST 2026\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nNow 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\n ... \n] = 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. | | \n[line removed by the dashboard scrubber]\nStill 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\n ... \n`. | 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. |"},{"i":49,"kind":"tool","long":false,"text":"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'"},{"i":50,"kind":"output","long":false,"text":"Thu Nov 12 08:48:10 PM EST 2026\njob1\nlogs\nmock\nroot 2724 0.0 0.0 49444 30316 ? Ss 17:57 0:00 /usr/bin/python3 /usr/bin/networkd-dispatcher --run-startup-triggers\nroot 2809 0.0 0.0 235864 43528 ? Ssl 17:57 0:09 /usr/bin/python3 /usr/local/sbin/wrx90-gpu-fan-controller.py\nroot 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\nroot 3738 3.3 0.0 309296 77752 tty1 SNsl+ 17:57 5:46 /usr/bin/python3 /opt/wrx90-dash/dash.py\nkeith 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\nkeith 184766 0.0 0.0 39564 27488 ? Ss 19:50 0:00 python3 wsp31/queue/runner.py loop\nkeith 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)\nkeith 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\nkeith 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\nkeith 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\n0, 91772 MiB\n1, 90512 MiB\nBRIEF.md\nclaude\nturn_close.sh\nturn_open.sh"},{"i":51,"kind":"tool","long":false,"text":"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'"},{"i":52,"kind":"output","long":false,"text":"#!/bin/bash\n# turn_close.sh : report what the turn left behind. Kills nothing.\nA=$1; N=$2; RC=$3; D=/home/keith/gemma-v2/wsp31/authors/$A\ncase $A in claude|gpt|gemini) ;; *) echo \"unknown author $A\"; exit 2 ;; esac\nT0=$(cat $D/.turn_start 2>/dev/null || echo 0)\nnew=0; names=\"\"\nfor r in $D/queue/*/READY; do\n [ -f \"$r\" ] || continue\n if [ \"$(stat -c %Y \"$r\")\" -ge \"$T0\" ]; then new=$((new+1)); names=\"$names $(basename $(dirname $r))\"; fi\ndone\nhalf=$(find $D/queue -mindepth 1 -maxdepth 1 -type d -newermt \"@$T0\" ! -exec test -e {}/READY \\; -print 2>/dev/null | wc -l)\nsz=$(du -sm $D 2>/dev/null | cut -f1)\necho \"$(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\n# GPU processes still alive on GPU 0 (for the supervisor to look at; nothing is killed here)\nnvidia-smi --query-compute-apps=gpu_uuid,pid,used_memory --format=csv,noheader 2>/dev/null | head -8\n1794533816\nkeith 215044 0.0 0.0 10136 3912 ? S 20:01 0:00 /bin/bash ./run_gpu.sh logs/job2.log ./job2.sh\nkeith 215046 0.0 0.0 8372 2168 ? S 20:01 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job2.sh\nkeith 270626 0.0 0.0 8372 2172 ? S 20:24 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessB.sh 1620\nkeith 302424 0.0 0.0 17652 7948 ? S 20:37 0:00 timeout 1740 ./job2.sh\nkeith 302425 0.0 0.0 10136 4008 ? S 20:37 0:00 /bin/bash ./job2.sh\nkeith 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\nkeith 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\nserver_18200_1112_195910.log\nsessA.log\nsessB.log\nsmoke.log\nverify_dequant_ref.log"},{"i":53,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nt=open(\"think_job.py\").read()\nt=t.replace('DEADLINE_CAPTURE, DEADLINE = 600, 1400','''DEADLINE_CAPTURE, DEADLINE = 600, 1400\nTURN_END = 1794533816 + 90 * 60 # this turn is stopped then; leave 13 minutes after the job for the queue entries\nif not (\"--mock\" in sys.argv or \"--dry\" in sys.argv):\n DEADLINE = min(DEADLINE, TURN_END - 13 * 60 - T0)\n DEADLINE_CAPTURE = min(DEADLINE_CAPTURE, 0.42 * DEADLINE)''')\nt=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)]\nCTRL = [(\"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)\nGRID = [(\"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)]\nCTRL = [(\"B\", 0.3), (\"C\", 8.0), (\"A\", 0.1)]''')\nt=t.replace(''' GRID[:] = GRID[:1] + GRID[3:4] + GRID[6:7]''',''' GRID[:] = GRID[:3]''')\nt=t.replace(''' log(\"train\", len(train), \"eval\", len(ev),''',''' log(\"deadline\", DEADLINE, \"capture deadline\", DEADLINE_CAPTURE)\n if DEADLINE < 420:\n log(\"not enough time left in the turn for a useful hold; releasing the lock\")\n return\n log(\"train\", len(train), \"eval\", len(ev),''')\nopen(\"think_job.py\",\"w\").write(t)\nEOF\ngrep -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 \"\nWait\" 2>&1 | tail -2'"},{"i":54,"kind":"output","long":false,"text":"29:DEADLINE_CAPTURE, DEADLINE = 600, 1400\n32: DEADLINE = min(DEADLINE, TURN_END - 13 * 60 - T0)\n33: DEADLINE_CAPTURE = min(DEADLINE_CAPTURE, 0.42 * DEADLINE)\n130: if time.time() - T0 > DEADLINE_CAPTURE:\n222: if time.time() - T0 > DEADLINE - 60:\n247: if time.time() - T0 > DEADLINE:\n330: log(\"deadline\", DEADLINE, \"capture deadline\", DEADLINE_CAPTURE)\n331: if DEADLINE < 420:\n335: global DEV, DEADLINE_CAPTURE\nThu Nov 12 08:48:46 PM EST 2026\n global DEV, DEADLINE_CAPTURE\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nSyntaxError: name 'DEADLINE_CAPTURE' is used prior to global declaration\n[20470]\n['Wait']\n[29202]\n[' Wait']\n[107, 20470]\n['\\\\n', 'Wait']"},{"i":55,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nt=open(\"think_job.py\").read()\nt=t.replace(\" global DEV, DEADLINE_CAPTURE\\n\",\"\")\nt=t.replace(\"def main():\\n import g31\\n\",\"def main():\\n global DEV\\n import g31\\n\")\nopen(\"think_job.py\",\"w\").write(t)\nimport ast; ast.parse(t)\nEOF\ncd .. && 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'"},{"i":56,"kind":"output","long":false,"text":"Thu Nov 12 08:49:04 PM EST 2026\n[ 19.0] grid built\n[ 35.8] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 5\n[ 45.4] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 5\n[ 45.4] done\ndry"},{"i":57,"kind":"tool","long":false,"text":"Bash: cat > code/wait_stats.py <<'EOF'\n[line removed by the dashboard scrubber]\nimport json,os,collections\nR=\"/home/keith/gemma-v2/results/wsp31\"\ncnt=collections.defaultdict(lambda:[0,0,0,0]) # cls -> [wait<400, tok<400, wait>=400, tok>=400]\nper=collections.defaultdict(list)\nfor arm in (\"base\",\"aa\",\"zero\"):\n d=R+\"/batches/b001/\"+arm+\"/rebench/shard0_gpu0\"\n for l in open(d+\"/task_results.jsonl\"):\n r=json.loads(l)\n p=r.get(\"request_log_path\") or \"\"\n if not os.path.exists(p): p=d+\"/requests/%s__r%d.jsonl\"%(r[\"instance_id\"],r.get(\"rollout_idx\",0))\n if not os.path.exists(p): continue\n p1=r.get(\"phase1_seconds\") or 0\n cls=\"solved\" if r[\"resolved\"] else (\"limit\" if p1>=420 else \"wrong\")\n w=0\n for x in open(p):\n[line removed by the dashboard scrubber]\n t=ids.index(101) if 101 in ids else len(ids)\n for o in range(t):\n k=0 if o<400 else 2\n cnt[cls][k+1]+=1\n if ids[o] in (20470,29202): cnt[cls][k]+=1; w+=1\n per[cls].append(w)\nfor cls,v in cnt.items():\n[line removed by the dashboard scrubber]\nEOF\nscp -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'"},{"i":58,"kind":"output","long":false,"text":"Thu Nov 12 08:50:01 PM EST 2026\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]"},{"i":59,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\code\\finalize.py"},{"i":60,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\code\\finalize.py (file state is current in your context \u2014 no need to Read it back)"},{"i":61,"kind":"tool","long":false,"text":"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'"},{"i":62,"kind":"output","long":false,"text":"Thu Nov 12 08:50:50 PM EST 2026\ndry\nkeith 270626 0.0 0.0 8372 2172 ? S 20:24 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessB.sh 1620\nkeith 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\n# wsp31 queue status (2026-11-12 20:50:33 EST)\n\nRule: not fixed yet (waits for the unedited baseline, ledger record 0001)\n\n## Batches\n\n| batch | stage | gpu | state | arms | rows | note |\n|---|---|---|---|---|---|---|\n| b001 | s1 | 1 | running | base aa zero | 65 | |\n\n## Edits"},{"i":63,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nt=open(\"build_think.py\").read()\nt=t.replace('else A_DIR + \"/code/data/think_stats.pt\"','else os.environ.get(\"THINK_STATS\", A_DIR + \"/code/data/think_stats.pt\")')\nopen(\"build_think.py\",\"w\").write(t)\nt=open(\"think_report.py\").read()\nt=t.replace('import sys\\nimport torch','import os\\nimport sys\\nimport torch')\nt=t.replace('else A_DIR + \"/scratch/job1/eval.pt\")','else os.environ.get(\"THINK_EVAL\", A_DIR + \"/scratch/job1/eval.pt\"))')\nopen(\"think_report.py\",\"w\").write(t)\nEOF\ncd .. && 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'"},{"i":64,"kind":"output","long":true,"text":"saved queue/zz-test/edit r 1 modules 39 max rel_fro 0.07436\nsaved queue/zz-test/ctrl r 1 modules 39 max rel_fro 0.0743\nOK edit: 39 modules changed (of 39 present), r=1, max |dW|_F/|W|_F 0.07436 at 34.down_proj\nOK ctrl: 39 modules changed (of 39 present), r=1, max |dW|_F/|W|_F 0.0743 at 34.down_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/zz-test/record.md\ntitle: test title\n\n## Hypothesis\n\nIn the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the\n7.5-minute session limit, 10 of them with an empty patch and 9 of those without a single file edit. At about\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\"Wait, ...\" restarts. Making long thoughts end sooner should return that wall-clock to tool calls, so that episodes\nwhich now time out without a patch get to edit, test and submit.\n\n\nBuilt as intended; approximations: bf16 storage of the factors (8 significant bits), the 5% `clipdiv` mask, and the\nbias-free threshold (the constant is read from the mean-input direction).\n[... 49 lines cut ...]\nqueue/zz-test/edit:\ntotal 2080\ndrwxrwxr-x 2 keith keith 4096 Nov 12 20:51 .\ndrwxrwxr-x 4 keith keith 4096 Nov 12 20:51 ..\n-rw-rw-r-- 1 keith keith 371 Nov 12 20:51 adapter_config.json\n-rw------- 1 keith keith 2107864 Nov 12 20:51 adapter_model.safetensors\n-rw-rw-r-- 1 keith keith 6078 Nov 12 20:51 edit.json\nThu Nov 12 08:51:07 PM EST 2026"},{"i":65,"kind":"tool","long":false,"text":"Bash: cat > NOTES.md <<'EOF'\n# Notes of author claude (turn 0, written 12 Nov 2026 about 8:55 PM ET; DRAFT, updated at end of turn)\n\n## What I learned (unedited b001, 61 episodes; code/fail_stats.py, req_stats.py, cf_budget.py)\n- 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.\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n## What I built (code/)\n- thinklib.py, think_job.py (one GPU hold: capture class means, fit gates, build dose grid, teacher-forced eval),\n think_report.py (table), build_think.py (final builder from code/data/think_stats.pt), finalize.py (queue entry).\n- gpu_hold.sh / gpu_job.sh: queue for the lock with `nohup flock $R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh &`.\n The lock queue was 45+ minutes at the start of this turn: get in line in the first minutes.\n- `think_job.py --mock` tests all plumbing on CPU in 45 s. Use it before any hold.\n\n## State at the time of writing\n- Waiting for the GPU lock; no entry queued yet. See the end of this file for the final state.\nEOF\nscp -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'"},{"i":66,"kind":"output","long":false,"text":"Thu Nov 12 08:51:24 PM EST 2026\njob1\nlogs\nmock\n=== replay base + kernel (20:49:20, 1620 s left)\n0, 50108 MiB\n1, 90512 MiB"},{"i":67,"kind":"tool","long":false,"text":"Bash: cat > code/end_neurons.py <<'EOF'\n\"\"\"end_neurons.py (CPU, weights only): MLP neurons of layers 50..59 whose write points along the direction that\nraises the logit, r = (E[101] - mean_vocab E) * final_norm_weight, after the layer's post-feedforward norm\nweight. Saves code/data/end_neurons.pt: per layer the top K neurons by cosine, their down columns, and K control\nneurons (random among the neurons with |cosine| below the layer median, each matched to a top neuron's column norm\nrank: the nearest unused neuron in norm). Family N of thinklib.py scales these columns.\"\"\"\nimport sys\nimport torch\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\")\nimport g31\nfrom edits import EditSet\nK = 16\nLAYERS = list(range(50, 60))\ne = EditSet()\n[line removed by the dashboard scrubber]\ntot = torch.zeros(emb.shape[1], dtype=torch.float64)\nfor s in range(0, emb.shape[0], 32768):\n tot += emb[s:s + 32768].double().sum(0)\nemean = (tot / emb.shape[0]).float()\ne101 = emb[101].float()\nw_final = e.norm(0, \"pre_feedforward_layernorm\") * 0 + e._file().get_tensor(g31.LM + \"norm.weight\").float()\nr = (e101 - emean) * w_final\nout = dict(K=K, layers=LAYERS, r=r, sel={})\ng = torch.Generator().manual_seed(4242)\nfor L in LAYERS:\n W = e.W(L, \"down_proj\", cache=False) # [5376, 21504]\n wp = e.norm(L, \"post_feedforward_layernorm\").float()\n rw = (r * wp).double()\n s = (rw[None, :] @ W.double())[0] # direct logit-direction content of each column\n cn = (W.double() * wp.double()[:, None]).norm(dim=0) # column norm after the post-norm weight\n cos = (s / (cn * r.double().norm() + 1e-30)).float()\n raw = W.double().norm(dim=0).float()\n top = cos.topk(K).indices\n pool = (cos.abs() < cos.abs().median()).nonzero()[:, 0]\n pool = pool[torch.randperm(len(pool), generator=g)][:4000]\n used, ctrl = set(top.tolist()), []\n for j in top.tolist():\n d = (raw[pool] - raw[j]).abs()\n for k in d.argsort().tolist():\n if int(pool[k]) not in used:\n used.add(int(pool[k]))\n ctrl.append(int(pool[k]))\n break\n ctrl = torch.tensor(ctrl)\n out[\"sel\"][L] = dict(top=top, ctrl=ctrl, cols=W[:, top].clone(), ctrl_cols=W[:, ctrl].clone(), cos=cos[top],\n ctrl_cos=cos[ctrl], norm=raw[top], ctrl_norm=raw[ctrl])\n z = (cos - cos.mean()) / cos.std()\n 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()),\n \"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)\n del W\nimport os\nos.makedirs(A_DIR + \"/code/data\", exist_ok=True)\ntorch.save(out, A_DIR + \"/code/data/end_neurons.pt\")\nprint(\"saved\")\nEOF\nscp -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'"},{"i":68,"kind":"output","long":false,"text":"Thu Nov 12 08:52:12 PM EST 2026\n50 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]\n51 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]\n52 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]\n53 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]\n54 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]\n55 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]\n56 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]\n57 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]\n58 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]\n59 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]\nsaved\n\nreal\t0m7.117s\nuser\t0m45.959s\nsys\t0m31.476s"},{"i":69,"kind":"tool","long":false,"text":"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'"},{"i":70,"kind":"output","long":false,"text":"layer 57 neuron 4238 (local layer, layer_scalar 0.793)\n |gate| 1.170 (pct 92) |up| 0.914 (pct 11) |down| 0.324 (pct 0) cos(gate, up) -0.225\n write lens, promotes: (+0.55) <(+0.31) )<(+0.23) !<(+0.23) :<(+0.21) ,<(+0.21) <(+0.20) ?<(+0.20) .<(+0.19)
    (+0.18) \".\"(+0.18) ()<(+0.18)\n write lens, suppresses: **:(-0.15) Mitt(-0.14) $[(-0.14) \\u200e(-0.14) dato(-0.14) \u5c31\u5f00\u59cb(-0.13) \u6cf3(-0.13) itatea(-0.13) \u603b\u662f(-0.13) \\u200b(-0.13) ker\u00fcl(-0.13) \\\\,(-0.13)\n gate read, embedding side (+): el(+615.65) \u0641(+571.43) \uc2a4(+569.63) \u0633(+532.49) K(+527.18) \u091a\u093e\u0902\u0917(+523.33) \u00e4(+518.61) \u554f\u984c(+491.09) AL(+477.11) ick(+472.34) oll(+466.94) \u091f\u093e(+455.98)\n gate read, embedding side (-): \u094d\\u200d\u092f\u093e\u0926\u093e(+683.45) \u0d2c\u0d30\u0d3f\u0d2e\u0d32(+622.32) \\U000f0855(+613.95) \u7136\u800c(+609.61) oniazid(+564.82) \u0e40\u0e18\u0e2d(+560.16) \u09c2\u09a4\u09a8(+539.93) \u0924\u0930\u0902\u091c(+522.27) \u3057\u304b\u3057(+521.74) \u092c\u0940\u092f\u0924(+518.07) elaborate(+512.79) \u0924\u092e\u0902\u0926(+507.96)\n up read, embedding side (+): \u00f6(+343.34) \u0d2c\u0d4d\u0d30\u0d3f\u0d1f\u0d4d\u0d1f(+313.66) \uac83(+312.97) s\u00e5(+306.99) \u0905\u0902\u0917(+299.44) \u96be\u4ee5(+298.91) \ub554(+297.19) \u09b0\u09cd\u0995\u09c7(+297.04) \ub3c8(+292.92) \ub18b(+291.81) larghezza(+289.34) skyrocketed(+289.06)\n up read, embedding side (-): \u9674(+442.86) \u63ab(+434.66) \u09ae\u09a7\u09cd\u09af\u09c7\u0987(+428.34) \u6c8d(+411.88) \u610a(+410.23) \u8156(+402.10) \u62c3(+398.39) \u98e5(+390.00) \u4f93(+389.33) \u8488(+385.98) \u6dc8(+377.66) \u0917\u094d\u0935\u093f\u091c\u092f(+376.74)\n 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)\n 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)\n 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)\nlayer 59 neuron 14974 (global layer, layer_scalar 0.032)\n |gate| 1.231 (pct 93) |up| 1.232 (pct 95) |down| 0.622 (pct 98) cos(gate, up) +0.145\n write lens, promotes: (+3.02) \u0e2b\u0e31\u0e27(+0.86) \u0e08\u0e34\u0e15(+0.85) >(+0.78) \u601d\u8003(+0.77) \u0e14(+0.75) \u610f\u8bc6(+0.75) \u7701\u7565(+0.73) Thinking(+0.73) END(+0.73) \u6d88\u5931(+0.72) punctuation(+0.71)\n write lens, suppresses: \u0643\u0644(-0.47) \u0644\u0623\u0646(-0.43) \u0637\u0641(-0.42) \u0637(-0.41) ifah(-0.40) UnitPrice(-0.40) periodicity(-0.39) \u0627\u0644\u0633(-0.39) wh(-0.39) ch\u00ednh(-0.38) \u1ec1u(-0.38) mez(-0.38)\n gate read, embedding side (+): \u0628(+50.39) ass(+44.32) ash(+44.00) las(+43.87) Does(+42.62) \u0627\u0644\u0623(+42.48) ame(+40.09) shower(+40.08) ast(+39.96) remodeled(+39.70) ting(+39.14) \u0627\u0644(+38.96)\n gate read, embedding side (-): \u0987\u09b9\u09be\u09a4\u09c7(+64.75) BlueprintName(+56.57) Balliye(+55.91) \u53ef\u80fd\u4f1a(+53.85) \u093f\u092b\u093f\u0936\u093f\u092f\u0932(+53.31) txtbtn(+53.27) \u0918\u091f\u0938\u094d\u092b\u094b\u091f(+53.22) \u098f\u0997\u09c1\u09b2\u09cb(+52.15) \u094d\u092f\u0941\u0928\u093f\u0915\u0947\u0936\u0928(+51.88) pcMove(+51.64) i\u0107(+51.27) o\u015bci(+51.19)\n up read, embedding side (+): \u093f\u0932\u094d\\u200d\u0932\u0940(+40.61) \u2019,(+35.36) \u0917\u094d\u0930\u0940\u0915\u0932\u094d\u091a\u0930(+35.01) ):[\"(+34.90) \ub77c\uc778(+34.88) olu(+34.28) \u0928\u093f\u0930\u094d\u092e\u093e\u0924\u093e\u0913\u0902(+33.91) ospor(+33.45) phenylsulfanyl(+33.14) \u09aa\u09cd\u09b0\u09a4\u09c0\u0995(+32.85) \u043d\u043e\u0435(+32.40) \u067e(+32.25)\n up read, embedding side (-): clockwise(+32.56) st(+32.04) \u540c\u65f6\u4e5f(+31.77) \u0433\u0456\u0434\u043d\u043e(+31.38) ert(+30.48) ick(+29.24) acakt\u0131r(+29.12) \u06c1\u0648\u06d4(+28.90) \u7bd2(+28.54) memang(+28.43) \u0995\u09a5\u09be\u099f\u09be(+28.38) \u0b95\u0bcd\u0b95\u0bca\u0ba3\u0bcd\u0b9f(+27.48)\n 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)\n 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)\n 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)\nThu Nov 12 08:52:43 PM EST 2026"},{"i":71,"kind":"tool","long":false,"text":"Bash: cat > code/end_neurons.py <<'EOF'\n[line removed by the dashboard scrubber]\nFor 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] *\ndown_proj[L][:, j]. z_j = (lens_j[101] - mean_vocab lens_j) / sd_vocab lens_j, computed exactly from the vocabulary\nmean and covariance of the embedding matrix (no 262144 x 21504 product). \"End\" neurons: z >= ZMIN, at most K per\nlayer (largest z). \"Anti\" neurons: z <= -ZMIN. Controls: for each selected neuron the unused neuron of the same layer\nwith |z| < 1 nearest in raw column norm (pool of 4000 drawn with seed 4242).\nSaves code/data/end_neurons.pt. Family N of thinklib.py scales the down columns of the end neurons.\"\"\"\nimport os\nimport sys\nimport torch\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\")\nimport g31\nfrom edits import EditSet\nK, ZMIN = 16, 6.0\nLAYERS = list(range(48, 60))\ne = EditSet()\n[line removed by the dashboard scrubber]\nV, D = emb.shape\ntot = torch.zeros(D, dtype=torch.float64)\nS = torch.zeros(D, D, dtype=torch.float64)\nfor s in range(0, V, 16384):\n c = emb[s:s + 16384].float()\n tot += c.double().sum(0)\n S += (c.T @ c).double()\nemean = tot / V\nC = (S / V - torch.outer(emean, emean)).float()\ne101 = emb[101].double()\nw_final = e._file().get_tensor(g31.LM + \"norm.weight\").double()\ndirv = (e101 - emean)\nout = dict(K=K, ZMIN=ZMIN, layers=LAYERS, sel={})\ng = torch.Generator().manual_seed(4242)\nfor L in LAYERS:\n W = e.W(L, \"down_proj\", cache=False)\n wp = e.norm(L, \"post_feedforward_layernorm\").double()\n X = (W.double() * (w_final * wp)[:, None]).float()\n num = (dirv.float()[None, :] @ X)[0]\n den = (X * (C @ X)).sum(0).clamp_min(1e-30).sqrt()\n z = num / den\n raw = W.double().norm(dim=0).float()\n order = z.argsort(descending=True)\n top = order[:K][z[order[:K]] >= ZMIN]\n anti = order.flip(0)[:K]\n anti = anti[z[anti] <= -ZMIN]\n pool = (z.abs() < 1).nonzero()[:, 0]\n pool = pool[torch.randperm(len(pool), generator=g)][:4000]\n used = set(top.tolist()) | set(anti.tolist())\n\n def match(sel):\n res = []\n for j in sel.tolist():\n d = (raw[pool] - raw[j]).abs()\n for k in d.argsort().tolist():\n if int(pool[k]) not in used:\n used.add(int(pool[k]))\n res.append(int(pool[k]))\n break\n return torch.tensor(res, dtype=torch.long)\n ctrl, actrl = match(top), match(anti)\n out[\"sel\"][L] = dict(top=top, anti=anti, ctrl=ctrl, actrl=actrl, cols=W[:, top].clone(), anti_cols=W[:, anti].clone(),\n ctrl_cols=W[:, ctrl].clone(), actrl_cols=W[:, actrl].clone(), z=z[top], anti_z=z[anti],\n norm=raw[top], ctrl_norm=raw[ctrl])\n print(L, \"n z>=6:\", int((z >= 6).sum()), \"n z<=-6:\", int((z <= -6).sum()), \"n |z|>=10:\", int((z.abs() >= 10).sum()),\n \"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]],\n \"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)\n del W, X\nos.makedirs(A_DIR + \"/code/data\", exist_ok=True)\ntorch.save(out, A_DIR + \"/code/data/end_neurons.pt\")\nprint(\"saved; end neurons\", sum(len(v[\"top\"]) for v in out[\"sel\"].values()), \"anti\", sum(len(v[\"anti\"]) for v in out[\"sel\"].values()))\nEOF\nscp -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'"},{"i":72,"kind":"output","long":false,"text":"Thu Nov 12 08:53:27 PM EST 2026\n48 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\n49 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\n50 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\n51 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\n52 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\n53 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\n54 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\n55 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\n56 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\n57 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\n58 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\n59 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\nsaved; end neurons 20 anti 19\n\nreal\t0m24.314s\nuser\t5m37.957s\nsys\t0m45.550s"},{"i":73,"kind":"tool","long":true,"text":"Bash: cd code && python - <<'EOF'\ns=open(\"thinklib.py\").read()\ns=s.replace(''' else:\n raise ValueError(fam)\n return info''',''' elif fam in (\"N\", \"M\"):\n # weights-only families: scale the down columns of the model's own neurons (code/end_neurons.py).\n[line removed by the dashboard scrubber]\n en = torch.load(A_DIR + \"/code/data/end_neurons.pt\")\n for L, s in en[\"sel\"].items():\n if fam == \"N\":\n js, cols, f = (s[\"ctrl\"], s[\"ctrl_cols\"], dose) if ctrl else (s[\"top\"], s[\"cols\"], dose)\n else:\n js, cols, f = (s[\"actrl\"], s[\"actrl_cols\"], -dose) if ctrl else (s[\"anti\"], s[\"anti_cols\"], -dose)\n for k, j in enumerate(js.tolist()):\n e.col_add(L, \"down_proj\", j, f * cols[:, k].float())\n if len(js):\n info[L] = dict(n=len(js), neurons=js.tolist(), rho=0.0)\n else:\n raise ValueError(fam)\n return info''')\ns=s.replace('''sys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\")\n\nLA = ''','''sys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\")\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\n\nLA = ''')\nopen(\"thinklib.py\",\"w\").write(s)\nt=open(\"think_job.py\").read()\nt=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)]\nCTRL = [(\"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),\n (\"B\", 0.1), (\"C\", 3.0)]\nCTRL = [(\"B\", 0.3), (\"C\", 8.0), (\"A\", 0.1), (\"N\", 3.0)]\nGRID_TAIL = [(\"A\", 0.03), (\"N\", 8.0)]''')\nt=t.replace(''' for fam, dose, ctrl in [(f, d, False) for f, d in GRID] + [(f, d, True) for f, d in CTRL]:\n e = EditSet()\n info = tl.build(e, st, fam, dose, ctrl)\n nm = name_of(fam, dose, ctrl)\n 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]\n + [(f, d, False) for f, d in GRID_TAIL]):\n nm = name_of(fam, dose, ctrl)\n try:\n e = EditSet()\n info = tl.build(e, st, fam, dose, ctrl)\n rep = e.save(f\"{out}/{nm}\", note=dict(family=fam, dose=dose, ctrl=ctrl))\n except Exception as ex: # one family failing must not cost the hold\n log(\"BUILD FAILED\", nm, repr(ex)[:300])\n continue''')\nt=t.replace(''' GRID[:] = GRID[:3]''',''' GRID[:] = GRID[:4]\n GRID_TAIL[:] = []''')\nt=t.replace(''' ads = {nm: tl_adapter(f\"{addir}/{nm}\") for nm in names}\n arms = [(\"base\", None)] + [(nm, ads[nm]) for nm in names]''',''' ads = {}\n for nm in names:\n try:\n ads[nm] = tl_adapter(f\"{addir}/{nm}\")\n except Exception as ex:\n log(\"ADAPTER LOAD FAILED\", nm, repr(ex)[:300])\n arms = [(\"base\", None)] + [(nm, ads[nm]) for nm in names if nm in ads]''')\nopen(\"think_job.py\",\"w\").write(t)\nf=open(\"finalize.py\").read()\nf=f.replace('''}\nrecipe = ''',''' \"N\": \"\"\"Family N (weights only, the model's own end-of-thought neurons): the down_proj columns of the MLP neurons of\n[line removed by the dashboard scrubber]\n(`code/end_neurons.py`, CPU, no activations): for neuron j, `x_j = final_norm_w * post_ff_norm_w * down[:, j]`,\n`z_j = ((E[101] - mean_vocab E) . x_j) / sqrt(x_j^T Cov_vocab(E) x_j)` (the z-score of `` in the neuron's\n[line removed by the dashboard scrubber]\nsearched: 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\n59 (14974 z 22.7, 6539 z 18.6, 3378 z 12.0, 18515 z 11.2, ...); none in layers 48..56. Lists and columns:\n`code/data/end_neurons.pt`. Control for this family: for each selected neuron, a neuron of the same layer with\n`|z| < 1` nearest in column norm (pool of 4000, seed 4242), scaled by the same factor.\"\"\",\n \"M\": \"\"\"Family M (weights only): t\n[... 735 characters cut ...]"},{"i":74,"kind":"output","long":false,"text":"Thu Nov 12 08:54:27 PM EST 2026\n[ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-']\n[ 0.1] model loaded\n[ 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\n[ 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']\n[ 17.0] built C8p0 r 1 max rel_fro 0.0165 rho ['0.004', '0.004', '0.004']\n[ 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']\n[ 17.0] built N3p0 r 16 max rel_fro 0.0750 rho ['0.000', '0.000', '0.000']\n[ 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']\n[ 17.0] grid built\n[ 34.4] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 6\n[ 43.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 6\n[ 43.5] done\n17\nThu Nov 12 08:55:14 PM EST 2026"},{"i":75,"kind":"tool","long":false,"text":"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'"},{"i":76,"kind":"output","long":false,"text":"Thu Nov 12 09:04:48 PM EST 2026\n24 points, layers 40..41 (2), resid_post (24, 2, 5376), mlp_act (24, 2, 21504) -> data/capture_test.pt (2 MB)\n=== single-module + consist (21:03:47, 753 s left)"},{"i":77,"kind":"tool","long":false,"text":"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'"},{"i":78,"kind":"output","long":false,"text":"Thu Nov 12 09:14:28 PM EST 2026"},{"i":79,"kind":"tool","long":false,"text":"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'"},{"i":80,"kind":"output","long":false,"text":"Thu Nov 12 09:15:53 PM EST 2026\nThu Nov 12 09:15:51 PM EST 2026\n[ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-']\n[ 2.3] model loaded\n[ 14.2] captured 0 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]\n[ 35.9] captured 1 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]\n[ 46.7] captured 2 Peter-van-Tol__pydantic-shapel n 11000 pos 346 cls [6, 80, 60, 120, 80]\nkeith 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\nkeith 394515 0.0 0.0 8372 104 ? S 21:06 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job3.sh"},{"i":81,"kind":"tool","long":false,"text":"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'"},{"i":82,"kind":"output","long":true,"text":"Thu Nov 12 09:20:04 PM EST 2026\n[ 0.1] deadline 1400 capture deadline 588.0\n[ 0.1] train 22 eval 8 ['PyCQA__flake8.cf1542', 'Mimino666__langdetec', '[task]', '[task]', 'HIPS__autograd.ac044', 'Nikoleta-v3__blackbo', 'EvoluxBR__greenswitc', 'Textualize__textual-']\n[ 2.3] model loaded\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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'}\n[ 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\n[ 248.1] d_end 30 norm 41.27 resid norm late 287.12\n[ 248.1] d_end 32 norm 39.66 resid norm late 321.94\n[... 18 lines cut ...]\n[ 248.3] built C3p0 r 1 max rel_fro 0.0120 rho ['0.001', '0.001', '0.001']\n[ 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']\n[ 248.3] built C8p0c r 1 max rel_fro 0.0120 rho ['0.004', '0.003', '0.001']\n[ 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']\n[ 248.3] built N3p0c r 16 max rel_fro 0.0750 rho ['0.000', '0.000', '0.000']\n[ 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']\n[ 248.3] built N8p0 r 16 max rel_fro 0.1999 rho ['0.000', '0.000', '0.000']\n[ 248.3] grid built"},{"i":83,"kind":"tool","long":false,"text":"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'"},{"i":84,"kind":"output","long":true,"text":"Thu Nov 12 09:24:16 PM EST 2026\n[ 374.2] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 18\n[ 499.9] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 18\nepisodes 2 ['PyCQA__flake8.cf15', 'Mimino666__langdet'] arms per episode [18, 18]\n== all episodes 2\nn by class {'E': 8, 'early': 633, 'mid': 1072, 'late': 3305, 'non': 300, 'think_pt': 8, 'tool_pt': 8}\n[line removed by the dashboard scrubber]\nbase mean lp101: early -25.27 mid -26.47 late -24.94 E -1.29 non -20.25\narm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non\nB0p3 | 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)\nC8p0 | 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)\nA0p1 | 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)\nN3p0 | -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)\nB1p0 | 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)\nC20p0 | 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)\nA0p3 | 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)\nN1p0 | -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)\nM0p75 | -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)\nB0p1 | 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)\nC3p0 | 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)\n[... 12 lines cut ...]\n40 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'}\n42 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'}\n44 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'}\n46 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'}\n48 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'}\n54 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'}\n56 auc 0.714 {'E': '0.00', 'early': '0.09', 'mid': '0.14', 'late': '0.34', 'non': \n[... 455 characters cut ...]"},{"i":85,"kind":"tool","long":false,"text":"Bash: cat > code/think_greedy.py <<'EOF'\n\"\"\"think_greedy.py [eval.pt]: greedy (temperature 0) reading of the teacher-forced evaluation: along each logged\n[line removed by the dashboard scrubber]\nimport os, sys, torch, statistics as st\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nres = torch.load(sys.argv[1] if len(sys.argv) > 1 else os.environ.get(\"THINK_EVAL\", A_DIR + \"/scratch/job1/eval.pt\"))\neps = res[\"episodes\"]\narms = [a for a in res[\"arms\"] if all(a in e[\"arms\"] for e in eps)]\nprint(\"episodes\", len(eps), \"arms complete\", len(arms), \"of\", len(res[\"arms\"]))\n[line removed by the dashboard scrubber]\nfor a in arms:\n S = dict(short=[0, 0, 0, 0, 0], long=[0, 0, 0, 0, 0]); cuts = []; etop = [0, 0]; inner = [0, 0]\n for e in eps:\n rows = e[\"rows\"]; top = e[\"arms\"][a][\"top\"]\n for ci in rows[:, 4].unique().tolist():\n if ci < 0: continue\n mk = (rows[:, 4] == ci) & (rows[:, 1] <= 3)\n d = rows[mk, 2]; o = d.argsort(); t = top[mk][o]; c = rows[mk, 1][o]; d = d[o]\n n = len(t)\n if n < 3 or int(c[-1]) != 0: continue # need the logged end (E) as last position\n[line removed by the dashboard scrubber]\n hit = (t == 101).nonzero()\n[line removed by the dashboard scrubber]\n etop[1] += 1; etop[0] += int(t[-1] == 101)\n inner[1] += n - 1; inner[0] += int((t[:-1] == 101).sum())\n if len(hit) == 0:\n[line removed by the dashboard scrubber]\n else:\n k = int(hit[0])\n[line removed by the dashboard scrubber]\n if k < n - 1:\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n 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))\n 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)))\nEOF\nscp -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'"},{"i":86,"kind":"output","long":false,"text":"Thu Nov 12 09:25:12 PM EST 2026\n[ 374.2] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 18\n[ 499.9] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 18\nepisodes 2 arms complete 18 of 18\n[line removed by the dashboard scrubber]\nbase | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nB0p3 | 5 0.00 0.20 1.00 | 3 1.00 0.00 0.22 369 | 0.62 (8) | 0.0443\nC8p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nA0p1 | 5 0.80 0.20 0.96 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0010\nN3p0 | 5 0.20 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0004\nB1p0 | 5 0.00 0.20 1.00 | 3 1.00 0.00 0.23 361 | 0.62 (8) | 0.0218\nC20p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nA0p3 | 5 1.00 0.00 0.05 | 3 1.00 0.00 0.00 7 | 1.00 (8) | 0.4353\nN1p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nM0p75 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nB0p1 | 5 0.00 0.20 1.00 | 3 1.00 0.00 0.27 369 | 0.62 (8) | 0.0283\nC3p0 | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nB0p3c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nC8p0c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nA0p1c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nN3p0c | 5 0.00 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.75 (8) | 0.0002\nA0p03 | 5 0.40 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0006\nN8p0 | 5 0.20 0.20 1.00 | 3 0.33 0.00 0.99 1284 | 0.88 (8) | 0.0004"},{"i":87,"kind":"tool","long":true,"text":"Bash: cat > code/think_refit.py <<'EOF'\n\"\"\"think_refit.py (CPU): refit the \"deep in a long thought\" gates from the raw MLP inputs saved by think_job.py with a\nsharper gate (k = 12, so that positions below the threshold sit where gelu_tanh is ~0 instead of at its negative lobe)\nand, for g3, a deeper positive class. Writes code/data/think_stats_g2.pt (positives depth >= 400, negatives depth < 100\nor outside thoughts) and think_stats_g3.pt (positives depth >= 800, negatives depth < 200 or outside thoughts), same\nneurons and same class means as think_stats.pt, and builds the stage-2 adapters into scratch/job2/ad.\"\"\"\nimport os, sys, torch\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0, A_DIR + \"/code\")\nimport thinklib as tl\nfrom edits import EditSet\nst = torch.load(A_DIR + \"/code/data/think_stats.pt\")\nraw = torch.load(A_DIR + \"/scratch/job1/raw_mlpin.pt\")\ncls, dep = raw[\"cls\"], raw[\"depth\"]\nthink = (cls >= 1) & (cls <= 3)\nlab = {\"g2\": torch.where(cls == 3, 3, torch.where((cls == 1) | (cls == 4), 1, 2)),\n \"g3\": torch.where(think & (dep >= 800), 3, torch.where((think & (dep < 200)) | (cls == 4), 1, 2))}\nS = {}\n[line removed by the dashboard scrubber]\n s2 = dict(st); s2[\"gate\"] = {}\n for gi, L in enumerate(tl.GL):\n if L not in tl.LA: continue\n X = raw[\"X\"][gi].float(); G = st[\"gate\"][L]\n gr, ur, fs = tl.fit_gate(X, lb, k=12.0)\n greal = G[\"gate_old\"] + (gr - G[\"gate_old\"]).to(torch.bfloat16).float()\n ureal = G[\"up_old\"] + (ur - G[\"up_old\"]).to(torch.bfloat16).float()\n g = X @ greal; a = tl.gelu_tanh(g) * (X @ ureal)\n gbar = max(float(a[lb == 3].mean()), 0.5)\n 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)\n fire = lambda m: \"%.2f\" % float((g[m] > 0).float().mean())\n[line removed by the dashboard scrubber]\n \"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)\n[line removed by the dashboard scrubber]\nS[\"g1\"] = st\nARMS = [(\"G2B0p1\", [(\"B\", 0.1, \"g2\")], False), (\"G3B0p1\", [(\"B\", 0.1, \"g3\")], False), (\"G3B0p3\", [(\"B\", 0.3, \"g3\")], False),\n (\"G2B0p1N8\", [(\"B\", 0.1, \"g2\"), (\"N\", 8.0, \"g1\")], False), (\"B0p1N8\", [(\"B\", 0.1, \"g1\"), (\"N\", 8.0, \"g1\")], False),\n (\"G3B0p1N8\", [(\"B\", 0.1, \"g3\"), (\"N\", 8.0, \"g1\")], False), (\"G2B0p03\", [(\"B\", 0.03, \"g2\")], False),\n (\"G2B0p1c\", [(\"B\", 0.1, \"g2\")], True), (\"G3B0p1c\", [(\"B\", 0.1, \"g3\")], True), (\"N24p0\", [(\"N\", 24.0, \"g1\")], False)]\nout = A_DIR + \"/scratch/job2/ad\"; os.makedirs(out, exist_ok=True)\nfor nm, parts, ctrl in ARMS:\n e = EditSet()\n[line removed by the dashboard scrubber]\n rep = e.save(f\"{out}/{nm}\", note=dict(parts=[list(p) for p in parts], ctrl=ctrl)); print(\"built\", nm, rep[\"r\"])\nopen(A_DIR + \"/scratch/job2/arms.txt\", \"w\").write(\"\\n\".join(a[0] for a in ARMS))\nEOF\ncat > code/think_job2.py <<'EOF'\n\"\"\"think_job2.py: second stage inside the same GPU-0 hold: teacher-forced evaluation of the stage-2 adapters\n(scratch/job2/ad, built on CPU by think_refit.py) on the first held-out episodes, same rows as stage 1.\"\"\"\nimport json, os, sys, time, torch\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, A_DIR + \"/code\")\nimport think_job as tj\nimport thinklib as tl\nimport g31\nhold_start = os.path.getmtime(A_DIR + \"/scratch/hold_start\")\ntj.S = A_DIR + \"/scratch/job2\"\ntj.DEADLINE = hold_start + 1560 - 45 - tj.T0\ntj.log(\"stage 2; seconds available\", tj.DEADLINE)\nnames = open(tj.S + \"/arms.txt\").read().split()\nst = torch.load(A_DIR + \"/code/data/think_stats_g2.pt\")\nst3 = torch.load(A_DIR + \"/code/data/think_stats_g3.pt\")\nfor L in tl.LC: # evaluate() computes gate pre-activations for every layer of tl.GL\n st[\"gate\"][L] = st3[\"gate\"][tl.LA[tl.LC.index(L)]] # slots 54/56/5\n[... 637 characters cut ...]"},{"i":88,"kind":"output","long":true,"text":"Thu Nov 12 09:26:39 PM EST 2026\n[ 374.2] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 18\n[ 499.9] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 18\n[ 623.6] evaluated 2 [task] rows 1949 arms 18\ng2 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\ng2 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\ng2 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\ng2 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\ng2 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\ng2 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\ng2 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\ng2 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\ng2 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\ng2 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\ng3 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\ng3 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\ng3 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\ng3 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\ng3 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\ng3 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\n[... 7 lines cut ...]\nbuilt G2B0p1N8 16\nbuilt B0p1N8 16\nbuilt G3B0p1N8 16\nbuilt G2B0p03 1\nbuilt G2B0p1c 1\nbuilt G3B0p1c 1\nbuilt N24p0 16\nThu Nov 12 09:26:41 PM EST 2026"},{"i":89,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nt=open(\"think_job2.py\").read()\nt=t.replace('''st3 = torch.load(A_DIR + \"/code/data/think_stats_g3.pt\")\nfor L in tl.LC: # evaluate() computes gate pre-activations for every layer of tl.GL\n st[\"gate\"][L] = st3[\"gate\"][tl.LA[tl.LC.index(L)]] # slots 54/56/58 carry the g3 gates of layers 30/32/34\n''','''st1 = torch.load(A_DIR + \"/code/data/think_stats.pt\")\nfor L in tl.LC: # evaluate() computes gate pre-activations for every layer of tl.GL\n st[\"gate\"][L] = st1[\"gate\"][L] # layers 54/56/58: stage-1 gates (not used by any stage-2 arm)\n''')\nopen(\"think_job2.py\",\"w\").write(t)\nEOF\ncd .. && 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; $PY -c \"import ast;ast.parse(open(\\\"code/think_job2.py\\\").read())\"; date; grep \"evaluated\" scratch/logs/job1.log | tail -1 | cut -c1-80; cat code/gpu_job.sh | tail -2; pgrep -f \"think_job.py\" -a | cut -c1-100'"},{"i":90,"kind":"output","long":false,"text":"appended-ran\nThu Nov 12 09:27:04 PM EST 2026\n[ 623.6] evaluated 2 [task] rows 1949 arms 18\nexport WSP31_WORK=$PWD/scratch CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0\n$PY code/think_job.py > scratch/logs/job1.log 2>&1\n423047 /home/keith/gemma-v2/tii-venv/bin/python code/think_job.py\n478005 bash -c cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; mkdir -p"},{"i":91,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; echo \"\\$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 $(seq 1 100); do grep -q \"evaluated 4\" scratch/logs/job1.log && break; sleep 4; done; date; cp scratch/job1/eval.pt scratch/job1/eval1.pt; pkill -f \"code/think_job.py\" ; sleep 12; date; tail -2 scratch/logs/job1.log | cut -c1-100; cat scratch/logs/job2.log | cut -c1-200; cat scratch/logs/hold1.log; ls scratch/hold_end 2>/dev/null'"},{"i":92,"kind":"output","long":false,"text":"Exit code 255\n$PY code/think_job.py > scratch/logs/job1.log 2>&1\n$PY code/think_job2.py 5 > scratch/logs/job2.log 2>&1\nThu Nov 12 09:30:29 PM EST 2026"},{"i":93,"kind":"tool","long":false,"text":"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'"},{"i":94,"kind":"output","long":false,"text":"Thu Nov 12 09:30:34 PM EST 2026\n[ 749.2] evaluated 3 [task] rows 1504 arms 18\n[ 874.6] evaluated 4 HIPS__autograd.ac044f0d.lm_rew rows 991 arms 18\n[ 0.0] stage 2; seconds available 635.9578437805176\nTerminated $PY code/think_job.py > scratch/logs/job1.log 2>&1\ntotal 918400\ndrwxrwxr-x 4 keith keith 4096 Nov 12 21:30 .\ndrwxrwxr-x 6 keith keith 4096 Nov 12 21:27 ..\ndrwxrwxr-x 19 keith keith 4096 Nov 12 21:20 ad\ndrwxrwxr-x 19 keith keith 4096 Nov 12 20:55 dry\n-rw-rw-r-- 1 keith keith 3780149 Nov 12 21:30 eval.pt\n-rw-rw-r-- 1 keith keith 3780149 Nov 12 21:30 eval1.pt\n-rw-rw-r-- 1 keith keith 2055869 Nov 12 21:25 eval_snap.pt\n-rw-rw-r-- 1 keith keith 930790769 Nov 12 21:19 raw_mlpin.pt\n487355 /home/keith/gemma-v2/tii-venv/bin/python code/think_job2.py 5\n487540 bash -c cd /home/keith/gemma-v2/wsp31/authors/claude; date; tail -2 scratch/logs/jo"},{"i":95,"kind":"tool","long":false,"text":"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'"},{"i":96,"kind":"output","long":true,"text":"episodes 5 ['PyCQA__flake8.cf15', 'Mimino666__langdet', '[task]', '[task]', 'HIPS__autograd.ac0'] arms per episode [18, 18, 18, 18, 18]\n== all episodes 5\nn by class {'E': 29, 'early': 2051, 'mid': 3020, 'late': 3870, 'non': 750, 'think_pt': 29, 'tool_pt': 29}\n[line removed by the dashboard scrubber]\nbase mean lp101: early -24.99 mid -25.83 late -24.72 E -0.79 non -20.54\narm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non\nB0p3 | 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)\nC8p0 | 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)\nA0p1 | 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)\nN3p0 | -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)\nB1p0 | 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)\nC20p0 | 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)\nA0p3 | 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)\nN1p0 | -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)\nM0p75 | -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)\nB0p1 | 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)\nC3p0 | 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)\nB0p3c | 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)\nC8p0c | 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)\nA0p1c | 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)\n[... 20 lines cut ...]\nB0p3c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001\nC8p0c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001\nA0p1c | 23 0.00 0.17 1.00 | 6 0.17 0.17 0.99 1284 | 0.79 (29) | 0.0001\nN3p0c | 23 0.00 0.13 1.00 | 6 0.17 0.17 0.99 1284 | 0.83 (29) | 0.0001\nA0p03 | 23 0.17 0.09 1.00 | 6 0.17 0.17 0.99 1284 | 0.90 (29) | 0.0006\nN8p0 | 23 0.22 0.09 0.95 | 6 0.33 0.00 0.90 701 | 0.93 (29) | 0.0008\nThu Nov 12 09:30:40 PM EST 2026\n[ 0.0] stage 2; seconds available 635.9578437805176"},{"i":97,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nf=open(\"finalize.py\").read()\nf=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 \"\"\ngv = sys.argv[sys.argv.index(\"--gate\") + 1] if \"--gate\" in sys.argv else \"g1\" # g1 (stage 1), g2 or g3 (think_refit.py)\nevf = A + (\"/scratch/job1/eval1.pt\" if gv == \"g1\" and \"--eval2\" not in sys.argv else \"/scratch/job2/eval.pt\")\nsopt = \"\" if gv == \"g1\" else f\" --stats code/data/think_stats_{gv}.pt\"''')\nf=f.replace('''$PY code/build_think.py queue/{slug}/edit {spec}\n$PY code/build_think.py queue/{slug}/ctrl {spec} --ctrl''','''$PY code/build_think.py queue/{slug}/edit {spec}{sopt}\n$PY code/build_think.py queue/{slug}/ctrl {spec} --ctrl{sopt}''')\nf=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()\ngre = subprocess.run([PY, A + \"/code/think_greedy.py\", evf], capture_output=True, text=True).stdout.splitlines()\ngrows = [l for l in gre if l.startswith((\"arm \", \"base \")) or any(l.startswith(a + \" \") for a in arms)]\nif gv != \"g1\":\n 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(``) at the true ends of thoughts); classes as above.\",\n \"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]\nelse:\n gate_note = \"\"''')\nf=f.replace('''if \"B\" in spec or \"C\" in spec:''','''if (\"B\" in spec or \"C\" in spec) and gv in (\"g1\", \"g2\"):''')\nf=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 \"\")''')\nf=f.replace('''```\n{nl.join(base)}\n{nl.join(hdr)}\n{nl.join(urows)}\n```''','''```\n{nl.join(base)}\n{nl.join(hdr)}\n{nl.join(urows)}\n```\n\nGreedy reading of the same passes (the harness decodes at temperature 0): along each logged thought, the first\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n(the unedited forward pass itself reproduces only this share of the logged ends, see `base`).\n\n```\n{nl.join(grows)}\n```''')\nopen(\"finalize.py\",\"w\").write(f)\nimport ast; ast.parse(f)\nEOF\ncd .. && 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'"},{"i":98,"kind":"output","long":false,"text":"Thu Nov 12 09:31:10 PM EST 2026"},{"i":99,"kind":"tool","long":false,"text":"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'"},{"i":100,"kind":"output","long":true,"text":"Thu Nov 12 09:34:26 PM EST 2026\n[ 80.5] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 11\n[ 157.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 11\n[ 233.1] evaluated 2 [task] rows 1949 arms 11\n== all episodes 3\nn by class {'E': 19, 'early': 1240, 'mid': 1894, 'late': 3642, 'non': 450, 'think_pt': 19, 'tool_pt': 19}\n[line removed by the dashboard scrubber]\nbase mean lp101: early -24.90 mid -25.66 late -24.81 E -0.82 non -22.33\narm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non\nG2B0p1 | 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)\nG3B0p1 | 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)\nG3B0p3 | 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)\nG2B0p1N8 | 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)\nB0p1N8 | 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)\nG3B0p1N8 | 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)\nG2B0p03 | 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)\nG2B0p1c | 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)\nG3B0p1c | 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)\nN24p0 | 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)\n== held-out gate pre-activation > 0 by class (and AUC late vs early+non), per gate layer\n[... 5 lines cut ...]\nG3B0p3 | 15 0.00 0.07 1.00 | 4 0.50 0.25 0.44 317 | 0.79 (19) | 0.0037\nG2B0p1N8 | 15 0.33 0.07 0.73 | 4 1.00 0.00 0.19 209 | 0.79 (19) | 0.0717\nB0p1N8 | 15 0.40 0.07 0.73 | 4 1.00 0.00 0.19 209 | 0.79 (19) | 0.0704\nG3B0p1N8 | 15 0.27 0.07 0.91 | 4 0.75 0.00 0.45 340 | 0.84 (19) | 0.0047\nG2B0p03 | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001\nG2B0p1c | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001\nG3B0p1c | 15 0.00 0.07 1.00 | 4 0.25 0.25 0.99 1284 | 0.84 (19) | 0.0001\nN24p0 | 15 0.47 0.00 0.78 | 4 0.75 0.00 0.32 63 | 0.95 (19) | 0.0031"},{"i":101,"kind":"tool","long":true,"text":"Bash: mkdir -p extras && cat > extras/common_tail.txt <<'EOF'\n[line removed by the dashboard scrubber]\ngain between -4 and +4 (identical unedited arms already differ by a few tasks).\n\n[line removed by the dashboard scrubber]\ntransfer to free-running episodes (the model re-opens the thought it was made to close); or (b) thinking falls by\n20% or more and the limit rate falls, but the edit does not gain tasks: long thoughts are then a symptom of hard\n[line removed by the dashboard scrubber]\nthis direction. A rise of episodes that stop at the 30-tool-call cap above 15% would mean the saved time went into\nmore calls and not into finishing.\n\nCaveats written before the run: the pre-test has only 5 held-out episodes (6 long thoughts); the lane's forward pass\nreproduces only 83% of the logged thought ends on the unedited model (agreement with the server NOT VERIFIED YET);\nnothing was generated under the edit, so the first tool call after a thought that was cut short is untested.\nUnedited reference (b001, 61 episodes when read): solved 54%, at the session limit 23%, empty patch 16%, thinking\n[line removed by the dashboard scrubber]\n27% in limit episodes.\nEOF\ncat > extras/e1.txt <<'EOF'\nBehaviour that should move: thoughts are ended at a sentence boundary once they are deep (the cut falls at depth\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n- episodes ending at the 7.5-minute limit: from 23% to 10-17%; empty patches from 16% to 8-13%;\n- tool calls per episode: up by 1 to 4; first file edit about as early as now in call number, earlier in seconds.\nEOF\ncat > extras/e2.txt <<'EOF'\nBehaviour that should move: every thought ends at the first place where the model itself already gives ``\nsome weight; short and long thoughts both get shorter (pre-test: 47% of short thoughts cut before their logged end with\n[line removed by the dashboard scrubber]\n(`E top=101` 0.95 against 0.84 unedited). This is the \"think briefly\" kind of edit that was neutral to harmful on E4B;\nit is queued because here the session limit binds, because it is built from weights alone (the model's own 20\nend-of-thought neurons, found by their write lens), and because its collateral in the pre-test is the smallest of all\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n- episodes ending at the 7.5-minute limit: from 23% to 6-15%; empty patches from 16% to 6-12%;\n- tool calls per episode: up by 2 to 5; episodes at the 30-call cap up from 7% to at most 15%;\n- risk I can name: p(``) outside thoughts rises from 0.0000 to 0.006 on average; a stray `` inside a\n tool call would show up as malformed calls (predicted: under 2% of calls).\nEOF\ncat > extras/e3.txt <<'EOF'\nBehaviour that should move: the deep-thought gate (family B, dose 0.1, gate g3) ends long thoughts, and the model's own\nend-of-thought neurons at 9 times their write (family N, dose 8) keep natural ends decisive, which the gate alone\nweakens slightly (`E top=101` 0.79 for the gate alone, 0.84 unedited, 0.84 for the combination). Pre-test of the\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n- episodes ending at the 7.5-minute limit: from 23% to 10-17%; empty patches from 16% to 8-13%;\n- tool calls per episode: up by 1 to 4.\nEOF\ncd code && python - <<'EOF'\nt=open(\"think_greedy.py\").read()\nt=t.replace(''' f = lambda k:''',''' nn = [0, 0, 0]\n for e in eps:\n rows = e[\"rows\"]; top = e[\"arms\"][a][\"top\"]; btop = e[\"arms\"][\"base\"][\"top\"]\n mk = rows[:, 1] >= 4\n nn[0] += int(mk.sum()); nn[1] += int((top[mk] == 101).sum()); nn[2] += int((top[mk] == btop[mk]).sum())\n 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))\n f = lambda k:''')\nt=t.replace('''inner[0] / max(inner[1], 1)))''','''inner[0] / max(inner[1], 1)) + extra)''')\nopen(\"think_gree\n[... 631 characters cut ...]"},{"i":102,"kind":"output","long":false,"text":"Thu Nov 12 09:35:22 PM EST 2026\n[ 80.5] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 11\n[ 157.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 11\n[ 233.1] evaluated 2 [task] rows 1949 arms 11\nepisodes 3 arms complete 11 of 11\n[line removed by the dashboard scrubber]\nbase | 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\nG2B0p1 | 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\nG3B0p1 | 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\nG3B0p3 | 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\nG2B0p1N8 | 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\nB0p1N8 | 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\nG3B0p1N8 | 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\nG2B0p03 | 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\nG2B0p1c | 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\nG3B0p1c | 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\nN24p0 | 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\nbase | 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\nA0p1 | 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\nN3p0 | 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\nN8p0 | 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"},{"i":103,"kind":"tool","long":false,"text":"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'"},{"i":104,"kind":"output","long":true,"text":"Thu Nov 12 09:36:58 PM EST 2026\nThu Nov 12 09:36:58 PM EST 2026\n[ 80.5] evaluated 0 PyCQA__flake8.cf1542ce.func_pm rows 3573 arms 11\n[ 157.5] evaluated 1 Mimino666__langdetect.a1598f1a rows 1761 arms 11\n[ 233.1] evaluated 2 [task] rows 1949 arms 11\n[ 310.1] evaluated 3 [task] rows 1504 arms 11\n[ 386.9] evaluated 4 HIPS__autograd.ac044f0d.lm_rew rows 991 arms 11\n[ 386.9] stage 2 done\nTerminated $PY code/think_job.py > scratch/logs/job1.log 2>&1\n184976, 90492 MiB\n503061 bash -c cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma\n== all episodes 5\nn by class {'E': 29, 'early': 2051, 'mid': 3020, 'late': 3870, 'non': 750, 'think_pt': 29, 'tool_pt': 29}\n[line removed by the dashboard scrubber]\nbase mean lp101: early -24.99 mid -25.83 late -24.72 E -0.79 non -20.54\narm | dlp101 early mid late E | explen ratio short long | dlpnext early late non think tool | top1 agree early late think tool | p101 non\nG2B0p1 | 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)\nG3B0p1 | 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)\nG3B0p3 | 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)\nG2B0p1N8 | 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)\n[... 12 lines cut ...]\nG3B0p3 | 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\nG2B0p1N8 | 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\nB0p1N8 | 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\nG3B0p1N8 | 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\nG2B0p03 | 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\nG2B0p1c | 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\nG3B0p1c | 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\nN24p0 | 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 0.918"},{"i":105,"kind":"tool","long":false,"text":"Bash: cat > code/non_split.py <<'EOF'\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nimport json, sys, torch\nA_DIR = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nrecs = {}\nfor l in open(\"/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl\"):\n r = json.loads(l); recs.setdefault(r[\"instance\"], []).append(r)\nfor f in sys.argv[1:]:\n res = torch.load(f); eps = res[\"episodes\"]\n arms = [a for a in res[\"arms\"] if all(a in e[\"arms\"] for e in eps)]\n for a in arms:\n if a == \"base\": continue\n c = {\"gen\": [0, 0, 0], \"read\": [0, 0, 0]}\n for e in eps:\n cand = [r for r in recs[e[\"instance\"]] if len(r[\"ids\"]) >= e[\"n\"]] or recs[e[\"instance\"]]\n[line removed by the dashboard scrubber]\n spans = [(k[\"start\"], k[\"start\"] + len(k[\"comp\"])) for k in rec[\"calls\"]]\n rows = e[\"rows\"]; top = e[\"arms\"][a][\"top\"]; bt = e[\"arms\"][\"base\"][\"top\"]\n for i in (rows[:, 1] == 4).nonzero()[:, 0].tolist():\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n print(\"%-9s generated: n %d top=101 %d changed %d | read-only: n %d top=101 %d changed %d\" % (a, *c[\"gen\"], *c[\"read\"]))\nEOF\nscp -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'"},{"i":106,"kind":"output","long":false,"text":"saved queue/deep-thought-gate-g3-b03/edit r 1 modules 30 max rel_fro 0.10156\nsaved queue/deep-thought-gate-g3-b03/ctrl r 1 modules 30 max rel_fro 0.10156\nOK edit: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj\nOK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/record.md\nsaved queue/deep-gate-b01-end-neurons-x9/edit r 16 modules 33 max rel_fro 0.1999\nsaved queue/deep-gate-b01-end-neurons-x9/ctrl r 16 modules 33 max rel_fro 0.19988\nOK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nOK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/record.md\nThu Nov 12 09:37:32 PM EST 2026\nG3B0p1 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6\nG3B0p3 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 8\nG3B0p1N8 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 1 changed 19\nG3B0p1c generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 7\nN24p0 generated: n 152 top=101 0 changed 1 | read-only: n 598 top=101 8 changed 65\nA0p1 generated: n 152 top=101 0 changed 2 | read-only: n 598 top=101 0 changed 82\nN3p0 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6\nN1p0 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6\nB0p1 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 8\nN3p0c generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 0 changed 6\nN8p0 generated: n 152 top=101 0 changed 0 | read-only: n 598 top=101 2 changed 16"},{"i":107,"kind":"tool","long":false,"text":"Bash: cd extras && python - <<'EOF'\nt=open(\"e2.txt\").read()\ni=t.index(\"- risk I can name\")\nt=t[:i]+'''- risk I can name: p(``) outside thoughts rises from 0.0001 to 0.011 on average. Split by\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n and is `` at 8. A stray `` inside a tool call would show up as malformed calls (predicted: under\n 2% of calls).\n[line removed by the dashboard scrubber]\n `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 /\n 0.93; norm-matched control neurons x4: 1.00 / 0.99 / 0.83 (= unedited). The control of this entry (x25 on the\n control neurons) was not pre-tested.\n'''\nopen(\"e2.txt\",\"w\").write(t)\nEOF\n[line removed by the dashboard scrubber]"},{"i":108,"kind":"output","long":true,"text":"saved queue/end-neurons-x25/edit r 16 modules 3 max rel_fro 0.59969\nsaved queue/end-neurons-x25/ctrl r 16 modules 3 max rel_fro 0.59963\nOK edit: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5997 at 59.down_proj\nOK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/record.md\nThu Nov 12 09:37:52 PM EST 2026\n\nStatistics (`code/data/think_stats.pt`, written by `code/think_job.py` in one GPU-0 hold with `tools/g31.py`, the\ntools lane's forward pass, whose agreement with the served model is NOT VERIFIED YET in TOOLS.md): one rollout per\ninstance of `tools/data/probe_screen.jsonl` (unedited run s1_base), every fourth instance held out for the pre-test,\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\nFamily B (gated, state side), layers 30, 32, ..., 48, one neuron per layer replaced outright (`set`): the\nneuron `j_L` with the smallest mean |activation| over all sampled positions. Gate row = `(3 / sigma) (w - (theta /\ncbar) m)`: `w` = unit diagonal-LDA probe on `mlp_in[L]` (late thought positions, depth >= 400, against early thought\npositions, depth < 100, and positions outside thoughts), `theta` = 95% quantile of the probe on the negatives,\n`sigma` = sd on the positives, `m` = unit mean `mlp_in` (the model has no bias; `m . x` is `cbar` at every position).\nUp row = `m / cbar` (reads 1). Down column = `clipdiv(d_L / |d_L|, w_post_L) * dose * |d_L| * rms_y_L / (gbar_L *\n[... 75 lines cut ...]\nmore calls and not into finishing.\n\nCaveats written before the run: the pre-test has only 5 held-out episodes (6 long thoughts); the lane's forward pass\nreproduces only 83% of the logged thought ends on the unedited model (agreement with the server NOT VERIFIED YET);\nnothing was generated under the edit, so the first tool call after a thought that was cut short is untested.\nUnedited reference (b001, 61 episodes when read): solved 54%, at the session limit 23%, empty patch 16%, thinking\n[line removed by the dashboard scrubber]\n27% in limit episodes."},{"i":109,"kind":"tool","long":false,"text":"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 - <=420; emp+=not r[\"patch_chars\"]; cap+=r[\"tool_calls\"]>=30\n for x in open(p):\n[line removed by the dashboard scrubber]\n t=ids.index(101) if 101 in ids else len(ids); n+=1; k8+=t>=800; k4+=t>=400\nprint(\"episodes\",ep,\"solved\",sol,\"limit\",lim,\"empty\",emp,\"cap30\",cap,\"calls\",n,\"thought>=800: %.3f\"%(k8/n),\">=400: %.3f\"%(k4/n))\nEOF'"},{"i":110,"kind":"output","long":false,"text":"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\n41: gate_note = \"\"\n94:recipe = \"\\n\\n\".join(fam[p.split(\":\")[0]] for p in spec.split(\",\"))\nepisodes 110 solved 48 limit 39 empty 27 cap30 19 calls 2496 thought>=800: 0.086 >=400: 0.176"},{"i":111,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nf=open(\"finalize.py\").read()\nold='recipe = \"\\\\n\\\\n\".join(fam[p.split(\":\")[0]] for p in spec.split(\",\"))'\nassert old in f, \"recipe line\"\nf=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 \"\")')\nold2='''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the\n7.5-minute session limit, 10 of them with an empty patch and 9 of those without a single file edit.'''\nassert old2 in f\nf=f.replace(old2,'''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the\n7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110\nepisodes, 9:40 PM ET: solved 44%, at the limit 35%, empty patch 25%).''')\nf=f.replace(\"here it ends 23% of unedited episodes\",\"here it ends 23% to 35% of unedited episodes\")\nopen(\"finalize.py\",\"w\").write(f)\nimport ast; ast.parse(f)\nc=open(\"../extras/common_tail.txt\").read()\ni=c.index(\"Unedited reference\")\n[line removed by the dashboard scrubber]\n(solved 4,637, limit 9,149). At 110 episodes (9:40 PM ET): solved 44%, at the session limit 35%, empty patch 25%, at\n[line removed by the dashboard scrubber]\nabove are relative to whatever the unedited arm of the same batch shows.\n'''\nc=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\")\nopen(\"../extras/common_tail.txt\",\"w\").write(c)\nfor n in (\"e1\",\"e2\",\"e3\"):\n t=open(f\"../extras/{n}.txt\").read()\n t=t.replace(\"0 of 15 cut\",\"0 of 23 cut\").replace(\"from about 8% to under 3%\",\"from 8.6% to under 4%\")\n 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%)\")\n 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%)\")\n 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%)\")\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n open(f\"../extras/{n}.txt\",\"w\").write(t)\n print(n, \"23%\" in t, \"16%\" in t)\nEOF\n[line removed by the dashboard scrubber]"},{"i":112,"kind":"output","long":true,"text":"Traceback (most recent call last):\n File \"\", line 3, in \nAssertionError: recipe line\nOK edit: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj\nOK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/record.md\nOK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nOK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/record.md\nOK edit: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5997 at 59.down_proj\nOK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/record.md\nThu Nov 12 09:39:00 PM EST 2026\n==> queue/deep-gate-b01-end-neurons-x9/record.md <==\ntitle: Deep-thought gate (dose 0.1) plus the model s own end-of-thought neurons at 9 times their write\n\n==> queue/deep-thought-gate-g3-b03/record.md <==\ntitle: 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)\n\n==> queue/end-neurons-x25/record.md <==\n[... 27 lines cut ...]\n control neurons) was not pre-tested.\n[line removed by the dashboard scrubber]\ngain between -4 and +4 (identical unedited arms already differ by a few tasks).\n\n[line removed by the dashboard scrubber]\ntransfer to free-running episodes (the model re-opens the thought it was made to close); or (b) thinking falls by\n20% or more and the limit rate falls, but the edit does not gain tasks: long thoughts are then a symptom of hard\n[line removed by the dashboard scrubber]"},{"i":113,"kind":"tool","long":false,"text":"Bash: cd code && python - <<'EOF'\nf=open(\"finalize.py\").read()\nfor l in f.splitlines():\n if l.startswith(\"recipe\"): print(repr(l))\nold='recipe = \"\\\\n\\\\n\".join(fam[p.split(\":\")[0]] for p in spec.split(\",\"))'\nprint(repr(old), old in f)\nEOF"},{"i":114,"kind":"output","long":false,"text":"'recipe = \"\\\\n\\\\n\".join(fam[p.split(\":\")[0]] for p in spec.split(\",\"))'\n'recipe = \"\\n\\n\".join(fam[p.split(\":\")[0]] for p in spec.split(\",\"))' False"},{"i":115,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nBS=chr(92)\nf=open(\"finalize.py\").read()\nlines=f.split(chr(10))\nfor i,l in enumerate(lines):\n if l.startswith(\"recipe = \"):\n nn='\"'+BS+'n'+BS+'n\"'\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 \"\")'\n print(lines[i][:200])\nf=chr(10).join(lines)\nold2='''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the\n7.5-minute session limit, 10 of them with an empty patch and 9 of those without a single file edit.'''\nassert old2 in f\nf=f.replace(old2,'''In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the\n7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110\nepisodes, 9:40 PM ET: solved 44%, at the limit 35%, empty patch 25%).''')\nf=f.replace(\"here it ends 23% of unedited episodes\",\"here it ends 23% to 35% of unedited episodes\")\nopen(\"finalize.py\",\"w\",newline=chr(10)).write(f)\nimport ast; ast.parse(f)\nc=open(\"../extras/common_tail.txt\").read()\ni=c.index(\"Unedited reference\")\n[line removed by the dashboard scrubber]\n(solved 4,637, limit 9,149). At 110 episodes (9:40 PM ET): solved 44%, at the session limit 35%, empty patch 25%, at\n[line removed by the dashboard scrubber]\nabove are relative to whatever the unedited arm of the same batch shows.\n'''\nc=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\")\nopen(\"../extras/common_tail.txt\",\"w\",newline=chr(10)).write(c)\nfor n in (\"e1\",\"e2\",\"e3\"):\n t=open(f\"../extras/{n}.txt\").read()\n t=t.replace(\"0 of 15 cut\",\"0 of 23 cut\").replace(\"from about 8% to under 3%\",\"from 8.6% to under 4%\")\n 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%)\")\n 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%)\")\n 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%)\")\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n open(f\"../extras/{n}.txt\",\"w\",newline=chr(10)).write(t)\n print(n, \"from 23%\" in t, \"0.84\" in t, \"78%\" in t)\nEOF\n[line removed by the dashboard scrubber]"},{"i":116,"kind":"output","long":false,"text":"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\ne1 False False False\ne2 False False False\ne3 False False False\nOK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/record.md\nOK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/record.md\nOK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/record.md\nThu Nov 12 09:39:50 PM EST 2026\nqueue/deep-gate-b01-end-neurons-x9/record.md:1\nqueue/deep-thought-gate-g3-b03/record.md:1\nqueue/end-neurons-x25/record.md:0\nqueue/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 \n[line removed by the dashboard scrubber]\nqueue/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\nqueue/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\nqueue/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\nqueue/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\n[line removed by the dashboard scrubber]\nqueue/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\nqueue/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\n#!/bin/bash\n# rebuilds edit/ and ctrl/ of deep-thought-gate-g3-b03 (CPU, seconds). Needs code/data/think_stats.pt (written by code/think_job.py\n# in one GPU-0 hold: `flock $R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh`, which runs code/gpu_job.sh).\n. /home/keith/gemma-v2/env.sh\ncd /home/keith/gemma-v2/wsp31/authors/claude\n$PY code/build_think.py queue/deep-thought-gate-g3-b03/edit B:0.3 --stats code/data/think_stats_g3.pt\n$PY code/build_think.py queue/deep-thought-gate-g3-b03/ctrl B:0.3 --ctrl --stats code/data/think_stats_g3.pt\n$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/ctrl"},{"i":117,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; . /home/keith/gemma-v2/env.sh; $PY - < code/fix_records.py <<'EOF'\nimport sys\nA = \"/home/keith/gemma-v2/wsp31/authors/claude/queue/\"\ndef paras(p): return open(p).read().split(\"\\n\\n\")\n# end-neurons-x25: weights-only entry\np = A + \"end-neurons-x25/record.md\"; P = paras(p)\nfor i, x in enumerate(P):\n if x.startswith(\"Statistics (`code/data/think_stats.pt`\"):\n P[i] = (\"No activation statistics enter this edit: it is built from the checkpoint alone (`code/end_neurons.py`, CPU).\\n\"\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\"\n \"the served model is NOT VERIFIED YET in TOOLS.md) on 5 held-out episodes of `tools/data/probe_screen.jsonl`\\n\"\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n if x.startswith(\"Control (`ctrl/`): same code path and statistics\"):\n P[i] = (\"Control (`ctrl/`): same code path (`build_think.py N:24 --ctrl`): the same factor 25 applied to the down columns of\\n\"\n \"20 other neurons, one per selected neuron, same layer, `|z| < 1` for ``, nearest in column norm. Same\\n\"\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\"\n \"\\\"these neurons, not any 20 late-layer neurons made 25 times louder\\\".\")\n if x.startswith(\"Built as intended; approximations\"):\n P[i] = \"Built as intended; the only approximation is bf16 storage of the added columns (8 significant bits).\"\nopen(p, \"w\").write(\"\\n\\n\".join(P))\np = A + \"deep-gate-b01-end-neurons-x9/record.md\"; P = paras(p)\nfor i, x in enumerate(P):\n if x.startswith(\"Control (`ctrl/`): same code path and statistics\"):\n 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\"\n \"`|z| < 1`, nearest in column norm). The control of the combination was not pre-tested as one adapter; its two parts\\n\"\n \"were (rows `G3B0p1c` below; family N control at factor 4 in the first pass: no change from unedited).\")\nopen(p, \"w\").write(\"\\n\\n\".join(P))\np = A + \"deep-thought-gate-g3-b03/record.md\"; P = paras(p)\nfor i, x in enumerate(P):\n if x.startswith(\"Control (`ctrl/`): same code path and statistics\"):\n 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\"\n \"control at dose 0.3 with the first gates left every thought uncut.\")\nopen(p, \"w\").write(\"\\n\\n\".join(P))\nprint(\"fixed\")\nEOF\nscp -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'"},{"i":120,"kind":"output","long":true,"text":"fixed\n[line removed by the dashboard scrubber]\n\n## Hypothesis\n\nIn the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the\n7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110\nepisodes, 9:40 PM ET: solved 44%, at the limit 35%, empty patch 25%). At about\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\"Wait, ...\" restarts. Making long thoughts end sooner should return that wall-clock to tool calls, so that episodes\nwhich now time out without a patch get to edit, test and submit.\n\nDifferent from the E4B \"think briefly\" edits (28 / 28 / 25 vs 29, PRIOR_E4B.md): there the session limit was not\nreported as binding; here it ends 23% to 35% of unedited episodes, and the edit is built from the state at the logged ends of\nthoughts (and, in families B and C, acts only deep inside a long thought), not from a short-vs-long contrast.\n\n## Exact recipe\n\n[... 56 lines cut ...]\n`|z| < 1` nearest in column norm (pool of 4000, seed 4242), scaled by the same factor.\n\nGate 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\n\nControl (`ctrl/`): same code path and statistics; the written unit direction has the sign of every coordinate\nflipped at random (seed 1000 + layer) before `clipdiv`. Per-coordinate magnitudes are unchanged, so the control has\nthe same norm in the stream, in the raw MLP output and in |dW|_F, the same read vector / gate, and a write direction\nThu Nov 12 09:40:26 PM EST 2026"},{"i":121,"kind":"tool","long":false,"text":"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'"},{"i":122,"kind":"output","long":false,"text":"OK edit: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj\nOK ctrl: 30 modules changed (of 30 present), r=1, max |dW|_F/|W|_F 0.1016 at 32.gate_proj\nREADY deep-thought-gate-g3-b03\nOK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nOK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nREADY deep-gate-b01-end-neurons-x9\nOK edit: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5997 at 59.down_proj\nOK ctrl: 3 modules changed (of 3 present), r=16, max |dW|_F/|W|_F 0.5996 at 59.down_proj\nREADY end-neurons-x25\nThu Nov 12 09:40:42 PM EST 2026\nqueue/deep-gate-b01-end-neurons-x9/READY\nqueue/deep-thought-gate-g3-b03/READY\nqueue/end-neurons-x25/READY"},{"i":123,"kind":"tool","long":false,"text":"Bash: cat > NOTES.md <<'EOF'\n# Notes of author claude (rewritten end of turn 0, 12 Nov 2026 9:45 PM ET)\n\n## What the unedited model does wrong (b001; code/fail_stats.py, req_stats.py, cf_budget.py)\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n- Unedited log p() inside thoughts is about -25: thoughts end only where the model decides; hazard-style\n \"expected length\" is useless at temperature 0. Use the greedy table (code/think_greedy.py).\n\n## Queued this turn (all READY; edits are bit-identical to the pre-tested adapters)\n1. deep-thought-gate-g3-b03: one \"set\" neuron per layer 30..48 (even) detects depth >= 800 in a thought (diag-LDA\n gate, AUC 0.88-0.91) and writes d_end = mean resid(thought end) - mean resid(other thought positions), dose 0.3.\n Pre-test (5 held-out episodes, 23 short / 6 long thoughts): short kept 1.00, long kept 0.53. Predicted -4..9.\n2. end-neurons-x25: weights only. 20 MLP neurons in layers 57-59 whose write lens is (z >= 6;\n code/end_neurons.py), down columns x25. Short kept 0.86, long 0.34, logged ends more decisive. Predicted -8..9.\n3. 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.\nControls: sign-flipped write direction (gate) / norm-matched other neurons (N). Controls cut nothing in the pre-test.\n\n## Measured nulls and traps (do not repeat)\n- Family C (gated write of the unembedding direction in layers 54-58): no effect at my doses (3-20 \"nats\").\n- Always-on Concept Country form (family A): 0.1 shortens only short thoughts (kept 0.81) and leaves long ones; 0.3\n wrecks the model (tool top-1 agreement 0). No usable window for long thoughts.\n- First gate (sharpness 3) leaked through the negative lobe of gelu and lowered p() at true thought ends.\n Use sharpness 12 (think_refit.py). \"Anti\" neurons scaled to 0.25 lowered p(): wrong sign, dropped.\n- g31 forward reproduces only 83% of logged thought ends on the unedited model (forward vs server still unverified).\n[line removed by the dashboard scrubber]\n- Git Bash heredocs halve backslashes: do not patch files with \"\\\\n\" inside `python - <= 1500, dose series, and check the first tool call after a cut thought with fastprobe /\n generation (not done: no served probe this turn).\n- Untested ideas: gate \"N tool calls and no edit yet\" writing an edit-now direction; read_file loops in limit episodes.\n\n## Missing tools that would have changed the plan\nServed generation under an adapter (to see what follows a cut thought); likelihood of successful trajectories;\nverified forward-vs-server agreement.\nEOF\nwc -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'"},{"i":124,"kind":"output","long":false,"text":"626 NOTES.md\nThu Nov 12 09:41:05 PM EST 2026\n/home/keith/gemma-v2/wsp31/authors/claude/queue/deep-gate-b01-end-neurons-x9/:\nREADY\nbuild.sh\nctrl\nedit\nrecord.md\n\n/home/keith/gemma-v2/wsp31/authors/claude/queue/deep-thought-gate-g3-b03/:\nREADY\nbuild.sh\nctrl\nedit\nrecord.md\n\n/home/keith/gemma-v2/wsp31/authors/claude/queue/end-neurons-x25/:\nREADY\nbuild.sh\nctrl\nedit\nrecord.md\n\n## Edits\n\n| ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict |\n|---|---|---|---|---|---|---|---|---|\n\nWaiting for stage 1: 0. Waiting for stage 2: 0."},{"i":125,"kind":"tool","long":false,"text":"Bash: cat > extras/e4.txt <<'EOF'\nBehaviour that should move: this is the strong end of the gated family. The gate is the g2 version (fitted on depth\n>= 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\ntimes their write. Pre-test of this adapter: every one of the 6 long thoughts is cut (median depth 148, 21% of their\n[line removed by the dashboard scrubber]\nas decisive as unedited (`E top=101` 0.83). With entries deep-thought-gate-g3-b03 (long 0.53, short 1.00) and\ndeep-gate-b01-end-neurons-x9 (long 0.48, short 0.95) it gives three doses of \"how much thinking is cut\" by the same\nmechanism.\n[line removed by the dashboard scrubber]\n- episodes ending at the 7.5-minute limit: down by a third to two thirds of the unedited arm's rate (35% at 110\n episodes: to 12-24%); empty patches down in the same proportion (25%: to 9-17%);\n- tool calls per episode: up by 2 to 5; episodes at the 30-call cap up by at most 8 points (unedited 17%);\n- if cutting thoughts is good at all, this entry should gain at least as much as the two weaker doses; if it gains\n less than they do, the cut has started to remove thinking the tasks need (collateral inside deep thoughts is the\n[line removed by the dashboard scrubber]\n of which is the intended early end).\nEOF\nscp -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 - <`) at the true ends of thoughts); classes as above. This entry uses `code/data/think_st\n56:`|z| < 1`, nearest in column norm). The control of the combination was not pre-tested as one adapter; its two parts\n85: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)\n86: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)\n87: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)\n99: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\n100: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\n101: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\n106:30 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'}\n115:48 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'}\nThu Nov 12 09:41:36 PM EST 2026"},{"i":127,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nt=open(\"NOTES.md\").read()\nt=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)\")\nt=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.\n Short 0.82, long 0.21. Predicted -7..9.\nControls: sign-flipped write direction (gate) / norm-matched other neurons (N). Controls cut nothing in the pre-test.''')\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n''')\nt=t.replace('''- Git Bash heredocs halve backslashes: do not patch files with \"\\\\\\\\n\" inside `python - <= 400 (g2), dose 0.1, plus neurons x9: the strong dose.\n30:- Local heredocs halve backslashes; `pkill -f` matches your own ssh command line.\nOK edit: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nOK ctrl: 33 modules changed (of 33 present), r=16, max |dW|_F/|W|_F 0.1999 at 59.down_proj\nREADY early-gate-b01-end-neurons-x9\n1017M\tscratch\n611 NOTES.md\nqueue/deep-gate-b01-end-neurons-x9/READY\nqueue/deep-thought-gate-g3-b03/READY\nqueue/early-gate-b01-end-neurons-x9/READY\nqueue/end-neurons-x25/READY\nThu Nov 12 09:41:53 PM EST 2026\n505422, 71764 MiB\n184976, 90492 MiB"},{"i":129,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nt=open(\"NOTES.md\").read()\nt=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.\")\n[line removed by the dashboard scrubber]\nt=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).\")\nt=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).\")\nopen(\"NOTES.md\",\"w\",newline=chr(10)).write(t)\nprint(len(t.split()))\nEOF\nscp -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'"},{"i":130,"kind":"output","long":false,"text":"598\n598 NOTES.md\n## Edits\n\n| ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict |\n|---|---|---|---|---|---|---|---|---|\n| 0007 | claude | deep-gate-b01-end-neurons-x9 | queued_s1 | -5 to +9 | | | | - |\n| 0008 | claude | deep-thought-gate-g3-b03 | queued_s1 | -4 to +9 | | | | - |\n| 0009 | claude | end-neurons-x25 | queued_s1 | -8 to +9 | | | | - |\n\nWaiting for stage 1: 3. Waiting for stage 2: 0.\nThu Nov 12 09:42:02 PM EST 2026"},{"i":131,"kind":"tool","long":false,"text":"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'"},{"i":132,"kind":"output","long":false,"text":"## Edits\n\n| ledger | author | slug | state | predicted (120) | stage 1 | stage 2 | control | verdict |\n|---|---|---|---|---|---|---|---|---|\n| 0007 | claude | deep-gate-b01-end-neurons-x9 | queued_s1 | -5 to +9 | | | | - |\n| 0008 | claude | deep-thought-gate-g3-b03 | queued_s1 | -4 to +9 | | | | - |\n| 0009 | claude | end-neurons-x25 | queued_s1 | -8 to +9 | | | | - |\n| 0010 | claude | early-gate-b01-end-neurons-x9 | queued_s1 | -7 to +9 | | | | - |\n\nWaiting for stage 1: 4. Waiting for stage 2: 0.\nThu Nov 12 09:42:51 PM EST 2026"},{"i":133,"kind":"assistant","long":false,"text":"Four edits are queued and accepted by the runner (ledger 0007\u20130010, all `queued_s1`, none rejected); `NOTES.md` is current.\n\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n4. **`deep-gate-b01-end-neurons-x9`** (\u22125 to +9) and **`early-gate-b01-end-neurons-x9`** (\u22127 to +9): the gate at dose 0.1 plus the same neurons \u00d79, as the medium and strong doses (long thoughts keep 48% and 21%). Together with entry 2 they give three doses of one mechanism.\n[line removed by the dashboard scrubber]\n\nCaveats on the evidence:\n- The pre-test is teacher-forced on only 5 held-out episodes (23 short, 6 long thoughts).\n- 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.\n- Nothing was generated under any edit, so the first tool call after a cut thought is untested.\n- 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.\n\nTwo 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).\n\nI 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."},{"i":134,"kind":"assistant","long":false,"text":"Final message of the turn:\n[claude-code:unrecognized_model] {\"model\":\"claude-fable-5-2\",\"query_source\":\"sdk\"}\nFour edits are queued and accepted by the runner (ledger 0007\u20130010, all `queued_s1`, none rejected); `NOTES.md` is current.\n\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n4. **`deep-gate-b01-end-neurons-x9`** (\u22125 to +9) and **`early-gate-b01-end-neurons-x9`** (\u22127 to +9): the gate at dose 0.1 plus the same neurons \u00d79, as the medium and strong doses (long thoughts keep 48% and 21%). Together with entry 2 they give three doses of one mechanism.\n[line removed by the dashboard scrubber]\n\nCaveats on the evidence:\n- The pre-test is teacher-forced on only 5 held-out episodes (23 short, 6 long thoughts).\n- 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.\n- Nothing was generated under any edit, so the first tool call after a cut thought is untested.\n- 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.\n\nTwo 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).\n\nI 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."}],"updated":"2026-11-15T02:05:58Z"}