{"chunks":1,"id":"t003","last_seq":0,"rows":[{"i":0,"kind":"user","long":false,"text":"You are author 'claude' in the wsp31 weight-editing programme. This is turn 3. 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\nchampion in the same batch (the unedited model plus the edits that have already passed; see \"The champion\"), on the\nmargin 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[... 184 lines cut ...]\n- An edit that passes both stages is promoted: it becomes part of the champion for everything after it.\n- The champion is confirmed on tasks you do not see, against the unedited model, and the final champion gets one\n pre-registered held-out run. A changed edit is a new edit.\n- A combination of your own edits is a new queue entry with its own record, prediction and control. Combining with\n edits that already passed needs nothing from you: that is what the champion does.\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 'cd /home/keith/gemma-v2/wsp31; cat authors/claude/NOTES.md; echo ======; cat queue/STATUS.md | head -150; echo ======; cat queue/CHAMPION.md; ls authors/claude/queue authors/claude/code authors/claude/scratch | head -80'"},{"i":4,"kind":"output","long":true,"text":"# 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.\n4. 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[... 84 lines cut ...]\n\nauthors/claude/scratch:\nhold_end\nhold_start\njob1\njob2\nlogs\ncat: queue/CHAMPION.md: No such file or directory"},{"i":5,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2; date; cat tii-wsp31-doc/research/wsp31/LEDGER.md; ls tii-wsp31-doc/research/wsp31/ledger/; ls wsp31/queue/; cat wsp31/queue/config.json; ls -la wsp31/authors/*/queue/ wsp31/authors/claude/'"},{"i":6,"kind":"output","long":true,"text":"Thu Nov 12 11:57:01 PM EST 2026\n# 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 | - | - |\n| [0004](ledger/0004-claude-size-of-real-training-updates.md) | 2026-11-12 | claude | baseline | How far real training moves the weights (size reference for edits) | - / - | done | n/a | research/wsp31/tools/train_size.py (numbers below) |\n| [0005](ledger/0005-claude-tools-lane-edit-toolchain.md) | 2026-11-12 | claude | tools | Tools lane (31B edit toolchain: exact loader and forward pass, LoRA builders, checker, pre-tests, champion support) | wsp31_screen_v1 / - | running | - | research/wsp31/edit_tools/REPORT.md |\n| [0006](ledger/0006-claude-interp-lane-readers-failure-modes-directions.md) | 2026-11-12 | claude | interp | Interpretability lane (can LLMs read the weights; failure modes; per-layer directions for training-sized edits) | wsp31_screen_v1 / - | running | - | research/wsp31/interp/REPORT.md |\n| [0007](ledger/0007-claude-deep-gate-b01-end-neurons-x9.md) | 2026-11-12 | claude | edit | Deep-thought gate (dose 0.1) plus the model s own end-of-thought neurons at 9 times their write | wsp31_screen_v1 / - | queued | - | - |\n| [0008](ledger/0008-claude-deep-thought-gate-g3-b03.md) | 2026-11-12 | claude | edit | 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) | wsp31_screen_v1 / - | queued | - | - |\n[line removed by the dashboard scrubber]\n| [0010](ledger/0010-claude-early-gate-b01-end-neurons-x9.md) | 2026-11-12 | claude | edit | 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 | wsp31_screen_v1 / - | queued | - | - |\n| [0011](ledger/0011-gpt-writer-close-amplify-033.md) | 2026-11-12 | gpt | edit | Amplify natural thought-ending writes across all layers at 0.33 percent | wsp31_screen_v1 / - | queued | - | - |\n| [0012](ledger/0012-gpt-writer-close-amplify-050.md) | 2026-11-12 | gpt | edit | Amplify natural thought-ending writes across all layers at 0.50 percent | wsp31_screen_v1 / - | queued | - | - |\n[... 105 lines cut ...]\n\nwsp31/authors/gpt/queue/:\ntotal 20\ndrwxrwxr-x 5 keith keith 4096 Nov 12 22:37 .\ndrwxrwxr-x 5 keith keith 4096 Nov 12 21:44 ..\ndrwxrwxr-x 4 keith keith 4096 Nov 12 22:35 writer-close-amplify-033\ndrwxrwxr-x 4 keith keith 4096 Nov 12 22:37 writer-close-amplify-050\ndrwxrwxr-x 4 keith keith 4096 Nov 12 22:39 writer-close-shift-011"},{"i":7,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31; cat tools/TOOLS.md'"},{"i":8,"kind":"output","long":true,"text":"# wsp31 tools for edit authors\n\nChangelog (newest first; times US Eastern):\n- Thu 12 Nov, 11:40 PM: first served runs of `dose.py`, `validate.py`, `vgen.py` and first run of `closeprobe.py`:\n all work as documented (section 5, with numbers); **any active adapter slows every stream of its batch** (trap\n[line removed by the dashboard scrubber]\n- Thu 12 Nov, 10:55 PM: rank 32 on all 420 matrices and stacks of rank 34, 64, 96 are applied as written\n (sections 1, 4); new `vgen.py`. 10:30 PM: **thought-end pre-tests need exact contexts** (section 5;\n `closeprobe.py`, `reqset.py --per-call`); rank limits above 32 are 64 or 128, never 96; b001 probe sets.\n- Thu 12 Nov, 10:00 PM: champion support (section 4); network-wide builders, `stack.py`, `rescale.py`, `dose.py`,\n `train_size.py`, `capture.py`, `replay.py`. 8:36 PM: first version. Every earlier command still works unchanged.\n\nEverything here lives in `$R/wsp31/tools` (below `$T`; `$R = /home/keith/gemma-v2`). Each entry ends with\n**Verified by** (a measurement that was made) or **NOT VERIFIED YET** (written, first real run still queued; use it,\nbut do not build a claim on its numbers alone). Architecture facts are in `$T/ARCH.md` (read \"Consequences for\nweight edits\" there before designing an edit); measurements with commands are in `$T/REPORT.md`.\n\n . /home/keith/gemma-v2/env.sh; T=$R/wsp31/tools\n export WSP31_WORK=$R/wsp31/authors//scratch # caches and server logs of the tools go here, not into $T\n\n[... 288 lines cut ...]\n\n## 8. Not available yet\n\n[line removed by the dashboard scrubber]\n- Activation atlas and its views; `--champion` runs of `dose.py`, `validate.py`, `closeprobe.py`, `vgen.py`.\n- Any single entry beyond rank 32 per matrix, and edits of norms, `layer_scalar` or embeddings (`overlay.py` exists\n as code, has not been served, and the runner does not accept it).\n- A probe verdict that predicts rollout outcome: not shown for any tool here."},{"i":9,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31; cat authors/claude/code/gpu_hold.sh; echo ---; cat authors/claude/code/gpu_job.sh | head -30; echo ---; ps aux | grep -E \"flock|gpu0_session|vllm|g31|capture|causal\" | grep -v grep | cut -c1-230; nvidia-smi --query-gpu=index,memory.used,utilization.gpu --format=csv; free -g | head -2; cat authors/claude/.turn_start; ls authors/claude/scratch/* | head -40; du -sh authors/claude/scratch'"},{"i":10,"kind":"output","long":true,"text":"#!/bin/bash\n# runs under the GPU0 lock; executes code/gpu_job.sh as it exists at acquisition time\ncd /home/keith/gemma-v2/wsp31/authors/claude\ndate > scratch/hold_start\n[ -f code/gpu_job.sh ] || { echo \"no job\"; exit 3; }\nbash code/gpu_job.sh\ndate > scratch/hold_end\n---\n#!/bin/bash\n# GPU-0 job of author claude (run only through code/gpu_hold.sh under the gpu0 lock)\n. /home/keith/gemma-v2/env.sh\ncd /home/keith/gemma-v2/wsp31/authors/claude\nexport WSP31_WORK=$PWD/scratch CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0\n$PY code/think_job.py > scratch/logs/job1.log 2>&1\n$PY code/think_job2.py 5 > scratch/logs/job2.log 2>&1\n---\nkeith 184764 2.3 0.4 20440536 877080 ? Ssl 19:50 5:44 /home/keith/gemma-v2/tii-venv/bin/python -m vllm.entrypoints.openai.api_server --model /home/keith/gemma-v2/models/gemma-4-31b-it-qat-w4a16-ct --served-model-name \nkeith 893374 0.0 0.0 8372 2176 ? S 23:26 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\nkeith 957831 0.0 0.0 8372 2172 ? S 23:38 0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\nindex, memory.used [MiB], utilization.gpu [%]\n[... 18 lines cut ...]\neval.pt\nsnap.pt\n\nauthors/claude/scratch/logs:\nhold1.log\njob1.log\njob2.log\n1017M\tauthors/claude/scratch"},{"i":11,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; 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[ -f code/gpu_job3.sh ] || { echo \"no job3 yet\"; exit 0; }\nbash code/gpu_job3.sh\nEOF\nrm -f code/gpu_job3.sh; nohup flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh > scratch/logs/hold3.log 2>&1 &\nsleep 1; nvidia-smi --query-compute-apps=pid,used_memory --format=csv; ps -o pid,etime,cmd -p $(nvidia-smi --query-compute-apps=pid --format=csv,noheader | tr \"\\n\" \",\" | sed \"s/,$//\") | cut -c1-200; cat /home/keith/gemma-v2/wsp31/interp/REPORT.md | head -150'"},{"i":12,"kind":"output","long":true,"text":"pid, used_gpu_memory [MiB]\n1026421, 35750 MiB\n184976, 90492 MiB\n PID ELAPSED CMD\n 184976 04:06:54 VLLM::EngineCore\n1026421 06:43 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_act --sets 0-59 --alphas 0.01,0.03,0.1,0.3 --budget_s 1390\n# Interpretability lane: what is known about the inside of Gemma 4 31B\n\nLane `wsp31/interp`. Updated Thu 12 Nov 2026, 11:50 PM ET. Everything under \"Measured\" is a number from this lane's\nown runs; anything else is labelled. Pre-registration: `PROTOCOL.md` (sections 0-5 and dated amendments 1, 2, 3, 3a,\n3b, 3c, 3d, 3e, 4, 4a, 5, 5a, 5b). Located-and-causally-checked components: `ATLAS.md` (currently none). Lens-only readings:\n`ATLAS_lens.md`.\n\n**Forward pass, as measured (this wording replaces two earlier ones: \"verified\", then \"not yet verified\";\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nre-rendered; agreement on an edited model: not measured beyond the tools lane's rank-4 comparison.\nSection 3 uses the unedited model only. The dose check (3.5) will change activations inside the pass through\nhooks; that has no served counterpart and is labelled where it is reported.\n[... 128 lines cut ...]\npermutations), at the \"agent\" position rule (mean over the episode's sampled decision points). Controls beside each:\nthe same test at the \"early\" rule (positions up to 6,000) and the \"task\" rule (first agent turn only), a 5-fold\nestimate over all tasks, and the AUC of two activation-free baselines.\n\n[line removed by the dashboard scrubber]\n|---|---|---|---|---|---|---|---|\n| solved | 23/23 | 0.617 (0.586, p .014) | yes | 0.575 (0.620, p .155) no | 0.595 (0.599, p .063) no | 0.480 (0.572, p .62) no | 0.489 / 0.593 |\n| tests_run | 23/23 | 0.677 (0.627, p .019) | yes | 0.527 (p .38) no | 0.643 (0.600, p .011) yes | 0.690 (0.590, p .005) yes | 0.553 / 0.655 |"},{"i":13,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31; sed -n 150,330p interp/REPORT.md | cut -c1-900'"},{"i":14,"kind":"output","long":true,"text":"| tests_run | 23/23 | 0.677 (0.627, p .019) | yes | 0.527 (p .38) no | 0.643 (0.600, p .011) yes | 0.690 (0.590, p .005) yes | 0.553 / 0.655 |\n| patch | 37/9 | 0.797 (0.652, p .001) | yes | 0.591 (p .14) no | 0.604 (0.590, p .034) yes | 0.710 (p .005) yes | 0.755 / 0.589 |\n| finished | 30/16 | 0.674 (0.651, p .029) | yes | 0.549 (p .25) no | 0.604 (0.606, p .054) no | 0.737 (p .005) yes | 0.679 / 0.485 |\n| brief | 37/9 | 0.721 (0.684, p .020) | yes | 0.614 (p .084) no | 0.593 (p .17) no | 0.698 (p .005) yes | 0.725 / 0.471 |\n\np-values are per contrast, not corrected over the five contrasts (Holm over five leaves only patch below 0.05);\n5-fold p-values use 200 permutations (floor .005). The pre-registered permutation test shuffles episode labels and\nignores that the 46 check episodes come from 15 tasks.\n\nPost hoc, exploratory (amendment 3e, written after the table above was seen, before these were computed):\n\n| contrast | same-task pairs ordered correctly, check tasks (pairs; p) | same, 5-fold directions, all tasks (pairs; p) | task-level Spearman, 15 check tasks (null 95%, p) |\n|---|---|---|---|\n| solved | 0.470 (9; p .60) | 0.411 (14; p .81) | 0.312 (0.278, p .035) |\n| tests_run | 0.365 (17; p .81) | 0.398 (35; p .85) | 0.392 (0.396, p .051) |\n| patch | 0.423 (14; p .67) | 0.454 (28; p .67) | 0.472 (0.384, p .016) |\n| finished | 0.443 (17; p .65) | 0.539 (30; p .37) | 0.298 (p .11) |\n| brief | 0.408 (11; p .74) | 0.609 (22; p .14) | 0.292 (p .13) |\n\nResult: the criterion is met by all five contrasts at the main rule and by none at the \"early\" rule; the\n[... 153 lines cut ...]\n (19 of 66 against 44 of 70). \"Edits before reading\" never happens; loops do not predict failure.\n4. **Per-layer directions for training-sized edits** (section 3). The honest state: the five episode-level\n contrasts (solved, tests run, patch, finished, brief thoughts) are **weak**. Each separates held-out episodes a\n little better than shuffled labels (AUC 0.62 to 0.80 against a null 95th percentile of 0.59 to 0.68), no better\n than the number of model calls does without any activation, and none of them puts the better of two episodes of\n the **same** task first more often than chance. Four of the five are one shared direction (cosines 0.65 to 0.91).\n They describe which tasks and runs go well, not a decision the agent takes; an edit along them is not expected to\n carry the outcome difference. The two within-episode decision contrasts are strong as read-outs: edit-or-write"},{"i":15,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors; sed -n 1,60p gemini/queue/late-preedit-commit-gate/record.md | cut -c1-1200; echo =====; sed -n 1,45p gpt/queue/writer-close-shift-011/record.md | cut -c1-1000; echo; ls /home/keith/gemma-v2/wsp31/interp/dirs/out_partial/ /home/keith/gemma-v2/wsp31/interp/dirs/out/ | head -60'"},{"i":16,"kind":"output","long":true,"text":"title: Coordinate-whitened GeGLU late pre-edit commit gate across 13 high-signal layers (dose 1.20)\n\n## Hypothesis\n\nAcross the 210 `b001` episodes, a second major failure bottleneck\u2014completely untouched by all seven queued thought-shortening edits (`0007`--`0013`)\u2014is **indecisive `read_file` spiralling into the 30-tool-call hard cap**:\n- Solved episodes (`n=108`) average `5.48` `read_file` calls and make their first file edit on call `10.3` (`108/108` edit at least once).\n- Unsolved episodes average `8.90`--`11.20` `read_file` calls and delay their first edit to call `15.0` (or never edit). In **5 of the 10 within-task discordant `b001` tasks** (`kayak__pypika`, `[task]`, `mozilla__bleach`, `[task]`), failing arms still had up to `210s` of wall-clock left (`max_think = 787`--`1270`, so deep-thought gates never fire!) yet burned 25--30 tool calls on 20-line `read_file` slices until `BudgetExceeded` at call 30 blocked `edit_file` and forced an empty `submit_patch`.\n\nWhy this differs from `PRIOR_E4B.md` (\"always-on edit sooner: 12 vs 28\") and `0007`--`0013`:\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n## Exact recipe\n\nbase: champion generation 0\n\nDesigned and pre-tested on generation 0 (unedited checkpoint); entry contains only this change. Completely orthogonal in mechanism and target positions to all queued thought-ending edits (`0007`--`0013`), so it stacks cleanly if any thought-ending edit is promoted to champion generation 1.\n\nsize: whole network 1.055181% over 39 matrices, largest matrix 9.659172%\n\n[... 111 lines cut ...]\nfinished.pt\npatch.pt\npost.json\nsolved.pt\nsummary.json\ntests_run.pt\ntool_act.pt\ntool_submit.pt"},{"i":17,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/tii-wsp31-doc; cut -c1-420 research/wsp31/PRIOR_E4B.md | head -45; echo ======; grep -n \"^## \\|^### \\|^| *[0-9]* *|\" reports/2026-11-12-weight-editing-survey.md | cut -c1-330 | sed -n 1,80p; cat /home/keith/gemma-v2/wsp31/interp/dirs/out/dose.json | head -c 1500'"},{"i":18,"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[... 52 lines cut ...]\n[line removed by the dashboard scrubber]\n212:| 17 | **Causal head zero/scale search** under a budget | 2611.09382 (GPQA +4.42 at 0.8B from one head) | Dev tasks | High variance and path-dependent; multiple-comparison trap |\n213:| 18 | **Singular-value rescaling search** on selected matrices (black-box) | Transformer\u00b2 (SVF learned by RL) | SVDs; dev tasks | High overfit risk |\n214:| 19 | **Long-trajectory persona and coherence capping** (gated-neuron approximation of activation capping) | Assistant Axis | Persona-axis estimate; long-context traces | Low benefit expected for solve rate; possibly helps very long episodes |\n215:| 20 | **Hypernetwork skill-to-LoRA** (an author LLM writes skill text, a hypernetwork emits the LoRA) | Parametric Skills, LatentSkill, T2L | A trained Gemma-4-31B hypernetwork (none exists) | Not training-free for you. Parked |\n224:## D. Tooling recommendations for the LLM-author harness\n241:## E. Gaps, open questions, and inaccessible sources\n{\"configs\": {\"close_thought|0-59|real|+0.01\": {\"all\": {\"n\": 80, \"d_next\": -0.12025429308414459, \"d_next_min\": -1.4565130472183228, \"p_next_base\": 0.9643121957778931, \"p_next_edit\": 0.889710545539856, \"kl\": 0.2102956473827362, \"kl_max\": 1.5913411378860474, \"flip\": 0.03750000149011612, \"mass\": {\"<|channel>\": [0.3678768277168274, 0.35935646295547485], \"<|tool_call>\": [0.23126783967018127, 0.17472825944423676], \"\": [3.965581072407076e-07, 7.914503612482804e-07]}, \"up\": [[\"\", 0.04161743447184563], [\"run\", 0.009498253464698792], [\"---\", 0.0052758632227778435], [\"<\", 0.005135818384587765], [\"\", 0.004065435845404863]], \"down\": [[\"<|tool_call>\", -0.056539542973041534], [\"<|channel>\", -0.00852038525044918], [\"read\", -0.006944986991584301], [\"\\\\\\\\\", -0.004442206583917141], [\"//\", -0.0033264285884797573]]}, \"turn\": {\"n\": 10, \"d_next\": -0.1638861745595932, \"d_next_min\": -0.32547277212142944, \"p_next_base\": 0.9979263544082642, \"p_next_edit\": 0.8495909571647644, \"kl\": 0.16121992468833923, \"kl_max\": 0.3222624361515045, \"flip\": 0.0, \"mass\": {\"<|channel>\": [0.9979263544082642, 0.8495909571647644], \"<|tool_call>\": [1.4870819597945228e-07, 0.00012044723553117365], \"\": [1.1040862446520805e-08, 3.992269739683252e-06]}, \"up\": [[\"---\", 0.04118669033050537], [\"\", 0.031182680279016495], [\"thought\", 0.023336807265877724], [\"(\\\"\", 0.0014389149146154523], [\"####\", 0.0014357835752889514]], \"down\": [[\"<|channel>\", -0.14833541214466095], [\"set\", -6.4170999394264"},{"i":19,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/interp/dirs; /home/keith/gemma-v2/tii-venv/bin/python - <<\"EOF\"\nimport json\nd=json.load(open(\"out/dose.json\"))\n[line removed by the dashboard scrubber]\nfor k,v in d[\"configs\"].items():\n s=k\n for kind in (\"think\",\"tool\",\"turn\",\"step\",\"cont\",\"close\"):\n if kind in v:\n x=v[kind]; s+=\" | %s n%d dn%.2f kl%.2f fl%.2f\"%(kind,x[\"n\"],x[\"d_next\"],x[\"kl\"],x[\"flip\"])\n print(s)\n if \"tool\" in v: print(\" tool up\",[(a,round(b,3)) for a,b in v[\"tool\"][\"up\"][:4]],\"down\",[(a,round(b,3)) for a,b in v[\"tool\"][\"down\"][:4]])\nprint({k:(v if not isinstance(v,dict) else list(v)[:8]) for k,v in d.items() if k!=\"configs\"})\nEOF\ngrep -n \"alpha\\|def hook\\|unit\\|rms\" dose.py | head -40; tail -5 ../logs/job5.log 2>/dev/null'"},{"i":20,"kind":"output","long":false,"text":"[line removed by the dashboard scrubber]\nclose_thought|0-59|real|+0.01 | think n20 dn-0.40 kl0.49 fl0.15 | tool n30 dn-0.01 kl0.06 fl0.00 | turn n10 dn-0.16 kl0.16 fl0.00 | step n20 dn0.01 kl0.18 fl0.00\n tool up [('run', 0.025), ('edit', 0.002), ('write', 0.0), ('get', 0.0)] down [('read', -0.019), ('grep', -0.009), ('//', -0.0), (' grep', -0.0)]\nclose_thought|0-59|real|-0.01 | think n20 dn-1.84 kl1.64 fl0.60 | tool n30 dn-0.06 kl0.10 fl0.03 | turn n10 dn0.00 kl0.01 fl0.00 | step n20 dn-1.29 kl1.28 fl0.40\n tool up [('read', 0.04), ('get', 0.0), ('red', 0.0), ('1', 0.0)] down [('run', -0.03), ('grep', -0.009), ('edit', -0.001), ('write', -0.0)]\nclose_thought|0-59|null|+0.01 | think n20 dn-0.24 kl0.21 fl0.05 | tool n30 dn-0.24 kl0.21 fl0.13 | turn n10 dn-0.02 kl0.02 fl0.00 | step n20 dn0.01 kl0.05 fl0.00\n tool up [('write', 0.05), ('grep', 0.001), ('//', 0.0), ('Write', 0.0)] down [('run', -0.036), ('read', -0.014), ('edit', -0.001), ('get', -0.0)]\nclose_thought|0-59|real|+0.03 | think n20 dn-13.42 kl12.86 fl1.00 | tool n30 dn-13.74 kl13.73 fl1.00 | turn n10 dn-2.18 kl2.18 fl1.00 | step n20 dn-2.59 kl2.88 fl1.00\n tool up [('', 0.716), ('', 0.091), ('<|channel>', 0.078), ('---', 0.012)] down [('run', -0.598), ('read', -0.391), ('grep', -0.01), ('edit', -0.001)]\nclose_thought|0-59|real|-0.03 | think n20 dn-28.05 kl26.60 fl1.00 | tool n30 dn-18.71 kl18.72 fl1.00 | turn n10 dn-1.78 kl1.78 fl0.40 | step n20 dn-8.59 kl8.71 fl1.00\n tool up [('1', 0.257), ('\\\\n', 0.111), (' {', 0.095), ('C', 0.032)] down [('run', -0.598), ('read', -0.391), ('grep', -0.01), ('edit', -0.001)]\nclose_thought|0-59|null|+0.03 | think n20 dn-10.03 kl9.89 fl1.00 | tool n30 dn-5.04 kl5.00 fl0.97 | turn n10 dn-3.84 kl3.84 fl1.00 | step n20 dn-5.41 kl5.55 fl1.00\n tool up [('//', 0.166), ('', 0.125), ('C', 0.069), ('\\\\n', 0.055)] down [('run', -0.547), ('read', -0.389), ('grep', -0.007), ('edit', -0.001)]\nclose_thought|0-59|real|+0.1 | think n20 dn-19.40 kl18.50 fl1.00 | tool n30 dn-19.21 kl19.21 fl1.00 | turn n10 dn-2.78 kl2.78 fl1.00 | step n20 dn-2.68 kl3.02 fl1.00\n tool up [('', 0.893), ('<|channel>', 0.059), ('1', 0.003), ('\\\\n', 0.002)] down [('run', -0.598), ('read', -0.391), ('grep', -0.01), ('edit', -0.001)]\n{'episodes': ['s1__Mimino666__langdetect.a1598f1a.lm_rewrite__dk9wx21q__r0', 's1__DanielSank__constraintula-14__r1', '[task]', '[task]', '[task]', 's1__PyCQA__flake8.cf1542ce.lm_rewrite__zr4swpdd__r0', 'b001_zero__django-money__django-money.835c1ab8.combine_module__bqggrnha', 'b001_aa__cookiecutter__cookiecutter.b4451231.lm_rewrite__byoy8vaf', '[task]', 'b001_base__Mimino666__langdetect.a1598f1a.func_basic__rku38xih'], 'points': ['turn', 'step', 'think', 'tool', 'cont', 'close'], 'max_pos': 8000, 'close_pass': 'branch', 'base': ['close', 'cont', 'tool_odds']}\n2:PROTOCOL amendment 3 item 8, 3b item 4, 3c item 3, 3d item 3. Adds alpha * |h| * u_L to resid_post at every layer L\n14:Usage: python dose.py --contrasts close_thought,tool_act --sets 0-59,20-59 --alphas 0.01,0.03,0.1,0.3\n95: ap.add_argument(\"--alphas\", default=\"0.01,0.03,0.1,0.3\")\n145: alphas = [float(x) for x in a.alphas.split(\",\")]\n152: for al in alphas:\nclose files before this hold: 136"},{"i":21,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31; ls -la authors/claude/scratch/job1 authors/claude/scratch/job2 authors/claude/code/data authors/claude/code/extras | cut -c20-120; ls -la interp/dirs/ | cut -c20-120 | head -60; grep -n \"^def \\|^class \\|^ def \" authors/claude/code/thinklib.py | head -60; sed -n 1,40p authors/claude/queue/deep-thought-gate-g3-b03/record.md | cut -c1-700'"},{"i":22,"kind":"output","long":true,"text":"/data:\n\nkeith 4096 Nov 12 21:26 .\nkeith 4096 Nov 12 21:40 ..\nkeith 1710391 Nov 12 20:53 end_neurons.pt\nkeith 54909320 Nov 12 21:20 think_stats.pt\nkeith 54579909 Nov 12 21:26 think_stats_g2.pt\nkeith 54579909 Nov 12 21:26 think_stats_g3.pt\n\n/extras:\n\nkeith 4096 Nov 12 21:41 .\nkeith 4096 Nov 12 21:40 ..\nkeith 1598 Nov 12 21:39 common_tail.txt\nkeith 699 Nov 12 21:39 e1.txt\nkeith 2039 Nov 12 21:39 e2.txt\nkeith 742 Nov 12 21:39 e3.txt\nkeith 1476 Nov 12 21:41 e4.txt\n\ntch/job1:\n[... 86 lines cut ...]\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 *\nlayer_scalar_L)` with `gbar_L` = mean activation of the new neuron on the late positions and `d_L` as in family A\n(E minus other thought positions): at an average late position the write moves `resid_post` by `dose * d_L`.\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 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. This entry uses `code/data/think_stats_g3.pt`: the class means and neurons of `think_stats.pt` with these gates in place of the first version described under Family B.\n\nControl (`ctrl/`): same code path and statistics; the written unit direction has the sign of every coordinate"},{"i":23,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; /home/keith/gemma-v2/tii-venv/bin/python - <<\"EOF\"\nimport torch\nd=torch.load(\"scratch/job1/raw_mlpin.pt\",map_location=\"cpu\")\ndef show(x,pre=\"\"):\n if isinstance(x,dict):\n for k,v in list(x.items())[:25]:\n if isinstance(v,(dict,)): print(pre,k,\"dict\",len(v)); show(v,pre+\" \")\n elif torch.is_tensor(v): print(pre,k,tuple(v.shape),v.dtype)\n elif isinstance(v,(list,tuple)): print(pre,k,type(v).__name__,len(v),str(v[:6])[:200])\n else: print(pre,k,str(v)[:200])\nshow(d)\ns=torch.load(\"code/data/think_stats_g3.pt\",map_location=\"cpu\")\nshow(s)\nEOF\nsed -n 172,215p code/thinklib.py; grep -n \"raw_mlpin\\|torch.save\" code/think_job.py | head; sed -n 1,60p code/think_refit.py'"},{"i":24,"kind":"output","long":true,"text":" X (13, 6658, 5376) torch.float16\n cls (6658,) torch.int64\n depth (6658,) torch.int64\n ep (6658,) torch.int64\n n (5,) torch.float64\n resid_sum (5, 36, 5376) torch.float64\n mlpin_sum (5, 36, 5376) torch.float64\n act_sum (5, 36, 21504) torch.float64\n absact_sum (36, 21504) torch.float64\n rmsy_sum (5, 36) torch.float64\n rnorm_sum (5, 36) torch.float64\n CL list 36 [24, 25, 26, 27, 28, 29]\n train_instances list 22 ['HIPS__autograd.ac044f0d.func_pm_op_break_chains__mhdjtwmz', 'HIPS__autograd.ac044f0d.func_pm_ctrl_shuffle__rsn9e29k', '[task]', '[task]', 'StingraySoftware__st\n A_proj dict 5\n E dict 2\n mean list 10 [2.8446428775787354, 1.1685497760772705, 10.992581367492676, 1.9841610193252563, 1.4817982912063599, 5.0086140632629395]\n sd list 10 [0.3666640818119049, 0.151480033993721, 1.0210658311843872, 0.26093870401382446, 0.17310023307800293, 0.666898787021637]\n early dict 2\n mean list 10 [2.276689052581787, 1.0085142850875854, 9.492975234985352, 1.397033929824829, 1.4942704439163208, 4.794047832489014]\n sd list 10 [0.6681310534477234, 2.721149206161499, 1.5431852340698242, 0.5406621098518372, 0.5879179835319519, 2.2555582523345947]\n[... 242 lines cut ...]\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))"},{"i":25,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31; date; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH|speedbench|gpu0_session|job[0-9].sh\" | grep -v grep | cut -c1-160; cat authors/claude/scratch/logs/hold3.log; grep -n \"GL\\b\\|GL =\\|LA =\\|CL =\\|seed\\|random\\|sample\" authors/claude/code/thinklib.py authors/claude/code/think_job.py | head -40'"},{"i":26,"kind":"output","long":false,"text":"Fri Nov 13 12:01:28 AM EST 2026\n 893373 35:14 /bin/bash ./run_gpu.sh logs/job5.log ./job5.sh\n 893374 35:14 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 23:27 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026408 10:38 timeout 1740 ./job5.sh\n1026409 10:38 /bin/sh ./job5.sh\n1026421 10:38 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_act --sets 0-59 --alphas 0.01,0.03,0.1,0.3 --budget_s 1390\n1030456 09:19 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/sessH_2350.log ] && break; sleep 30; done; date +%T; head -n\n1054488 03:55 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\nauthors/claude/code/thinklib.py:11:Controls: same code path, the written unit direction has the sign of every coordinate flipped at random (seeded):\nauthors/claude/code/thinklib.py:22:LA = list(range(30, 50, 2))\nauthors/claude/code/thinklib.py:24:GL = LA + LC\nauthors/claude/code/thinklib.py:25:CL = list(range(24, 60))\nauthors/claude/code/thinklib.py:41:def signflip(d, seed):\nauthors/claude/code/thinklib.py:42: g = torch.Generator().manual_seed(seed)\nauthors/claude/code/think_job.py:8:import random\nauthors/claude/code/think_job.py:65: torch.manual_seed(0)\nauthors/claude/code/think_job.py:73: for L in tl.GL:\nauthors/claude/code/think_job.py:86: if L == tl.GL[1]:\nauthors/claude/code/think_job.py:88: for L in tl.GL[2:]:\nauthors/claude/code/think_job.py:89: st[\"gate\"][L] = dict(st[\"gate\"][tl.GL[0]])\nauthors/claude/code/think_job.py:127: gidx = [tl.CL.index(L) for L in tl.GL]\nauthors/claude/code/think_job.py:141: rng = random.Random(1234 + ei)\nauthors/claude/code/think_job.py:205: for gi, L in enumerate(tl.GL):\nauthors/claude/code/think_job.py:251: rng = random.Random(99 + ei)\nauthors/claude/code/think_job.py:265: r = m.forward(ids2, out_pos=[n - 1], capture=(\"mlp_in\",), cap_layers=tl.GL,\nauthors/claude/code/think_job.py:269: for gi, L in enumerate(tl.GL)])\nauthors/claude/code/think_job.py:302: \"\"\"Shape-faithful stand-in for g31.G31 (CPU, random numbers) to test this script's plumbing without the GPU.\"\"\""},{"i":27,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date +%s; sed -n 100,200p code/think_job.py; grep -n \"size:\" queue/*/record.md | cut -c1-200'"},{"i":28,"kind":"output","long":true,"text":"1794546110\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))\n\n\ndef pick_episodes(recs):\n inst = sorted({r[\"instance\"] for r in recs})\n held = {x for i, x in enumerate(inst) if i % 4 == 1}\n\n def nlate(r, cap):\n return sum(1 for p in tl.positions(r, cap)[1] if p[1] == 3)\n best_tr, best_ev = {}, {}\n for r in recs:\n if r[\"instance\"] in held:\n k = nlate(r, NEVAL_CAP)\n if r[\"instance\"] not in best_ev or k > best_ev[r[\"instance\"]][0]:\n best_ev[r[\"instance\"]] = (k, r)\n else:\n k = nlate(r, NTRAIN_CAP)\n if r[\"instance\"] not in best_tr or k > best_tr[r[\"instance\"]][0]:\n[... 74 lines cut ...]\n\ndef finish_stats(m, st, raw):\n st[\"w_post\"] = torch.stack([m.L[L][\"post_feedforward_layernorm\"].float().cpu() for L in tl.CL])\n st[\"ls\"] = torch.tensor([float(m.L[L][\"layer_scalar\"].float()) for L in tl.CL])\n st[\"w_final\"] = m.final_norm.float().cpu()\n st[\"e101\"] = m.emb[101].float().cpu()\n tot = torch.zeros(tl.D, dtype=torch.float64, device=m.emb.device)\n for s in range(0, m.emb.shape[0], 32768):"},{"i":29,"kind":"tool","long":false,"text":"Bash: mkdir -p /c/Users/Owner/wsp31_loop/work/claude/t3 && cd /c/Users/Owner/wsp31_loop/work/claude/t3 && cat > meta_check.py <<'EOF'\n\"\"\"meta_check.py (CPU): rebuild (pos, call) of every row of scratch/job1/raw_mlpin.pt (deterministic sampling of\n[line removed by the dashboard scrubber]\ndiagonal-LDA probe on mlp_in separates them on held-out episodes.\"\"\"\nimport sys, json, random, torch, collections\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0, A + \"/code\")\nimport thinklib as tl, probeset\nrecs = probeset.load(\"/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl\")\nraw = torch.load(A + \"/scratch/job1/raw_mlpin.pt\")\nst = torch.load(A + \"/code/data/think_stats.pt\")\nused = st[\"train_instances\"]\nimport think_job as tj\ntrain, ev = tj.pick_episodes(recs)\nassert [r[\"instance\"] for r in train[:len(used)]] == used, \"episode order differs\"\ntoolname = collections.Counter()\nrows = []\nfor ei, rec in enumerate(train[:len(used)]):\n n, P, non, ids2 = tl.positions(rec, tj.NTRAIN_CAP)\n rng = random.Random(1234 + ei)\n sel = []\n for k, cap in ((0, 40), (1, 80), (2, 60), (3, 120)):\n c = [p for p in P if p[1] == k]\n rng.shuffle(c)\n sel += [(p[0], k, p[2]) for p in c[:cap]]\n rng.shuffle(non)\n sel += [(p, 4, 0) for p in non[:80]]\n sel.sort()\n calls = rec[\"calls\"]\n # tool of each call: the 'tool' point inside the call\n tool = {}\n for pt in rec[\"points\"]:\n if pt[\"kind\"] == \"tool\":\n for ci, c in enumerate(calls):\n if c[\"start\"] <= pt[\"pos\"] + 1 < c[\"start\"] + len(c[\"comp\"]) + 1:\n tool[ci] = pt[\"next\"]; toolname[pt[\"next\"]] += 1\n starts = [c[\"start\"] for c in calls]\n gen_cum = [0]\n for c in calls: gen_cum.append(gen_cum[-1] + len(c[\"comp\"]))\n for (p, k, d) in sel:\n ci = max([i for i, s in enumerate(starts) if s <= p], default=0)\n rows.append(dict(ei=ei, pos=p, cls=k, depth=d, ci=ci, gen=gen_cum[ci] + (p - starts[ci] if starts else 0), tools=[tool.get(i) for i in range(ci)]))\nassert len(rows) == raw[\"cls\"].numel(), (len(rows), raw[\"cls\"].numel())\nassert all(r[\"cls\"] == int(c) for r, c in zip(rows, raw[\"cls\"])) and all(r[\"depth\"] == int(c) for r, c in zip(rows, raw[\"depth\"]))\n[line removed by the dashboard scrubber]\njson.dump(dict(rows=rows, toolname={str(k): v for k, v in toolname.items()}), open(A + \"/scratch/t3_rows.json\", \"w\"))\nEOF\nscp -q meta_check.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && grep -n \"^NTRAIN_CAP\\|^NEVAL_CAP\\|^if __name__\\|^def main\\|^T0\\|^DEADLINE\" think_job.py | head; $PY meta_check.py 2>&1 | tail -5; $PY /home/keith/gemma-v2/wsp31/tools/tok.py text 100 101 2>&1 | tail -3'"},{"i":30,"kind":"output","long":false,"text":"14:T0 = time.time()\n30:NTRAIN_CAP, NEVAL_CAP = 11000, 8000\n31:DEADLINE_CAPTURE, DEADLINE = 600, 1400\n336:def main():\n377:if __name__ == \"__main__\":\n[line removed by the dashboard scrubber]\n['<|channel>', '']"},{"i":31,"kind":"tool","long":false,"text":"Bash: cat > state_auc.py <<'EOF'\n\"\"\"state_auc.py (CPU): held-out-episode AUC of diagonal-LDA probes on mlp_in for episode-state labels.\"\"\"\nimport sys, json, torch\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0, A + \"/code\")\nimport thinklib as tl\nraw = torch.load(A + \"/scratch/job1/raw_mlpin.pt\")\nJ = json.load(open(A + \"/scratch/t3_rows.json\")); rows = J[\"rows\"]\nED = {8982, 5986}\nei = torch.tensor([r[\"ei\"] for r in rows]); cls = raw[\"cls\"]; dep = raw[\"depth\"]\ngen = torch.tensor([r[\"gen\"] for r in rows]); ci = torch.tensor([r[\"ci\"] for r in rows])\nedited = torch.tensor([any(t in ED for t in r[\"tools\"]) for r in rows])\nthink = (cls >= 1) & (cls <= 3)\nq = torch.tensor([0.1, 0.25, 0.5, 0.75, 0.9])\nprint(\"gen quantiles (thought rows)\", torch.quantile(gen[think].float(), q).tolist(), \"ci\", torch.quantile(ci[think].float(), q).tolist())\nprint(\"edited share of thought rows %.2f; episodes with an edit %d of %d\" % (float(edited[think].float().mean()), len({int(e) for e, x in zip(ei, edited) if x}), int(ei.max()) + 1))\nper_ep = [(int(e), int(gen[ei == e].max()), int(ci[ei == e].max()), bool(edited[ei == e].any())) for e in range(int(ei.max()) + 1)]\nprint(\"per episode (ei, max gen, max ci, edited)\", per_ep)\nheld = (ei % 3 == 1)\ndef run(name, pos, neg, layers=(0, 2, 4, 6, 9, 10, 12)):\n out = []\n for gi in layers:\n X = raw[\"X\"][gi].float()\n tr = ~held\n mp, mn = X[tr & pos].mean(0), X[tr & neg].mean(0)\n v = 0.5 * (X[tr & pos].var(0) + X[tr & neg].var(0)) + 1e-6\n w = (mp - mn) / v\n s = X @ w\n a_tr = tl.auc(s[tr & pos], s[tr & neg]); a_he = tl.auc(s[held & pos], s[held & neg])\n out.append(\"L%d %.2f/%.2f\" % (tl.GL[gi], a_tr, a_he))\n print(name, \"n+ %d/%d n- %d/%d (train/held)\" % (int((~held & pos).sum()), int((held & pos).sum()), int((~held & neg).sum()), int((held & neg).sum())), \" \".join(out), flush=True)\nprint(\"GL\", tl.GL)\nrun(\"depth>=800 vs <200|non\", think & (dep >= 800), (think & (dep < 200)) | (cls == 4))\nrun(\"late gen>=4000 vs gen<2000 (thought)\", think & (gen >= 4000), think & (gen < 2000))\nrun(\"late gen>=3000 vs gen<1500 (thought)\", think & (gen >= 3000), think & (gen < 1500))\nrun(\"ci>=10 vs ci<=4 (thought)\", think & (ci >= 10), think & (ci <= 4))\nrun(\"pre-edit vs post-edit (thought)\", think & ~edited, think & edited)\nrun(\"pre-edit vs post-edit (thought, ci>=6)\", think & ~edited & (ci >= 6), think & edited & (ci >= 6))\nrun(\"trouble: gen>=4000&no edit vs rest of thought\", think & (gen >= 4000) & ~edited, think & ~((gen >= 4000) & ~edited))\nrun(\"trouble3k: gen>=3000&no edit vs rest of thought\", think & (gen >= 3000) & ~edited, think & ~((gen >= 3000) & ~edited))\nrun(\"troubleD: gen>=3000&no edit&depth>=400 vs rest+non\", think & (gen >= 3000) & ~edited & (dep >= 400), (think & ~((gen >= 2000) & ~edited & (dep >= 200))) | (cls == 4))\nEOF\nscp -q state_auc.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && $PY state_auc.py 2>&1 | tail -16; $PY /home/keith/gemma-v2/wsp31/tools/tok.py text 4503 1399 8982 15037 5986 | tail -2'"},{"i":32,"kind":"output","long":false,"text":"gen quantiles (thought rows) [182.0, 498.0, 1168.0, 2155.0, 3019.0] ci [0.0, 2.0, 3.0, 7.0, 10.0]\nedited share of thought rows 0.07; episodes with an edit 5 of 22\nper episode (ei, max gen, max ci, edited) [(0, 5324, 4, False), (1, 5823, 4, False), (2, 4323, 5, False), (3, 4902, 10, False), (4, 3408, 6, False), (5, 2622, 9, False), (6, 3780, 7, False), (7, 3913, 4, False), (8, 2713, 3, False), (9, 4043, 16, True), (10, 4225, 3, False), (11, 3359, 14, True), (12, 5099, 8, True), (13, 3859, 10, False), (14, 1686, 8, False), (15, 3403, 19, True), (16, 3007, 7, False), (17, 2996, 9, False), (18, 3284, 5, False), (19, 5038, 3, False), (20, 3192, 11, True), (21, 3116, 13, False)]\nGL [30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 54, 56, 58]\ndepth>=800 vs <200|non n+ 369/241 n- 2857/1332 (train/held) L30 0.92/0.81 L34 0.93/0.81 L38 0.93/0.81 L42 0.92/0.76 L48 0.90/0.79 L54 0.85/0.74 L58 0.81/0.77\nlate gen>=4000 vs gen<2000 (thought) n+ 74/63 n- 2217/1152 (train/held) L30 0.99/0.66 L34 0.98/0.63 L38 0.97/0.67 L42 0.98/0.66 L48 0.96/0.74 L54 0.95/0.73 L58 0.92/0.75\nlate gen>=3000 vs gen<1500 (thought) n+ 364/143 n- 1835/954 (train/held) L30 0.95/0.74 L34 0.95/0.75 L38 0.95/0.77 L42 0.92/0.60 L48 0.93/0.66 L54 0.91/0.65 L58 0.88/0.74\nci>=10 vs ci<=4 (thought) n+ 495/70 n- 1771/1332 (train/held) L30 0.93/0.67 L34 0.95/0.64 L38 0.90/0.67 L42 0.84/0.64 L48 0.81/0.66 L54 0.76/0.65 L58 0.70/0.66\npre-edit vs post-edit (thought) n+ 2881/1496 n- 324/0 (train/held) L30 0.96/nan L34 0.97/nan L38 0.93/nan L42 0.88/nan L48 0.85/nan L54 0.81/nan L58 0.72/nan\npre-edit vs post-edit (thought, ci>=6) n+ 953/127 n- 306/0 (train/held) L30 0.98/nan L34 0.98/nan L38 0.95/nan L42 0.91/nan L48 0.87/nan L54 0.84/nan L58 0.72/nan\ntrouble: gen>=4000&no edit vs rest of thought n+ 62/63 n- 3143/1433 (train/held) L30 0.99/0.54 L34 0.99/0.53 L38 0.98/0.61 L42 0.97/0.68 L48 0.95/0.72 L54 0.93/0.71 L58 0.89/0.77\ntrouble3k: gen>=3000&no edit vs rest of thought n+ 228/143 n- 2977/1353 (train/held) L30 0.91/0.52 L34 0.92/0.54 L38 0.90/0.60 L42 0.91/0.58 L48 0.87/0.60 L54 0.88/0.60 L58 0.80/0.65\ntroubleD: gen>=3000&no edit&depth>=400 vs rest+non n+ 168/114 n- 3875/1818 (train/held) L30 0.95/0.57 L34 0.96/0.57 L38 0.96/0.63 L42 0.95/0.61 L48 0.93/0.66 L54 0.94/0.65 L58 0.89/0.70\n['run', 'read', 'edit', 'submit', 'write']"},{"i":33,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && date +%T && sed -n 1,12p fail_stats.py | cut -c1-200; ls $R/results/wsp31/batches/b001/; for a in base aa zero; do wc -l < $R/results/wsp31/batches/b001/$a/rebench/shard0_gpu0/task_results.jsonl; done; timeout 120 $PY fail_stats.py 2>&1 | tail -30 | cut -c1-260; $PY /home/keith/gemma-v2/wsp31/tools/train_size.py ../scratch/size_old.json g3=../queue/deep-thought-gate-g3-b03/edit n25=../queue/end-neurons-x25/edit 2>&1 | tail -6 | cut -c1-300'"},{"i":34,"kind":"output","long":true,"text":"00:04:04\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\"]),\naa\naa.launch.log\nbase\nbase.launch.log\nserver.log\nzero\nzero.launch.log\n[... 31 lines cut ...]\n F [task] dur 354 p1 352 tc 27 llm 29 patch 1801 err Test failures/errors recorded in JUnit XML (failures=6, erro {\"collected_tests\": 8, \"competition_junit_ok\": false, \"error\": \"Test failures/errors recorded in JUnit XML (failures=6, errors=0\n F [task] dur 467 p1 455 tc 28 llm 28 patch 3991 err Test failures/errors recorded in JUnit XML (failures=165, er {\"collected_tests\": 917, \"competition_junit_ok\": false, \"error\": \"Test failures/errors recorded in JUnit XML (failures=165,\ng3 {\"r\": 1, \"scale\": 2.0, \"changed\": 30, \"of\": 420, \"net_rel\": 0.01523, \"rel_median\": 0.01361, \"rel_min\": 0.006193, \"rel_max\": 0.1016, \"rel_max_mod\": \"32.gate_proj\", \"total_rel\": 0.04518, \"total_fro\": 31.4, \"stable_rank_median\": 1.0, \"part_rank_median\": 1.0}\n by type {'down_proj': 0.01361, 'gate_proj': 0.05059, 'up_proj': 0.00747}\n by depth (10-layer bands) [None, None, None, 0.00941, 0.03008, None]\nn25 {\"r\": 16, \"scale\": 2.0, \"changed\": 3, \"of\": 420, \"net_rel\": 0.02377, \"rel_median\": 0.1929, \"rel_min\": 0.1734, \"rel_max\": 0.5997, \"rel_max_mod\": \"59.down_proj\", \"total_rel\": 0.3916, \"total_fro\": 49.03, \"stable_rank_median\": 1.848, \"part_rank_median\": 1.997}\n by type {'down_proj': 0.19292}\n by depth (10-layer bands) [None, None, None, None, None, 0.19292]"},{"i":35,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && $PY - <<\"EOF\"\nimport json,collections\nR=\"/home/keith/gemma-v2/results/wsp31/batches/b001/\"\ns1=set(open(\"/home/keith/gemma-v2/wsp31/queue/sets/stage1_ids.txt\").read().split())\ntot=collections.Counter(); per={}\nfor arm in (\"base\",\"aa\",\"zero\"):\n for l in open(R+arm+\"/rebench/shard0_gpu0/task_results.jsonl\"):\n r=json.loads(l)\n if r[\"duration_seconds\"]<5: tot[\"crash\"]+=1; continue\n lim=r[\"duration_seconds\"]>=445; cap=r[\"tool_calls\"]>=30; emp=not r[\"patch_chars\"]\n k=(\"solved\" if r[\"resolved\"] else \"fail\")+(\"/limit\" if lim else (\"/cap\" if cap else \"/finished\"))+(\"/empty\" if emp else \"\")\n tot[k]+=1; tot[\"n\"]+=1; tot[\"in_s1\"]+=r[\"instance_id\"] in s1\n per.setdefault(r[\"instance_id\"],[]).append(int(bool(r[\"resolved\"])))\nfor k,v in sorted(tot.items()): print(k,v)\nc=collections.Counter(tuple(sorted(v)) for v in per.values() if len(v)==3)\nprint(\"tasks with 3 arms:\",sum(c.values()),dict(c))\nEOF'"},{"i":36,"kind":"output","long":false,"text":"crash 7\nfail/cap 19\nfail/cap/empty 7\nfail/finished 47\nfail/limit 29\nfail/limit/empty 31\nin_s1 288\nn 288\nsolved/cap 14\nsolved/finished 133\nsolved/limit 8\ntasks with 3 arms: 94 {(1, 1, 1): 43, (0, 0, 0): 36, (0, 0, 1): 7, (0, 1, 1): 8}"},{"i":37,"kind":"tool","long":false,"text":"Bash: cat > seq_stats.py <<'EOF'\n\"\"\"seq_stats.py (CPU): tool sequences of b001 episodes by outcome class (did it test after its last edit, when did it submit).\"\"\"\nimport json, collections, sys, os, re\nR = \"/home/keith/gemma-v2/results/wsp31/batches/b001/\"\nrows = []\nfor arm in (\"base\", \"aa\", \"zero\"):\n for l in open(R + arm + \"/rebench/shard0_gpu0/task_results.jsonl\"):\n r = json.loads(l)\n if r[\"duration_seconds\"] < 5: continue\n p = r.get(\"request_log_path\")\n if not p or not os.path.exists(p): continue\n seq = []; think = []\n for ln in open(p):\n q = json.loads(ln); m = q.get(\"response_message\") or {}\n tcs = m.get(\"tool_calls\") or []\n nm = tcs[0][\"function\"][\"name\"] if tcs else \"none\"\n arg = tcs[0][\"function\"][\"arguments\"] if tcs else \"\"\n if not isinstance(arg, str): arg = json.dumps(arg)\n istest = nm.startswith(\"run\") and re.search(r\"pytest|unittest|tox|nose|test\", arg) is not None\n seq.append((\"T\" if istest else nm[0]) if nm != \"none\" else \"-\")\n[line removed by the dashboard scrubber]\n think.append(ct.index(101) if 101 in ct else len(ct))\n lim = r[\"duration_seconds\"] >= 445; cap = r[\"tool_calls\"] >= 30; emp = not r[\"patch_chars\"]\n k = (\"solved\" if r[\"resolved\"] else \"fail\") + (\"/limit\" if lim else (\"/cap\" if cap else \"/finished\")) + (\"/empty\" if emp else \"\")\n rows.append(dict(k=k, seq=\"\".join(seq), think=think, dur=r[\"duration_seconds\"], inst=r[\"instance_id\"], arm=arm))\nby = collections.defaultdict(list)\nfor r in rows: by[r[\"k\"]].append(r)\ndef last_edit_then_test(s):\n i = max(s.rfind(\"e\"), s.rfind(\"w\"))\n return i >= 0 and \"T\" in s[i:]\nimport statistics as st\nfor k, v in sorted(by.items()):\n n = len(v)\n print(\"%-20s n %3d | calls med %4.1f | dur med %3.0f | any edit %.2f | any test %.2f | test after last edit %.2f | submit call %.2f | first edit idx med %s | think tok med %5.0f | max thought med %5.0f\" % (\n k, n, st.median(len(r[\"seq\"]) for r in v), st.median(r[\"dur\"] for r in v),\n sum((\"e\" in r[\"seq\"] or \"w\" in r[\"seq\"]) for r in v) / n, sum(\"T\" in r[\"seq\"] for r in v) / n,\n sum(last_edit_then_test(r[\"seq\"]) for r in v) / n, sum(\"s\" in r[\"seq\"] for r in v) / n,\n st.median([min([i for i, c in enumerate(r[\"seq\"]) if c in \"ew\"]) for r in v if (\"e\" in r[\"seq\"] or \"w\" in r[\"seq\"])] or [-1]),\n st.median(sum(r[\"think\"]) for r in v), st.median(max(r[\"think\"] or [0]) for r in v)))\nfor k in (\"fail/finished\", \"solved/finished\", \"fail/limit\", \"fail/limit/empty\", \"fail/cap\"):\n print(k)\n for r in by[k][:9]: print(\" %-45s %3.0fs %s\" % (r[\"inst\"][:45], r[\"dur\"], r[\"seq\"]))\njson.dump(rows, open(\"/home/keith/gemma-v2/wsp31/authors/claude/scratch/t3_seq.json\", \"w\"))\nEOF\nscp -q seq_stats.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && $PY seq_stats.py 2>&1 | tail -60 | cut -c1-250; date +%T; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH\" | grep -v grep | cut -c1-120'"},{"i":38,"kind":"output","long":true,"text":"fail/cap n 19 | calls med 32.0 | dur med 341 | any edit 0.84 | any test 0.68 | test after last edit 0.42 | submit call 1.00 | first edit idx med 20.5 | think tok med 6311 | max thought med 1311\nfail/cap/empty n 7 | calls med 34.0 | dur med 293 | any edit 0.86 | any test 0.43 | test after last edit 0.14 | submit call 1.00 | first edit idx med 30.0 | think tok med 6052 | max thought med 1345\nfail/finished n 47 | calls med 23.0 | dur med 266 | any edit 1.00 | any test 0.87 | test after last edit 0.74 | submit call 1.00 | first edit idx med 9 | think tok med 4082 | max thought med 1155\nfail/limit n 29 | calls med 24.0 | dur med 458 | any edit 0.97 | any test 0.66 | test after last edit 0.52 | submit call 0.10 | first edit idx med 12.0 | think tok med 8444 | max thought med 2594\nfail/limit/empty n 31 | calls med 21.0 | dur med 455 | any edit 0.29 | any test 0.74 | test after last edit 0.06 | submit call 0.06 | first edit idx med 16 | think tok med 8934 | max thought med 2631\nsolved/cap n 14 | calls med 31.0 | dur med 356 | any edit 1.00 | any test 0.93 | test after last edit 0.71 | submit call 1.00 | first edit idx med 22.5 | think tok med 5822 | max thought med 1282\nsolved/finished n 133 | calls med 17.0 | dur med 209 | any edit 1.00 | any test 0.94 | test after last edit 0.90 | submit call 1.00 | first edit idx med 7 | think tok med 3571 | max thought med 962\nsolved/limit n 8 | calls med 26.0 | dur med 456 | any edit 1.00 | any test 0.75 | test after last edit 0.75 | submit call 0.00 | first edit idx med 8.5 | think tok med 8873 | max thought med 2802\nfail/finished\n [task] 224s rrrrrTrrTTeeeTeeeererrTrrrTrs\n [task] 133s rrrTTeTTTTrs\n [task] 272s rrrTTTeTTTTTrrrrrreTTrrs\n cool-RR__PySnooper.57472b46.lm_rewrite__031xv 419s rrrrrrerrrrrrrrrrrrrrrrrTrs\n [task] 260s rrrrrrrrrrrrrrrrrrrereerrrrers\n [task] 159s rrrrrrrrrrrTreeerrrTTTrrs\n[line removed by the dashboard scrubber]\n [task] 403s rrrrrrrTrrrrrrrre-rrrrrrs\n jd__tenacity.0d40e76f.combine_module__jua0mom 241s rrrrrrerrrTerTTTTTrTrTTTrrrrs\nsolved/finished\n Mimino666__langdetect.a1598f1a.func_basic__rk 181s rrrrrerTrrrerrrrrrrTeeeeeerTrTrrrrs\n[... 36 lines cut ...]\n jd__tenacity.0d40e76f.combine_module__jua0mom 298s rrrrrerrrrrrrrrrrrrrrrrrrrrrrrrs\n jd__tenacity.0d40e76f.func_basic__85q611gl 331s rTrrrrrrrrrrrrrrrrrrrrrrrrrrres\n00:04:45\n 893374 38:31 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 26:45 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 13:55 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_act --sets 0-59 --al\n1054488 07:12 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1067894 02:57 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/sessH_2350.log ] &&"},{"i":39,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && $PY - <<\"EOF\"\nimport json,collections,statistics as st\nrows=json.load(open(\"../scratch/t3_seq.json\"))\ndef fe(s):\n i=[j for j,c in enumerate(s) if c in \"ew\"]\n return i[0] if i else 999\nprint(\"P(solved | no edit within first k calls), n\")\nfor k in (0,4,6,8,10,12,15,18,22):\n v=[r for r in rows if fe(r[\"seq\"])>=k and len(r[\"seq\"])>=k]\n print(k, len(v), \"%.2f\"%(sum(r[\"k\"].startswith(\"solved\") for r in v)/max(len(v),1)), \"| edited at exactly k..k+1:\", \"%.2f\"%(lambda u: sum(r[\"k\"].startswith(\"solved\") for r in u)/max(len(u),1))([r for r in rows if k<=fe(r[\"seq\"])&1 | cut -c1-430 | head -90"},{"i":42,"kind":"output","long":true,"text":"{\"type\": \"function\", \"function\": {\"name\": \"run_command\", \"description\": \"Execute a shell command inside the repository sandbox.\\n\\nYou can run any shell command (Python scripts, tests, git commands, etc.).\\nThe command runs in the repository workspace. No network access.\\n\\nArgs:\\n command: Shell command to execute (e.g., \\\"pytest tests/\\\", \\\"python3 script.py\\\").\\n\", \"parameters\": {\"type\": \"object\", \"properties\":\n{\"type\": \"function\", \"function\": {\"name\": \"read_file\", \"description\": \"Read the contents of a file from the repository workspace.\\n\\nSupports reading specific line ranges using start_line and end_line (1-indexed).\\nTo avoid overwhelming the context window, large files are automatically truncated\\nto a maximum number of lines (default: 150 lines).\\n\\nArgs:\\n filepath: Relative path within the workspace directory (e\n[line removed by the dashboard scrubber]\n{\"type\": \"function\", \"function\": {\"name\": \"write_file\", \"description\": \"Create or overwrite a file in the workspace.\\n\\nArgs:\\n filepath: Relative path to the file to create or overwrite.\\n content: Full content of the file.\\n\", \"parameters\": {\"type\": \"object\", \"properties\": {\"filepath\": {\"type\": \"string\"}, \"content\": {\"type\": \"string\"}}, \"required\": [\"filepath\", \"content\"]}}}\n{\"type\": \"function\", \"function\": {\"name\": \"get_status\", \"description\": \"Check active budget details (time, tool calls) and active patch details.\", \"parameters\": {\"type\": \"object\", \"properties\": {}}}}\n{\"type\": \"function\", \"function\": {\"name\": \"submit_patch\", \"description\": \"Capture the current working tree modifications as the agent's submission.\\n\\nThis generates a unified git diff against the baseline commit. The agent\\ncan submit multiple times; only the final submission is evaluated.\\n\", \"parameters\": {\"type\": \"object\", \"properties\": {}}}}\n[task].jsonl\n think 200 run_command {\"command\": \"grep -rn --include=\\\"*.py\\\" \\\"update_incumbents\\\" .\"}\n think 45 run_command {\"command\": \"grep -rn --include=\\\"*.py\\\" \\\"_rejected_config_ids\\\" .\"}\n think 58 read_file {\"filepath\": \"smac/intensifier/abstract_intensifier.py\", \"start_line\": 450}\n think 353 read_file {\"filepath\": \"smac/intensifier/abstract_intensifier.py\", \"start_line\": 710, \"end_line\": 730}\n think 3910 read_file {\"filepath\": \"smac/facade/hyperparameter_optimization_facade.py\"}\n think 54 read_file {\"filepath\": \"smac/main/smbo.py\"}\n think 15 run_command {\"command\": \"grep -n \\\"def tell\\\" smac/main/smbo.py\"}\n think 25 read_file {\"end_line\": 200, \"filepath\": \"smac/main/smbo.py\", \"start_line\": 166}\n think 13 read_file {\"end_line\": 250, \"filepath\": \"smac/main/smbo.py\", \"start_line\": 201}\n think 143 read_file {\"end_line\": 650, \"filepath\": \"smac/intensifier/abstract_intensifier.py\", \"start_line\": 600}\n think 19 run_command {\"command\": \"grep -n \\\"def __post_init__\\\" smac/intensifier/abstract_intensifier.py\"}\n think 15 read_file {\"end_line\": 200, \"filepath\": \"smac/intensifier/abstract_intensifier.py\", \"start_line\": 134}\n think 325 read_file {\"filepath\": \"smac/intensifier/intensifier.py\"}\n[... 41 lines cut ...]\n think 14 read_file {\"end_line\": 100, \"filepath\": \"copier/_cli.py\", \"start_line\": 76}\n think 1171 run_command {\"command\": \"grep -n \\\"def run_copy\\\" copier/_main.py\"}\n think 21 read_file {\"end_line\": 1700, \"filepath\": \"copier/_main.py\", \"start_line\": 1664}\n think 1713 run_command {\"command\": \"grep -rn \\\"clone(\\\" . | grep \\\".py\\\"\"}\n think 657 read_file {\"filepath\": \"copier/_vcs.py\", \"start_line\": 178}\nCounter({'run_command': 3730, 'read_file': 2130, 'edit_file': 546, 'submit_patch': 239, 'write_file': 37, 'get_status': 19})\n[(('end_line', 'filepath', 'start_line'), 1221), (('filepath',), 663), (('filepath', 'start_line'), 220), (('filepath', 'start_line\"'), 13), (('end_line', 'filepath', 'start_line\"'), 8)]\nspan quantiles [16.0, 21.0, 25.0, 31.0, 41.0, 50.0, 59.4, 95.0, 101.0] n 1221"},{"i":43,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && date +%T && sed -n 1,170p thinklib.py'"},{"i":44,"kind":"output","long":true,"text":"00:06:41\n\"\"\"thinklib.py (author claude): closed-form \"end the thought\" edits for Gemma 4 31B.\n\nThree families, all built from class means of activations at logged thought positions (no optimiser anywhere):\n A always-on (Concept Country form): rank-one down_proj term c (x) v^T in layers 30..48 even; v = unit mean\n neuron-activation vector over thought positions, c writes the stream direction d_end = mean(resid at the last\n[line removed by the dashboard scrubber]\n B gated: one neuron per layer 30..48 even is replaced (\"set\") by a detector of \"deep inside a long thought\"\n[line removed by the dashboard scrubber]\n C gated, output side: the same kind of detector neuron in layers 54, 56, 58 writes the direction that raises the\n logit: (E[101] - mean E) * final_norm_weight.\nControls: same code path, the written unit direction has the sign of every coordinate flipped at random (seeded):\nidentical per-coordinate magnitudes, so identical norm in the stream, in the raw MLP output and in |dW|_F.\n\"\"\"\nimport math\nimport sys\n\nimport torch\n\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\")\n[... 142 lines cut ...]\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"},{"i":45,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && sed -n 14,98p think_job.py; sed -n 200,377p think_job.py'"},{"i":46,"kind":"output","long":true,"text":"T0 = time.time()\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 thinklib as tl # noqa: E402\nfrom edits import EditSet # noqa: E402\n\nPROBE = \"/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl\"\nMOCK = \"--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\")\n# 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), (\"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)]\nNTRAIN_CAP, NEVAL_CAP = 11000, 8000\nDEADLINE_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[... 235 lines cut ...]\n log(\"d_end\", L, \"norm %.2f\" % float(d.norm()), \"resid norm late %.2f\" % float(st[\"rnorm_sum\"][3, i] / st[\"n\"][3]))\n names = build_grid(st, S + \"/ad\")\n log(\"grid built\")\n evaluate(m, st, names, S + \"/ad\", ev[:8])\n log(\"done\")\n\n\nif __name__ == \"__main__\":"},{"i":47,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31 && . /home/keith/gemma-v2/env.sh && date +%T && grep -E \"captured|model loaded|stats saved|grid built|evaluated|done\" authors/claude/scratch/logs/job1.log | sed -n \"1,4p;20,24p;\\$p\" | cut -c1-150; $PY - <<\"EOF\"\nimport json\nf=open(\"/home/keith/gemma-v2/wsp31/tools/data/probe_b001.jsonl\")\nr=json.loads(f.readline()); print({k:(v if not isinstance(v,(list,dict)) else (type(v).__name__,len(v))) for k,v in r.items()})\nprint(r[\"points\"][:3], {k:(v if not isinstance(v,list) else len(v)) for k,v in r[\"calls\"][0].items()})\nn=1; inst={r[\"instance\"]}; L=[len(r[\"ids\"])]\nfor l in f:\n r=json.loads(l); n+=1; inst.add(r[\"instance\"]); L.append(len(r[\"ids\"]))\ns1=set(open(\"/home/keith/gemma-v2/wsp31/queue/sets/stage1_ids.txt\").read().split())\nprint(n,len(inst),\"in s1\",len(inst&s1), \"len med\", sorted(L)[len(L)//2], \"max\", max(L))\nEOF\n$PY tools/vgen.py -h 2>&1 | head -40; grep -n \"def forward\\|def base_weight\\|def rms\\|def logprobs_from_final\\|stop_after\\|cap_pos\" tools/g31.py | head -20; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH\" | grep -v grep | cut -c1-100'"},{"i":48,"kind":"output","long":true,"text":"00:07:47\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]\n[ 214.4] captured 18 HIPS__autograd.ac044f0d.lm_rew n 11000 pos 226 cls [6, 80, 60, 0, 80]\n[ 224.6] captured 19 Mimino666__langdetect.a1598f1a n 11000 pos 207 cls [3, 80, 44, 0, 80]\n[ 235.0] captured 20 PyCQA__flake8.cf1542ce.lm_rewr n 11000 pos 232 cls [12, 80, 60, 0, 80]\n[ 245.2] captured 21 PyCQA__flake8.cf1542ce.lm_rewr n 11000 pos 234 cls [14, 80, 60, 0, 80]\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, 10.992581367\n[ 874.6] evaluated 4 HIPS__autograd.ac044f0d.lm_rew rows 991 arms 18\n{'trace': '/home/keith/gemma-v2/results/wsp31/batches/b001/base/rebench/shard0_gpu0/requests/Mimino666__langdetect.a1598f1a.func_basic__rku38xih.jsonl', 'run': '/home/keith/gemma-v2/results/wsp31/batches/b001/base', 'instance': 'Mimino666__langdetect.a1598f1a.func_basic__rku38xih', 'rollout': 0, 'resolved': True, 'tool_calls': 28, 'n': 16000, 'full_n': 17470, 'calls_total': 35, 'ids': ('list', 16000), 'points': ('list', 48), 'calls': ('list', 16)}\n[{'pos': 3318, 'kind': 'turn', 'turn': 0, 'next': 100, 'exact': True}, {'pos': 3455, 'kind': 'think', 'turn': 0, 'next': 48, 'exact': False}, {'pos': 3458, 'kind': 'tool', 'turn': 0, 'next': 4503, 'exact': False}] {'start': 3319, 'comp': 166, 'lp': 166}\n166 58 in s1 58 len med 14898 max 16000\nusage: vgen.py [-h] [--port PORT] [--at {after,close,cont,forced,turn}]\n [--n N] [--per-record PER_RECORD] [--records RECORDS]\n[line removed by the dashboard scrubber]\n [--streams STREAMS] [--show SHOW] [--show-chars SHOW_CHARS]\n [--probeset PROBESET] [--champion CHAMPION] [--budget BUDGET]\n [--out OUT] [--selftest]\n[... 29 lines cut ...]\n307: caps[name].append(x[cap_pos_t].float().cpu())\n344: if stop_after is not None and l >= stop_after:\n357: def logprobs_from_final(self, hp):\n 893374 41:34 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 29:47 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 16:57 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_\n1054488 10:15 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1067894 05:59 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/"},{"i":49,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\net_job.py"},{"i":50,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\net_job.py (file state is current in your context \u2014 no need to Read it back)"},{"i":51,"kind":"tool","long":false,"text":"Bash: scp -q net_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && date +%T && (time timeout 600 $PY code/net_job.py --mock) 2>&1 | tail -40 | cut -c1-330; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH\" | grep -v grep | cut -c1-100'"},{"i":52,"kind":"output","long":true,"text":"00:09:56\n[ 0.4] deadline 1120 capture deadline 420 train 43 eval 15\n[ 0.4] model loaded\n[ 0.8] captured 0 [task] n 11000 rows 298 groups [6, 252, 56, 196, 70, 120]\n[line removed by the dashboard scrubber]\n[ 1.4] captured 2 cool-RR__PySnooper.57472b46.lm n 11000 rows 378 groups [6, 332, 175, 9, 116, 138]\n[ 1.8] captured 3 benoitc__gunicorn.bacbf8aa.fun n 11000 rows 383 groups [10, 333, 94, 15, 103, 19]\n[ 1.8] directions: episodes for d_commit 3 split-half cos (every 6th layer) ['1.00'] cos(end, com) ['0.00']\n[ 2.3] gate deep 0 j 10591 auc 0.998 pre/post-deep auc 0.536 gbar 31.60 {'E': '0.59', 'd_lt100': '0.02', 'd100_400': '0.45', 'd400_800': '0.68', 'd_ge800': '1.00', 'non': '0.04', 'pre_deep': '0.86', 'post_deep': '0.86', 'post_all': '0.67'}\n[ 2.3] gate pre 0 j 11465 auc 0.992 pre/post-deep auc 0.999 gbar 17.93 {'E': '0.11', 'd_lt100': '0.01', 'd100_400': '0.12', 'd400_800': '0.55', 'd_ge800': '0.65', 'non': '0.01', 'pre_deep': '0.98', 'post_deep': '0.01', 'post_all': '0.01'}\n[ 2.8] gate deep 20 j 1419 auc 0.998 pre/post-deep auc 0.548 gbar 28.69 {'E': '0.37', 'd_lt100': '0.03', 'd100_400': '0.41', 'd400_800': '0.64', 'd_ge800': '1.00', 'non': '0.03', 'pre_deep': '0.85', 'post_deep': '0.82', 'post_all': '0.66'}\n[ 2.8] gate pre 20 j 15832 auc 0.994 pre/post-deep auc 1.000 gbar 22.04 {'E': '0.19', 'd_lt100': '0.01', 'd100_400': '0.13', 'd400_800': '0.56', 'd_ge800': '0.64', 'non': '0.01', 'pre_deep': '0.98', 'post_deep': '0.00', 'post_all': '0.01'}\n[ 3.3] gate deep 40 j 15774 auc 0.999 pre/post-deep auc 0.550 gbar 34.30 {'E': '0.78', 'd_lt100': '0.03', 'd100_400': '0.45', 'd400_800': '0.62', 'd_ge800': '1.00', 'non': '0.03', 'pre_deep': '0.85', 'post_deep': '0.80', 'post_all': '0.65'}\n[ 3.3] gate pre 40 j 13349 auc 0.995 pre/post-deep auc 1.000 gbar 23.73 {'E': '0.19', 'd_lt100': '0.02', 'd100_400': '0.17', 'd400_800': '0.56', 'd_ge800': '0.65', 'non': '0.01', 'pre_deep': '0.99', 'post_deep': '0.01', 'post_all': '0.01'}\n[ 3.8] gate deep 59 j 7874 auc 0.999 pre/post-deep auc 0.521 gbar 29.96 {'E': '0.52', 'd_lt100': '0.01', 'd100_400': '0.34', 'd400_800': '0.58', 'd_ge800': '1.00', 'non': '0.03', 'pre_deep': '0.81', 'post_deep': '0.82', 'post_all': '0.63'}\n[ 3.8] gate pre 59 j 19256 auc 0.993 pre/post-deep auc 1.000 gbar 18.02 {'E': '0.15', 'd_lt100': '0.01', 'd100_400': '0.11', 'd400_800': '0.54', 'd_ge800': '0.66', 'non': '0.00', 'pre_deep': '0.98', 'post_deep': '0.01', 'post_all': '0.01'}\n[ 3.9] stats saved; rows 1304 episodes 4\n[ 3.9] raw saved\n[ 3.9] built D_E05 r 1 rho min/med/max 0.047 0.049 0.051\n[ 3.9] built P_C05 r 1 rho min/med/max 0.036 0.039 0.040\n[... 7 lines cut ...]\nreal\t0m18.023s\nuser\t0m48.191s\nsys\t0m24.771s\n 893374 44:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 32:13 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 19:23 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_\n1054488 12:41 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1067894 08:25 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/"},{"i":53,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/tools && sed -n 1,60p vgen.py | cut -c1-220; grep -n \"add_argument\" vgen.py | cut -c1-200; cat gpu0_session.sh | head -50 | cut -c1-200'"},{"i":54,"kind":"output","long":true,"text":"#!/usr/bin/env python\n[line removed by the dashboard scrubber]\n[--champion current|DIR] [--probeset data/probe_calls.jsonl] [--out FILE.json] | vgen.py --selftest: what does the\nserved model WRITE from a decision point, unedited (or champion) next to each edit? Run inside a gpu0 session (a\n[line removed by the dashboard scrubber]\nagent then produced (a per-call probe set from `reqset.py --per-call`), cut at a labelled point:\n turn the prompt of the call: the model writes its whole answer (thought, then tool call);\n cont inside the logged thought, at a place where the agent went on thinking;\n[line removed by the dashboard scrubber]\n forced a cont position with appended: the thought is cut there by force, what follows a cut thought;\n after just after the real .\n[line removed by the dashboard scrubber]\nthe tool prints: the outcome classes of dose.py (well-formed tool call, malformed, thinking never closed, empty,\n[line removed by the dashboard scrubber]\nthe share that closes at once; cont / close / turn only), the share choosing the tool the agent chose in the log,\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nloaded under a content-hash name (as champion + edit when --champion is given) and unloaded afterwards. These are\nsingle greedy continuations of contexts from unedited trajectories: they show what an edit does to the next turn,\nnot whether a task gets solved. --selftest runs the bookkeeping on logged answers (CPU, no server).\"\"\"\n[... 98 lines cut ...]\nif [ -n \"$MODEL\" ]; then\n setsid $T/serve_dir.sh $MODEL 0 $PORT $UTIL $LORAS > $LOG 2>&1 &\nelse\n setsid /home/keith/gemma-v2/serve_31b.sh 0 $PORT $UTIL $LORAS > $LOG 2>&1 &\nfi\nSPID=$!\necho $SPID > $W/logs/server_${PORT}.pid\nstop() {"},{"i":55,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/tools && . /home/keith/gemma-v2/env.sh && date +%T && $PY - <<\"EOF\"\nimport json,collections\nf=open(\"data/probe_calls_b001.jsonl\"); n=0; kinds=collections.Counter(); deep=0\nfor l in f:\n r=json.loads(l); n+=1\n if n==1:\n print({k:(v if not isinstance(v,(list,dict)) else (type(v).__name__,len(v))) for k,v in r.items()}); print(r[\"points\"][:6]); print({k:(v if not isinstance(v,list) else len(v)) for k,v in (r.get(\"calls\") or [{}])[0].items()})\n for p in r[\"points\"]:\n kinds[p[\"kind\"]]+=1\n if p[\"kind\"]==\"cont\" and p.get(\"depth\",0)>=800 and p[\"pos\"]<10000: deep+=1\nprint(n,kinds,\"deep cont\",deep)\nEOF\ngrep -n \"def select\" -A22 probeset.py | cut -c1-170 | head -40; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH\" | grep -v grep | cut -c1-100'"},{"i":56,"kind":"output","long":true,"text":"00:10:51\n{'trace': '/home/keith/gemma-v2/results/wsp31/batches/b001/base/rebench/shard0_gpu0/requests/Mimino666__langdetect.a1598f1a.func_basic__rku38xih.jsonl', 'run': '/home/keith/gemma-v2/results/wsp31/batches/b001/base', 'instance': 'Mimino666__langdetect.a1598f1a.func_basic__rku38xih', 'rollout': 0, 'resolved': True, 'call': 0, 'calls_total': 35, 'n': 3485, 'ids': ('list', 3485), 'points': ('list', 5), 'calls': ('list', 1)}\n[{'pos': 3318, 'kind': 'turn', 'turn': 0, 'next': 100, 'exact': True}, {'pos': 3403, 'kind': 'cont', 'turn': 0, 'next': 107, 'alt': 101, 'depth': 84, 'exact': True}, {'pos': 3453, 'kind': 'close', 'turn': 0, 'next': 101, 'depth': 134, 'exact': True}, {'pos': 3454, 'kind': 'think', 'turn': 0, 'next': 48, 'exact': True}, {'pos': 3457, 'kind': 'tool', 'turn': 0, 'next': 4503, 'exact': True}]\n{'start': 3319, 'comp': 166, 'lp': 166}\n1325 Counter({'cont': 3946, 'turn': 1325, 'close': 1325, 'think': 1325, 'tool': 1322}) deep cont 70\n71:def select(recs, per_trace=4, kinds=(\"turn\", \"step\", \"think\", \"tool\"), max_pos=None, traces=None):\n72- \"\"\"Deterministic spread of decision points: up to per_trace points of each kind per trace, evenly spaced.\n73- Returns [(trace_index, point_dict)], ordered by trace then position (so prefixes share the server cache).\"\"\"\n74- out = []\n75- for ti, r in enumerate(recs if traces is None else recs[:traces]):\n76- chosen = []\n77- for k in kinds:\n78- c = [p for p in r[\"points\"] if p[\"kind\"] == k and (max_pos is None or p[\"pos\"] < max_pos)]\n79- if len(c) > per_trace:\n80- c = [c[round(i * (len(c) - 1) / (per_trace - 1))] for i in range(per_trace)] if per_trace > 1 else c[:1]\n81- chosen += c\n[line removed by the dashboard scrubber]\n83- return out\n84-\n85-\n[... 5 lines cut ...]\n91- ap.add_argument(\"--limit\", type=int, default=0)\n92- a = ap.parse_args()\n93- assert \"golden\" not in a.glob, \"golden sets are sealed\"\n 893374 44:38 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 32:51 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 20:01 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_\n1054488 13:19 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1067894 09:03 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/"},{"i":57,"kind":"tool","long":false,"text":"Bash: cat > mk_callset.py <<'EOF'\n\"\"\"mk_callset.py (CPU): per-call probe sets for tools/vgen.py with ONE deep `cont` point per logged call, on the\nheld-out tasks of net_job.py (sorted stage-1 instances of probe_b001, index % 4 == 1). Writes\nscratch/job3/deep_pre.jsonl (no file edit made before the call) and deep_post.jsonl (after the first edit).\n[line removed by the dashboard scrubber]\nimport json, sys, collections\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nSRC = \"/home/keith/gemma-v2/wsp31/tools/data/probe_calls_b001.jsonl\"\nDEPTH = int(sys.argv[1]) if len(sys.argv) > 1 else 850\nMAXPOS = 8000\ns1 = set(open(\"/home/keith/gemma-v2/wsp31/queue/sets/stage1_ids.txt\").read().split())\nrecs = [json.loads(l) for l in open(SRC)]\ninst = sorted({r[\"instance\"] for r in recs if r[\"instance\"] in s1})\nheld = {x for i, x in enumerate(inst) if i % 4 == 1}\nep = collections.defaultdict(list)\nfor r in recs: ep[(r[\"run\"], r[\"instance\"])].append(r)\nout = dict(pre=[], post=[])\nfor (run, ins), lst in sorted(ep.items()):\n if ins not in held: continue\n[line removed by the dashboard scrubber]\n edited = False; got = collections.Counter()\n for r in lst:\n c = r[\"calls\"][0]; comp = c[\"comp\"]; s = c[\"start\"]\n tool = [p[\"next\"] for p in r[\"points\"] if p[\"kind\"] == \"tool\"]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n assert r[\"ids\"][s + DEPTH + 1] == comp[DEPTH + 1]\n q = dict(r)\n q[\"points\"] = [p for p in r[\"points\"] if p[\"kind\"] != \"cont\"] + [dict(pos=s + DEPTH, kind=\"cont\", turn=0, next=comp[DEPTH + 1], alt=101, depth=DEPTH, exact=True)]\n[line removed by the dashboard scrubber]\n q[\"thought_len\"] = comp.index(101)\n[line removed by the dashboard scrubber]\n if tool and tool[0] in (8982, 5986): edited = True\nfor k, v in out.items():\n # spread over instances: round-robin\n by = collections.defaultdict(list)\n for q in v: by[q[\"instance\"]].append(q)\n order = []\n while any(by.values()):\n for i in sorted(by):\n if by[i]: order.append(by[i].pop(0))\n with open(f\"{A}/scratch/job3/deep_{k}.jsonl\", \"w\") as f:\n for q in order[:24]: f.write(json.dumps(q) + \"\\n\")\n print(k, len(v), \"kept\", min(len(order), 24), \"instances\", len({q[\"instance\"] for q in order[:24]}), \"thought lens\", [q[\"thought_len\"] for q in order[:12]])\nEOF\nscp -q mk_callset.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/job3 && $PY code/mk_callset.py 850 2>&1 | tail -4; date +%T; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH\" | grep -v grep | cut -c1-100'"},{"i":58,"kind":"output","long":false,"text":"pre 6 kept 6 instances 3 thought lens [1034, 1476, 4097, 4097, 3933, 2844]\npost 0 kept 0 instances 0 thought lens []\n00:11:22\n 893374 45:08 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 33:21 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 20:31 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_\n1054488 13:49 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1094379 00:00 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/"},{"i":59,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]\nimport json\nfor k in (\"pre\",\"post\"):\n L=[json.loads(l) for l in open(f\"/home/keith/gemma-v2/wsp31/authors/claude/scratch/job3/deep_{k}.jsonl\")]\n print(k,[(q[\"instance\"][:14],q[\"call\"],[p[\"pos\"] for p in q[\"points\"] if p[\"kind\"]==\"cont\"][0]) for q in L])\nEOF'"},{"i":60,"kind":"output","long":false,"text":"pre 22 kept 22 instances 9 thought lens [1034, 3910, 2502, 1381, 1243, 4097, 1211, 4097, 1363, 1476, 1489, 1369]\npost 1 kept 1 instances 1 thought lens [1731]\npre [('EdinburghGenom', 3, 7107), ('automl__smac3-', 4, 9983), ('borgbackup__bo', 8, 9482), ('[task]', 17, 10116), ('enthought__env', 17, 8995), ('gawel__pyquery', 3, 5792), ('googleapis__py', 7, 9368), ('hdmf-dev__hdmf', 16, 9952), ('janehmueller__', 3, 8744), ('borgbackup__bo', 19, 5742), ('[task]', 15, 8783), ('enthought__env', 14, 12022), ('gawel__pyquery', 5, 7798), ('googleapis__py', 31, 9335), ('borgbackup__bo', 4, 9566), ('gawel__pyquery', 3, 5739), ('borgbackup__bo', 3, 8562), ('gawel__pyquery', 5, 10712), ('gawel__pyquery', 7, 9835), ('gawel__pyquery', 3, 5792), ('gawel__pyquery', 4, 8794), ('gawel__pyquery', 6, 8000)]\npost [('kurtmckee__fee', 9, 12256)]"},{"i":61,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nimport re\ns=open(\"net_job.py\").read()\ns=s.replace(\"DEADLINE_CAPTURE, DEADLINE = 420, 1120\",\"DEADLINE_CAPTURE, DEADLINE = 380, 1000\")\ns=s.replace(\"\"\" (\"P_EC12\", [(\"pre\", dict(end=0.12, com=0.12))], False),\n (\"P_C05c\", [(\"pre\", dict(com=0.05))], True)]\"\"\",\"\"\" (\"P_EC12\", [(\"pre\", dict(end=0.12, com=0.12))], False)]\"\"\")\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n if G[\"fit\"][\"auc_z\"] < 0.8:\n continue\n vec = sum(\"\"\")\ns=s.replace(\"\"\" if \"--no-raw\" not in sys.argv:\n torch.save(raw, S + \"/raw_mlpin.pt\")\n log(\"raw saved\")\n del raw\"\"\",\"\"\" if \"--no-raw\" not in sys.argv:\n import threading\n threading.Thread(target=lambda r=raw: (torch.save(r, S + \"/raw_mlpin.pt\"), log(\"raw saved\"))).start()\n del raw\"\"\")\ns=s.replace(\"evaluate(m, st, names, S + \\\"/ad\\\", ev[:6])\",\"evaluate(m, st, names, S + \\\"/ad\\\", ev[:5])\")\nopen(\"net_job.py\",\"w\").write(s)\nEOF\ncat > gpu_job3.sh <<'EOF'\n#!/bin/bash\n# GPU-0 job 3 of author claude (run only through code/gpu_hold.sh under the gpu0 lock): capture, fit, build, evaluate\n# with the forward pass, then one served vgen check of the arms named in scratch/job3/vgen_arms.txt.\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\nH0=$(date +%s)\n$PY code/net_job.py > scratch/logs/job3.log 2>&1\nNOW=$(date +%s)\nLEFTH=$((1560 - 100 - (NOW - H0)))\nLEFTT=$((1794545791 + 5400 - 15 * 60 - NOW))\n[ $LEFTT -lt $LEFTH ] && LEFTH=$LEFTT\necho \"python part done after $((NOW - H0)) s; session budget $LEFTH\" >> scratch/logs/job3.log\nif [ $LEFTH -ge 280 ] && [ -s scratch/job3/vgen_arms.txt ]; then\n ARMS=$(cat scratch/job3/vgen_arms.txt)\n[line removed by the dashboard scrubber]\nfi\nEOF\nscp -q net_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && scp -q gpu_job3.sh wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/gpu_job3.tmp && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && rm -rf scratch/mock3 && timeout 300 $PY code/net_job.py --mock 2>&1 | tail -4 | cut -c1-200 && rm -rf scratch/mock3 && printf \"scratch/job3/ad/D_E05 scratch/job3/ad/D_E12 scratch/job3/ad/P_EC05 scratch/job3/ad/P_C12\\n\" > scratch/job3/vgen_arms.txt && mv code/gpu_job3.tmp code/gpu_job3.sh && date +%T && cat scratch/logs/hold3.log; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH\" | grep -v grep | cut -c1-100; ls /home/keith/gemma-v2/serve_31b.sh && grep -n \"CUDA_VISIBLE\" /home/keith/gemma-v2/serve_31b.sh | head -3'"},{"i":62,"kind":"output","long":false,"text":"[ 3.8] raw saved\n[ 9.7] evaluated 0 [task] rows 1478 arms 5\n[ 16.8] evaluated 1 [task] rows 1796 arms 5\n[ 16.9] done\n00:12:41\n 893374 46:27 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 34:41 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 21:51 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_\n1054488 15:08 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1094379 01:20 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/\n/home/keith/gemma-v2/serve_31b.sh\n9:CUDA_VISIBLE_DEVICES=$G exec $PY -m vllm.entrypoints.openai.api_server --model $R/models/gemma-4-31b-it-qat-w4a16-ct \\"},{"i":63,"kind":"tool","long":false,"text":"Bash: sed -i 's| BUDGET=$LEFTH /home/keith| env -u CUDA_DEVICE_ORDER -u CUDA_VISIBLE_DEVICES BUDGET=$LEFTH /home/keith|' gpu_job3.sh && grep -n \"gpu0_session\" gpu_job3.sh | cut -c1-120 && scp -q gpu_job3.sh wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/gpu_job3.tmp && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && mv code/gpu_job3.tmp code/gpu_job3.sh && grep -n \"CUDA\\|cuda:\" /home/keith/gemma-v2/wsp31/tools/g31.py | head -8; cat scratch/logs/hold3.log; ls scratch/logs/ | tail -3; date +%T'"},{"i":64,"kind":"output","long":false,"text":"16: env -u CUDA_DEVICE_ORDER -u CUDA_VISIBLE_DEVICES BUDGET=$LEFTH /home/keith/gemma-v2/wsp31/tools/gpu0_session.sh 184\n106: def __init__(self, path, device=\"cuda:0\"):\n130: def __init__(self, device=\"cuda:0\", model_dir=MODEL_DIR, mirror=True, cache_dense=False, layers=None):\nhold3.log\njob1.log\njob2.log\n00:12:56"},{"i":65,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && cat code/finalize.py | cut -c1-260 | head -120; cat queue/deep-thought-gate-g3-b03/build.sh; cat code/build_think.py | head -40; ls queue/deep-thought-gate-g3-b03/ queue/deep-thought-gate-g3-b03/edit'"},{"i":66,"kind":"output","long":true,"text":"\"\"\"finalize.py SLUG SPEC LOW HIGH \"TITLE\" [--arms EDITARM,CTRLARM] [--extra FILE]\nBuilds queue/SLUG/{edit,ctrl} with build_think.py, runs the checker, writes build.sh and record.md (no READY).\nSPEC like \"B:0.3\" or \"B:0.3,C:8\". LOW/HIGH: predicted_gain_120 interval. --extra FILE: text appended to the\nprediction section (the behaviour predictions written by the author for this entry).\"\"\"\nimport os\nimport subprocess\nimport sys\n\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nPY = \"/home/keith/gemma-v2/tii-venv/bin/python\"\nT = \"/home/keith/gemma-v2/wsp31/tools\"\nslug, spec, low, high, title = sys.argv[1:6]\narms = sys.argv[sys.argv.index(\"--arms\") + 1].split(\",\") if \"--arms\" in sys.argv else []\nextra = 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\"\nq = f\"{A}/queue/{slug}\"\nos.makedirs(q, exist_ok=True)\nbuild = f\"\"\"#!/bin/bash\n[... 141 lines cut ...]\nctrl\nedit\nrecord.md\n\nqueue/deep-thought-gate-g3-b03/edit:\nadapter_config.json\nadapter_model.safetensors\nedit.json"},{"i":67,"kind":"tool","long":true,"text":"Bash: cat > net_report.py <<'EOF'\n\"\"\"net_report.py [EVAL.pt] (CPU): tables from the teacher-forced evaluation written by net_job.py (held-out tasks).\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nimport sys, torch, collections\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, A + \"/code\")\nimport thinklib as tl\nres = torch.load(sys.argv[1] if len(sys.argv) > 1 else A + \"/scratch/job3/eval.pt\")\neps = res[\"episodes\"]; arms = [a for a in res[\"arms\"] if all(a in e[\"arms\"] for e in eps)]\nprint(\"episodes\", len(eps), [e[\"instance\"][:22] for e in eps], \"arms\", arms)\nR = torch.cat([e[\"rows\"] for e in eps]); epi = torch.cat([torch.full((e[\"rows\"].shape[0],), i) for i, e in enumerate(eps)])\ncls, dep, ci, ed, tool = R[:, 1], R[:, 2], R[:, 4], R[:, 5].bool(), R[:, 6]\nthink = (cls >= 1) & (cls <= 3)\nprint(\"rows by class\", {k: int((cls == k).sum()) for k in range(7)}, \"| thought rows pre-edit\", int((think & ~ed).sum()), \"post-edit\", int((think & ed).sum()))\nif \"gate_pre\" in eps[0]:\n print(\"== held-out gates (pre-activation g > 0 = fires), layers\", res[\"gate_layers\"])\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 for gi, L in enumerate(res[\"gate_layers\"]):\n g = G[gi]; f = lambda m: (\"%.2f\" % float((g[m] > 0).float().mean())) if m.any() else \"-\"\n print(\" %-4s L%-2d auc %.3f | fire d<100 %s 100-400 %s 400-800 %s >=800 %s | pre>=400 %s post>=400 %s post all %s | non %s E %s tool-pt %s\" % (\n[line removed by the dashboard scrubber]\n f(think & (dep >= 400) & ~ed), f(think & (dep >= 400) & ed), f(think & ed), f(cls == 4), f(cls == 0), f(cls == 6)))\ndef cat(arm, k): return torch.cat([e[\"arms\"][arm][k] for e in eps])\nbase_top = cat(\"base\", \"top\"); base101 = cat(\"base\", \"lp101\"); base_tool = cat(\"base\", \"lptool\")\n# thoughts: (episode, call) -> rows sorted by depth\nth = collections.defaultdict(list)\nfor i in torch.nonzero(think | (cls == 0))[:, 0].tolist():\n th[(int(epi[i]), int(ci[i]))].append(i)\n[line removed by the dashboard scrubber]\nprint(\"arm | cut share d<200 200-400 400-800 >=800 | dlp101 >=800 | kept short(<400) long(>=800) n | long thoughts cut | E top=101 | agree non think tool | d lp(edit) pre/post | d lp(read) pre | tool argmax changed\")\nfor arm in arms:\n top = cat(arm, \"top\"); l101 = cat(arm, \"lp101\"); lt = cat(arm, \"lptool\")\n cut = lambda m: (\"%.3f\" % float((top[m] == 101).float().mean())) if m.any() else \" - \"\n ks, kl, ncut = [], [], 0\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n if int(cls[idx[-1]]) != 0: continue\n first = next((int(dep[i]) for i in idx[:-1] if int(top[i]) == 101), None)\n kept = 1.0 if first is None else first / Lg\n if Lg < 400: ks.append(kept)\n elif Lg >= 800:\n kl.append(kept); ncut += first is not None\n mean = lambda v: (sum(v) / len(v)) if v else float(\"nan\")\n tp = cls == 6\n ag = lambda m: (\"%.3f\" % float((top[m] == base_top[m]).float().mean())) if m.any() else \"-\"\n dl = lambda col, m: (\"%+.2f\" % float((lt[m, col] - base_tool[m, col]).mean())) if m.any() else \"-\"\n chg = float((lt[tp].argmax(1) != base_tool[tp].argmax(1)).float().mean()) if tp.any() else float(\"nan\")\n print(\"%-8s | %s %s %s %s | %+6.2f | %.2f %.2f n %d/%d | %d of %d | %s | %s %s %s | %s / %s | %s | %.3f\" % (\n arm, cut(think & (dep < 200)), cut(think & (dep >= 200) & (dep < 400)), cut(think & (dep >= 400) & (dep < 800)), cut(think & (dep >= 800)),\n float((l101 - base101)[think & (dep >= 800)].mean()) if (think & (dep >= 800)).any() else float(\"nan\"),\n mean(ks), mean(kl), len(ks), len(kl), ncut, len(kl), cut(cls == 0), ag(cls == 4), ag(cls == 5), ag(tp),\n dl(2, tp & ~ed), dl(2, tp & ed), dl(0, tp & ~ed), chg))\nEOF\ncat > net_build.py <<'EOF'\n\"\"\"net_build.py OUT_DIR ARM [--stats FILE] (CPU): reb\n[... 1696 characters cut ...]"},{"i":68,"kind":"output","long":false,"text":"episodes 2 ['[task]', 'Nikoleta-v3__blackbook'] arms ['base', 'D_E05', 'P_C05', 'P_EC05', 'D_E05c']\nrows by class {0: 16, 1: 1070, 2: 1181, 3: 675, 4: 300, 5: 16, 6: 16} | thought rows pre-edit 2356 post-edit 570\n== held-out gates (pre-activation g > 0 = fires), layers [0, 59]\n deep L0 auc 0.473 | fire d<100 0.67 100-400 0.65 400-800 0.63 >=800 0.58 | pre>=400 0.62 post>=400 - post all 0.66 | non 0.66 E 0.56 tool-pt 0.44\n deep L59 auc 0.523 | fire d<100 0.65 100-400 0.65 400-800 0.64 >=800 0.65 | pre>=400 0.65 post>=400 - post all 0.63 | non 0.65 E 0.75 tool-pt 0.62\n pre L0 auc 0.484 | fire d<100 0.20 100-400 0.19 400-800 0.18 >=800 0.18 | pre>=400 0.18 post>=400 - post all 0.21 | non 0.17 E 0.31 tool-pt 0.06\n pre L59 auc 0.486 | fire d<100 0.33 100-400 0.29 400-800 0.26 >=800 0.28 | pre>=400 0.27 post>=400 - post all 0.30 | non 0.34 E 0.44 tool-pt 0.56\n[line removed by the dashboard scrubber]\narm | cut share d<200 200-400 400-800 >=800 | dlp101 >=800 | kept short(<400) long(>=800) n | long thoughts cut | E top=101 | agree non think tool | d lp(edit) pre/post | d lp(read) pre | tool argmax changed\nbase | 0.000 0.000 0.000 0.000 | +0.00 | 1.00 1.00 n 14/1 | 0 of 1 | 0.000 | 1.000 1.000 1.000 | +0.00 / +0.00 | +0.00 | 0.000\nD_E05 | 0.000 0.000 0.000 0.000 | -0.06 | 1.00 1.00 n 14/1 | 0 of 1 | 0.000 | 0.000 0.000 0.000 | +0.56 / -0.72 | -0.69 | 0.812\nP_C05 | 0.000 0.002 0.000 0.000 | +0.17 | 0.99 1.00 n 14/1 | 0 of 1 | 0.000 | 0.000 0.000 0.000 | -0.08 / -0.64 | -0.48 | 0.812\nP_EC05 | 0.000 0.000 0.000 0.000 | +0.10 | 1.00 1.00 n 14/1 | 0 of 1 | 0.000 | 0.000 0.000 0.000 | +0.35 / -0.78 | -0.32 | 1.000\nD_E05c | 0.000 0.000 0.000 0.000 | +0.19 | 1.00 1.00 n 14/1 | 0 of 1 | 0.000 | 0.000 0.000 0.000 | +0.30 / -1.86 | -0.41 | 0.938\n00:14:02\n 893374 47:48 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 36:01 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 23:12 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_\n1054488 16:29 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1094379 02:41 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/"},{"i":69,"kind":"tool","long":true,"text":"Bash: cat > finalize3.py <<'EOF'\n\"\"\"finalize3.py SLUG ARM CTRLARM TEXTFILE (CPU): writes queue/SLUG/{edit,ctrl,build.sh,record.md,size.json} for one\narm of net_job.py (no READY). TEXTFILE: the record with the placeholders @SIZE@ @CTRLSIZE@ @CHECK@ @IDENT@ @GATES@\n@ROWS@ @VGEN@ @DIRS@, which are filled with measured output. ARM / CTRLARM: names or specs of net_build.py.\"\"\"\nimport hashlib, json, os, subprocess, sys, torch\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nPY = \"/home/keith/gemma-v2/tii-venv/bin/python\"\nT = \"/home/keith/gemma-v2/wsp31/tools\"\nslug, arm, carm, textfile = sys.argv[1:5]\nnames = sys.argv[5].split(\",\") if len(sys.argv) > 5 else [arm, carm] # arm names in the evaluation tables\nq = f\"{A}/queue/{slug}\"\nassert not os.path.exists(q + \"/READY\")\nos.makedirs(q, exist_ok=True)\nslim = A + \"/code/data/net_stats_slim.pt\"\nif not os.path.exists(slim):\n st = torch.load(A + \"/scratch/job3/net_stats.pt\")\n torch.save({k: st[k] for k in (\"layers\", \"gate\", \"dirs\", \"w_post\", \"ls\", \"dir_info\", \"instances\")}, slim)\nbuild = f\"\"\"#!/bin/bash\n# rebuilds edit/ and ctrl/ of {slug} (CPU, about a minute). Needs code/data/net_stats_slim.pt: the per-layer gates,\n# directions and norm weights fitted by code/net_job.py in one GPU-0 hold (`flock $R/wsp31/gpu0.lock timeout 1560\n# code/gpu_hold.sh`, which runs code/gpu_job.sh -> code/gpu_job3.sh); net_stats_slim.pt is the subset of\n# scratch/job3/net_stats.pt that code/net_job.build_arm reads (written by code/finalize3.py).\n. /home/keith/gemma-v2/env.sh\ncd {A}\n$PY code/net_build.py queue/{slug}/edit '{arm}'\n$PY code/net_build.py queue/{slug}/ctrl '{carm}'\n$PY $R/wsp31/tools/check_adapter.py queue/{slug}/edit && $PY $R/wsp31/tools/check_adapter.py queue/{slug}/ctrl\n\"\"\"\nopen(q + \"/build.sh\", \"w\").write(build)\nos.chmod(q + \"/build.sh\", 0o755)\nout = subprocess.run([\"bash\", q + \"/build.sh\"], capture_output=True, text=True)\ncheck = \"\\n\".join(l for l in (out.stdout + out.stderr).splitlines() if l.startswith((\"OK\", \"FAIL\", \"saved\")) or \"rror\" in l)\nprint(check)\nsha = lambda p: hashlib.sha256(open(p, \"rb\").read()).hexdigest()\nident = []\nfor d, nm in ((\"edit\", arm), (\"ctrl\", carm)):\n ref = f\"{A}/scratch/job3/ad/{nm}/adapter_model.safetensors\"\n h = sha(f\"{q}/{d}/adapter_model.safetensors\")\n ident.append(f\"- `{d}/adapter_model.safetensors` sha256 `{h}`: \" + ((\"identical to\" if sha(ref) == h else \"DIFFERS from\") + f\" the adapter `{nm}` evaluated in the hold\" if os.path.exists(ref) else \"no evaluated adapter of this name (built after the hold from the same statistics)\"))\nprint(\"\\n\".join(ident))\nsz = subprocess.run([PY, T + \"/train_size.py\", q + \"/size.json\", f\"edit={q}/edit\", f\"ctrl={q}/ctrl\"], capture_output=True, text=True)\nJ = json.load(open(q + \"/size.json\"))\ndef find(J, k):\n if isinstance(J, dict):\n if k in J and isinstance(J[k], dict) and \"net_rel\" in J[k]: return J[k]\n for v in J.values():\n r = find(v, k)\n if r: return r\n if isinstance(J, list):\n for v in J:\n r = find(v, k)\n if r: return r\n return None\nse, sc = find(J, \"edit\"), find(J, \"ctrl\")\nif se is None:\n print(\"size.json layout unexpected:\", sz.stdout[-600:], sz.stderr[-300:]); se = sc = dict(net_rel=float(\"nan\"), changed=0, rel_max=float(\"nan\"))\nsize = \"size: whole network %.4f%% over %d matrices, largest matrix %.4f%%\" % (100 * se[\"net_rel\"], se[\"changed\"], 100 * se[\"rel_max\"])\ncsize = \"control size: whole network %.4f%% over %d matrices, largest matrix %.4f%%\" % (100 * sc[\"net_rel\"], sc[\"changed\"], 100 * sc[\"rel_max\"])\nprint(size); print(csize)\nrep = open(A + \"/scratch/job3/report.txt\").read().splitlines() if os.path.exists(A + \"/scratch/job3/report.txt\") else []\ngates = [l for l in rep if l.startswith((\" deep\", \" pre\"))]\nwant = set(a for part in arm.replace(\":ctrl\", \"\").split(\",\") for a in [part.split(\":\")[0]]) if \":\" in arm else None\nif want is None:\n import importlib; sys.path.insert(0, A + \"/code\"); sys.path.insert(0, T)\n nj = importlib.\n[... 1844 characters cut ...]"},{"i":70,"kind":"output","long":false,"text":"00:14:45\n 893374 48:31 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh\n 957831 36:44 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1026421 23:55 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_\n1054488 17:12 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1094379 03:23 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/\n{\"g3\": {\"summary\": {\"r\": 1, \"scale\": 2.0, \"changed\": 30, \"of\": 420, \"net_rel\": 0.015227495797693908, \"rel_median\": 0.013608398515171798, \"rel_min\": 0.00619325226990479, \"rel_max\": 0.10158920439295836, \"rel_max_mod\": \"32.gate_proj\", \"total_rel\": 0.04517873411435002, \"total_fro\": 31.403525438747394, \"stable_rank_median\": 1.0, \"part_rank_median\": 1.0, \"by_type\": {\"down_proj\": 0.013608398515171798, \"gate_proj\": 0.050591640821837994, \"up_proj\": 0.007471890436004857}, \"by_depth\": [null, null, null, 0.009407278722231701, 0.03008385993679317, null]}, \"rows\": [{\"mod\": \"30.down_proj\", \"fro\": 0.731494223"},{"i":71,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\").read()\na=s.index(\"def find(J, k):\"); b=s.index(\"if se is None:\")\ns=s[:a]+'se, sc = (J.get(\"edit\") or {}).get(\"summary\"), (J.get(\"ctrl\") or {}).get(\"summary\")\\n'+s[b:]\nopen(\"finalize3.py\",\"w\").write(s)\nEOF\ncat > NOTES_draft.md <<'EOF'\n# Notes of author claude (rewritten during turn 3, 13 Nov 2026, about 0:20 AM ET; turn-0 entries 0007-0010 still unscored)\n\n## What the unedited model does wrong (b001, 288 episodes, three unedited arms; code/seq_stats.py, fail_stats.py)\n- Solved 54%. Failures per 120 tasks: about 25 at the 7.5-min limit (13 with an empty patch), 11 at the 30-call cap,\n 20 finished with a wrong patch. 15 of 94 tasks differ between identical arms (noise).\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n- P(solved | no edit within the first 12 calls) = 0.31 against 0.70-0.80 for a first edit at calls 6-11.\n[line removed by the dashboard scrubber]\n little to a depth condition.\n- read_file truncates at 150 lines; the model's ranged reads have a median span of 41 lines.\n\n## Measured nulls and traps (do not repeat)\n[line removed by the dashboard scrubber]\n calls so far\" reach AUC 0.9+ on the fit episodes and 0.52-0.77 on held-out episodes (22 episodes, layers 30-58;\n code/state_auc.py). The same data gives depth >= 800 held-out AUC 0.76-0.81 (the 0.88-0.91 in records 0007-0010 is the\n fit-set value). Do not build a gate on episode progress without many more episodes.\n- All-layer residual steering by hooks (interp lane, dirs/out/dose.json): 0.03 |h| per layer wrecks the model with the\n real and the null direction alike; 0.01 does little. The window for always-on all-layer pushes is narrow.\n- From turn 0: family C (unembedding direction in layers 54-58) no effect; always-on Concept Country form has no window\n for long thoughts; gate sharpness 3 leaks through the negative lobe of gelu (use 12); \"anti\" neurons wrong sign.\n[line removed by the dashboard scrubber]\n- Local heredocs halve backslashes; `pkill -f` matches your own ssh command line. Exporting CUDA_DEVICE_ORDER into\n gpu0_session.sh could change which card the server takes: call it with `env -u CUDA_DEVICE_ORDER -u CUDA_VISIBLE_DEVICES`.\nEOF\nscp -q finalize3.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && date +%T; cat scratch/logs/hold3.log; tail -3 scratch/logs/job3.log 2>/dev/null | cut -c1-200; ps -eo pid,etime,cmd | grep -E \"flock|dose.py|sessH|net_job|speed|vllm.*184|vllm.*182\" | grep -v grep | cut -c1-110'"},{"i":72,"kind":"output","long":false,"text":"00:15:11\n 957831 37:11 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1054488 17:38 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1094379 03:50 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/sessH_2350\n1104886 00:08 /bin/bash ./sessH.sh\n1104901 00:08 /bin/bash ./gpu0_session.sh 18200 0.90 -- ./sessH_body.sh\n1104910 00:07 /home/keith/gemma-v2/tii-venv/bin/python -m vllm.entrypoints.openai.api_server --model /ho"},{"i":73,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && $PY - <<\"EOF\"\nimport json,statistics as st\nrows=json.load(open(\"../scratch/t3_seq.json\"))\ndef fe(s):\n i=[j for j,c in enumerate(s) if c in \"ew\"]\n return i[0] if i else None\nfor grp,sel in ((\"solved\",lambda r:r[\"k\"].startswith(\"solved\")),(\"failed\",lambda r:not r[\"k\"].startswith(\"solved\"))):\n F=[];O=[];deepF=0;n=0;deep_any=[];longest_is_fe=0\n for r in rows:\n if not sel(r): continue\n f=fe(r[\"seq\"])\n if f is None or f>=len(r[\"think\"]): continue\n n+=1; F.append(r[\"think\"][f]); O+=r[\"think\"][:f]\n deepF+= r[\"think\"][f]>=800\n pre=r[\"think\"][:f+1]; longest_is_fe += (max(pre)==r[\"think\"][f])\n q=lambda v:[round(x) for x in st.quantiles(v,n=10)]\n print(grp,\"episodes with an edit\",n,\"| first-edit thought length deciles\",q(F),\"| share >=800: %.2f\"%(deepF/n),\"| other pre-edit thoughts deciles\",q(O),\"share>=800 %.3f\"%(sum(o>=800 for o in O)/len(O)),\"| longest pre-edit thought is the first-edit thought: %.2f\"%(longest_is_fe/n))\n[line removed by the dashboard scrubber]\nimport collections\nc=collections.Counter(); cs=collections.Counter()\nfor r in rows:\n f=fe(r[\"seq\"]); f=10**9 if f is None else f\n for i,(t,ch) in enumerate(zip(r[\"think\"],r[\"seq\"])):\n if i<=f:\n (c if t>=800 else cs)[ch]+=1\ntot=sum(c.values()); tots=sum(cs.values())\nprint(\"tool after a pre-edit thought >=800:\",{k:round(v/tot,2) for k,v in c.most_common()},\"n\",tot,\"| after shorter pre-edit thoughts:\",{k:round(v/tots,2) for k,v in cs.most_common()},\"n\",tots)\nEOF\ndate +%T'"},{"i":74,"kind":"output","long":false,"text":"solved episodes with an edit 155 | first-edit thought length deciles [27, 52, 89, 204, 327, 483, 692, 904, 1351] | share >=800: 0.24 | other pre-edit thoughts deciles [22, 33, 45, 67, 106, 151, 229, 391, 686] share>=800 0.074 | longest pre-edit thought is the first-edit thought: 0.23\nfailed episodes with an edit 106 | first-edit thought length deciles [59, 152, 293, 439, 553, 684, 901, 1097, 1376] | share >=800: 0.35 | other pre-edit thoughts deciles [20, 28, 38, 55, 81, 123, 194, 361, 716] share>=800 0.091 | longest pre-edit thought is the first-edit thought: 0.26\ntool after a pre-edit thought >=800: {'r': 0.61, 'T': 0.18, 'e': 0.16, 'w': 0.03, '-': 0.01, 's': 0.01, 'g': 0.0} n 397 | after shorter pre-edit thoughts: {'r': 0.85, 'T': 0.09, 'e': 0.05, 'w': 0.0, '-': 0.0, 'g': 0.0, 's': 0.0} n 3373\n00:15:54"},{"i":75,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"net_job.py\").read()\ns=s.replace(\"\"\"def evaluate(m, st, names, addir, ev):\n import think_job as tj\n ads = {}\n for nm in names:\n try:\n ads[nm] = tj.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]\n\"\"\",\"\"\"def evaluate(m, st, names, addir, ev, extra=()):\n import think_job as tj\n ads = {}\n paths = {nm: f\"{addir}/{nm}\" for nm in names}\n names = list(names)\n for k, (nm, path) in enumerate(extra): # reference adapters built elsewhere (already queued entries)\n paths[nm] = path\n names.insert(min(len(names), 4 + 8 * k), nm)\n for nm in names:\n try:\n ads[nm] = tj.tl_adapter(paths[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]\n\"\"\")\ns=s.replace(\"\"\" evaluate(m, st, names, S + \"/ad\", ev[:5])\"\"\",\"\"\" extra = [(\"Q0008\", A_DIR + \"/queue/deep-thought-gate-g3-b03/edit\"),\n (\"Q0010\", A_DIR + \"/queue/early-gate-b01-end-neurons-x9/edit\")]\n evaluate(m, st, names, S + \"/ad\", ev[:5], extra)\"\"\")\nopen(\"net_job.py\",\"w\").write(s)\nEOF\ngrep -n \"Q0008\\|names.insert\" net_job.py | head; scp -q net_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/net_job.tmp && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && ps -eo cmd | grep -c \"[n]et_job.py\"; mv code/net_job.tmp code/net_job.py && timeout 300 $PY code/net_job.py --mock 2>&1 | tail -3 | cut -c1-200; $PY code/net_report.py scratch/mock3/eval.pt 2>&1 | grep -c \"^Q00\"; rm -rf scratch/mock3; date +%T; cat scratch/logs/hold3.log'"},{"i":76,"kind":"output","long":false,"text":"301: names.insert(min(len(names), 4 + 8 * k), nm)\n412: extra = [(\"Q0008\", A_DIR + \"/queue/deep-thought-gate-g3-b03/edit\"),\n1\n[ 11.7] evaluated 0 [task] rows 1478 arms 7\n[ 21.5] evaluated 1 [task] rows 1796 arms 7\n[ 21.5] done\n2\n00:16:44"},{"i":77,"kind":"tool","long":true,"text":"Bash: cat > common_recipe.txt <<'EOF'\nbase: champion generation 0\n\nNo edit had passed when this entry was built (no `CHAMPION.md`); statistics, weights and pre-tests are those of the\nunedited model. The entry holds only this change.\n\n@SIZE@\n\n@CTRLSIZE@\n\nBuilt by `code/net_job.py` (one GPU-0 hold, forward pass `tools/g31.py` only, no optimiser anywhere) and rebuilt\nbit for bit by `build.sh` (`code/net_build.py` from `code/data/net_stats_slim.pt`).\n\nData. `tools/data/probe_b001.jsonl`: unedited arms `base`, `aa`, `zero` of batch b001, all 58 instances are stage-1\ntasks. Instances sorted by name; every fourth (index % 4 == 1, 15 tasks) is held out for the pre-test, the other 43\nare fit tasks. One episode per task (the one with most deep, post-edit and first-edit thought positions), truncated\n[line removed by the dashboard scrubber]\nNo task outcome enters any statistic.\n\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\npositions at depth < 100 (40), 100-399 (40), 400-799 (50), >= 800 (70), positions outside thoughts (40), up to 60\nmore thought positions at depth >= 300 after the episode's first edit, and up to 80 thought positions of the call\n[line removed by the dashboard scrubber]\n`edit_file` or `write_file`. Captured at all 60 layers: `mlp_in`, `resid_post`, `mlp_raw`.\n\nGate (one neuron per layer, replaced outright, mode \"set\"): the neuron with the smallest mean |activation| over the\nsampled positions of the first five episodes (second smallest for the `pre` gate, so both gates can share a layer).\nGate row = `(12 / sigma) (w - (theta / cbar) m)`: `w` = unit diagonal-LDA probe on `mlp_in[L]` (positives against\nnegatives as listed below), `theta` = 95% quantile of the probe on the negatives, `sigma` = sd on the positives,\n`m` = unit mean `mlp_in` (`m . x = cbar` at every position; the model has no bias). Up row = `m / cbar` (reads 1).\n- `deep` gate: positives = thought positions at depth >= 800; negatives = thought positions at depth < 200 and\n positions outside thoughts.\n- `pre` gate: positives = thought positions at depth >= 400 while no file edit has been made; negatives = thought\n positions at depth < 150, positions outside thoughts, and every thought position after the first edit.\nLayers whose fit-set AUC is below 0.8 are skipped (none unless listed under the tables).\n\nWrite (down column of the same neuron) = `clipdiv(v_L, w_post_L) * rms_y_L / (gbar_L * layer_scalar_L)`: `v_L` the\nstream vector below, `w_post_L` the post-feedforward norm weights (`clipdiv(d, w) = d / w`, zero where `|w|` is under\nits 5% quantile), `rms_y_L` the rms of the raw MLP output at the gate's positives, `gbar_L` the neuron's mean\nactivation at its positives. At an average positive position the write moves `resid_post[L]` by `v_L`.\n- `d_end_L` = mean `resid_post[L]` at E positions minus mean at the other thought positions (pooled over episodes).\n- `d_commit_L` = mean over episodes of [mean `resid_post[L]` over the thought positions of the call that makes the\n first edit minus mean over the thought positions of earlier read / run calls of the same episode] (episodes with\n at least 8 positions on each side). @DIRS@.\n\nControl (`ctrl/`): same code path, same neurons, identical gate and up rows (it fires at the same positions with the\nsame strength); the written stream vector `v_L` has the sign of every coordinate flipped at random (seed 1000 + L)\nbefore the division by `w_post`: identical per-coordinate magnitudes, hence identical norm in the stream and\nidentical `|dW|_F` in every matrix. It tests the direction against an equal-sized, equally gated write.\n\nApproximations: bf16 storage of the factors; the gate is a linear probe, so it fires on a share of other positions\n(table); `clipdiv` zeroes 5% of the coordinates; the write formula ignores the change of the rms of the raw MLP\noutput. `d_commit` is a difference of thought states, not a verified control direction.\n\nChecker and rebuild:\n\n```\n@CHECK@\n```\n@IDENT@\n\n[line\n[... 1106 characters cut ...]"},{"i":78,"kind":"output","long":false,"text":"00:17:18\n 957831 39:17 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1104886 02:14 /bin/bash ./sessH.sh\n1104901 02:14 /bin/bash ./gpu0_session.sh 18200 0.90 -- ./sessH_body.sh\n1109577 01:00 timeout 565 ./sessH_body.sh\n1109578 01:00 /bin/bash ./sessH_body.sh\nhold start Fri Nov 13 12:15:03 AM EST 2026\n##### A MAXRANK=128 MAXLORAS=6 util=0.90 budget=640 (00:15:03, 1680 s left in hold)\nserver up after 75s (pid/pgid 1104910, log /home/keith/gemma-v2/wsp31/tools/logs/server_18200_1113_001503.log)\n[line removed by the dashboard scrubber]\n=== speedbench A_r128_l6_u0.90 (00:16:18, 550 s left)"},{"i":79,"kind":"tool","long":false,"text":"Bash: sed -i 's/^DEADLINE_CAPTURE, DEADLINE = 380, 1000/DEADLINE_CAPTURE, DEADLINE = 300, 900/' net_job.py && grep -n \"^DEADLINE_CAPTURE\" net_job.py && python -c \"import ast,sys; ast.parse(open('net_job.py').read())\" && scp -q net_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/net_job.tmp && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && cat scratch/logs/hold3.log && mv code/net_job.tmp code/net_job.py && date +%T && grep -c . code/net_job.py'"},{"i":80,"kind":"output","long":false,"text":"38:DEADLINE_CAPTURE, DEADLINE = 300, 900\n00:17:38\n383\n[This command modified 1 file you've previously read: net_job.py. Call Read before editing.]"},{"i":81,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && date +%T; cat scratch/logs/hold3.log; tail -4 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log 2>/dev/null | cut -c1-160; cat /home/keith/gemma-v2/wsp31/tools/sessH.sh 2>/dev/null | head -30 | cut -c1-200'"},{"i":82,"kind":"output","long":true,"text":"00:18:29\n##### A MAXRANK=128 MAXLORAS=6 util=0.90 budget=640 (00:15:03, 1680 s left in hold)\nserver up after 75s (pid/pgid 1104910, log /home/keith/gemma-v2/wsp31/tools/logs/server_18200_1113_001503.log)\n[line removed by the dashboard scrubber]\n=== speedbench A_r128_l6_u0.90 (00:16:18, 550 s left)\n#!/bin/bash\n# sessH.sh: one GPU0 hold for speed by adapter shape (speedbench.py), three server configurations one after another\n# (each through gpu0_session.sh, so each server is stopped before the next starts), all at memory share 0.90 like the\n# runner's batch server:\n# A rank limit 128, 6 slots: unedited, rank 32 on 420 matrices, rank 96 on 420; mixed batches (4 + 4 streams)\n# unedited + rank 32 and unedited + rank 96, with the speed of every stream\n# B rank limit 64, 6 slots: unedited, rank 32 on 420; mixed unedited + rank 32\n# C rank limit 32, 8 slots: the same\n# (The rank and coverage sweep at limit 32 and the zero-padding question were answered by the throughput lane.)\n# Queue: nohup flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh > logs/sessH_$(date +%H%M).log 2>&1 &\n# Env: HOLD seconds (1680, never above 1740), CONFIGS (\"tag:maxrank:maxloras:util:budget\"), OUT, REPS.\nT=/home/keith/gemma-v2/wsp31/tools\ncd $T\nSTART=$(date +%s); HOLD=${HOLD:-1680}; END=$(( START + HOLD ))\necho \"hold start $(date)\"; echo $START > logs/sessH_hold_start\n[... 5 lines cut ...]\n [ $l -lt 200 ] && { echo \"##### skip $tag: $l s left\"; continue; }\n [ $bud -gt $(( l - 12 )) ] && bud=$(( l - 12 ))\n echo \"##### $tag MAXRANK=$mr MAXLORAS=$ml util=$ut budget=$bud ($(date +%H:%M:%S), $l s left in hold)\"\n TAG=$tag UTIL=$ut HOLD_END=$END MAXRANK=$mr MAXLORAS=$ml EXTRA=\"\" BUDGET=$bud ./gpu0_session.sh 18200 $ut -- ./sessH_body.sh\n echo \"##### $tag rc=$?\"\n sleep 3\ndone\necho \"hold end $(date), used $(( $(date +%s) - START )) s\"; date +%s > logs/sessH_hold_end"},{"i":83,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/tools && sed -n 127,215p vgen.py | cut -c1-210'"},{"i":84,"kind":"output","long":true,"text":"def main():\n ap = argparse.ArgumentParser()\n ap.add_argument(\"edits\", nargs=\"*\")\n ap.add_argument(\"--port\", type=int)\n ap.add_argument(\"--at\", choices=sorted(KIND), default=\"turn\")\n ap.add_argument(\"--n\", type=int, default=16)\n ap.add_argument(\"--per-record\", type=int, default=1)\n ap.add_argument(\"--records\", type=int, default=200)\n[line removed by the dashboard scrubber]\n ap.add_argument(\"--max-pos\", type=int, default=6000)\n ap.add_argument(\"--streams\", type=int, default=4)\n ap.add_argument(\"--show\", type=int, default=2)\n ap.add_argument(\"--show-chars\", type=int, default=500)\n ap.add_argument(\"--probeset\", default=os.environ.get(\"WSP31_CALLSET\", os.path.join(T, \"data/probe_calls.jsonl\")))\n ap.add_argument(\"--champion\", default=None)\n ap.add_argument(\"--budget\", type=float, default=float(os.environ.get(\"LEFT\", 1500)) - 45)\n ap.add_argument(\"--out\", default=None)\n ap.add_argument(\"--selftest\", action=\"store_true\")\n a = ap.parse_args()\n import wlib\n[... 47 lines cut ...]\n[line removed by the dashboard scrubber]\n f\"{tok.decode(x['ids'])[:a.show_chars]!r}\")\n if a.out:\n json.dump(res, open(a.out, \"w\"), indent=1)\n\n\nif __name__ == \"__main__\":\n main()"},{"i":85,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"mk_callset.py\").read()\ns=s.replace(\"MAXPOS = 8000\",\"MAXPOS = 12500\")\n[line removed by the dashboard scrubber]\ns=s.replace(\"\"\" q[\"points\"] = [p for p in r[\"points\"] if p[\"kind\"] != \"cont\"] + [dict(pos=s + DEPTH, kind=\"cont\", turn=0, next=comp[DEPTH + 1], alt=101, depth=DEPTH, exact=True)]\n[line removed by the dashboard scrubber]\n q[\"ids\"] = r[\"ids\"][:s + DEPTH + 2]; q[\"n\"] = len(q[\"ids\"]) # vgen filters records by n\"\"\")\nopen(\"mk_callset.py\",\"w\").write(s)\ng=open(\"gpu_job3.sh\").read()\n[line removed by the dashboard scrubber]\nopen(\"gpu_job3.sh\",\"w\").write(g)\nEOF\nscp -q mk_callset.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && scp -q gpu_job3.sh wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/gpu_job3.tmp && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && cat scratch/logs/hold3.log && mv code/gpu_job3.tmp code/gpu_job3.sh && $PY code/mk_callset.py 850 | tail -2 && printf \"scratch/job3/ad/D_E05 scratch/job3/ad/P_EC05 scratch/job3/ad/D_E12 scratch/job3/ad/P_C12\\n\" > scratch/job3/vgen_arms.txt && $PY - <<\"EOF\"\nimport sys,json; sys.path.insert(0,\"/home/keith/gemma-v2/wsp31/tools\")\nimport probeset, vgen\nrecs=[r for r in probeset.load(\"/home/keith/gemma-v2/wsp31/authors/claude/scratch/job3/deep_pre.jsonl\") if r[\"n\"]<=12600]\nc=vgen.contexts(recs,\"cont\",8,1); print(len(recs),[(recs[ri][\"instance\"][:12],p[\"depth\"],len(x)) for ri,p,x in c])\nEOF\ngrep -n \"vgen\" code/gpu_job3.sh | cut -c1-80; date +%T'"},{"i":86,"kind":"output","long":false,"text":"pre 22 kept 22 instances 9 thought lens [1034, 3910, 2502, 1381, 1243, 4097, 1211, 4097, 1363, 1476, 1489, 1369]\npost 1 kept 1 instances 1 thought lens [1731]\n22 [('EdinburghGen', 850, 7108), ('automl__smac', 850, 9984), ('borgbackup__', 850, 9483), ('conda__conda', 850, 10117), ('enthought__e', 850, 8996), ('gawel__pyque', 850, 5793), ('googleapis__', 850, 9369), ('hdmf-dev__hd', 850, 9953)]\n3:# with the forward pass, then one served vgen check of the arms named in scrat\n14:if [ $LEFTH -ge 280 ] && [ -s scratch/job3/vgen_arms.txt ]; then\n15: ARMS=$(cat scratch/job3/vgen_arms.txt)\n16: env -u CUDA_DEVICE_ORDER -u CUDA_VISIBLE_DEVICES BUDGET=$LEFTH /home/keith/\n00:19:17"},{"i":87,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n(mean |activation|)^2) with k . mean_act(class) = 0 for thought ends, thought depth < 100 and positions outside\nthoughts, 0.25 for depth 100-399 and 1 for depth >= 400; c_L writes DOSE * d_end_L (thinklib.d_end) at response 1.\nStatistics: code/data/think_stats.pt (turn 0, class means of mlp_act at 36 layers, 22 episodes of s1_base).\"\"\"\nimport sys, torch\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0, A + \"/code\")\nimport thinklib as tl\nfrom edits import EditSet\nout, dose = sys.argv[1], float(sys.argv[2]); ctrl = \"--ctrl\" in sys.argv\nst = torch.load(A + \"/code/data/think_stats.pt\")\nn = st[\"n\"].double(); t = torch.tensor([0, 0, 0.25, 1.0, 0]).double()\ne = EditSet(); info = []\nfor i, L in enumerate(st[\"CL\"]):\n Am = st[\"act_sum\"][:, i].double() / n[:, None]\n s = st[\"absact_sum\"][i].double() / n.sum()\n Cinv = 1.0 / (s ** 2 + 0.1 * s.median() ** 2)\n M = (Am * Cinv) @ Am.T\n k = Cinv * (Am.T @ torch.linalg.solve(M, t))\n noise = 1.25 * float(((k ** 2) * (s ** 2)).sum().sqrt())\n d = tl.d_end(st, L)\n v = dose * d\n if ctrl: v = tl.signflip(v, 1000 + L)\n rmsy = float(st[\"rmsy_sum\"][3, i] / st[\"n\"][3])\n c = tl.clipdiv(v, st[\"w_post\"][i].float()) * (rmsy / float(st[\"ls\"][i]))\n info.append((L, noise, float(torch.linalg.cond(M)), float(k.norm()), int((k.abs() > 0.1 * k.abs().max()).sum())))\n if \"--diag\" not in sys.argv:\n e.add(L, \"down_proj\", c[:, None].float(), k[None, :].float())\nprint(\"layer: noise-sd proxy of the response (signal 1), cond(M), |k|, neurons above 10% of max\")\nfor x in info[::4] + info[-1:]: print(\" L%d noise %.2f cond %.1e |k| %.3f big %d\" % x)\nif \"--diag\" not in sys.argv:\n[line removed by the dashboard scrubber]\n print(\"saved\", out, \"r\", rep[\"r\"], \"modules\", len(rep[\"modules\"]), \"max rel_fro %.4f\" % max(v.get(\"rel_fro\", 0) for v in rep[\"modules\"].values()))\nEOF\n[line removed by the dashboard scrubber]"},{"i":88,"kind":"output","long":false,"text":"layer: noise-sd proxy of the response (signal 1), cond(M), |k|, neurons above 10% of max\n L24 noise 0.07 cond 2.0e+02 |k| 1.402 big 8541\n L28 noise 0.07 cond 2.1e+02 |k| 3.121 big 10735\n L32 noise 0.08 cond 1.9e+02 |k| 3.489 big 12494\n L36 noise 0.08 cond 2.8e+02 |k| 2.871 big 12448\n L40 noise 0.07 cond 2.5e+02 |k| 1.154 big 10199\n L44 noise 0.06 cond 3.5e+02 |k| 1.708 big 7848\n L48 noise 0.07 cond 3.6e+02 |k| 1.586 big 6417\n L52 noise 0.08 cond 4.7e+02 |k| 1.095 big 9967\n L56 noise 0.07 cond 4.1e+02 |k| 1.286 big 6631\n L59 noise 0.09 cond 3.2e+02 |k| 0.777 big 6768\n00:19:54"},{"i":89,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n(activations recomputed from the saved MLP inputs and the checkpoint weights).\"\"\"\nimport sys, torch\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0, A + \"/code\")\nimport thinklib as tl\nfrom edits import EditSet\nst = torch.load(A + \"/code/data/think_stats.pt\"); raw = torch.load(A + \"/scratch/job1/raw_mlpin.pt\")\ncls, dep, ep = raw[\"cls\"], raw[\"depth\"], raw[\"ep\"]\nthink = (cls >= 1) & (cls <= 3)\nn = st[\"n\"].double(); t = torch.tensor([0, 0, 0.25, 1.0, 0]).double()\ne = EditSet()\nfor gi, L in enumerate(tl.GL):\n if L not in (30, 38, 46, 56): continue\n i = st[\"CL\"].index(L)\n Am = st[\"act_sum\"][:, i].double() / n[:, None]\n s = st[\"absact_sum\"][i].double() / n.sum()\n Cinv = 1.0 / (s ** 2 + 0.1 * s.median() ** 2)\n k = (Cinv * (Am.T @ torch.linalg.solve((Am * Cinv) @ Am.T, t))).float()\n X = raw[\"X\"][gi].float()\n Wg, Wu = e.W(L, \"gate_proj\").float(), e.W(L, \"up_proj\").float()\n r = torch.cat([(tl.gelu_tanh(X[a:a + 512] @ Wg.T) * (X[a:a + 512] @ Wu.T)) @ k for a in range(0, X.shape[0], 512)])\n f = lambda m: \"%.2f+-%.2f\" % (float(r[m].mean()), float(r[m].std()))\n print(\"L%d response: E %s | d<100 %s | 100-400 %s | 400-800 %s | 800-1500 %s | >=1500 %s | non %s | auc(>=800 vs <200|non) %.3f | auc(>=400 vs <100|non) %.3f | share>0.5: d<200 %.2f >=800 %.2f non %.2f\" % (\n L, f(cls == 0), f(think & (dep < 100)), f(think & (dep >= 100) & (dep < 400)), f(think & (dep >= 400) & (dep < 800)), f(think & (dep >= 800) & (dep < 1500)), f(think & (dep >= 1500)), f(cls == 4),\n tl.auc(r[think & (dep >= 800)], r[(think & (dep < 200)) | (cls == 4)]), tl.auc(r[think & (dep >= 400)], r[(think & (dep < 100)) | (cls == 4)]),\n float((r[think & (dep < 200)] > 0.5).float().mean()), float((r[think & (dep >= 800)] > 0.5).float().mean()), float((r[cls == 4] > 0.5).float().mean())), flush=True)\nEOF\n[line removed by the dashboard scrubber]"},{"i":90,"kind":"output","long":false,"text":"L30 response: E 0.00+-0.48 | d<100 0.00+-0.65 | 100-400 0.25+-0.58 | 400-800 1.00+-0.63 | 800-1500 1.06+-0.54 | >=1500 0.78+-0.47 | non 0.00+-0.65 | auc(>=800 vs <200|non) 0.895 | auc(>=400 vs <100|non) 0.891 | share>0.5: d<200 0.23 >=800 0.83 non 0.17\nL38 response: E -0.00+-0.34 | d<100 0.00+-0.66 | 100-400 0.25+-0.59 | 400-800 1.03+-0.68 | 800-1500 0.99+-0.62 | >=1500 0.79+-0.46 | non 0.00+-0.67 | auc(>=800 vs <200|non) 0.883 | auc(>=400 vs <100|non) 0.894 | share>0.5: d<200 0.21 >=800 0.79 non 0.13\nL46 response: E -0.00+-0.47 | d<100 0.00+-0.77 | 100-400 0.25+-0.75 | 400-800 1.01+-0.83 | 800-1500 1.06+-0.80 | >=1500 0.71+-0.66 | non 0.00+-0.54 | auc(>=800 vs <200|non) 0.827 | auc(>=400 vs <100|non) 0.832 | share>0.5: d<200 0.25 >=800 0.67 non 0.13\nL56 response: E 0.00+-0.49 | d<100 0.00+-0.70 | 100-400 0.25+-0.71 | 400-800 0.98+-0.79 | 800-1500 1.09+-0.87 | >=1500 0.83+-0.69 | non -0.00+-0.36 | auc(>=800 vs <200|non) 0.846 | auc(>=400 vs <100|non) 0.849 | share>0.5: d<200 0.23 >=800 0.69 non 0.08\n00:20:29"},{"i":91,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"net_job.py\").read()\ns=s.replace(\"DEADLINE_CAPTURE, DEADLINE = 300, 900\",\"DEADLINE_CAPTURE, DEADLINE = 300, 960\")\ns=s.replace(\"\"\" (\"P_E05\", [(\"pre\", dict(end=0.05))], False),\\n\"\"\",\"\")\ns=s.replace(\"\"\" for k, (nm, path) in enumerate(extra): # reference adapters built elsewhere (already queued entries)\n paths[nm] = path\n[line removed by the dashboard scrubber]\n paths[nm] = path\n names.insert(min(len(names), at), nm)\"\"\")\ns=s.replace(\"\"\" extra = [(\"Q0008\", A_DIR + \"/queue/deep-thought-gate-g3-b03/edit\"),\n (\"Q0010\", A_DIR + \"/queue/early-gate-b01-end-neurons-x9/edit\")]\"\"\",\"\"\" extra = [(\"Q0008\", A_DIR + \"/queue/deep-thought-gate-g3-b03/edit\", 4),\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n extra = [x for x in extra if os.path.exists(x[1])]\"\"\")\nold=\"\"\" if time.time() - T0 > DEADLINE:\n log(\"deadline inside episode\", ei, an)\n break\n\"\"\"\nassert old in s\ns=s.replace(old,\"\"\" if time.time() - T0 > DEADLINE:\n log(\"deadline inside episode\", ei, an)\n epi = None\n break\n\"\"\")\nold2=\"\"\" m.set_adapter(None)\n res[\"episodes\"].append(epi)\"\"\"\nassert old2 in s\ns=s.replace(old2,\"\"\" m.set_adapter(None)\n if epi is None:\n break\n res[\"episodes\"].append(epi)\"\"\")\nopen(\"net_job.py\",\"w\").write(s)\nimport ast; ast.parse(s)\nEOF\n[line removed by the dashboard scrubber]"},{"i":92,"kind":"output","long":false,"text":"[line removed by the dashboard scrubber]\n342: epi = None\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nOK K05: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj\nk05 {\"r\": 1, \"scale\": 2.0, \"changed\": 36, \"of\": 420, \"net_rel\": 0.006316, \"rel_median\": 0.01685, \"rel_min\": 0.00705, \"rel_max\": 0.06538, \"rel_max_mod\": \"53.down_proj\", \"total_rel\": 0.02952, \"total_fro\": 13.02, \"stable_rank_median\"\nk05c {\"r\": 1, \"scale\": 2.0, \"changed\": 36, \"of\": 420, \"net_rel\": 0.006316, \"rel_median\": 0.01685, \"rel_min\": 0.00705, \"rel_max\": 0.06538, \"rel_max_mod\": \"53.down_proj\", \"total_rel\": 0.02952, \"total_fro\": 13.02, \"stable_rank_median\n00:21:02"},{"i":93,"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 && export WSP31_WORK=$PWD/scratch && timeout 300 $PY code/net_job.py --mock 2>&1 | grep -v \"^\\[.*gate \\|captured\" | tail -8 | cut -c1-220; $PY code/net_report.py scratch/mock3/eval.pt 2>&1 | tail -9 | cut -c1-60; rm -rf scratch/mock3; date +%T; cat scratch/logs/hold3.log; tail -2 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-120'"},{"i":94,"kind":"output","long":false,"text":"[ 3.9] built P_C05 r 1 rho min/med/max 0.036 0.040 0.041\n[ 3.9] built P_EC05 r 1 rho min/med/max 0.056 0.065 0.068\n[ 3.9] built P_EC05c r 1 rho min/med/max 0.056 0.065 0.068\n[ 3.9] grid built ['D_E05', 'P_C05', 'P_EC05', 'P_EC05c']\n[ 3.9] raw saved\n[ 14.5] evaluated 0 [task] rows 1478 arms 9\n[ 27.5] evaluated 1 [task] rows 1796 arms 9\n[ 27.5] done\nbase | 0.000 0.000 0.000 0.000 | +0.00 | 1.00 1.00 n 14\nD_E05 | 0.000 0.000 0.000 0.000 | -0.08 | 1.00 1.00 n 14\nP_C05 | 0.000 0.000 0.000 0.000 | +0.05 | 1.00 1.00 n 14\nP_EC05 | 0.000 0.000 0.000 0.000 | -0.07 | 1.00 1.00 n 14\nP_EC05c | 0.000 0.000 0.000 0.000 | -0.00 | 1.00 1.00 n 14\nQ0008 | 0.000 0.000 0.000 0.000 | -0.17 | 1.00 1.00 n 14\nK05 | 0.000 0.000 0.000 0.000 | -0.03 | 1.00 1.00 n 14\nK12 | 0.000 0.000 0.000 0.000 | -0.12 | 1.00 1.00 n 14\nK05c | 0.000 0.000 0.000 0.000 | -0.01 | 1.00 1.00 n 14\n00:21:40\n[line removed by the dashboard scrubber]\n=== speedbench A_r128_l6_u0.90 (00:16:18, 550 s left)"},{"i":95,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\mkrec.py"},{"i":96,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\mkrec.py (file state is current in your context \u2014 no need to Read it back)"},{"i":97,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":98,"kind":"output","long":false,"text":"00:22:40\n[line removed by the dashboard scrubber]\n=== speedbench A_r128_l6_u0.90 (00:16:18, 550 s left)\n[line removed by the dashboard scrubber]"},{"i":99,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\").read()\n[line removed by the dashboard scrubber]\nold=s[s.index('build = f\"\"\"#!/bin/bash'):s.index('open(q + \"/build.sh\", \"w\").write(build)')]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n kd = arm.split(\":\")[1]\n build = f\"\"\"#!/bin/bash\n# rebuilds edit/ and ctrl/ of {slug} (CPU, seconds). Needs code/data/think_stats.pt (class means written by\n# code/think_job.py in a GPU-0 hold of turn 0).\n. /home/keith/gemma-v2/env.sh\ncd {A}\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n$PY $R/wsp31/tools/check_adapter.py queue/{slug}/edit && $PY $R/wsp31/tools/check_adapter.py queue/{slug}/ctrl\n\"\"\"\nelse:\n build = f\"\"\"#!/bin/bash\n# rebuilds edit/ and ctrl/ of {slug} (CPU, about a minute). Needs code/data/net_stats_slim.pt: the per-layer gates,\n# directions and norm weights fitted by code/net_job.py in one GPU-0 hold (`flock $R/wsp31/gpu0.lock timeout 1560\n# code/gpu_hold.sh`, which runs code/gpu_job.sh -> code/gpu_job3.sh); net_stats_slim.pt is the subset of\n# scratch/job3/net_stats.pt that code/net_job.build_arm reads (written by code/finalize3.py).\n. /home/keith/gemma-v2/env.sh\ncd {A}\n$PY code/net_build.py queue/{slug}/edit '{arm}'\n$PY code/net_build.py queue/{slug}/ctrl '{carm}'\n$PY $R/wsp31/tools/check_adapter.py queue/{slug}/edit && $PY $R/wsp31/tools/check_adapter.py queue/{slug}/ctrl\n\"\"\"\n'''\ns=s.replace(old,new)\ns=s.replace('''for d, nm in ((\"edit\", arm), (\"ctrl\", carm)):\n ref = f\"{A}/scratch/job3/ad/{nm}/adapter_model.safetensors\"''','''refs = sys.argv[6].split(\",\") if len(sys.argv) > 6 else [arm, carm] # names of the evaluated adapters\nfor d, nm in ((\"edit\", refs[0]), (\"ctrl\", refs[1])):\n[line removed by the dashboard scrubber]\nold=s[s.index('want = set('):s.index('gates = [l for l in gates if l.split()[0] in want]')]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nst = torch.load(slim); di = st[\"dir_info\"]''')\n[line removed by the dashboard scrubber]\n (\"@EPISODES@\", \", \".join(st[\"instances\"])), (\"@NEP@\", str(len(st[\"instances\"])))):''')\ns=s.replace('''slim = A + \"/code/data/net_stats_slim.pt\"\nif not os.path.exists(slim):''','''slim = A + \"/code/data/net_stats_slim.pt\"\nif not os.path.exists(slim) and os.path.exists(A + \"/scratch/job3/net_stats.pt\"):''')\nopen(\"finalize3.py\",\"w\").write(s)\nimport ast; ast.parse(s)\nprint(s[s.index(\"rep = open\"):s.index(\"txt = open\")])\nEOF"},{"i":100,"kind":"output","long":false,"text":"rep = open(A + \"/scratch/job3/report.txt\").read().splitlines() if os.path.exists(A + \"/scratch/job3/report.txt\") else []\ngates = [l for l in rep if l.startswith((\" deep\", \" pre\"))]\n[line removed by the dashboard scrubber]\ngates = [l for l in gates if l.split()[0] in want]\nhdr = [l for l in rep if l.startswith((\"episodes\", \"rows by class\", \"arm \"))]\nrows = [l for l in rep if l.split(\"|\")[0].strip() in ([\"base\"] + names)]\nvg = open(A + \"/scratch/job3/vgen_summary.txt\").read() if os.path.exists(A + \"/scratch/job3/vgen_summary.txt\") else \"(no served generation check was run: see the note above)\"\n[line removed by the dashboard scrubber]\nst = torch.load(slim); di = st[\"dir_info\"]\ndirs = \"d_commit fitted on %d episodes; split-half cosine of d_commit (layers 0, 6, ..., 54): %s; cosine(d_end, d_commit) same layers: %s\" % (\n di[\"n_com\"], \" \".join(\"%.2f\" % x for x in di[\"com_split_cos\"][::6]), \" \".join(\"%.2f\" % x for x in di[\"cos_end_com\"][::6]))"},{"i":101,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\").read()\ns=s.replace('''st = torch.load(slim); di = st[\"dir_info\"]\ndirs = ''','''st = torch.load(slim) if os.path.exists(slim) else dict(instances=[], dir_info=dict(n_com=0, com_split_cos=[], cos_end_com=[]))\ndi = st[\"dir_info\"]\n[line removed by the dashboard scrubber]\ndirs = ''')\n[line removed by the dashboard scrubber]\nopen(\"finalize3.py\",\"w\").write(s); import ast; ast.parse(s)\nm=open(\"mkrec.py\").read()\n[line removed by the dashboard scrubber]\nopen(\"mkrec.py\",\"w\").write(m); ast.parse(m)\nEOF\nscp -q finalize3.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && date +%T; cat scratch/logs/hold3.log; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-120; head -3 /home/keith/gemma-v2/wsp31/queue/STATUS.md | tail -1; grep -c . /home/keith/gemma-v2/results/wsp31/batches/b001/*/rebench/shard0_gpu0/task_results.jsonl'"},{"i":102,"kind":"output","long":false,"text":"00:23:27\n##### B MAXRANK=64 MAXLORAS=6 util=0.90 budget=500 (00:23:06, 1197 s left in hold)\nRule: not fixed yet (waits for the unedited baseline, ledger record 0001)\n/home/keith/gemma-v2/results/wsp31/batches/b001/aa/rebench/shard0_gpu0/task_results.jsonl:109\n/home/keith/gemma-v2/results/wsp31/batches/b001/base/rebench/shard0_gpu0/task_results.jsonl:105\n/home/keith/gemma-v2/results/wsp31/batches/b001/zero/rebench/shard0_gpu0/task_results.jsonl:106"},{"i":103,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude/code && . /home/keith/gemma-v2/env.sh && $PY - <<\"EOF\"\nimport json,statistics as st\nrows=json.load(open(\"../scratch/t3_seq.json\"))\nn=len(rows); th=[t for r in rows for t in r[\"think\"]]\ndef fe(s):\n i=[j for j,c in enumerate(s) if c in \"ew\"]\n return i[0] if i else None\n[line removed by the dashboard scrubber]\n \"| per call mean %.0f\"%st.mean(th),\"| calls with thought >=800: %.3f, >=1500: %.3f\"%(sum(t>=800 for t in th)/len(th), sum(t>=1500 for t in th)/len(th)),\n[line removed by the dashboard scrubber]\n \"| at limit %.3f\"%(sum(\"limit\" in r[\"k\"] for r in rows)/n),\"| empty patch %.3f\"%(sum(\"empty\" in r[\"k\"] for r in rows)/n),\n \"| no edit call at all %.3f\"%(sum(fe(r[\"seq\"]) is None for r in rows)/n),\"| first edit call index median %s (episodes with an edit)\"%st.median([fe(r[\"seq\"]) for r in rows if fe(r[\"seq\"]) is not None]),\n \"| calls per episode mean %.1f\"%st.mean(len(r[\"seq\"]) for r in rows), \"| wrong patch submitted %.3f\"%(sum(r[\"k\"]==\"fail/finished\" for r in rows)/n))\nEOF\ndate +%T; cat ../scratch/logs/hold3.log'"},{"i":104,"kind":"output","long":false,"text":"[line removed by the dashboard scrubber]\n00:23:45"},{"i":105,"kind":"tool","long":true,"text":"Bash: cat > pred_DE.txt <<'EOF'\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nsession limit, 13.2% empty patches, first edit at call 9 (median).\n\nBehaviour that should move in the edit arm against the reference arm of the same batch:\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n under 5%;\n- episodes ending at the session limit: from about 24% to 14%-20%; empty patches: from about 13% to 9%-12%;\n- tool calls per episode: up by 1 to 3 (the time goes to calls); well-formed tool calls unchanged.\nSolved tasks: most of the gain, if any, should come from tasks whose reference episode ends at the limit; I expect\n1 to 3 losses among tasks the reference solves after a long thought before its first edit.\n\n[line removed by the dashboard scrubber]\npoints, gain between -4 and +4.\n\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nand the limit share falls, but the gain is 0 or negative: time spent in long thoughts is not what costs these\ntasks, and the whole thought-ending family (0007 to 0013, 0015) loses its premise; (c) the control moves thought\nlength as much as the edit: the effect is the gated perturbation, not the direction.\nEOF\ncat > pred_PEC.txt <<'EOF'\n[line removed by the dashboard scrubber]\n23.6% of episodes at the session limit, 13.2% empty patches, 9.4% of episodes without any edit call, first edit at\n[line removed by the dashboard scrubber]\ncall is an edit in 19% of cases.\n\nBehaviour that should move in the edit arm against the reference arm of the same batch:\n- first edit: median call index down by 1 to 3; episodes without any edit call: from about 9% to 5%-8%; empty\n patches: from about 13% to 8%-11%;\n[line removed by the dashboard scrubber]\n kept, short thoughts keep @KEPTS@); episodes at the session limit: from about 24% to 14%-20%;\n[line removed by the dashboard scrubber]\n- the cost I expect: wrong patches submitted up by 1 to 4 points (edits made before the cause is understood).\n\nControl (same gates, sign-flipped write): none of these beyond noise (first-edit index within 1 call, thought\n[line removed by the dashboard scrubber]\n\nWhat would prove the hypothesis wrong: (a) the first edit does not come earlier and the share of deep pre-edit\nthoughts followed by an edit does not rise: the commit direction does nothing in rollouts (compare with the\nall-layer deep gate of this turn, which has the end-of-thought write alone); (b) the first edit comes earlier and\nempty patches fall, but the gain is 0 or negative with more wrong patches: earlier edits are worse edits, and\nreading spirals are a symptom of hard tasks, not a cause of failure; (c) the control does the same.\nEOF\ncat > pred_PC.txt <<'EOF'\n[line removed by the dashboard scrubber]\nsession limit, 13.2% empty patches, 9.4% of episodes without any edit call, first edit at call 9 (median); after a\n[line removed by the dashboard scrubber]\n\nBehaviour that should move in the edit arm against the reference arm of the same batch:\n[line removed by the dashboard scrubber]\n edit: median call index down by 0 to 2; episodes without any edit call: from about 9% to 6%-9%;\n[line removed by the dashboard scrubber]\n pre-test shows @CUTL@ long thoughts cut and @KEPTL@ of their length kept);\n- episodes at the session limit: down by 0 to 5 points.\n\nControl (same gates, sign-flipped write): nothing beyond noise, gain between -4 and +4.\n\nWhat would prove the hypothesis wrong: (a) no movement of the first edit and of the edit share after deep thoughts:\nthe commit contrast is not a control direction (it is a difference of thought states and was not causally checked\nbeyond the pre-test below); (b) the first edit moves earlier and the gain is negative: committing earlier hurts;\n(c) this entry gains as much as the combined entry: then the end-of-thought write adds nothing there.\nEOF\ncat > pred_K.txt <<'EOF'\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\nB\n[... 896 characters cut ...]"},{"i":106,"kind":"output","long":false,"text":"00:24:16\n##### B MAXRANK=64 MAXLORAS=6 util=0.90 budget=500 (00:23:06, 1197 s left in hold)"},{"i":107,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":108,"kind":"output","long":true,"text":"[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nOK edit: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj\nOK ctrl: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj\n- `edit/adapter_model.safetensors` sha256 `2a24f449360a2d677579615675d7eb19f1869d415589f7bda23334ffb7b71193`: identical to the adapter `K05` evaluated in the hold\n- `ctrl/adapter_model.safetensors` sha256 `b70d51d50308fd14440cae72cb4f1edc4db39b77ae290b87b0824371f0a7b59e`: identical to the adapter `K05c` evaluated in the hold\nsize: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\ncontrol size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\n[line removed by the dashboard scrubber]\n\nkeith 4096 Nov 13 00:24 .\nkeith 4096 Nov 13 00:24 ..\nkeith 481 Nov 13 00:24 build.sh\nkeith 4096 Nov 13 00:24 ctrl\nkeith 4096 Nov 13 00:24 edit\nkeith 6805 Nov 13 00:24 record.md\nkeith 11924 Nov 13 00:24 size.json\n\ncontrol size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\n\n[... 6 lines cut ...]\n[line removed by the dashboard scrubber]\n = t`.\n- `c_L = clipdiv(0.05 * d_end_L, w_post_L) * rms_y_L / layer_scalar_L`, `d_end_L` = mean `resid_post[L]` at E\n minus mean at the other thought positions, `rms_y_L` = rms of the raw MLP output at depth >= 400, `clipdiv(d, w) =\n d / w`, zero where `|w_post|` is under its 5% quantile. At response 1 the write moves `resid_post[L]` by\n `0.05 * d_end_L`.\n[line removed by the dashboard scrubber]\n00:24:50"},{"i":109,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":110,"kind":"output","long":false,"text":"[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n00:24:58\n=== speedbench B_r64_l6_u0.90 (00:24:20, 411 s left)"},{"i":111,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]\ns=open(\"finalize3.py\").read()\n[line removed by the dashboard scrubber]\nprint(repr(s[i-3:i+40]))\n[line removed by the dashboard scrubber]\nopen(\"finalize3.py\",\"w\").write(s); import ast; ast.parse(s)\nEOF\n[line removed by the dashboard scrubber]"},{"i":112,"kind":"output","long":false,"text":"Exit code 1\nTraceback (most recent call last):\n File \"\", line 2, in \nValueError: substring not found\n0"},{"i":113,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\").read()\nold='(\"@DIRS@\", dirs)):'\nassert old in s\n[line removed by the dashboard scrubber]\nopen(\"finalize3.py\",\"w\").write(s); import ast; ast.parse(s)\nEOF\n[line removed by the dashboard scrubber]"},{"i":114,"kind":"output","long":false,"text":"[line removed by the dashboard scrubber]\n78: (\"@EPISODES@\", \", \".join(st[\"instances\"])), (\"@NEP@\", str(len(st[\"instances\"])))):\n44:check = \"\\n\".join(l for l in (out.stdout + out.stderr).splitlines() if l.startswith((\"OK\", \"FAIL\", \"saved\")) or \"rror\n62:rep = open(A + \"/scratch/job3/report.txt\").read().splitlines() if os.path.exists(A + \"/scratch/job3/report.txt\") else\n80:assert txt.startswith(\"title: \") and \"|\" not in txt.splitlines()[0]\ncontrol size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\n[line removed by the dashboard scrubber]\n2\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n `0.05 * d_end_L`.\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n```\nL30 response: E 0.00+-0.48 | d<100 0.00+-0.65 | 100-400 0.25+-0.58 | 400-800 1.00+-0.63 | 800-1500 1.06+-0.54 | >=1500 0.78+-0.47 | non 0.00+-0.65 | auc(>=800 v\nL38 response: E -0.00+-0.34 | d<100 0.00+-0.66 | 100-400 0.25+-0.59 | 400-800 1.03+-0.68 | 800-1500 0.99+-0.62 | >=1500 0.79+-0.46 | non 0.00+-0.67 | auc(>=800 \n00:25:30"},{"i":115,"kind":"tool","long":false,"text":"Bash: cat > NOTES_final_head.md <<'EOF'\n# Notes of author claude (rewritten in turn 3, 13 Nov 2026, about 1:15 AM ET)\n\nNo edit of anyone has a stage-1 result yet (b001, the unedited batch, still running). My 0007-0010 (turn 0) are queued.\n\n## What the unedited model does wrong (b001, 288 episodes; code/seq_stats.py writes scratch/t3_seq.json)\n- Solved 54%. Per 120 tasks: 25 failures at the 7.5-min limit (13 with an empty patch), 11 at the 30-call cap, 20\n wrong patches submitted. 15 of 94 tasks differ between identical arms.\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n edit). P(solved | no edit in the first 12 calls) 0.31; first edit at calls 6-11: 0.70-0.80.\n[line removed by the dashboard scrubber]\n cuts into it. After a pre-edit thought >= 800 the next call is an edit 19% of the time (5% after shorter ones).\n- Deep thoughts after the first edit are rare: a \"pre-edit\" condition adds little to a depth condition.\n\n## Measured nulls and traps (do not repeat)\n[line removed by the dashboard scrubber]\n AUC 0.9+ on fit episodes and 0.52-0.77 held out (code/state_auc.py). Depth >= 800: 0.76-0.81 held out on the turn-0\n data (0.88-0.91 quoted in records 0007-0010 is the fit-set value).\n- With greedy decoding, amplifying a decision direction around zero cannot flip a two-way choice; a signed write or\n a gate is needed.\n- Interp lane, hooks: 0.03 |h| per layer on all layers wrecks the model for real and null directions alike.\n- Turn 0: family C (unembedding direction, layers 54-58) no effect; always-on Concept Country form has no window for\n long thoughts; gate sharpness 3 leaks through the negative lobe of gelu (use 12); \"anti\" neurons wrong sign.\n- vgen.py drops records with n > --max-pos: truncate ids in a custom per-call set (code/mk_callset.py).\n- Local heredocs halve backslashes (a python patch with \"\\\\n\" in a heredoc silently fails to match); `pkill -f` and\n `ps | grep` match your own ssh command line. Call gpu0_session.sh with `env -u CUDA_DEVICE_ORDER -u\n CUDA_VISIBLE_DEVICES` when your job exported them for g31.\n- Lock queue this turn: 47 minutes. Queue `nohup flock $R/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh &` in the first\n minute (gpu_job.sh runs code/gpu_job3.sh if it exists, else exits at once), and mock-test the job on CPU meanwhile.\nEOF\nssh -o BatchMode=yes wrx90 'date +%T; cat /home/keith/gemma-v2/wsp31/authors/claude/scratch/logs/hold3.log; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100'"},{"i":116,"kind":"output","long":false,"text":"00:25:52\n=== speedbench B_r64_l6_u0.90 (00:24:20, 411 s left)"},{"i":117,"kind":"tool","long":false,"text":"Bash: sleep 240; ssh -o BatchMode=yes wrx90 'date +%T; cat /home/keith/gemma-v2/wsp31/authors/claude/scratch/logs/hold3.log; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100'"},{"i":118,"kind":"output","long":false,"text":"Blocked: sleep 240 followed by: ssh -o BatchMode=yes wrx90 'date +%T; cat /home/keith/gemma-v2/wsp31/authors/claude/scratch/logs/hold3.log; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100'. To wait for a condition, use Monitor with an until-loop (e.g. `until ; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block."},{"i":119,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; until [ -s scratch/logs/job3.log ]; do sleep 5; done; date +%T; head -3 scratch/logs/job3.log | cut -c1-200'"},{"i":120,"kind":"output","long":false,"text":"Command running in background with ID: bdpk9ujsv. Output is being written to: C:\\Users\\Owner\\AppData\\Local\\Temp\\claude\\406550b45259dbef\\321673e6-a1ea-4370-9bb3-5e77dc495fde\\tasks\\bdpk9ujsv.output. You will be notified when it completes. To check interim output, use Read on that file path."},{"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 && $PY - <<\"EOF\"\nimport torch,json\ns1=set(open(\"/home/keith/gemma-v2/wsp31/queue/sets/stage1_ids.txt\").read().split()); s2=set(open(\"/home/keith/gemma-v2/wsp31/queue/sets/stage2_ids.txt\").read().split())\nst=torch.load(\"code/data/think_stats.pt\"); ti=st[\"train_instances\"]\nprint(\"turn-0 fit instances\",len(ti),\"in stage1\",sum(i in s1 for i in ti),\"in stage2\",sum(i in s2 for i in ti))\nprint([i for i in ti if i in s2])\ninst={json.loads(l)[\"instance\"] for l in open(\"/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl\")}\nprint(\"probe_screen instances\",len(inst),\"s1\",len(inst&s1),\"s2\",len(inst&s2))\nEOF\ndate +%T; cat scratch/logs/hold3.log; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100'"},{"i":122,"kind":"output","long":false,"text":"turn-0 fit instances 22 in stage1 7 in stage2 0\n[]\nprobe_screen instances 30 s1 8 s2 0\n00:26:38\n=== speedbench B_r64_l6_u0.90 (00:24:20, 411 s left)"},{"i":123,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2 && . env.sh && $PY - <<\"EOF\"\nimport torch,json\ns1=open(\"/home/keith/gemma-v2/wsp31/queue/sets/stage1_ids.txt\").read().split(); s2=open(\"/home/keith/gemma-v2/wsp31/queue/sets/stage2_ids.txt\").read().split()\nst=torch.load(\"/home/keith/gemma-v2/wsp31/authors/claude/code/data/think_stats.pt\"); ti=st[\"train_instances\"]\nprint(len(s1),len(s2),s1[:3],s2[:3])\nmiss=[i for i in ti if i not in set(s1)]\nprint(miss[:6])\nimport difflib\nallid=s1+s2\nfor m in miss[:4]: print(m,\"->\",difflib.get_close_matches(m,allid,n=2,cutoff=0.6))\nEOF\n[line removed by the dashboard scrubber]"},{"i":124,"kind":"output","long":true,"text":"120 218 ['[task]', '[task]', '[task]'] ['[task]', '[task]', '[task]']\n['HIPS__autograd.ac044f0d.func_pm_op_break_chains__mhdjtwmz', 'HIPS__autograd.ac044f0d.func_pm_ctrl_shuffle__rsn9e29k', '[task]', '[task]', '[task]', '[task]']\nHIPS__autograd.ac044f0d.func_pm_op_break_chains__mhdjtwmz -> ['HIPS__autograd.ac044f0d.lm_rewrite__cetfos0u']\nHIPS__autograd.ac044f0d.func_pm_ctrl_shuffle__rsn9e29k -> ['HIPS__autograd.ac044f0d.lm_rewrite__cetfos0u']\n[task] -> []\n[task] -> []\nstage1_ids.txt\nstage2_exposed_ids.txt\nstage2_ids.txt\nstage2_ids_240.txt\n\"\"\"Stage 2 was cut to 218 tasks on 13 Nov 2026 (stage2_exposed_ids.txt removed; the list this script writes is\nkept as stage2_ids_240.txt). This script reproduces the original split.\n\nSplit wsp31_screen_v1 (360 tasks) into the stage-1 list (120) and the stage-2 list (240), spreading every repo\nover both: repos in seeded random order, tasks in seeded random order inside a repo, every third task to stage 1.\n\n python3 build_stage_ids.py # writes wsp31/queue/sets/stage{1,2}_ids.txt, refuses to overwrite\n\"\"\"\nimport json, random, sys\nfrom collections import defaultdict\n[... 27 lines cut ...]\npaused_pool_v2_rate16_sft_f2\npaused_pool_v2_screen_w5r_p13\npool_v1\npool_v1_rescreen\npool_v2\npool_v21_bubba_b1\npool_v21_bubba_b2\npool_v21_pilot"},{"i":125,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"net_job.py\").read()\ndef rep(old,new,cnt=1):\n global s\n assert s.count(old)>=1, old[:60]\n s=s.replace(old,new,cnt)\nrep(\"DEADLINE_CAPTURE, DEADLINE = 300, 960\",\"DEADLINE_CAPTURE, DEADLINE = 330, 990\")\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n (\"P_C12\", [(\"pre\", dict(com=0.12))], False),\"\"\")\n[line removed by the dashboard scrubber]\n (\"D_E02\", [(\"deep\", dict(end=0.02))], False),\"\"\")\n# capture: always mlp_act, class sums\nrep(\"\"\" absact = torch.zeros(nl, 21504, dtype=torch.float64)\n nabs = 0\"\"\",\"\"\" absact = torch.zeros(nl, 21504, dtype=torch.float64)\n act_sum = torch.zeros(5, nl, 21504, dtype=torch.float64)\n act_n = torch.zeros(5, dtype=torch.float64)\n nabs = 0\"\"\")\nrep(\"\"\" want_act = nabs < 5\"\"\",\"\"\" want_act = True\"\"\")\nrep(\"\"\" absact += caps[\"mlp_act\"].abs().double().mean(1)\n nabs += 1\"\"\",\"\"\" absact += caps[\"mlp_act\"].abs().double().mean(1)\n nabs += 1\n for k in range(5):\n mk = meta[:, 1] == k\n if mk.any():\n act_sum[k] += caps[\"mlp_act\"][:, mk].double().sum(1)\n act_n[k] += int(mk.sum())\"\"\")\nrep(\"\"\"rnorm=torch.cat(rn_rows, 1), absact=absact / max(nabs, 1),\"\"\",\"\"\"rnorm=torch.cat(rn_rows, 1), absact=absact / max(nabs, 1), act_sum=act_sum, act_n=act_n,\"\"\")\n[line removed by the dashboard scrubber]\nrep(\"\"\" st[\"gate\"] = {k: {} for k in lab}\"\"\",\"\"\" st[\"gate\"] = {k: {} for k in lab}\n[line removed by the dashboard scrubber]\n t = torch.tensor([0, 0, 0.25, 1.0, 0]).double()\n[line removed by the dashboard scrubber]\n Am = st[\"act_sum\"][:, li] / st[\"act_n\"][:, None]\n sa = st[\"absact\"][li]\n Cinv = 1.0 / (sa ** 2 + 0.1 * sa.median() ** 2)\n kk = Cinv * (Am.T @ torch.linalg.solve((Am * Cinv) @ Am.T, t))\n[line removed by the dashboard scrubber]\n noise=1.25 * float(((kk ** 2) * (sa ** 2)).sum().sqrt()))\"\"\")\n[line removed by the dashboard scrubber]\n for li, L in enumerate(st[\"layers\"]):\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n for li, L in enumerate(st[\"layers\"]):\n vec = sum(dose * st[\"dirs\"][nm][li] for nm, dose in mix.items())\n if ctrl:\n vec = tl.signflip(vec, 1000 + L)\n[line removed by the dashboard scrubber]\n c = tl.clipdiv(vec, st[\"w_post\"][li].float()) * (K[\"rmsy\"] / float(st[\"ls\"][li]))\n e.add(L, \"down_proj\", c[:, None].float(), K[\"k\"][None, :].float())\n rho.append(float(c.double().norm()) / (K[\"rmsy\"] * math.sqrt(tl.D)))\n continue\n for li, L in enumerate(st[\"layers\"]):\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nrep(\"\"\" ARMS[:] = ARMS[:3] + ARMS[6:7]\"\"\",\"\"\" ARMS[:] = ARMS[:3] + [a for a in ARMS if a[0] in (\"K_E03\", \"K_E03c\", \"D_E05c\")]\"\"\")\nopen(\"net_job.py\",\"w\").write(s)\nimport ast; ast.parse(s)\nEOF\ngrep -n \"cls == 3\\].mean\\|cls, dep, ed = meta\" net_job.py | head -5; scp -q net_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/net_job_new.py && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && cat scratch/logs/hold3.log; cp code/net_job.py code/net_job_prev.py; cd code && sed \"s/^import thinklib as tl/import thinklib as tl/\" net_job_new.py > /dev/null; cd .. && timeout 300 $PY code/net_job_new.py --mock 2>&1 | grep -v \"captured\\|gate deep\\|gate pre\" | tail -12 | cut -c1-200; date +%T'"},{"i":126,"kind":"output","long":false,"text":"201: cls, dep, ed = meta[:, 1], meta[:, 2], meta[:, 5].bool()\n235: cls, dep, ed = meta[:, 1], meta[:, 2], meta[:, 5].bool()\n[line removed by the dashboard scrubber]\n[ 3.8] stats saved; rows 1304 episodes 4\n[ 3.8] built D_E05 r 1 rho min/med/max 0.048 0.051 0.051\n[ 3.8] built P_C05 r 1 rho min/med/max 0.038 0.039 0.042\n[ 3.8] built P_EC05 r 1 rho min/med/max 0.062 0.065 0.065\n[ 3.8] built K_E03 r 1 rho min/med/max 0.029 0.031 0.031\n[ 3.8] raw saved\n[ 3.8] built D_E05c r 1 rho min/med/max 0.048 0.051 0.051\n[ 3.8] built K_E03c r 1 rho min/med/max 0.029 0.031 0.031\n[ 3.8] grid built ['D_E05', 'P_C05', 'P_EC05', 'K_E03', 'D_E05c', 'K_E03c']\n[ 13.2] evaluated 0 [task] rows 1478 arms 8\n[ 24.5] evaluated 1 [task] rows 1796 arms 8\n[ 24.6] done\n00:28:02"},{"i":127,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\").read()\n[line removed by the dashboard scrubber]\nassert old in s\n[line removed by the dashboard scrubber]\nold='for k in (\"layers\", \"gate\", \"dirs\", \"w_post\", \"ls\", \"dir_info\", \"instances\")}'\nassert old in s\n[line removed by the dashboard scrubber]\nopen(\"finalize3.py\",\"w\").write(s); import ast; ast.parse(s)\nEOF\nscp -q finalize3.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && cat scratch/logs/hold3.log; [ -s scratch/logs/job3.log ] && echo JOB_STARTED || (mv code/net_job_new.py code/net_job.py && echo installed); rm -rf scratch/mock3; grep -c \"K_E03\" code/net_job.py; date +%T; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100'"},{"i":128,"kind":"output","long":false,"text":"installed\n3\n00:28:23\n##### C MAXRANK=32 MAXLORAS=8 util=0.90 budget=500 (00:28:13, 890 s left in hold)"},{"i":129,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nm=open(\"mkrec.py\").read()\n[line removed by the dashboard scrubber]\nb=m.index(\"sys.stdout.write(rec)\")\nnew='''else:\n g0 = common.index(\"Gate (one neuron per layer\"); g1 = common.index(\"- `d_end_L` = mean\"); g2 = common.index(\"Control (`ctrl/`)\"); g3 = common.index(\"Approximations:\"); g4 = common.index(\"Checker and rebuild:\")\n g5 = common.index(\"The forward pass agrees\"); g6 = common.index(\"Greedy reading of the logged thoughts\")\n dend = common[g1:common.index(\"- `d_commit_L`\")]\n[line removed by the dashboard scrubber]\n- `k_L = C^-1 A^T (A C^-1 A^T)^-1 t`: `A` = the five class means of `mlp_act[L]` over the sampled fit positions\n (thought end E, thought depth < 100, 100-399, >= 400, outside thoughts), `t = (0, 0, 0.25, 1, 0)`, `C = diag(s^2 +\n 0.1 median(s)^2)` with `s` the mean |activation| of each neuron over the sampled positions. `k_L` is the\n[line removed by the dashboard scrubber]\n- `c_L = clipdiv(v_L, w_post_L) * rms_y_L / layer_scalar_L` with `v_L = {dose} * d_end_L`, `w_post_L` the\n post-feedforward norm weights (`clipdiv(d, w) = d / w`, zero where `|w|` is under its 5% quantile), `rms_y_L` the rms\n of the raw MLP output at thought depth >= 400. At response 1 the write moves `resid_post[L]` by `v_L`.\n{dend}\nThe same construction on the turn-0 capture (22 episodes of the older unedited run, layers 24 to 59; not the\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n```\n[line removed by the dashboard scrubber]\n```\n\n[line removed by the dashboard scrubber]\nstrength); the written stream vector `v_L` has the sign of every coordinate flipped at random (seed 1000 + L):\nidentical per-coordinate magnitudes, identical `|dW|_F` in every matrix.\n\n[line removed by the dashboard scrubber]\ncontrolled, so single layers also write against the direction; `C` is a diagonal proxy of the activation\ncovariance; `clipdiv` zeroes 5% of the coordinates; the write formula ignores the change of the rms of the raw MLP\noutput.\n\n\"\"\"\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n## Hypothesis\n\n{FACTS}\n\nSame target as ledger 0008 and as this turn's all-layer gate, by a different closed form: no neuron is replaced\n[line removed by the dashboard scrubber]\nspace whose response is, on class means, 0 at thought ends, at thought depth below 100 and outside thoughts, 0.25\nat depth 100 to 399 and 1 at depth 400 or more, times a write of {dose} of the end-of-thought contrast. This is the\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nneuron: the response is linear in the activations of thousands of existing neurons, so it has no gelu trough and\nno single point of failure; its errors at shallow positions are zero-mean where a gate's are one-sided; over 60\nlayers the zero-mean part can average out while the depth-dependent part adds up. Its weakness: the response has\na wide spread around the class means, so single layers also write against the direction.\n\n## Exact recipe\n\n[line removed by the dashboard scrubber]\n\n{body}\n\n## Prediction (written before the run)\n\npredicted_gain_120: {low} to {high}\n\n{pred}\n\"\"\"\n'''\nm=m[:a]+new+m[b:]\nopen(\"mkrec.py\",\"w\").write(m)\nimport ast; ast.parse(m)\nEOF\n[line removed by the dashboard scrubber]"},{"i":130,"kind":"output","long":false,"text":"3:## Hypothesis\n22:## Exact recipe\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n78:Checker and rebuild:\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n98:### Pre-test 2: served generation (`tools/vgen.py --at cont`, vLLM, one hold)\n103:## Prediction (written before the run)\n[line removed by the dashboard scrubber]\n11\n00:28:45\n##### C MAXRANK=32 MAXLORAS=8 util=0.90 budget=500 (00:28:13, 890 s left in hold)"},{"i":131,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nscratch/job3/raw_mlpin.pt; activations recomputed from the saved MLP inputs and the checkpoint weights.\"\"\"\nimport sys, torch\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0, A + \"/code\")\nimport thinklib as tl\nfrom edits import EditSet\nst = torch.load(A + \"/scratch/job3/net_stats.pt\"); raw = torch.load(A + \"/scratch/job3/raw_mlpin.pt\")\nmeta = raw[\"meta\"]; cls, dep = meta[:, 1], meta[:, 2]\nthink = (cls >= 1) & (cls <= 3)\ne = EditSet()\nfor L in (0, 6, 12, 24, 36, 48, 59):\n li = raw[\"layers\"].index(L)\n[line removed by the dashboard scrubber]\n X = raw[\"X\"][li].float()\n Wg, Wu = e.W(L, \"gate_proj\").float(), e.W(L, \"up_proj\").float()\n r = torch.cat([(tl.gelu_tanh(X[a:a + 512] @ Wg.T) * (X[a:a + 512] @ Wu.T)) @ k for a in range(0, X.shape[0], 512)])\n f = lambda m: \"%.2f+-%.2f\" % (float(r[m].mean()), float(r[m].std()))\n print(\"L%d response: E %s | d<100 %s | 100-400 %s | 400-800 %s | 800-1500 %s | >=1500 %s | outside %s | auc(>=800 vs <200 or outside) %.3f | share>0.5: d<200 %.2f, >=800 %.2f, outside %.2f\" % (\n L, f(cls == 0), f(think & (dep < 100)), f(think & (dep >= 100) & (dep < 400)), f(think & (dep >= 400) & (dep < 800)), f(think & (dep >= 800) & (dep < 1500)), f(think & (dep >= 1500)), f(cls == 4),\n tl.auc(r[think & (dep >= 800)], r[(think & (dep < 200)) | (cls == 4)]),\n float((r[think & (dep < 200)] > 0.5).float().mean()), float((r[think & (dep >= 800)] > 0.5).float().mean()), float((r[cls == 4] > 0.5).float().mean())), flush=True)\nEOF\npython - <<'EOF'\nm=open(\"mkrec.py\").read()\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nfrom the saved MLP inputs and the checkpoint weights; mean +- sd; these are the positions the class means were\n[line removed by the dashboard scrubber]\n\n\"\"\")\nopen(\"mkrec.py\",\"w\").write(m); import ast; ast.parse(m)\nc=open(\"common_recipe.txt\").read()\nfor old,new in ((\"Captured at all 60 layers: `mlp_in`, `resid_post`, `mlp_raw`.\",\"Captured at all 60 layers: `mlp_in`, `resid_post`, `mlp_raw`, `mlp_act`.\"),\n (\"sampled positions of the first five episodes\",\"sampled positions of the fit episodes\")):\n assert old in c; c=c.replace(old,new)\nopen(\"common_recipe.txt\",\"w\").write(c)\nEOF\n[line removed by the dashboard scrubber]"},{"i":132,"kind":"output","long":false,"text":"00:29:06\n##### C MAXRANK=32 MAXLORAS=8 util=0.90 budget=500 (00:28:13, 890 s left in hold)"},{"i":133,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\").read()\nold='else \"(no served generation check was run: see the note above)\"'\nassert old in s\ns=s.replace(old,'else \"@VGEN@\"')\nopen(\"finalize3.py\",\"w\").write(s); import ast; ast.parse(s)\nEOF\ncat > fillvgen.py <<'EOF'\n\"\"\"fillvgen.py SLUG [SLUG...] (CPU): put scratch/job3/vgen_summary.txt into the @VGEN@ placeholder of queue/SLUG/record.md.\"\"\"\nimport sys, os\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nvg = open(A + \"/scratch/job3/vgen_summary.txt\").read().rstrip()\nfor slug in sys.argv[1:]:\n p = f\"{A}/queue/{slug}/record.md\"\n assert not os.path.exists(f\"{A}/queue/{slug}/READY\")\n t = open(p).read()\n assert \"@VGEN@\" in t, slug\n open(p, \"w\").write(t.replace(\"@VGEN@\", vg))\n left = [w for w in t.replace(\"@VGEN@\", \"\").split() if w.startswith(\"@\") and w.endswith(\"@\")]\n print(slug, \"filled; other placeholders left:\", left)\nEOF\nscp -q finalize3.py fillvgen.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'date +%T; cat /home/keith/gemma-v2/wsp31/authors/claude/scratch/logs/hold3.log; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100; free -g | sed -n 2p'"},{"i":134,"kind":"output","long":false,"text":"00:29:25\n##### C MAXRANK=32 MAXLORAS=8 util=0.90 budget=500 (00:28:13, 890 s left in hold)\nMem: 183 23 112 1 51 160"},{"i":135,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"net_job.py\").read()\nold1='absact += caps[\"mlp_act\"].abs().double().mean(1)'\nold2='act_sum[k] += caps[\"mlp_act\"][:, mk].double().sum(1)'\nassert old1 in s and old2 in s\ns=s.replace(old1,'absact += caps[\"mlp_act\"].abs().sum(1, dtype=torch.float64) / len(pos)')\ns=s.replace(old2,'act_sum[k] += caps[\"mlp_act\"][:, mk].sum(1, dtype=torch.float64)')\nopen(\"net_job.py\",\"w\").write(s); import ast; ast.parse(s)\nEOF\nscp -q net_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/net_job_new.py && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && timeout 300 $PY code/net_job_new.py --mock 2>&1 | grep -v \"captured\\|gate deep\\|gate pre\\|built\" | tail -5 | cut -c1-200; [ -s scratch/logs/job3.log ] && echo JOB_STARTED_KEEP_OLD || (mv code/net_job_new.py code/net_job.py && echo installed); rm -rf scratch/mock3; date +%T; tail -1 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100'"},{"i":136,"kind":"output","long":false,"text":"[ 3.9] stats saved; rows 1304 episodes 4\n[ 3.9] raw saved\n[ 13.3] evaluated 0 [task] rows 1478 arms 8\n[ 24.8] evaluated 1 [task] rows 1796 arms 8\n[ 24.9] done\ninstalled\n00:30:12\n=== speedbench C_r32_l8_u0.90 (00:29:26, 412 s left)"},{"i":137,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; until [ -s scratch/logs/job3.log ] && grep -q \"stats saved\\|Traceback\\|Error\" scratch/logs/job3.log; do sleep 5; done; date +%T; grep -v \"captured\" scratch/logs/job3.log | cut -c1-330 | head -60; grep -c captured scratch/logs/job3.log'"},{"i":138,"kind":"output","long":false,"text":"Command running in background with ID: bwr5p1339. Output is being written to: C:\\Users\\Owner\\AppData\\Local\\Temp\\claude\\406550b45259dbef\\321673e6-a1ea-4370-9bb3-5e77dc495fde\\tasks\\bwr5p1339.output. You will be notified when it completes. To check interim output, use Read on that file path."},{"i":139,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'date +%T; cat /home/keith/gemma-v2/wsp31/authors/claude/scratch/logs/hold3.log; tail -2 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-100; ps -eo pid,etime,cmd | grep -E \"flock\" | grep -v grep | cut -c1-110'"},{"i":140,"kind":"output","long":false,"text":"00:30:25\n11-13_00:29 tag=HC maxrank=32 maxloras=8 util=0.90 gpu0_mib=90500 model_gib=23.32 kv_gib=57.02 kv_to\n=== speedbench C_r32_l8_u0.90 (00:29:26, 412 s left)\n 957831 52:25 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh\n1054488 32:52 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n1155059 00:08 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh"},{"i":141,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; until [ -s scratch/hold_start ] && [ scratch/hold_start -nt code/net_job.py ] || ! pgrep -f \"sessH.sh\" >/dev/null; do sleep 3; done; sleep 4; date +%T; cat scratch/hold_start; cat scratch/logs/hold3.log; head -2 scratch/logs/job3.log 2>/dev/null | cut -c1-150; ps -eo pid,etime,cmd | grep -E \"flock|net_job|dose.py\" | grep -v grep | cut -c1-110; nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader'"},{"i":142,"kind":"output","long":false,"text":"Command running in background with ID: bc81titww. Output is being written to: C:\\Users\\Owner\\AppData\\Local\\Temp\\claude\\406550b45259dbef\\321673e6-a1ea-4370-9bb3-5e77dc495fde\\tasks\\bc81titww.output. You will be notified when it completes. To check interim output, use Read on that file path."},{"i":143,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31 && . /home/keith/gemma-v2/env.sh && $PY - <<\"EOF\"\nimport torch\nd=torch.load(\"/home/keith/gemma-v2/wsp31/interp/dirs/out/close_thought.pt\",map_location=\"cpu\")\n[line removed by the dashboard scrubber]\nfor k,v in (d.items() if isinstance(d,dict) else []):\n print(k, tuple(v.shape) if torch.is_tensor(v) else (type(v).__name__, (len(v) if hasattr(v,\"__len__\") else v)))\nEOF\nsed -n 1,40p tools/example_net.py | cut -c1-170; date +%T'"},{"i":144,"kind":"output","long":true,"text":" ['dir', 'null', 'meta', 'resid.0', 'resid.1', 'resid.2', 'resid.3', 'resid.4', 'resid.5', 'resid.6', 'resid.7', 'resid.8']\ndir (60, 5376)\nnull (60, 5376)\nmeta ('dict', 6)\nresid.0 (5376,)\nresid.1 (5376,)\nresid.2 (5376,)\nresid.3 (5376,)\nresid.4 (5376,)\nresid.5 (5376,)\nresid.6 (5376,)\nresid.7 (5376,)\nresid.8 (5376,)\nresid.9 (5376,)\nresid.10 (5376,)\nresid.11 (5376,)\nresid.12 (5376,)\nresid.13 (5376,)\nresid.14 (5376,)\nresid.15 (5376,)\n[... 77 lines cut ...]\ndef build(D):\n e = EditSet()\n e.write_dir(layers, D)\n if not a.writers_only:\n e.read_dir(layers, D)\n return e\n\n00:30:54"},{"i":145,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; n=0; until [ scratch/hold_start -nt code/net_job.py ] || [ $n -ge 80 ]; do sleep 5; n=$((n+1)); done; date +%T; cat scratch/hold_start; cat scratch/logs/hold3.log; head -2 scratch/logs/job3.log 2>/dev/null | cut -c1-150; ps -eo pid,etime,cmd | grep -E \"flock |net_job.py|dose.py\" | grep -v grep | cut -c1-110; tail -2 /home/keith/gemma-v2/wsp31/tools/logs/sessH_*.log | cut -c1-80'"},{"i":146,"kind":"output","long":false,"text":"00:33:19\nFri Nov 13 12:33:17 AM EST 2026\ncode/gpu_job3.sh: line 4: /home/keith/gemma-v2/env.sh\n: No such file or directory\ncode/gpu_job3.sh: line 5: cd: $'/home/keith/gemma-v2/wsp31/authors/claude\\r': No such file or directory\ncode/gpu_job3.sh: line 8: 1\n: ambiguous redirect\ncode/gpu_job3.sh: line 10: 1794547997\n[line removed by the dashboard scrubber]\n\")\ncode/gpu_job3.sh: line 11: 1794547997\n[line removed by the dashboard scrubber]\n\")\ncode/gpu_job3.sh: line 13: 1794547997\n[line removed by the dashboard scrubber]\n\")\ncode/gpu_job3.sh: line 18: syntax error: unexpected end of file from `if' command on line 14\n1155059 03:01 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n##### C rc=0\nhold end Fri Nov 13 12:33:17 AM EST 2026, used 1094 s"},{"i":147,"kind":"tool","long":false,"text":"Bash: sed -i 's/\\r$//' gpu_job3.sh && sed -i 's/TURN_END - 16 \\* 60 - T0/TURN_END - 14 * 60 - T0/' net_job.py && grep -n \"TURN_END - \" net_job.py && file gpu_job3.sh net_job.py | cut -c1-120 && scp -q gpu_job3.sh net_job.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && sed -i \"s/\\r$//\" code/gpu_job3.sh code/net_job.py code/*.py && bash -n code/gpu_job3.sh && echo syntax_ok && grep -c $(printf \"\\r\") code/gpu_job3.sh; rm -f scratch/logs/job3.log; nohup flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh > scratch/logs/hold4.log 2>&1 & sleep 1; date +%T; ps -eo pid,etime,cmd | grep -E \"flock |dose.py|job6\" | grep -v grep | cut -c1-150; tail -3 /home/keith/gemma-v2/wsp31/interp/logs/job6.log 2>/dev/null | cut -c1-200'"},{"i":148,"kind":"output","long":false,"text":"58: DEADLINE = min(DEADLINE, TURN_END - 14 * 60 - T0)\ngpu_job3.sh: Bourne-Again shell script, ASCII text executable, with very long lines (381)\nnet_job.py: Python script, ASCII text executable\nsyntax_ok\n0\n00:34:30\n1155058 04:12 /bin/bash ./run_gpu.sh logs/job6.log ./job6.sh\n1155059 04:12 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n1161482 01:12 timeout 1740 ./job6.sh\n1161483 01:12 /bin/sh ./job6.sh\n1161878 01:00 bash -c sleep 893; exec flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessI.sh\n1163926 00:01 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n[This command modified 1 file you've previously read: net_job.py. Call Read before editing.]"},{"i":149,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"net_job.py\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\ndef rep(old,new):\n global s\n assert old in s, old[:50]\n s=s.replace(old,new)\nrep(\"TURN_END - 14 * 60 - T0\",\"TURN_END - 17 * 60 - T0\")\nrep(\"\"\" (\"D_E02\", [(\"deep\", dict(end=0.02))], False),\\n\"\"\",\"\")\nrep(\"\"\" (\"P_EC12\", [(\"pre\", dict(end=0.12, com=0.12))], False)]\"\"\",\"\"\" ]\"\"\")\nrep(\"\"\" ]\n extra = [x for x in extra if os.path.exists(x[1])]\"\"\",\"\"\" (\"K05t\", A_DIR + \"/scratch/fb/K05t\", 6)]\n extra = [x for x in extra if os.path.exists(x[1])]\"\"\")\nrep(\"\"\"evaluate(m, st, names, S + \"/ad\", ev[:5], extra)\"\"\",\"\"\"evaluate(m, st, names, S + \"/ad\", ev[:4], extra)\"\"\")\nopen(\"net_job.py\",\"w\",newline=\"\\n\").write(s)\nimport ast; ast.parse(s)\ni=s.index(\"ARMS = [\"); print(s[i:i+900])\n[line removed by the dashboard scrubber]\nold=\"\"\"if \"--diag\" not in sys.argv:\n rep = e.save(\"\"\"\nassert old in k\nk=k.replace(old,\"\"\"if \"--n\" in sys.argv: # add family N of thinklib (the model's own end-of-thought neurons, layers 57-59)\n tl.build(e, st, \"N\", float(sys.argv[sys.argv.index(\"--n\") + 1]), ctrl)\nif \"--diag\" not in sys.argv:\n rep = e.save(\"\"\")\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nEOF\n[line removed by the dashboard scrubber]"},{"i":150,"kind":"output","long":true,"text":"ARMS = [(\"D_E05\", [(\"deep\", dict(end=0.05))], False),\n (\"P_C05\", [(\"pre\", dict(com=0.05))], False),\n (\"P_EC05\", [(\"pre\", dict(end=0.05, com=0.05))], False),\n (\"D_E12\", [(\"deep\", dict(end=0.12))], False),\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n (\"P_C12\", [(\"pre\", dict(com=0.12))], False),\n (\"D_E05c\", [(\"deep\", dict(end=0.05))], True),\n (\"P_EC05c\", [(\"pre\", dict(end=0.05, com=0.05))], True),\n[line removed by the dashboard scrubber]\n ]\nif not MOCK:\n DEADLINE = min(DEADLINE, TURN_END - 17 * 60 - T0)\n DEADLINE_CAPTURE = min(DEADLINE_CAPTURE, 0.4 * DEADLINE)\n\n\ndef log(*a):\n print(f\"[{time.time() - T0:7.1f}]\", *a, flush=True)\n\n\n[... 5 lines cut ...]\nsaved scratch/fb/K05t r 1 modules 36 max rel_fro 0.0654\nsaved scratch/fb/K05tc r 1 modules 36 max rel_fro 0.0654\n[ 4.1] raw saved\n[ 14.7] evaluated 0 [task] rows 1478 arms 9\n[ 27.5] evaluated 1 [task] rows 1796 arms 9\n[ 27.5] done\ninstalled\n00:35:52"},{"i":151,"kind":"tool","long":true,"text":"Bash: cat > mkrec_fb.py <<'PYEOF'\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nimport sys\ndose, nd, low, high = sys.argv[1:5]\nnd = float(nd)\nfacts = open(\"facts.txt\").read().strip()\npred = open(\"pred_K.txt\").read().strip()\nntitle = f\" plus own end-of-thought neurons x{1 + nd:g}\" if nd else \"\"\nnhyp = f\"\"\"\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nend-of-thought state in the middle and late layers when the thought is deep; the neurons turn that state into the\n[line removed by the dashboard scrubber]\nnrec = f\"\"\"\nFamily N (`thinklib.build(e, st, \"N\", {nd:g})`, weights only, `code/end_neurons.py`, as in ledger 0009): down\ncolumns of the 20 selected neurons of layers 57, 58, 59 times {1 + nd:g}; in the control the norm-matched neurons with\n|z| < 1 of ledger 0009 are scaled by the same factor.\"\"\" if nd else \"\"\n[line removed by the dashboard scrubber]\n\n## Hypothesis\n\n{facts}\n\nSame target as ledger 0008 (make long thoughts end sooner), by a different closed form: no neuron is replaced and\n[line removed by the dashboard scrubber]\nneuron-activation space whose response is, on class means, 0 at thought ends, at thought depth below 100 and\noutside thoughts, 0.25 at depth 100 to 399 and 1 at depth 400 or more, times a write of {dose} of the\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\nbehave differently from a gate neuron: the response is linear in the activations of about 10,000 existing neurons\nper layer, so it has no gelu trough and no single point of failure; its errors at shallow positions are zero-mean\nwhere a gate's are one-sided; over 36 layers the zero-mean part can average out while the depth-dependent part\nadds up. Its weakness: the response has a spread of 0.5 to 0.8 around the class means (table), so single layers\nalso write against the direction.{nhyp}\n\n## Exact recipe\n\nbase: champion generation 0\n\nNo edit had passed when this entry was built; statistics and weights are those of the unedited model.\n\n@SIZE@\n\n@CTRLSIZE@\n\n[line removed by the dashboard scrubber]\n`resid_post` and `mlp_raw` at layers 24 to 59 over the sampled positions of 22 episodes of the unedited run s1_base\n(`tools/data/probe_screen.jsonl`), written by `code/think_job.py` in turn 0 (position classes as in ledger 0008).\nOf the 22 fit tasks 7 are stage-1 tasks and 15 are tasks that were removed from stage 2 on 13 Nov because that run\n[line removed by the dashboard scrubber]\nFor L = 24..59: `dW_down[L] = c_L k_L^T` (rank 1).\n- `k_L = C^-1 A^T (A C^-1 A^T)^-1 t`: `A` = the five class means of `mlp_act[L]` (thought end E, depth < 100,\n 100-399, >= 400, outside thoughts), `t = (0, 0, 0.25, 1, 0)`, `C = diag(s^2 + 0.1 median(s)^2)`, `s` = mean\n[line removed by the dashboard scrubber]\n- `c_L = clipdiv({dose} * d_end_L, w_post_L) * rms_y_L / layer_scalar_L`: `d_end_L` = mean `resid_post[L]` at E minus\n mean at the other thought positions, `rms_y_L` = rms of the raw MLP output at depth >= 400, `clipdiv(d, w) = d / w`,\n zero where `|w_post|` is under its 5% quantile. At response 1 the write moves `resid_post[L]` by `{dose} * d_end_L`.{nrec}\n\n[line removed by the dashboard scrubber]\nstrength); the written stream vector has the sign of every coordinate flipped at random (seed 1000 + L): identical\nper-coordinate magnitudes, identical `|dW|_F` in every matrix.\n\n[line removed by the dashboard scrubber]\nactivation covariance; the write formula ignores the change of the rms of the raw MLP output.\n\nChecker and rebuild:\n\n```\n@CHECK@\n```\n@IDENT@\n\n[line removed by the dashboard scrubber]\n\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n```\n[line removed by the dashboard scrubber]\n```\n\n### Pre-test 2 (model under the edit)\n\n@GPUNOTE@\n\n## Prediction (written before the run)\n\npredicted_gain_120: {low} to {high}\n\n{pred}\n\"\"\")\nPYEOF\npython - <<'EOF'\nm=open(\"mkrec.py\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\ni=m.index('FACTS = \"\"\"')+len('FAC\n[... 1166 characters cut ...]"},{"i":152,"kind":"output","long":false,"text":":9: SyntaxWarning: \"\\+\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\+\"? A raw string is also an option.\n[line removed by the dashboard scrubber]\nrec_fb1.txt:68\nrec_fb3.txt:75\nfit 22 stage1 7 exposed 15 neither []\n00:36:37"},{"i":153,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":154,"kind":"output","long":false,"text":"OK edit: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj\nOK ctrl: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj\n- `edit/adapter_model.safetensors` sha256 `2a24f449360a2d677579615675d7eb19f1869d415589f7bda23334ffb7b71193`: identical to the adapter `K05t` evaluated in the hold\n- `ctrl/adapter_model.safetensors` sha256 `b70d51d50308fd14440cae72cb4f1edc4db39b77ae290b87b0824371f0a7b59e`: identical to the adapter `K05tc` evaluated in the hold\nsize: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\ncontrol size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\n[line removed by the dashboard scrubber]\n- `ctrl/adapter_model.safetensors` sha256 `825e8bbe156b1fd219afbedeae351f13185b520c6e7762a60912c99f33a7945c`: no evaluated adapter of this name (built after the hold from the same statistics)\nsize: whole network 1.5158% over 36 matrices, largest matrix 15.6941%\ncontrol size: whole network 1.5158% over 36 matrices, largest matrix 15.6941%\n[line removed by the dashboard scrubber]\nOK ctrl: 36 modules changed (of 36 present), r=17, max |dW|_F/|W|_F 0.2007 at 59.down_proj\n- `edit/adapter_model.safetensors` sha256 `4d55c53d2754b12b1123b2672f7a251ce46921492b40b7b36e7b763b5e933e9a`: no evaluated adapter of this name (built after the hold from the same statistics)\n- `ctrl/adapter_model.safetensors` sha256 `46b8c7b046afe679a75a4233b9871472d27752838b535e2a29051f771e2c1cc3`: no evaluated adapter of this name (built after the hold from the same statistics)\nsize: whole network 1.0134% over 36 matrices, largest matrix 20.0706%\ncontrol size: whole network 1.0133% over 36 matrices, largest matrix 20.0691%\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[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n00:37:04"},{"i":155,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nimport re\nfor f in (\"pred_DE.txt\",\"pred_PEC.txt\",\"pred_PC.txt\",\"pred_K.txt\"):\n s=open(f,newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\n s=re.sub(r\"\\(the pre-test cuts @CUTL@ long thoughts and keeps @KEPTL@ of\\s+their length, short thoughts keep @KEPTS@\\)\",\"(pre-test table: columns `kept` and `long thoughts cut`)\",s)\n s=re.sub(r\"\\(pre-test: @CUTL@ long thoughts cut, @KEPTL@ of their length\\s+kept, short thoughts keep @KEPTS@\\)\",\"(pre-test table: columns `kept` and `long thoughts cut`)\",s)\n s=re.sub(r\"pre-test shows @CUTL@ long thoughts cut and @KEPTL@ of their length kept\",\"pre-test table shows how many long thoughts it cuts all the same\",s)\n assert \"@\" not in s, (f, [l for l in s.splitlines() if \"@\" in l])\n open(f,\"w\",newline=\"\\n\").write(s)\nEOF\ncat > fin_gpu.sh <<'EOF'\n#!/bin/bash\n[line removed by the dashboard scrubber]\n. /home/keith/gemma-v2/env.sh\ncd /home/keith/gemma-v2/wsp31/authors/claude\n$PY code/net_report.py scratch/job3/eval.pt > scratch/job3/report.txt 2>&1\n[line removed by the dashboard scrubber]\n$PY - <<'PY'\nimport torch, os\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nst = torch.load(A + \"/scratch/job3/net_stats.pt\")\n[line removed by the dashboard scrubber]\nPY\nfor spec in \"$@\"; do\n IFS='|' read slug arm ctrl rec names refs <<< \"$spec\"\n ( $PY code/finalize3.py \"$slug\" \"$arm\" \"$ctrl\" \"$rec\" \"$names\" \"$refs\" > scratch/job3/fin_$slug.log 2>&1; echo \"== $slug\"; tail -6 scratch/job3/fin_$slug.log | cut -c1-220 ) &\ndone\nwait\nEOF\n[line removed by the dashboard scrubber]"},{"i":156,"kind":"output","long":false,"text":"[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n00:37:46"},{"i":157,"kind":"tool","long":false,"text":"Bash: cat > NOTES_tail_pending.md <<'EOF'\n\n## This turn (state at 0:40 AM; the GPU hold had not run yet, see the end of this file if it did)\n- Built, mock-tested and queued for the lock: code/net_job.py (capture at all 60 layers on stage-1 episodes of\n probe_b001, gates `deep` (depth >= 800) and `pre` (depth >= 400, no edit yet), directions d_end and d_commit, linear\n[line removed by the dashboard scrubber]\n finalize3.py, fin_gpu.sh, mkrec.py (local, copy in code/extras3). My first hold was lost: gpu_job3.sh had CRLF line\n endings (rewritten by python on Windows). ALWAYS `sed -i 's/\\r$//'` and `bash -n` every shell file after scp.\n[line removed by the dashboard scrubber]\n neuron-activation space with response 0 / 0 / 0.25 / 1 / 0 on the class means (thought end, depth < 100, 100-399,\n >= 400, outside). On the turn-0 capture its response follows depth with sd 0.5-0.8, AUC 0.83-0.90 (5 fitted numbers\n per layer). Not yet run through the model when this was written.\n- Turn-0 statistics (think_stats*.pt) were fitted on 7 stage-1 tasks and 15 tasks later removed from stage 2\n (stage2_exposed_ids.txt). New fits should use probe_b001 (all stage-1).\n\n## Next\n[line removed by the dashboard scrubber]\n If thought length falls and solves do not rise, the thought-ending family is dead; then work on wrong patches\n (20 per 120) and the 30-call cap (11 per 120), which nothing queued addresses.\n- If the commit direction moves the first edit earlier: fit it with more episodes and a causal check (vgen --at cont\n on scratch/job3/deep_pre.jsonl, built by code/mk_callset.py; never run this turn).\n- Untested: larger read slices (median ranged read is 41 lines; fewer calls and thoughts per file).\n\n## Missing tools that would have changed the plan\nA second GPU or a shorter lock queue (47 + 25 minutes of waiting this turn); a per-author CPU-side activation cache\n[line removed by the dashboard scrubber]\nEOF\ncat NOTES_final_head.md NOTES_tail_pending.md > NOTES.md; sed -i 's/\\r$//' NOTES.md; wc -w NOTES.md; scp -q NOTES.md wrx90:/home/keith/gemma-v2/wsp31/authors/claude/NOTES.md && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && mkdir -p code/extras3 && date +%T; cat scratch/logs/hold4.log; tail -2 /home/keith/gemma-v2/wsp31/interp/logs/job6.log 2>/dev/null | cut -c1-160; ps -eo pid,etime,cmd | grep -E \"flock |dose.py\" | grep -v grep | cut -c1-150'"},{"i":158,"kind":"output","long":false,"text":"784 NOTES.md\n00:38:11\n1155059 07:53 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n1163926 03:41 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh"},{"i":159,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'ps -eo pid,ppid,etime,cmd --forest | grep -A6 \"job6.sh\" | grep -v grep | cut -c1-200 | head -14; cat /home/keith/gemma-v2/wsp31/interp/dirs/job6.sh 2>/dev/null | head -20 | cut -c1-200; ls -t /home/keith/gemma-v2/wsp31/interp/dirs/logs 2>/dev/null | head -3; nvidia-smi --query-gpu=index,memory.used,utilization.gpu --format=csv,noheader'"},{"i":160,"kind":"output","long":false,"text":"1181203 1181202 00:00 \\_ ps -eo pid,ppid,etime,cmd --forest\n1181206 1181202 00:00 \\_ cut -c1-200\n1181207 1181202 00:00 \\_ head -14\n 3086 1 06:41:01 /usr/libexec/colord\n 3183 1 06:41:01 /usr/libexec/rtkit-daemon\n 3736 1 06:40:55 /usr/sbin/cups-browsed\n--\n1155058 1106734 08:00 \\_ /bin/bash ./run_gpu.sh logs/job6.log ./job6.sh\n1155059 1155058 08:00 \\_ flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n1161482 1155059 05:00 \\_ timeout 1740 ./job6.sh\n1161483 1161482 05:00 \\_ /bin/sh ./job6.sh\n1161491 1161483 05:00 \\_ /home/keith/gemma-v2/tii-venv/bin/python dose2.py --contrasts close_thought --sets 0-59 --alphas 0.001,0.003,0.01,0.02,0.03,0.1,0.3 --variants real+,rea\n1160079 1 05:16 /usr/bin/containerd-shim-runc-v2 -namespace moby -id f0d86e2234873f7eb49fe57c4f600ffb82ada31f293795f8dab0ecb3bf725465 -address /run/containerd/containerd.sock\n1160119 1160079 05:16 \\_ sleep infinity\n0, 35768 MiB, 100 %\n1, 90512 MiB, 100 %"},{"i":161,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\ndef rep(old,new):\n global s\n assert old in s, old[:60]\n s=s.replace(old,new)\nrep('''open(q + \"/build.sh\", \"w\").write(build)\nos.chmod(q + \"/build.sh\", 0o755)\nout = subprocess.run([\"bash\", q + \"/build.sh\"], capture_output=True, text=True)\ncheck = \"\\\\n\".join(l for l in (out.stdout + out.stderr).splitlines() if l.startswith((\"OK\", \"FAIL\", \"saved\")) or \"rror\" in l)\nprint(check)''','''NOREBUILD = \"--norebuild\" in sys.argv and os.path.exists(q + \"/check.txt\") and os.path.exists(q + \"/size.json\")\nif NOREBUILD:\n check = open(q + \"/check.txt\").read()\nelse:\n open(q + \"/build.sh\", \"w\").write(build)\n os.chmod(q + \"/build.sh\", 0o755)\n out = subprocess.run([\"bash\", q + \"/build.sh\"], capture_output=True, text=True)\n check = \"\\\\n\".join(l for l in (out.stdout + out.stderr).splitlines() if l.startswith((\"OK\", \"FAIL\", \"saved\")) or \"rror\" in l)\n open(q + \"/check.txt\", \"w\").write(check)\nprint(check)''')\nrep('''sz = subprocess.run([PY, T + \"/train_size.py\", q + \"/size.json\", f\"edit={q}/edit\", f\"ctrl={q}/ctrl\"], capture_output=True, text=True)''','''if not NOREBUILD:\n sz = subprocess.run([PY, T + \"/train_size.py\", q + \"/size.json\", f\"edit={q}/edit\", f\"ctrl={q}/ctrl\"], capture_output=True, text=True)''')\nrep('''refs = sys.argv[6].split(\",\") if len(sys.argv) > 6 else [arm, carm]''','''refs = sys.argv[6].split(\",\") if len(sys.argv) > 6 and not sys.argv[6].startswith(\"--\") else [arm, carm]''')\nrep('''names = sys.argv[5].split(\",\") if len(sys.argv) > 5 else [arm, carm]''','''names = sys.argv[5].split(\",\") if len(sys.argv) > 5 and not sys.argv[5].startswith(\"--\") else [arm, carm]''')\nopen(\"finalize3.py\",\"w\",newline=\"\\n\").write(s); import ast; ast.parse(s)\nassert '\"\\\\n\".join(l for l in (out.stdout' in s\ng=open(\"fin_gpu.sh\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\ng=g.replace('''( $PY code/finalize3.py \"$slug\" \"$arm\" \"$ctrl\" \"$rec\" \"$names\" \"$refs\" > scratch/job3/fin_$slug.log 2>&1;''','''( $PY code/finalize3.py \"$slug\" \"$arm\" \"$ctrl\" \"$rec\" \"$names\" \"$refs\" $NOREBUILD > scratch/job3/fin_$slug.log 2>&1;''')\ng=g.replace('''$PY code/net_report.py scratch/job3/eval.pt > scratch/job3/report.txt 2>&1''','''[ -s scratch/job3/eval.pt ] && $PY code/net_report.py scratch/job3/eval.pt > scratch/job3/report.txt 2>&1''')\nopen(\"fin_gpu.sh\",\"w\",newline=\"\\n\").write(g)\nEOF\ngrep -n \"check.txt\\|NOREBUILD\" finalize3.py | head -8 | cut -c1-140\nfor a in \"D_E 0.05 -6 8 pred_DE.txt de05\" \"D_E 0.12 -7 9 pred_DE.txt de12\" \"P_EC 0.05 -8 9 pred_PEC.txt pec05\" \"P_C 0.05 -6 7 pred_PC.txt pc05\" \"P_C 0.12 -7 7 pred_PC.txt pc12\" \"K 0.03 -6 8 pred_K.txt ke03\" \"K 0.08 -7 8 pred_K.txt ke08\"; do set -- $a; python mkrec.py x $1 $2 $3 $4 $5 > rec_$6.txt; done; sed -i 's/\\r$//' rec_*.txt; grep -c \"\" rec_de05.txt rec_ke03.txt; grep -n \"@[A-Z]*@\" rec_ke03.txt | cut -c1-60; scp -q finalize3.py fin_gpu.sh wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && scp -q rec_de05.txt rec_de12.txt rec_pec05.txt rec_pc05.txt rec_pc12.txt rec_ke03.txt rec_ke08.txt wrx90:/home/keith/gemma-v2/wsp31/authors/claude/scratch/job3/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && bash -n code/fin_gpu.sh && . /home/keith/gemma-v2/env.sh && $PY -c \"import ast;ast.parse(open(\\\"code/finalize3.py\\\").read())\" && date +%T && cat scratch/logs/hold4.log'"},{"i":162,"kind":"output","long":false,"text":"Traceback (most recent call last):\n File \"\", line 6, in \n File \"\", line 4, in rep\nAssertionError: open(q + \"/build.sh\", \"w\").write(build)\nos.chmod(q + \"/build\n[line removed by the dashboard scrubber]\nrec_de05.txt:136\nrec_ke03.txt:121\n31:@SIZE@\n33:@CTRLSIZE@\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n81:@CHECK@\n83:@IDENT@\n95:@ROWS@\n100:@VGEN@\n00:38:50"},{"i":163,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nBS=chr(92)\ns=open(\"finalize3.py\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\ndef rep(old,new):\n global s\n assert old in s, old[:60]\n s=s.replace(old,new)\nchk='check = \"'+BS+'n\".join(l for l in (out.stdout + out.stderr).splitlines() if l.startswith((\"OK\", \"FAIL\", \"saved\")) or \"rror\" in l)'\nrep('open(q + \"/build.sh\", \"w\").write(build)\\nos.chmod(q + \"/build.sh\", 0o755)\\nout = subprocess.run([\"bash\", q + \"/build.sh\"], capture_output=True, text=True)\\n'+chk+'\\nprint(check)',\n'NOREBUILD = \"--norebuild\" in sys.argv and os.path.exists(q + \"/check.txt\") and os.path.exists(q + \"/size.json\")\\nif NOREBUILD:\\n check = open(q + \"/check.txt\").read()\\nelse:\\n open(q + \"/build.sh\", \"w\").write(build)\\n os.chmod(q + \"/build.sh\", 0o755)\\n out = subprocess.run([\"bash\", q + \"/build.sh\"], capture_output=True, text=True)\\n '+chk+'\\n open(q + \"/check.txt\", \"w\").write(check)\\nprint(check)')\nrep('sz = subprocess.run([PY, T + \"/train_size.py\", q + \"/size.json\", f\"edit={q}/edit\", f\"ctrl={q}/ctrl\"], capture_output=True, text=True)','if not NOREBUILD:\\n sz = subprocess.run([PY, T + \"/train_size.py\", q + \"/size.json\", f\"edit={q}/edit\", f\"ctrl={q}/ctrl\"], capture_output=True, text=True)')\nrep('refs = sys.argv[6].split(\",\") if len(sys.argv) > 6 else [arm, carm]','refs = sys.argv[6].split(\",\") if len(sys.argv) > 6 and not sys.argv[6].startswith(\"--\") else [arm, carm]')\nrep('names = sys.argv[5].split(\",\") if len(sys.argv) > 5 else [arm, carm]','names = sys.argv[5].split(\",\") if len(sys.argv) > 5 and not sys.argv[5].startswith(\"--\") else [arm, carm]')\nopen(\"finalize3.py\",\"w\",newline=\"\\n\").write(s); import ast; ast.parse(s)\ng=open(\"fin_gpu.sh\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\na='( $PY code/finalize3.py \"$slug\" \"$arm\" \"$ctrl\" \"$rec\" \"$names\" \"$refs\" > scratch/job3/fin_$slug.log 2>&1;'\nassert a in g\ng=g.replace(a,'( $PY code/finalize3.py \"$slug\" \"$arm\" \"$ctrl\" \"$rec\" \"$names\" \"$refs\" $NOREBUILD > scratch/job3/fin_$slug.log 2>&1;')\nb='$PY code/net_report.py scratch/job3/eval.pt > scratch/job3/report.txt 2>&1'\nassert b in g\ng=g.replace(b,'[ -s scratch/job3/eval.pt ] && '+b)\nopen(\"fin_gpu.sh\",\"w\",newline=\"\\n\").write(g)\nEOF\n[line removed by the dashboard scrubber]"},{"i":164,"kind":"output","long":false,"text":"finalize3.py:43:NOREBUILD = \"--norebuild\" in sys.argv and os.path.exists(q + \"/check.txt\") and os.path.exists(q + \"/size\nfinalize3.py:44:if NOREBUILD:\nfinalize3.py:61:if not NOREBUILD:\nfin_gpu.sh:15: ( $PY code/finalize3.py \"$slug\" \"$arm\" \"$ctrl\" \"$rec\" \"$names\" \"$refs\" $NOREBUILD > scratch/job3/fin_$sl\n66: print(\"size.json layout unexpected:\", sz.stdout[-600:], sz.stderr[-300:]); se = sc = dict(net_rel=float(\"nan\"), changed=0, rel_max=float(\"nan\")\nsize: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\ncontrol size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%\n[line removed by the dashboard scrubber]\n00:39:15"},{"i":165,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/interp; f=$(ls dirs/job6.sh job6.sh 2>/dev/null | head -1); echo $f; cat $f | cut -c1-260 | head -12; ps -eo etime,cmd | grep \"dose2.py\" | grep -v grep | cut -c1-330; date +%T'"},{"i":166,"kind":"output","long":false,"text":"job6.sh\n#!/bin/sh\n# holds 6+: dose check, version 2 (PROTOCOL amendment 3g): perp control, low doses, per-point values, KL skip rule.\n# Resumable: dose2.py skips configurations already in out/dose.json.\ncd /home/keith/gemma-v2/wsp31/interp/dirs\nPY=/home/keith/gemma-v2/tii-venv/bin/python\nT0=$(date +%s)\nleft() { echo $((1560 - $(date +%s) + T0)); }\nwhile read C S A V; do\n L=$(left)\n if [ \"$L\" -le 300 ]; then echo \"time left $L: stop\"; break; fi\n TAG=$(echo \"${C}_${S}\" | tr \",\" \"_\")\n if grep -q \"^done\" ../logs/dose2_$TAG.out 2>/dev/null; then echo \"dose2 $TAG already done\"; continue; fi\n 06:08 /home/keith/gemma-v2/tii-venv/bin/python dose2.py --contrasts close_thought --sets 0-59 --alphas 0.001,0.003,0.01,0.02,0.03,0.1,0.3 --variants real+,real-,null+,perp+,perp- --skip_kl 5 --budget_s 1390\n00:39:25"},{"i":167,"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 && $PY - <<\"EOF\"\np=\"code/net_job.py\"; s=open(p).read()\na=\"\"\" (\"P_C05\", [(\"pre\", dict(com=0.05))], False),\\n\"\"\"\nb=\"\"\" extra = [(\"Q0008\", A_DIR + \"/queue/deep-thought-gate-g3-b03/edit\", 4),\\n (\"K05t\",\"\"\"\nassert a in s and b in s\ns=s.replace(a,\"\").replace(b,\"\"\" extra = [(\"K05t\",\"\"\")\nopen(p,\"w\").write(s)\nimport ast; ast.parse(s)\ni=s.index(\" extra = [\"); print(s[i:i+260])\nEOF\nexport WSP31_WORK=$PWD/scratch; [ -s scratch/logs/job3.log ] && echo STARTED; timeout 200 $PY code/net_job.py --mock 2>&1 | grep -v \"captured\\|gate deep\\|gate pre\\|built\" | tail -3 | cut -c1-160; rm -rf scratch/mock3; grep -c $(printf \"\\r\") code/net_job.py; date +%T; cat scratch/logs/hold4.log'"},{"i":168,"kind":"output","long":false,"text":" extra = [(\"K05t\", A_DIR + \"/scratch/fb/K05t\", 6)]\n extra = [x for x in extra if os.path.exists(x[1])]\n evaluate(m, st, names, S + \"/ad\", ev[:4], extra)\n log(\"done\")\n\n\nif __name__ == \"__main__\":\n main()\n\n[ 13.4] evaluated 0 [task] rows 1478 arms 8\n[ 24.8] evaluated 1 [task] rows 1796 arms 8\n[ 24.8] done\n0\n00:40:07"},{"i":169,"kind":"tool","long":false,"text":"Bash: cat > NOTES_head2.md <<'EOF'\n# Notes of author claude (rewritten end of turn 3, 13 Nov 2026, about 1:15 AM ET)\n\nNo edit of anyone has a stage-1 result yet (b001, the unedited batch, still filling). My 0007-0010 are queued.\n\n## What the unedited model does wrong (b001, 288 episodes; code/seq_stats.py)\n- Solved 54%. Per 120 tasks: 25 failures at the 7.5-min limit (13 with an empty patch), 11 at the 30-call cap, 20\n wrong patches submitted. 15 of 94 tasks differ between identical arms (sd of a one-run difference: about 4 tasks).\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n P(solved | no edit in the first 12 calls) 0.31; first edit at calls 6-11: 0.70-0.80.\n- The thought before the first edit is long (24% / 35% beyond 800 in solved / failed): a depth gate cuts into it.\n After a pre-edit thought >= 800 the next call is an edit 19% of the time (5% after shorter ones).\n- Deep thoughts after the first edit are rare: \"pre-edit\" adds little to a depth condition.\n\n## Measured nulls and traps (do not repeat)\n[line removed by the dashboard scrubber]\n 0.52-0.77 held out (code/state_auc.py). Depth >= 800 held out: about 0.8 (0.88-0.91 in records 0007-0010 is fit-set).\n- Interp lane (hooks): 0.03 |h| on all layers wrecks the model for real and null directions alike.\n- Turn 0: family C no effect; always-on Concept Country form has no window; gate sharpness 3 leaks through the\n negative lobe of gelu (use 12); \"anti\" neurons wrong sign.\n- Turn-0 statistics (think_stats*.pt) come from 7 stage-1 tasks and 15 tasks later removed from stage 2\n (stage2_exposed_ids.txt). New fits: probe_b001 (all stage-1).\n- Shell files written by python on Windows get CRLF: my first hold died in one second. After every scp:\n `sed -i 's/\\r$//' f; bash -n f`. Heredocs halve backslashes (patch with chr(92) or on the host). vgen.py drops\n records with n > --max-pos (code/mk_callset.py truncates). Call gpu0_session.sh with `env -u CUDA_DEVICE_ORDER -u\n CUDA_VISIBLE_DEVICES`. `ps | grep` matches your own ssh command line.\n- Lock queue this turn: 37 minutes, then 26 more after the lost hold. Queue `nohup flock $R/wsp31/gpu0.lock timeout\n 1560 code/gpu_hold.sh &` in the first minute and mock-test the job on CPU meanwhile (`net_job.py --mock`).\nEOF\nwc -w NOTES_head2.md; ssh -o BatchMode=yes wrx90 'date +%T; cat /home/keith/gemma-v2/wsp31/authors/claude/scratch/logs/hold4.log; ps -eo etime,cmd | grep -E \"dose2.py|flock \" | grep -v grep | cut -c1-90'"},{"i":170,"kind":"output","long":false,"text":"418 NOTES_head2.md\n00:40:31\n 10:13 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n 07:14 /home/keith/gemma-v2/tii-venv/bin/python dose2.py --contrasts close_thought --\n 06:02 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh"},{"i":171,"kind":"tool","long":false,"text":"Bash: cat > probe_cmp.py <<'EOF'\n\"\"\"probe_cmp.py (CPU): held-out-episode AUC of gate probes for depth >= 800 on the turn-0 MLP inputs:\ndiagonal LDA (thinklib.fit_gate), top-k coordinates, and ridge LDA with the full covariance (dual / Woodbury form).\"\"\"\nimport sys, torch\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nsys.path.insert(0, \"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0, A + \"/code\")\nimport thinklib as tl\ntorch.set_num_threads(16)\nraw = torch.load(A + \"/scratch/job1/raw_mlpin.pt\")\ncls, dep, ep = raw[\"cls\"], raw[\"depth\"], raw[\"ep\"]\nthink = (cls >= 1) & (cls <= 3)\npos = think & (dep >= 800); neg = (think & (dep < 200)) | (cls == 4)\nfor split in (0, 1, 2):\n held = (ep % 3 == split); tr = ~held\n for gi in (0, 4, 8, 11):\n X = raw[\"X\"][gi].float()\n Xp, Xn = X[tr & pos], X[tr & neg]\n d = Xp.mean(0) - Xn.mean(0)\n v = 0.5 * (Xp.var(0) + Xn.var(0)); v = v + 1e-3 * v.median()\n out = []\n def ev(w, name):\n s = X @ w\n out.append(\"%s %.3f/%.3f\" % (name, tl.auc(s[tr & pos], s[tr & neg]), tl.auc(s[held & pos], s[held & neg])))\n ev(d / v, \"diag\")\n tstat = d.abs() / v.sqrt()\n for k in (300, 1500):\n m = torch.zeros_like(d); idx = tstat.topk(k).indices; m[idx] = 1\n ev(m * d / v, \"top%d\" % k)\n Xc = torch.cat([Xp - Xp.mean(0), Xn - Xn.mean(0)]).double() # within-class centred\n N = Xc.shape[0]; K = Xc @ Xc.T\n for c in (0.3, 3.0, 30.0):\n lam = c * float(v.mean())\n a = torch.linalg.solve(K + N * lam * torch.eye(N, dtype=torch.float64), Xc @ d.double())\n w = ((d.double() - Xc.T @ a) / lam).float()\n ev(w, \"ridge%g\" % c)\n print(\"split\", split, \"L%d\" % tl.GL[gi], \"n+ %d/%d\" % (int((tr & pos).sum()), int((held & pos).sum())), \" | \".join(out), flush=True)\nEOF\nsed -i 's/\\r$//' probe_cmp.py; scp -q probe_cmp.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 && timeout 280 $PY code/probe_cmp.py 2>&1 | tail -13 | cut -c1-260; date +%T; cat scratch/logs/hold4.log'"},{"i":172,"kind":"output","long":false,"text":"split 0 L30 n+ 409/201 diag 0.932/0.819 | top300 0.927/0.807 | top1500 0.930/0.819 | ridge0.3 1.000/0.906 | ridge3 0.999/0.913 | ridge30 0.983/0.873\nsplit 0 L38 n+ 409/201 diag 0.923/0.815 | top300 0.924/0.819 | top1500 0.923/0.817 | ridge0.3 1.000/0.927 | ridge3 0.999/0.935 | ridge30 0.977/0.896\nsplit 0 L46 n+ 409/201 diag 0.916/0.828 | top300 0.910/0.824 | top1500 0.917/0.830 | ridge0.3 1.000/0.869 | ridge3 0.996/0.890 | ridge30 0.963/0.868\nsplit 0 L56 n+ 409/201 diag 0.850/0.766 | top300 0.865/0.768 | top1500 0.858/0.769 | ridge0.3 1.000/0.929 | ridge3 0.998/0.933 | ridge30 0.978/0.901\nsplit 1 L30 n+ 369/241 diag 0.921/0.809 | top300 0.912/0.806 | top1500 0.921/0.807 | ridge0.3 1.000/0.935 | ridge3 0.999/0.945 | ridge30 0.979/0.908\nsplit 1 L38 n+ 369/241 diag 0.929/0.805 | top300 0.921/0.790 | top1500 0.927/0.805 | ridge0.3 1.000/0.920 | ridge3 0.999/0.926 | ridge30 0.981/0.893\nsplit 1 L46 n+ 369/241 diag 0.914/0.787 | top300 0.901/0.788 | top1500 0.912/0.784 | ridge0.3 1.000/0.864 | ridge3 0.998/0.893 | ridge30 0.969/0.863\nsplit 1 L56 n+ 369/241 diag 0.833/0.759 | top300 0.845/0.766 | top1500 0.835/0.761 | ridge0.3 1.000/0.926 | ridge3 0.998/0.945 | ridge30 0.977/0.924\nsplit 2 L30 n+ 442/168 diag 0.905/0.906 | top300 0.902/0.905 | top1500 0.902/0.905 | ridge0.3 1.000/0.949 | ridge3 0.999/0.959 | ridge30 0.977/0.946\nsplit 2 L38 n+ 442/168 diag 0.911/0.867 | top300 0.909/0.874 | top1500 0.912/0.869 | ridge0.3 1.000/0.945 | ridge3 0.998/0.950 | ridge30 0.968/0.915\nsplit 2 L46 n+ 442/168 diag 0.891/0.804 | top300 0.895/0.818 | top1500 0.894/0.809 | ridge0.3 1.000/0.898 | ridge3 0.995/0.907 | ridge30 0.955/0.872\nsplit 2 L56 n+ 442/168 diag 0.879/0.806 | top300 0.889/0.810 | top1500 0.886/0.812 | ridge0.3 1.000/0.925 | ridge3 0.997/0.938 | ridge30 0.976/0.908\n00:41:00"},{"i":173,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\patch_ridge.py"},{"i":174,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\patch_ridge.py (file state is current in your context \u2014 no need to Read it back)"},{"i":175,"kind":"tool","long":false,"text":"Bash: sed -i 's/\\r$//' patch_ridge.py && scp -q patch_ridge.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/scratch/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && [ -s scratch/logs/job3.log ] && echo STARTED; cp code/net_job.py scratch/net_job_before_ridge.py && cp code/net_job.py code/net_job_r.py && sed -i \"s#code/net_job.py#code/net_job_r.py#\" scratch/patch_ridge.py && $PY scratch/patch_ridge.py && timeout 250 $PY code/net_job_r.py --mock 2>&1 | grep -v \"captured\\|built\" | tail -9 | cut -c1-230; date +%T; cat scratch/logs/hold4.log'"},{"i":176,"kind":"output","long":false,"text":"patched 2\n[ 3.5] gate deep 40 j 21280 auc 1.000 pre/post-deep auc 0.538 gbar 68.71 {'E': '0.93', 'd_lt100': '0.06', 'd100_400': '0.58', 'd400_800': '0.86', 'd_ge800': '1.00', 'non': '0.05', 'pre_deep': '0.94', 'post_deep': '0.93', 'post_\n[ 3.5] gate pre 40 j 1193 auc 1.000 pre/post-deep auc 1.000 gbar 53.57 {'E': '0.63', 'd_lt100': '0.05', 'd100_400': '0.40', 'd400_800': '0.59', 'd_ge800': '0.68', 'non': '0.05', 'pre_deep': '1.00', 'post_deep': '0.06', 'post_al\n[ 4.1] gate deep 59 j 5266 auc 1.000 pre/post-deep auc 0.565 gbar 70.81 {'E': '0.89', 'd_lt100': '0.06', 'd100_400': '0.60', 'd400_800': '0.84', 'd_ge800': '1.00', 'non': '0.07', 'pre_deep': '0.93', 'post_deep': '0.92', 'post_a\n[ 4.1] gate pre 59 j 947 auc 1.000 pre/post-deep auc 1.000 gbar 50.87 {'E': '0.70', 'd_lt100': '0.06', 'd100_400': '0.36', 'd400_800': '0.58', 'd_ge800': '0.67', 'non': '0.06', 'pre_deep': '1.00', 'post_deep': '0.05', 'post_all\n[ 4.1] stats saved; rows 1304 episodes 4\n[ 4.2] raw saved\n[ 13.7] evaluated 0 [task] rows 1478 arms 8\n[ 25.2] evaluated 1 [task] rows 1796 arms 8\n[ 25.2] done\n00:42:13\n[This command modified 1 file you've previously read: patch_ridge.py. Call Read before editing.]"},{"i":177,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\patch_ridge2.py"},{"i":178,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\patch_ridge2.py (file state is current in your context \u2014 no need to Read it back)"},{"i":179,"kind":"tool","long":false,"text":"Bash: sed -i 's/\\r$//' patch_ridge2.py && scp -q patch_ridge2.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/scratch/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && $PY scratch/patch_ridge2.py && grep -n \"return gate_row, up_row\" code/net_job_r.py | cut -c1-80 && timeout 250 $PY code/net_job_r.py --mock 2>&1 | grep -v \"captured\\|built\" | tail -8 | cut -c1-250; date +%T; cat scratch/logs/hold4.log'"},{"i":180,"kind":"output","long":false,"text":"patched2\n235: return gate_row, up_row, dict(theta=theta, sigma=sigma, cbar=cbar, csd=c\n main()\n ~~~~^^\n File \"/home/keith/gemma-v2/wsp31/authors/claude/code/net_job_r.py\", line 473, in main\n st = finish_stats(m, st, raw)\n File \"/home/keith/gemma-v2/wsp31/authors/claude/code/net_job_r.py\", line 294, in finish_stats\n (gr, ur, fs), cal = fit_gate_ridge(X, lb, raw[\"ep\"], k=12.0, dev=(\"cpu\" if MOCK else m.dev))\n ^^^^^^^^^^^^^^^^^\nValueError: too many values to unpack (expected 2)\n00:42:45\n[This command modified 1 file you've previously read: patch_ridge2.py. Call Read before editing.]"},{"i":181,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && sed -i \"s/ (gr, ur, fs), cal = fit_gate_ridge/ gr, ur, fs, cal = fit_gate_ridge/\" code/net_job_r.py && timeout 250 $PY code/net_job_r.py --mock 2>&1 | grep -v \"captured\\|built\" | tail -8 | cut -c1-250; rm -rf scratch/mock3; date +%T; cat scratch/logs/hold4.log'"},{"i":182,"kind":"output","long":false,"text":"[ 4.3] BUILD FAILED D_E05 AssertionError('empty edit')\n[ 4.3] BUILD FAILED P_EC05 AssertionError('empty edit')\n[ 4.3] BUILD FAILED D_E12 AssertionError('empty edit')\n[ 4.3] BUILD FAILED D_E05c AssertionError('empty edit')\n[ 4.3] raw saved\n[ 9.1] evaluated 0 [task] rows 1478 arms 4\n[ 14.9] evaluated 1 [task] rows 1796 arms 4\n[ 14.9] done\n00:43:07"},{"i":183,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && export WSP31_WORK=$PWD/scratch && sed -i \"s/ if G\\[\\\"fit\\\"\\]\\[\\\"auc_z\\\"\\] < 0.8:/ if G[\\\"fit\\\"][\\\"auc_z\\\"] < (0.0 if MOCK else 0.75):/\" code/net_job_r.py && grep -n \"auc_z\\\"\\] <\" code/net_job_r.py && timeout 250 $PY code/net_job_r.py --mock 2>&1 | grep -v \"captured\" | tail -14 | cut -c1-250; $PY code/net_report.py scratch/mock3/eval.pt | tail -8 | cut -c1-70; rm -rf scratch/mock3; date +%T; cat scratch/logs/hold4.log'"},{"i":184,"kind":"output","long":false,"text":"334: if G[\"fit\"][\"auc_z\"] < (0.0 if MOCK else 0.75):\n[ 4.2] gate deep 59 j 17187 auc cal 0.514 fit 1.000 pre/post-deep auc 0.514 gbar 0.50 {'E': '0.06', 'd_lt100': '0.04', 'd100_400': '0.05', 'd400_800': '0.03', 'd_ge800': '0.06', 'non': '0.03', 'pre_deep': '0.04', 'post_deep': '0.05', 'post_all': '\n[ 4.2] gate pre 59 j 369 auc cal 0.508 fit 1.000 pre/post-deep auc 0.512 gbar 0.50 {'E': '0.12', 'd_lt100': '0.04', 'd100_400': '0.08', 'd400_800': '0.07', 'd_ge800': '0.06', 'non': '0.04', 'pre_deep': '0.06', 'post_deep': '0.07', 'post_all': '0.0\n[ 4.3] stats saved; rows 1304 episodes 4\n[ 4.3] built D_E05 r 1 rho min/med/max 0.044 0.049 0.050\n[ 4.3] built P_EC05 r 1 rho min/med/max 0.056 0.062 0.063\n[ 4.3] built D_E12 r 1 rho min/med/max 0.106 0.117 0.119\n[ 4.3] built K_E03 r 1 rho min/med/max 0.026 0.029 0.030\n[ 4.3] raw saved\n[ 4.3] built D_E05c r 1 rho min/med/max 0.044 0.049 0.050\n[ 4.3] built K_E03c r 1 rho min/med/max 0.026 0.029 0.030\n[ 4.3] grid built ['D_E05', 'P_EC05', 'D_E12', 'K_E03', 'D_E05c', 'K_E03c']\n[ 13.9] evaluated 0 [task] rows 1478 arms 8\n[ 25.4] evaluated 1 [task] rows 1796 arms 8\n[ 25.4] done\nbase | 0.000 0.000 0.000 0.000 | +0.00 | 1.00 1.00 n 14/1 | 0 of \nD_E05 | 0.000 0.000 0.000 0.000 | +0.28 | 1.00 1.00 n 14/1 | 0 of \nP_EC05 | 0.000 0.000 0.000 0.000 | +0.15 | 1.00 1.00 n 14/1 | 0 of \nD_E12 | 0.000 0.000 0.000 0.000 | +0.29 | 1.00 1.00 n 14/1 | 0 of \nK_E03 | 0.000 0.000 0.000 0.000 | +0.19 | 1.00 1.00 n 14/1 | 0 of \nD_E05c | 0.000 0.000 0.000 0.000 | +0.17 | 1.00 1.00 n 14/1 | 0 of \nK_E03c | 0.000 0.000 0.000 0.000 | +0.19 | 1.00 1.00 n 14/1 | 0 of \nK05t | 0.000 0.000 0.000 0.000 | +0.21 | 1.00 1.00 n 14/1 | 0 of \n00:43:45"},{"i":185,"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 - <<\"EOF\"\nimport sys, time, torch\nA=\"/home/keith/gemma-v2/wsp31/authors/claude\"; sys.path.insert(0,\"/home/keith/gemma-v2/wsp31/tools\"); sys.path.insert(0,A+\"/code\")\nimport thinklib as tl, net_job_r as nj\nraw=torch.load(A+\"/scratch/job1/raw_mlpin.pt\"); cls,dep,ep=raw[\"cls\"],raw[\"depth\"],raw[\"ep\"]\nthink=(cls>=1)&(cls<=3)\nlb=torch.where(think&(dep>=800),3,torch.where((think&(dep<200))|(cls==4),1,2))\nfor gi in (0,4,11):\n X=raw[\"X\"][gi].float(); t=time.time()\n gr,ur,fs,cal=nj.fit_gate_ridge(X,lb,ep)\n g=X@gr; a=tl.gelu_tanh(g)*(X@ur)\n f=lambda m:\"%.2f\"%float((g[m&cal]>0).float().mean())\n print(\"L%d %.1fs auc cal %.3f fit %.3f | cal rows %d | fire cal: d<100 %s 100-400 %s 400-800 %s >=800 %s non %s E %s | gbar cal %.2f (all rows %.2f) | diag-LDA all-row auc %.3f\"%(\n tl.GL[gi],time.time()-t,fs[\"auc_z\"],fs[\"auc_fit\"],int(cal.sum()),f(think&(dep<100)),f(think&(dep>=100)&(dep<400)),f(think&(dep>=400)&(dep<800)),f(think&(dep>=800)),f(cls==4),f(cls==0),\n float(a[(lb==3)&cal].mean()),float(a[lb==3].mean()), tl.fit_gate(X,lb,k=12.0)[2][\"auc_z\"]),flush=True)\nEOF\ndate +%T; cat scratch/logs/hold4.log'"},{"i":186,"kind":"output","long":false,"text":"L30 0.2s auc cal 0.898 fit 0.995 | cal rows 2439 | fire cal: d<100 0.04 100-400 0.20 400-800 0.47 >=800 0.42 non 0.02 E 0.03 | gbar cal 3.56 (all rows 15.37) | diag-LDA all-row auc 0.906\nL38 0.2s auc cal 0.920 fit 0.993 | cal rows 2439 | fire cal: d<100 0.05 100-400 0.24 400-800 0.56 >=800 0.56 non 0.01 E 0.06 | gbar cal 5.22 (all rows 15.67) | diag-LDA all-row auc 0.905\nL56 0.2s auc cal 0.923 fit 0.990 | cal rows 2439 | fire cal: d<100 0.05 100-400 0.17 400-800 0.41 >=800 0.59 non 0.02 E 0.02 | gbar cal 5.60 (all rows 10.69) | diag-LDA all-row auc 0.835\n00:44:01"},{"i":187,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && [ -s scratch/logs/job3.log ] && echo STARTED_ALREADY || (mv code/net_job_r.py code/net_job.py && echo installed); grep -c \"fit_gate_ridge\" code/net_job.py; grep -c $(printf \"\\r\") code/net_job.py; date +%T; cat scratch/logs/hold4.log' && cd /c/Users/Owner/wsp31_loop/work/claude/t3 && python - <<'EOF'\nc=open(\"common_recipe.txt\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\na=c.index(\"Gate row = `(12 / sigma)\"); b=c.index(\"- `deep` gate:\")\nnew=\"\"\"Gate row = `(12 / sigma) (w - (theta / cbar) m)`: `w` = unit ridge-LDA probe on `mlp_in[L]`, `w ~ (S + lam I)^-1\n(mean+ - mean-)` with `S` the full within-class covariance (solved in the dual form on at most 4,000 rows, seed 0)\nand `lam` = 10 times the mean coordinate variance; `m` = unit mean `mlp_in` (`m . x = cbar` at every position; the\nmodel has no bias). Up row = `m / cbar` (reads 1). The probe direction is fitted on the fit episodes with index\n% 3 != 0; `theta` (95% quantile of the probe on the negatives), `sigma` (sd on the positives) and the neuron's mean\nactivation `gbar` are taken on the remaining third, so they are out-of-sample values. (On the turn-0 capture this\nprobe has a held-out-episode AUC of 0.86 to 0.96 for depth >= 800, the diagonal probe of ledger 0008 0.76 to 0.91:\n`code/probe_cmp.py`.)\n\"\"\"\nc=c[:a]+new+c[b:]\nold=\"Layers whose fit-set AUC is below 0.8 are skipped (none unless listed under the tables).\"\nassert old in c\nc=c.replace(old,\"Layers whose AUC on the calibration third is below 0.75 are skipped: @SKIPPED@.\")\nopen(\"common_recipe.txt\",\"w\",newline=\"\\n\").write(c)\nEOF\nfor a in \"D_E 0.05 -6 8 pred_DE.txt de05\" \"D_E 0.12 -7 9 pred_DE.txt de12\" \"P_EC 0.05 -8 9 pred_PEC.txt pec05\" \"P_C 0.12 -7 7 pred_PC.txt pc12\" \"K 0.03 -6 8 pred_K.txt ke03\" \"K 0.08 -7 8 pred_K.txt ke08\"; do set -- $a; python mkrec.py x $1 $2 $3 $4 $5 > rec_$6.txt; done; sed -i 's/\\r$//' rec_*.txt; grep -c \"SKIPPED\" rec_de05.txt rec_ke03.txt"},{"i":188,"kind":"output","long":false,"text":"installed\n2\n0\n00:44:17\nrec_de05.txt:1\nrec_ke03.txt:0"},{"i":189,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"finalize3.py\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\nold='txt = open(textfile).read()'\nassert old in s\ns=s.replace(old,'''skipped = []\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n sk = [L for L, x in au.items() if x < 0.75]\n v = sorted(au.values())\n skipped.append(\"gate `%s`: %s skipped (%d layers used); calibration AUC over the 60 layers min %.2f, median %.2f, max %.2f\" % (\n[line removed by the dashboard scrubber]\ntxt = open(textfile).read().replace(\"@SKIPPED@\", \"; \".join(skipped))''')\nopen(\"finalize3.py\",\"w\",newline=\"\\n\").write(s); import ast; ast.parse(s)\nm=open(\"mkrec.py\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\nold=\"write follows depth more closely than one gate does (a single gate has a held-out AUC near 0.8, table below);\"\nassert old in m\nm=m.replace(old,\"write follows depth more closely than one gate does (held-out firing of single gates: table below);\")\nm=m.replace(\"three episodes\", \"three episodes\")\nopen(\"mkrec.py\",\"w\",newline=\"\\n\").write(m); ast.parse(m)\nEOF\nfor a in \"D_E 0.05 -6 8 pred_DE.txt de05\" \"D_E 0.12 -7 9 pred_DE.txt de12\" \"P_EC 0.05 -8 9 pred_PEC.txt pec05\" \"P_C 0.12 -7 7 pred_PC.txt pc12\" \"K 0.03 -6 8 pred_K.txt ke03\" \"K 0.08 -7 8 pred_K.txt ke08\"; do set -- $a; python mkrec.py x $1 $2 $3 $4 $5 > rec_$6.txt; done; sed -i 's/\\r$//' rec_*.txt; grep -n \"5 episodes\\|five\\|Q0008\" rec_de05.txt | cut -c1-140; scp -q finalize3.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && scp -q rec_de05.txt rec_de12.txt rec_pec05.txt rec_pc12.txt rec_ke03.txt rec_ke08.txt wrx90:/home/keith/gemma-v2/wsp31/authors/claude/scratch/job3/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && . /home/keith/gemma-v2/env.sh && $PY -c \"import ast;ast.parse(open(\\\"code/finalize3.py\\\").read())\" && date +%T; cat scratch/logs/hold4.log; ps -eo etime,cmd | grep -E \"dose2.py|flock \" | grep -v grep | cut -c1-90'"},{"i":190,"kind":"output","long":false,"text":"[line removed by the dashboard scrubber]\n106:tool-name position before / after the first edit. `Q0008` = the queued 10-layer entry of ledger 0008 on the same\n00:44:37\n 14:19 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n 11:19 /home/keith/gemma-v2/tii-venv/bin/python dose2.py --contrasts close_thought --\n 10:07 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n[This command modified 1 file you've previously read: mkrec.py. Call Read before editing.]"},{"i":191,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\nc=open(\"common_recipe.txt\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n assert old in c, old\n c=c.replace(old,new)\nopen(\"common_recipe.txt\",\"w\",newline=\"\\n\").write(c)\nEOF\nfor a in \"D_E 0.05 -6 8 pred_DE.txt de05\" \"D_E 0.12 -7 9 pred_DE.txt de12\" \"P_EC 0.05 -8 9 pred_PEC.txt pec05\" \"P_C 0.12 -7 7 pred_PC.txt pc12\" \"K 0.03 -6 8 pred_K.txt ke03\" \"K 0.08 -7 8 pred_K.txt ke08\"; do set -- $a; python mkrec.py x $1 $2 $3 $4 $5 > rec_$6.txt; done; sed -i 's/\\r$//' rec_*.txt; grep -c \"K05t\" rec_de05.txt rec_ke03.txt; scp -q rec_de05.txt rec_de12.txt rec_pec05.txt rec_pc12.txt rec_ke03.txt rec_ke08.txt common_recipe.txt mkrec.py mkrec_fb.py pred_DE.txt pred_PEC.txt pred_PC.txt pred_K.txt facts.txt wrx90:/home/keith/gemma-v2/wsp31/authors/claude/scratch/job3/ && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && cp scratch/job3/common_recipe.txt scratch/job3/mkrec.py scratch/job3/mkrec_fb.py scratch/job3/pred_*.txt scratch/job3/facts.txt code/extras3/ && cp scratch/patch_ridge.py scratch/patch_ridge2.py code/extras3/ 2>/dev/null; date +%T; cat scratch/logs/hold4.log'"},{"i":192,"kind":"output","long":false,"text":"rec_de05.txt:1\nrec_ke03.txt:1\n00:44:52"},{"i":193,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ng=open(\"fin_gpu.sh\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\n[line removed by the dashboard scrubber]\nassert old in g\n[line removed by the dashboard scrubber]\nopen(\"fin_gpu.sh\",\"w\",newline=\"\\n\").write(g)\nEOF\ncat > vgen_summary.txt <<'EOF'\nNot run. The served check is prepared (`code/gpu_job3.sh` runs `tools/vgen.py --at cont` on\n[line removed by the dashboard scrubber]\n`code/mk_callset.py`) and needs about seven more minutes of a hold. My first hold of this turn was lost to a script\nerror (CRLF line endings in the job file) and the second began too late for it. What the served model writes after\na thought cut by these edits is therefore not measured; the teacher-forced table above is the only pre-test of the\nmodel under the edit.\nEOF\nsed -i 's/\\r$//' vgen_summary.txt fin_gpu.sh; scp -q fin_gpu.sh wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && scp -q vgen_summary.txt wrx90:/home/keith/gemma-v2/wsp31/authors/claude/scratch/job3/vgen_summary_notrun.txt && ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && bash -n code/fin_gpu.sh && echo ok; date +%T; cat scratch/logs/hold4.log; ps -eo etime,cmd | grep -E \"dose2.py|flock \" | grep -v grep | cut -c1-90'"},{"i":194,"kind":"output","long":false,"text":"ok\n00:45:22\n 15:04 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n 12:05 /home/keith/gemma-v2/tii-venv/bin/python dose2.py --contrasts close_thought --\n 10:53 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh"},{"i":195,"kind":"tool","long":false,"text":"Bash: cat > fill_fb.py <<'EOF'\n[line removed by the dashboard scrubber]\nimport sys, os\nA = \"/home/keith/gemma-v2/wsp31/authors/claude\"\nnote = open(sys.argv[1]).read().rstrip()\nfor slug in sys.argv[2:]:\n p = f\"{A}/queue/{slug}/record.md\"\n assert not os.path.exists(f\"{A}/queue/{slug}/READY\")\n t = open(p).read()\n assert \"@GPUNOTE@\" in t, slug\n t = t.replace(\"@GPUNOTE@\", note).replace(\"identical to the adapter `K05t` evaluated in the hold\", \"identical to the adapter `K05t` built before the hold\").replace(\"identical to the adapter `K05tc` evaluated in the hold\", \"identical to the adapter `K05tc` built before the hold\")\n open(p, \"w\").write(t)\n print(slug, \"filled; placeholders left:\", [w for w in t.split() if w.startswith(\"@\") and w.endswith(\"@\")])\nEOF\ncat > gpunote_notrun.txt <<'EOF'\nNot run. No forward pass of the model under this edit was made: the GPU-0 lock was not obtained in time (63 minutes\nof queue in this turn, and my first hold was lost to a script error). The only evidence that the edit does what is\nclaimed is the CPU response table above and, for the write direction, the teacher-forced pre-tests of ledger 0008,\nwhich writes the same `d_end` through gate neurons. Whether this dose cuts long thoughts, and whether it leaves\ntool calls well-formed, is unmeasured. The checker passes.\nEOF\nsed -i 's/\\r$//' fill_fb.py gpunote_notrun.txt; scp -q fill_fb.py wrx90:/home/keith/gemma-v2/wsp31/authors/claude/code/ && scp -q gpunote_notrun.txt wrx90:/home/keith/gemma-v2/wsp31/authors/claude/scratch/fb/ && ssh -o BatchMode=yes wrx90 'date +%T; cat /home/keith/gemma-v2/wsp31/authors/claude/scratch/logs/hold4.log; tail -3 /home/keith/gemma-v2/wsp31/interp/logs/job6.log | cut -c1-200'"},{"i":196,"kind":"output","long":false,"text":"00:45:43"},{"i":197,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && sed -n 262,318p code/net_job.py'"},{"i":198,"kind":"output","long":true,"text":" com_split_cos=cosf(half[0], half[1]).tolist(), cos_end_com=cosf(d_end, d_com).tolist())\n return dict(end=d_end.float(), com=d_com.float()), info\n\n\ndef finish_stats(m, st, raw):\n st[\"w_post\"] = torch.stack([m.L[L][\"post_feedforward_layernorm\"].float().cpu() for L in LAYERS])\n st[\"ls\"] = torch.tensor([float(m.L[L][\"layer_scalar\"].float()) for L in LAYERS])\n st[\"dirs\"], st[\"dir_info\"] = directions(st)\n di = st[\"dir_info\"]\n log(\"directions: episodes for d_commit\", di[\"n_com\"], \"split-half cos (every 6th layer)\",\n [\"%.2f\" % x for x in di[\"com_split_cos\"][::6]], \"cos(end, com)\", [\"%.2f\" % x for x in di[\"cos_end_com\"][::6]])\n meta = raw[\"meta\"]\n lab = labels(meta)\n cls, dep, ed = meta[:, 1], meta[:, 2], meta[:, 5].bool()\n think = (cls >= 1) & (cls <= 3)\n st[\"gate\"] = {k: {} for k in lab}\n[line removed by the dashboard scrubber]\n t = torch.tensor([0, 0, 0.25, 1.0, 0]).double()\n[line removed by the dashboard scrubber]\n Am = st[\"act_sum\"][:, li] / st[\"act_n\"][:, None]\n[... 29 lines cut ...]\n if L % 4 == 0 or L == 59:\n[line removed by the dashboard scrubber]\n \"gbar %.2f\" % gbar, {k: \"%.2f\" % v for k, v in fr.items()})\n del Wg, Wu, Wd, X\n return st\n\n\ndef build_arm(st, parts, ctrl):"},{"i":199,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; n=0; until [ -s scratch/logs/job3.log ] || [ $n -ge 108 ]; do sleep 5; n=$((n+1)); done; date +%T; cat scratch/hold_start; cat scratch/logs/hold4.log; head -3 scratch/logs/job3.log 2>/dev/null | cut -c1-200; ps -eo etime,cmd | grep -E \"dose2.py|flock |net_job\" | grep -v grep | cut -c1-90'"},{"i":200,"kind":"output","long":false,"text":"00:55:00\nFri Nov 13 12:33:17 AM EST 2026\n 24:42 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh\n 21:42 /home/keith/gemma-v2/tii-venv/bin/python dose2.py --contrasts close_thought --\n 20:30 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n 06:44 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessI.sh"},{"i":201,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; n=0; until [ -s scratch/logs/job3.log ] || [ $n -ge 100 ]; do sleep 3; n=$((n+1)); done; date +%T; cat scratch/hold_start; cat scratch/logs/hold4.log; head -3 scratch/logs/job3.log 2>/dev/null | cut -c1-200; ps -eo etime,cmd | grep -E \"dose2.py|flock |net_job\" | grep -v grep | cut -c1-90'"},{"i":202,"kind":"output","long":false,"text":"00:56:34\nFri Nov 13 12:56:30 AM EST 2026\n[ 0.4] deadline 899.5705749988556 capture deadline 330 train 43 eval 15\n 22:04 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n 08:18 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessI.sh\n 00:03 /home/keith/gemma-v2/tii-venv/bin/python code/net_job.py"},{"i":203,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; n=0; until [ $(grep -c captured scratch/logs/job3.log) -ge 6 ] || grep -q \"Traceback\" scratch/logs/job3.log || [ $n -ge 40 ]; do sleep 3; n=$((n+1)); done; date +%T; tail -7 scratch/logs/job3.log | cut -c1-200'"},{"i":204,"kind":"output","long":false,"text":"00:57:40\n[ 3.1] model loaded\n[ 14.3] captured 0 [task] n 11000 rows 298 groups [6, 252, 56, 196, 70, 120]\n[line removed by the dashboard scrubber]\n[ 35.9] captured 2 cool-RR__PySnooper.57472b46.lm n 11000 rows 378 groups [6, 332, 175, 9, 116, 138]\n[ 47.0] captured 3 benoitc__gunicorn.bacbf8aa.fun n 11000 rows 383 groups [10, 333, 94, 15, 103, 19]\n[ 57.9] captured 4 cool-RR__PySnooper.57472b46.lm n 11000 rows 333 groups [16, 277, 114, 128, 70, 153]\n[ 68.4] captured 5 [task] n 11000 rows 246 groups [6, 200, 0, 200, 70, 120]"},{"i":205,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; n=0; until grep -q \"grid built\\|Traceback\" scratch/logs/job3.log || [ $n -ge 150 ]; do sleep 3; n=$((n+1)); done; date +%T; grep -c captured scratch/logs/job3.log; grep -v \"captured\" scratch/logs/job3.log | cut -c1-400 | tail -52'"},{"i":206,"kind":"output","long":true,"text":"01:02:33\n32\n[ 0.4] deadline 899.5705749988556 capture deadline 330 train 43 eval 15\n[ 3.1] model loaded\n[ 335.2] capture stops at episode 32 rows 8877\n[ 335.6] directions: episodes for d_commit 18 split-half cos (every 6th layer) ['0.22', '0.54', '0.71', '0.70', '0.59', '0.40', '0.34', '0.24', '0.21', '0.16'] cos(end, com) ['0.43', '0.55', '0.50', '0.51', '0.21', '-0.22', '0.01', '0.06', '0.05', '0.05']\n[ 335.8] gate deep 0 j 20849 auc cal 0.975 fit 0.993 pre/post-deep auc 0.576 gbar 13.19 {'E': '0.14', 'd_lt100': '0.02', 'd100_400': '0.23', 'd400_800': '0.56', 'd_ge800': '0.85', 'non': '0.04', 'pre_deep': '0.73', 'post_deep': '0.60', 'post_all': '0.39'}\n[ 336.0] gate pre 0 j 16094 auc cal 0.867 fit 0.946 pre/post-deep auc 0.537 gbar 2.11 {'E': '0.09', 'd_lt100': '0.00', 'd100_400': '0.04', 'd400_800': '0.12', 'd_ge800': '0.44', 'non': '0.00', 'pre_deep': '0.28', 'post_deep': '0.24', 'post_all': '0.15'}\n[ 337.4] gate deep 4 j 20839 auc cal 0.972 fit 0.995 pre/post-deep auc 0.479 gbar 13.12 {'E': '0.20', 'd_lt100': '0.03', 'd100_400': '0.30', 'd400_800': '0.64', 'd_ge800': '0.86', 'non': '0.03', 'pre_deep': '0.73', 'post_deep': '0.78', 'post_all': '0.53'}\n[ 337.6] gate pre 4 j 4029 auc cal 0.858 fit 0.970 pre/post-deep auc 0.503 gbar 1.85 {'E': '0.05', 'd_lt100': '0.00', 'd100_400': '0.03', 'd400_800': '0.17', 'd_ge800': '0.36', 'non': '0.00', 'pre_deep': '0.26', 'post_deep': '0.25', 'post_all': '0.16'}\n[ 339.0] gate deep 8 j 20294 auc cal 0.943 fit 0.996 pre/post-deep auc 0.567 gbar 10.43 {'E': '0.16', 'd_lt100': '0.04', 'd100_400': '0.30', 'd400_800': '0.62', 'd_ge800': '0.75', 'non': '0.01', 'pre_deep': '0.71', 'post_deep': '0.63', 'post_all': '0.43'}\n[ 339.2] gate pre 8 j 15026 auc cal 0.868 fit 0.972 pre/post-deep auc 0.595 gbar 2.98 {'E': '0.05', 'd_lt100': '0.00', 'd100_400': '0.08', 'd400_800': '0.28', 'd_ge800': '0.46', 'non': '0.00', 'pre_deep': '0.40', 'post_deep': '0.26', 'post_all': '0.17'}\n[ 340.6] gate deep 12 j 14147 auc cal 0.867 fit 0.977 pre/post-deep auc 0.576 gbar 5.15 {'E': '0.04', 'd_lt100': '0.04', 'd100_400': '0.21', 'd400_800': '0.44', 'd_ge800': '0.55', 'non': '0.00', 'pre_deep': '0.52', 'post_deep': '0.41', 'post_all': '0.28'}\n[ 340.9] gate pre 12 j 7113 auc cal 0.807 fit 0.944 pre/post-deep auc 0.575 gbar 1.87 {'E': '0.02', 'd_lt100': '0.01', 'd100_400': '0.09', 'd400_800': '0.24', 'd_ge800': '0.33', 'non': '0.00', 'pre_deep': '0.31', 'post_deep': '0.21', 'post_all': '0.13'}\n[ 342.2] gate deep 16 j 13758 auc cal 0.872 fit 0.982 pre/post-deep auc 0.596 gbar 4.16 {'E': '0.02', 'd_lt100': '0.04', 'd100_400': '0.21', 'd400_800': '0.36', 'd_ge800': '0.50', 'non': '0.00', 'pre_deep': '0.46', 'post_deep': '0.33', 'post_all': '0.25'}\n[ 342.5] gate pre 16 j 8671 auc cal 0.811 fit 0.961 pre/post-deep auc 0.602 gbar 1.94 {'E': '0.00', 'd_lt100': '0.02', 'd100_400': '0.12', 'd400_800': '0.22', 'd_ge800': '0.34', 'non': '0.00', 'pre_deep': '0.31', 'post_deep': '0.19', 'post_all': '0.14'}\n[ 343.8] gate deep 20 j 1333 auc cal 0.916 fit 0.989 pre/post-deep auc 0.585 gbar 6.53 {'E': '0.08', 'd_lt100': '0.03', 'd100_400': '0.26', 'd400_800': '0.55', 'd_ge800': '0.59', 'non': '0.01', 'pre_deep': '0.60', 'post_deep': '0.48', 'post_all': '0.35'}\n[ 344.0] gate pre 20 j 20630 auc cal 0.858 fit 0.973 pre/post-deep auc 0.606 gbar 2.68 {'E': '0.02', 'd_lt100': '0.01', 'd100_400': '0.11', 'd400_800': '0.27', 'd_ge800': '0.36', 'non': '0.00', 'pre_deep': '0.36', 'post_deep': '0.21', 'post_all': '0.15'}\n[ 345.4] gate deep 24 j 13913 auc cal 0.917 fit 0.989 pre/post-deep auc 0.586 gbar 5.90 {'E': '0.05', 'd_lt100': '0.03', 'd100_400': '0.24', 'd400_800': '0.50', 'd_ge800': '0.55', 'non': '0.00', 'pre_deep': '0.55', 'post_deep': '0.44', 'post_all': '0.32'}\n[ 345.6] gate pre 24 j 17546 auc cal 0.853 fit 0.979 pre/post-deep auc 0.597 gbar 2.41 {'E': '0.01', 'd_lt100': '0.01', 'd100_400': '0.12', 'd400_800': '0.29', 'd_ge800': '0.35', 'non': '0.00', 'pre_deep': '0.36', 'post_deep': '0.22', 'post_all': '0.15'}\n[\n[... 513 characters cut ...]"},{"i":207,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":208,"kind":"output","long":true,"text":"01:03:51\n[ 361.6] raw saved\n[ 437.2] evaluated 0 [task] rows 1478 arms 11\nepisodes 1 ['[task]'] arms ['base', 'D_E05', 'P_EC05', 'D_E12', 'K_E03', 'K_E08', 'P_C12', 'K05t', 'D_E05c', 'P_EC05c', 'K_E03c']\nrows by class {0: 8, 1: 467, 2: 700, 3: 137, 4: 150, 5: 8, 6: 8} | thought rows pre-edit 847 post-edit 457\n== held-out gates (pre-activation g > 0 = fires), layers [0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 59]\n deep L0 auc nan | fire d<100 0.03 100-400 0.07 400-800 0.82 >=800 - | pre>=400 0.82 post>=400 - post all 0.03 | non 0.01 E 0.00 tool-pt 0.00\n deep L6 auc nan | fire d<100 0.00 100-400 0.02 400-800 0.35 >=800 - | pre>=400 0.35 post>=400 - post all 0.01 | non 0.00 E 0.12 tool-pt 0.00\n deep L12 auc nan | fire d<100 0.01 100-400 0.00 400-800 0.17 >=800 - | pre>=400 0.17 post>=400 - post all 0.00 | non 0.01 E 0.00 tool-pt 0.00\n deep L18 auc nan | fire d<100 0.01 100-400 0.01 400-800 0.23 >=800 - | pre>=400 0.23 post>=400 - post all 0.02 | non 0.01 E 0.00 tool-pt 0.00\n deep L24 auc nan | fire d<100 0.00 100-400 0.00 400-800 0.36 >=800 - | pre>=400 0.36 post>=400 - post all 0.00 | non 0.01 E 0.00 tool-pt 0.00\n deep L30 auc nan | fire d<100 0.00 100-400 0.01 400-800 0.26 >=800 - | pre>=400 0.26 post>=400 - post all 0.00 | non 0.02 E 0.00 tool-pt 0.00\n deep L36 auc nan | fire d<100 0.00 100-400 0.01 400-800 0.33 >=800 - | pre>=400 0.33 post>=400 - post all 0.00 | non 0.01 E 0.00 tool-pt 0.00\n deep L42 auc nan | fire d<100 0.00 100-400 0.01 400-800 0.30 >=800 - | pre>=400 0.30 post>=400 - post all 0.01 | non 0.01 E 0.00 tool-pt 0.00\n deep L48 auc nan | fire d<100 0.01 100-400 0.01 400-800 0.17 >=800 - | pre>=400 0.17 post>=400 - post all 0.02 | non 0.02 E 0.00 tool-pt 0.00\n deep L54 auc nan | fire d<100 0.01 100-400 0.01 400-800 0.10 >=800 - | pre>=400 0.10 post>=400 - post all 0.02 | non 0.02 E 0.00 tool-pt 0.00\n deep L59 auc nan | fire d<100 0.03 100-400 0.01 400-800 0.11 >=800 - | pre>=400 0.11 post>=400 - post all 0.03 | non 0.01 E 0.00 tool-pt 0.00\n pre L0 auc 0.957 | fire d<100 0.00 100-400 0.00 400-800 0.16 >=800 - | pre>=400 0.16 post>=400 - post all 0.00 | non 0.00 E 0.00 tool-pt 0.00\n pre L6 auc 0.980 | fire d<100 0.00 100-400 0.00 400-800 0.20 >=800 - | pre>=400 0.20 post>=400 - post all 0.00 | non 0.00 E 0.12 tool-pt 0.00\n pre L12 auc 0.922 | fire d<100 0.00 100-400 0.00 400-800 0.15 >=800 - | pre>=400 0.15 post>=400 - post all 0.00 | non 0.00 E 0.00 tool-pt 0.00\n[... 13 lines cut ...]\nD_E12 | 0.000 0.000 0.029 - | +nan | 1.00 nan n 7/0 | 0 of 0 | 1.000 | 0.967 1.000 1.000 | +1.10 / +0.10 | +0.61 | 0.000\nK_E03 | 0.003 0.000 0.036 - | +nan | 0.83 nan n 7/0 | 0 of 0 | 0.500 | 0.973 1.000 1.000 | -0.60 / -1.90 | -0.92 | 0.000\nK_E08 | 0.043 0.035 0.343 - | +nan | 0.53 nan n 7/0 | 0 of 0 | 0.250 | 0.860 0.000 1.000 | +0.47 / -4.08 | -0.17 | 0.000\nP_C12 | 0.000 0.000 0.000 - | +nan | 1.00 nan n 7/0 | 0 of 0 | 1.000 | 0.993 1.000 1.000 | -0.08 / +0.06 | +0.09 | 0.000\nK05t | 0.004 0.010 0.109 - | +nan | 0.80 nan n 7/0 | 0 of 0 | 0.500 | 0.953 1.000 1.000 | -2.72 / -4.54 | -2.87 | 0.000\nD_E05c | 0.000 0.000 0.000 - | +nan | 1.00 nan n 7/0 | 0 of 0 | 1.000 | 1.000 1.000 1.000 | -0.05 / -0.05 | -0.20 | 0.000\nP_EC05c | 0.000 0.000 0.000 - | +nan | 1.00 nan n 7/0 | 0 of 0 | 1.000 | 0.993 1.000 1.000 | -0.19 / -0.01 | -0.37 | 0.000\nK_E03c | 0.000 0.000 0.000 - | +nan | 1.00 nan n 7/0 | 0 of 0 | 1.000 | 1.000 1.000 1.000 | -0.21 / +0.03 | -0.20 | 0.000"},{"i":209,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":210,"kind":"output","long":true,"text":"01:06:25\n[ 514.7] evaluated 1 [task] rows 1796 arms 11\n[ 592.4] evaluated 2 [task] rows 1500 arms 11\nepisodes 3 ['[task]', 'Nikoleta-v3__blackbook', '[task]'] arms ['base', 'D_E05', 'P_EC05', 'D_E12', 'K_E03', 'K_E08', 'P_C12', 'K05t', 'D_E05c', 'P_EC05c', 'K_E03c']\nrows by class {0: 19, 1: 1301, 2: 1497, 3: 1469, 4: 450, 5: 19, 6: 19} | thought rows pre-edit 3697 post-edit 570\n== held-out gates (pre-activation g > 0 = fires), layers [0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 59]\n[line removed by the dashboard scrubber]\narm | cut share d<200 200-400 400-800 >=800 | dlp101 >=800 | kept short(<400) long(>=800) n | long thoughts cut | E top=101 | agree non think tool | d lp(edit) pre/post | d lp(read) pre | tool argmax changed\nbase | 0.000 0.000 0.000 0.000 | +0.00 | 1.00 1.00 n 16/2 | 0 of 2 | 0.895 | 1.000 1.000 1.000 | +0.00 / +0.00 | +0.00 | 0.000\nD_E05 | 0.001 0.004 0.002 0.000 | +4.59 | 1.00 0.69 n 16/2 | 1 of 2 | 0.895 | 0.982 1.000 1.000 | +0.39 / +0.14 | +0.28 | 0.000\nP_EC05 | 0.002 0.005 0.007 0.000 | +6.34 | 1.00 0.56 n 16/2 | 1 of 2 | 0.895 | 0.976 1.000 1.000 | +0.87 / +0.48 | +0.45 | 0.000\nD_E12 | 0.001 0.006 0.005 0.000 | +7.49 | 1.00 0.63 n 16/2 | 1 of 2 | 0.895 | 0.969 1.000 1.000 | +1.07 / +0.38 | +0.79 | 0.000\nK_E03 | 0.006 0.012 0.025 0.038 | +3.11 | 0.88 0.44 n 16/2 | 2 of 2 | 0.632 | 0.953 0.895 1.000 | -0.48 / -1.33 | -0.39 | 0.000\nK_E08 | 0.056 0.102 0.126 0.167 | +10.95 | 0.62 0.04 n 16/2 | 2 of 2 | 0.474 | 0.851 0.158 1.000 | +0.50 / -2.14 | -0.88 | 0.000\nP_C12 | 0.000 0.000 0.000 0.000 | +3.08 | 1.00 1.00 n 16/2 | 0 of 2 | 0.895 | 0.991 1.000 1.000 | +0.05 / +0.02 | +0.10 | 0.000\nK05t | 0.009 0.033 0.052 0.103 | +5.69 | 0.80 0.37 n 16/2 | 2 of 2 | 0.579 | 0.944 0.842 1.000 | -2.67 / -3.67 | -2.42 | 0.000\nD_E05c | 0.001 0.000 0.000 0.000 | +0.15 | 1.00 1.00 n 16/2 | 0 of 2 | 0.895 | 0.982 1.000 1.000 | +0.15 / +0.12 | +0.13 | 0.000\nP_EC05c | 0.000 0.000 0.000 0.000 | +0.31 | 1.00 1.00 n 16/2 | 0 of 2 | 0.895 | 0.978 1.000 1.000 | -0.02 / +0.03 | -0.01 | 0.000\nK_E03c | 0.000 0.000 0.000 0.000 | -0.03 | 1.00 1.00 n 16/2 | 0 of 2 | 0.895 | 0.984 1.000 1.000 | -0.04 / +0.14 | -0.10 | 0.000\n deep L0 auc 0.951 | fire d<100 0.02 100-400 0.14 400-800 0.70 >=800 0.72 | pre>=400 0.71 post>=400 - post all 0.02 | non 0.02 E 0.11 tool-pt 0.00\n deep L30 auc 0.837 | fire d<100 0.00 100-400 0.09 400-800 0.24 >=800 0.23 | pre>=400 0.23 post>=400 - post all 0.00 | non 0.01 E 0.00 tool-pt 0.00\n deep L59 auc 0.714 | fire d<100 0.01 100-400 0.03 400-800 0.10 >=800 0.15 | pre>=400 0.12 post>=400 - post all 0.02 | non 0.00 E 0.00 tool-pt 0.00\n pre L30 auc 0.844 | fire d<100 0.00 100-400 0.13 400-800 0.23 >=800 0.23 | pre>=400 0.23 post>=400 - post all 0.00 | non 0.00 E 0.00 tool-pt 0.00\nL0 response: E 0.00+-0.39 | d<100 0.00+-0.95 | 100-400 0.25+-1.15 | 400-800 0.94+-1.10 | 800-1500 1.04+-1.02 | >=1500 1.14+-1.50 | outside -0.00+-0.77 | auc(>=800 vs <200 or outside) 0.863 | share>0.5: d<200 0.21, >=800 0.76, outside 0.16\nL6 response: E 0.00+-0.41 | d<100 0.00+-0.89 | 100-400 0.25+-0.92 | 400-800 0.92+-0.94 | 800-1500 1.10+-1.24 | >=1500 1.06+-0.80 | outside 0.00+-0.60 | auc(>=800 vs <200 or outside) 0.899 | share>0.5: d<200 0.22, >=800 0.82, outside 0.13\nL12 response: E -0.00+-0.38 | d<100 -0.00+-0.98 | 100-400 0.25+-0.98 | 400-800 0.96+-0.92 | 800-1500 1.05+-0.92 | >=1500 1.02+-0.82 | outside 0.00+-0.79 | auc(>=800 vs <200 or outside) 0.814 | share>0.5: d<200 0.33, >=800 0.77, outside 0.20\nL24 response: E -0.00+-0.54 | d<100 0.00+-0.98 | 100-400 0.25+-0.83 | 400-800 0.95+-0.94 | 800-1500 1.08+-1.00 | >=1500 0.94+-0.92 | outside 0.00+-0.90 | auc(>=800 vs <200 or outside) 0.838 | share>0.5: d<200 0.27, >=800 0.78, outside 0.19\nL36 response: E 0.00+-0.43 | d<100 0.00+-0.84 | 100-400 0.25+-0.89 | 400-800 0.94+-1.00 | 800-1500 1.10+-0.91 | >=1500 0.95+-1.08 | outside 0.00+-1.00 | auc(>=800 vs <200 or outside) 0.843 | share>0.\n[... 518 characters cut ...]"},{"i":211,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]\nsleep 2; date +%T; tail -3 scratch/logs/job3.log | cut -c1-160; ls queue/'"},{"i":212,"kind":"output","long":false,"text":"Exit code 2\n01:06:58\ntail: cannot open 'scratch/logs/job3.log' for reading: No such file or directory\nls: cannot access 'queue/': No such file or directory"},{"i":213,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude; date +%T; tail -3 scratch/logs/job3.log | cut -c1-160; ls queue/; cat scratch/job3/fin_all.log | cut -c1-200 | tail -30; ps -eo etime,cmd | grep -E \"net_build|finalize3|train_size|net_job|flock \" | grep -v grep | cut -c1-100'"},{"i":214,"kind":"output","long":true,"text":"01:07:20\n[ 437.2] evaluated 0 [task] rows 1478 arms 11\n[ 514.7] evaluated 1 [task] rows 1796 arms 11\n[ 592.4] evaluated 2 [task] rows 1500 arms 11\ndeep-gate-b01-end-neurons-x9\ndeep-thought-gate-g3-b03\nearly-gate-b01-end-neurons-x9\nend-neurons-x25\nnet-deep-gate-close-b05\nnet-deep-gate-close-b12\nnet-preedit-commit-b12\nnet-preedit-commit-close-b05\n== net-deep-gate-close-b05\nOK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.2218 at 6.down_proj\n- `edit/adapter_model.safetensors` sha256 `b9b76229861ff7ab53e5211b216d840753ce2f2fd145da34737d7035eba0784d`: identical to the adapter `D_E05` evaluated in the hold\n- `ctrl/adapter_model.safetensors` sha256 `3b3d9e5c36cba18b4ba6252e0932f0c825cdb1a93f69bd0cdb02750aa479b8b9`: identical to the adapter `D_E05c` evaluated in the hold\nsize: whole network 4.4116% over 180 matrices, largest matrix 22.1827%\ncontrol size: whole network 4.4117% over 180 matrices, largest matrix 22.1814%\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/net-deep-gate-close-b05/record.md 2325 words\n== net-deep-gate-close-b12\n[... 15 lines cut ...]\n- `edit/adapter_model.safetensors` sha256 `1938741b536e0e74d8bbda953ee113a56088d74dc25e4f3c554d4d4c68ebd64c`: identical to the adapter `P_C12` evaluated in the hold\n- `ctrl/adapter_model.safetensors` sha256 `0ff75e1c4666fea7adad367f5faed26d0a7110fd68b0c39bf8dfafc03ea9f60c`: no evaluated adapter of this name (built after the hold from the same statistics)\nsize: whole network 4.4709% over 180 matrices, largest matrix 18.9846%\ncontrol size: whole network 4.4709% over 180 matrices, largest matrix 18.9846%\nwrote /home/keith/gemma-v2/wsp31/authors/claude/queue/net-preedit-commit-b12/record.md 2323 words\n 32:50 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh\n 19:04 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessI.sh\n 10:49 /home/keith/gemma-v2/tii-venv/bin/python code/net_job.py"},{"i":215,"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 && n=0; until grep -q \"^\\[.*done\\|Traceback\\|python part done\" scratch/logs/job3.log || [ $n -ge 50 ]; do sleep 3; n=$((n+1)); done; date +%T; tail -3 scratch/logs/job3.log | cut -c1-160; NOREBUILD=--norebuild bash code/fin_gpu.sh \"net-deep-gate-close-b12|deep:end=0.12|deep:end=0.12:ctrl|scratch/job3/rec_de12.txt|D_E12,D_E05,D_E05c|D_E12,none\" \"net-deep-gate-close-b05|deep:end=0.05|deep:end=0.05:ctrl|scratch/job3/rec_de05.txt|D_E05,D_E05c,D_E12|D_E05,D_E05c\" \"net-preedit-commit-close-b05|pre:end=0.05+com=0.05|pre:end=0.05+com=0.05:ctrl|scratch/job3/rec_pec05.txt|P_EC05,P_EC05c,P_C12,D_E05|P_EC05,P_EC05c\" \"net-preedit-commit-b12|pre:com=0.12|pre:com=0.12:ctrl|scratch/job3/rec_pc12.txt|P_C12,P_EC05,P_EC05c|P_C12,none\" 2>&1 | grep -c \"wrote\"; grep -v \"^ deep\\|^ pre\" scratch/job3/report.txt | cut -c1-330; grep \"^ deep L0 \\|^ deep L12\\|^ deep L30\\|^ deep L48\\|^ deep L59\\|^ pre L0 \\|^ pre L30\\|^ pre L59\" scratch/job3/report.txt | cut -c1-200; grep -n \"@[A-Z]*@\" queue/net-*/record.md | cut -c1-100; cat scratch/hold_end; date +%T'"},{"i":216,"kind":"output","long":true,"text":"01:07:43\n[ 670.1] evaluated 3 janehmueller__python-json-conf rows 1750 arms 11\n[ 670.1] done\npython part done after 672 s; session budget 229\n4\nepisodes 4 ['[task]', 'Nikoleta-v3__blackbook', '[task]', 'janehmueller__python-j'] arms ['base', 'D_E05', 'P_EC05', 'D_E12', 'K_E03', 'K_E08', 'P_C12', 'K05t', 'D_E05c', 'P_EC05c', 'K_E03c']\nrows by class {0: 22, 1: 1592, 2: 2157, 3: 2109, 4: 600, 5: 22, 6: 22} | thought rows pre-edit 5288 post-edit 570\n== held-out gates (pre-activation g > 0 = fires), layers [0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 59]\n[line removed by the dashboard scrubber]\narm | cut share d<200 200-400 400-800 >=800 | dlp101 >=800 | kept short(<400) long(>=800) n | long thoughts cut | E top=101 | agree non think tool | d lp(edit) pre/post | d lp(read) pre | tool argmax changed\nbase | 0.000 0.000 0.000 0.000 | +0.00 | 1.00 1.00 n 17/3 | 0 of 3 | 0.909 | 1.000 1.000 1.000 | +0.00 / +0.00 | +0.00 | 0.000\nD_E05 | 0.000 0.002 0.003 0.000 | +3.69 | 1.00 0.79 n 17/3 | 1 of 3 | 0.909 | 0.983 1.000 1.000 | +0.45 / +0.14 | +0.29 | 0.000\nP_EC05 | 0.001 0.003 0.007 0.000 | +5.51 | 1.00 0.71 n 17/3 | 1 of 3 | 0.909 | 0.982 1.000 1.000 | +1.01 / +0.48 | +0.53 | 0.000\nD_E12 | 0.000 0.004 0.008 0.001 | +6.63 | 1.00 0.67 n 17/3 | 2 of 3 | 0.864 | 0.972 1.000 1.000 | +1.35 / +0.38 | +1.00 | 0.000\nK_E03 | 0.005 0.012 0.022 0.034 | +2.60 | 0.88 0.54 n 17/3 | 3 of 3 | 0.636 | 0.955 0.909 1.000 | -0.51 / -1.33 | -0.41 | 0.000\nK_E08 | 0.049 0.102 0.124 0.168 | +11.13 | 0.59 0.06 n 17/3 | 3 of 3 | 0.455 | 0.837 0.136 1.000 | +0.43 / -2.14 | -0.64 | 0.000\nP_C12 | 0.000 0.000 0.000 0.000 | +2.55 | 1.00 1.00 n 17/3 | 0 of 3 | 0.909 | 0.993 1.000 1.000 | +0.06 / +0.02 | +0.13 | 0.000\nK05t | 0.007 0.024 0.046 0.084 | +4.94 | 0.81 0.38 n 17/3 | 3 of 3 | 0.591 | 0.950 0.818 1.000 | -2.71 / -3.67 | -2.27 | 0.000\nD_E05c | 0.000 0.000 0.000 0.000 | +0.09 | 1.00 1.00 n 17/3 | 0 of 3 | 0.909 | 0.982 1.000 1.000 | +0.08 / +0.12 | +0.06 | 0.000\nP_EC05c | 0.000 0.000 0.000 0.000 | +0.23 | 1.00 1.00 n 17/3 | 0 of 3 | 0.909 | 0.980 1.000 1.000 | -0.03 / +0.03 | -0.04 | 0.000\n[... 7 lines cut ...]\n pre L30 auc 0.815 | fire d<100 0.00 100-400 0.09 400-800 0.16 >=800 0.19 | pre>=400 0.17 post>=400 - post all 0.00 | non 0.00 E 0.00 tool-pt 0.00\n pre L59 auc 0.725 | fire d<100 0.01 100-400 0.04 400-800 0.09 >=800 0.15 | pre>=400 0.11 post>=400 - post all 0.01 | non 0.00 E 0.00 tool-pt 0.00\nqueue/net-deep-gate-close-b05/record.md:135:@VGEN@\nqueue/net-deep-gate-close-b12/record.md:135:@VGEN@\nqueue/net-preedit-commit-b12/record.md:139:@VGEN@\nqueue/net-preedit-commit-close-b05/record.md:143:@VGEN@\nFri Nov 13 01:07:42 AM EST 2026\n01:07:46"},{"i":217,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\addnotes.py"},{"i":218,"kind":"output","long":false,"text":"File created successfully at: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\addnotes.py (file state is current in your context \u2014 no need to Read it back)"},{"i":219,"kind":"tool","long":false,"text":"Bash: sed -i 's/\\r$//' addnotes.py && scp -q addnotes.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/addnotes.py 2>&1 | cut -c1-220; for s in net-deep-gate-close-b12 net-deep-gate-close-b05 net-preedit-commit-close-b05 net-preedit-commit-b12; do $PY $R/wsp31/tools/check_adapter.py queue/$s/edit | cut -c1-110; $PY $R/wsp31/tools/check_adapter.py queue/$s/ctrl | cut -c1-110; ls queue/$s | tr \"\\n\" \" \"; echo; done; sed -n 36,44p queue/net-deep-gate-close-b12/record.md | cut -c1-220; grep -n \"skipped\\|d_commit fitted\" queue/net-preedit-commit-close-b05/record.md | cut -c1-260; du -sh queue/net-* scratch | tr \"\\n\" \" \"; date +%T'"},{"i":220,"kind":"output","long":false,"text":"net-deep-gate-close-b12 ok 2599 words; ['size: whole network 5.0716% over 180 matrices, largest matrix 53.2333%', 'predicted_gain_120: -7 to 9']\nnet-deep-gate-close-b05 ok 2534 words; ['size: whole network 4.4116% over 180 matrices, largest matrix 22.1827%', 'predicted_gain_120: -6 to 8']\nnet-preedit-commit-close-b05 ok 2726 words; ['size: whole network 5.2924% over 180 matrices, largest matrix 52.8741%', 'predicted_gain_120: -8 to 9']\nnet-preedit-commit-b12 ok 2613 words; ['size: whole network 4.4709% over 180 matrices, largest matrix 18.9846%', 'predicted_gain_120: -7 to 7']\nOK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5323 at 6.down_proj\nOK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5323 at 6.down_proj\nbuild.sh check.txt ctrl edit record.md size.json \nOK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.2218 at 6.down_proj\nOK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.2218 at 6.down_proj\nbuild.sh check.txt ctrl edit record.md size.json \nOK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5287 at 6.down_proj\nOK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5287 at 6.down_proj\nbuild.sh check.txt ctrl edit record.md size.json \nOK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.1898 at 28.gate_proj\nOK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.1898 at 28.gate_proj\nbuild.sh check.txt ctrl edit record.md size.json \nBuilt by `code/net_job.py` (one GPU-0 hold, forward pass `tools/g31.py` only, no optimiser anywhere) and rebuilt\nbit for bit by `build.sh` (`code/net_build.py` from `code/data/net_stats_slim.pt`).\n\nData. `tools/data/probe_b001.jsonl`: unedited arms `base`, `aa`, `zero` of batch b001, all 58 instances are stage-1\ntasks. Instances sorted by name; every fourth (index % 4 == 1, 15 tasks) is held out for the pre-test, the other 43\nare fit tasks. One episode per task (the one with most deep, post-edit and first-edit thought positions), truncated\n[line removed by the dashboard scrubber]\nNo task outcome enters any statistic.\n\n73:Layers whose AUC on the calibration third is below 0.75 are skipped: gate `pre`: none skipped (60 layers used); calibration AUC over the 60 layers min 0.75, median 0.83, max 0.88.\n82: at least 8 positions on each side). d_commit fitted on 18 episodes; split-half cosine of d_commit (layers 0, 6, ..., 54): 0.22 0.54 0.71 0.70 0.59 0.40 0.34 0.24 0.21 0.16; cosine(d_end, d_commit) same layers: 0.43 0.55 0.50 0.51 0.21 -0.22 0.01 0.06 0.05\n19M\tqueue/net-deep-gate-close-b05 19M\tqueue/net-deep-gate-close-b12 19M\tqueue/net-preedit-commit-b12 19M\tqueue/net-preedit-commit-close-b05 7.1G\tscratch 01:08:35\n[This command modified 1 file you've previously read: addnotes.py. Call Read before editing.]"},{"i":221,"kind":"tool","long":false,"text":"Bash: ssh -o BatchMode=yes wrx90 'cd /home/keith/gemma-v2/wsp31/authors/claude && grep -c \"by a sixth of what it does in 0008\" queue/net-deep-gate-close-b12/record.md queue/net-deep-gate-close-b05/record.md && sed -i \"s/moves the stream by a sixth of what it does in 0008/moves the stream by 0.4 of what it does in 0008/\" queue/net-deep-gate-close-b12/record.md && grep -c \"by 0.4 of what it does\" queue/net-deep-gate-close-b12/record.md && head -1 queue/net-*/record.md | cut -c1-200 && for s in net-deep-gate-close-b12 net-deep-gate-close-b05 net-preedit-commit-close-b05 net-preedit-commit-b12; do bash -n queue/$s/build.sh && grep -c \"predicted_gain_120\" queue/$s/record.md && touch queue/$s/READY; done; ls queue/*/READY; mkdir -p code/extras3 && cp scratch/job3/rec_*.txt scratch/job3/vgen_summary_notrun.txt code/extras3/ 2>/dev/null; cp scratch/job3/report.txt code/data/net_report_t3.txt; cp scratch/logs/job3.log code/data/net_job_t3.log; date +%T'"},{"i":222,"kind":"output","long":false,"text":"queue/net-deep-gate-close-b12/record.md:1\nqueue/net-deep-gate-close-b05/record.md:1\n1\n==> queue/net-deep-gate-close-b05/record.md <==\ntitle: Deep-thought gate in all 60 layers: one neuron per layer detects depth >= 800 in a thought and writes the end-of-thought state (per-layer dose 0.05)\n\n==> queue/net-deep-gate-close-b12/record.md <==\ntitle: Deep-thought gate in all 60 layers: one neuron per layer detects depth >= 800 in a thought and writes the end-of-thought state (per-layer dose 0.12)\n\n==> queue/net-preedit-commit-b12/record.md <==\ntitle: Pre-edit commit gate in all 60 layers: deep in a thought with no edit made yet, write the first-edit thought state only (per-layer dose 0.12)\n\n==> queue/net-preedit-commit-close-b05/record.md <==\ntitle: Pre-edit rumination gate in all 60 layers: deep in a thought with no edit made yet, write end-of-thought plus first-edit state (per-layer dose 0.05 each)\n1\n1\n1\n1\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\nqueue/net-deep-gate-close-b05/READY\nqueue/net-deep-gate-close-b12/READY\nqueue/net-preedit-commit-b12/READY\nqueue/net-preedit-commit-close-b05/READY\n01:08:48"},{"i":223,"kind":"tool","long":false,"text":"Write: C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\NOTES.md"},{"i":224,"kind":"output","long":false,"text":"The file C:\\Users\\Owner\\wsp31_loop\\work\\claude\\t3\\NOTES.md has been updated successfully. (file state is current in your context \u2014 no need to Read it back)"},{"i":225,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":226,"kind":"output","long":false,"text":"640 NOTES.md\n640 NOTES.md\n6.9G\ndeep-gate-b01-end-neurons-x9\ndeep-thought-gate-g3-b03\nearly-gate-b01-end-neurons-x9\nend-neurons-x25\nnet-deep-gate-close-b05\nnet-deep-gate-close-b12\nnet-preedit-commit-b12\nnet-preedit-commit-close-b05\nREADY\nbuild.sh\ncheck.txt\nctrl\nedit\nrecord.md\nsize.json\n Nov 12 23:52 gemini__late-preedit-commit-gate.json\n Nov 12 23:48 gemini__rumination-damp-r2-net.json\n Nov 12 22:35 [task].json\n Nov 12 22:37 [task].json\n Nov 12 22:39 [task].json\n4\n01:09:16\n[This command modified 1 file you've previously read: NOTES.md. Call Read before editing.]"},{"i":227,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"NOTES.md\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\nreps=[(\"No edit of anyone has a stage-1 result yet (b001 still filling). Queued and unscored: 0007-0010 (turn 0) and the four below.\",\"No edit has a stage-1 result yet. Queued, unscored: 0007-0010 and the four below.\"),\n(\" (a one-run difference has sd about 4 tasks)\",\" (one-run sd about 4 tasks)\"),\n(\"One neuron per layer replaced by a ridge-LDA detector, writing a stream direction at a small per-layer dose.\\n\",\"One neuron per layer becomes a ridge-LDA detector that writes a stream direction at a small dose.\\n\"),\n(\" Do not retry without\\n controlling the spread of the response (sd about 1 around class means).\",\" Do not retry without\\n controlling the response spread (sd about 1).\"),\n[line removed by the dashboard scrubber]\n(\"- scratch/job3/raw_mlpin.pt (6 GB, 60 layers, 32 episodes with edit/tool labels) allows CPU refits; delete when done.\",\"- scratch/job3/raw_mlpin.pt (6 GB, 60 layers, 32 episodes, edit/tool labels) allows CPU refits; delete when done.\"),\n(\" first minute; gpu_job.sh runs code/gpu_job3.sh. `net_job.py --mock` tests the whole job on CPU in 30 s.\",\" first minute (gpu_job.sh runs gpu_job3.sh). `net_job.py --mock` tests the job on CPU in 30 s.\"),\n(\"- vgen.py drops records with n > --max-pos (code/mk_callset.py truncates). Call gpu0_session.sh with\\n `env -u CUDA_DEVICE_ORDER -u CUDA_VISIBLE_DEVICES`. `ps | grep` matches your own ssh command line.\",\"- vgen.py drops records with n > --max-pos (mk_callset.py truncates). Call gpu0_session.sh with\\n `env -u CUDA_DEVICE_ORDER -u CUDA_VISIBLE_DEVICES`.\"),\n(\" Deep thoughts after the first edit are rare: \\\"pre-edit\\\" adds almost nothing to a depth condition.\",\" Deep thoughts after the first edit are rare: \\\"pre-edit\\\" adds little to a depth condition.\"),\n]\nfor a,b in reps:\n assert a in s, a[:40]\n s=s.replace(a,b)\nopen(\"NOTES.md\",\"w\",newline=\"\\n\").write(s)\nprint(len(s.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 && wc -w authors/claude/NOTES.md; grep \"net-\" queue/STATUS.md | cut -c1-200; ls authors/claude/queue/net-*/REJECTED 2>/dev/null; tail -4 queue/runner.log | cut -c1-200; date +%T'"},{"i":228,"kind":"output","long":false,"text":"607\n607 authors/claude/NOTES.md\n| 0020 | claude | net-deep-gate-close-b05 | queued_s1 | -6 to +8 | | | | - |\n| 0021 | claude | net-deep-gate-close-b12 | queued_s1 | -7 to +9 | | | | - |\n| 0022 | claude | net-preedit-commit-b12 | queued_s1 | -7 to +7 | | | | - |\n| 0023 | claude | net-preedit-commit-close-b05 | queued_s1 | -8 to +9 | | | | - |\n2026-11-13 01:08:55 EST INGEST claude__net-deep-gate-close-b05 ledger 0020\n2026-11-13 01:08:56 EST INGEST claude__net-deep-gate-close-b12 ledger 0021\n2026-11-13 01:08:58 EST INGEST claude__net-preedit-commit-b12 ledger 0022\n2026-11-13 01:08:59 EST INGEST claude__net-preedit-commit-close-b05 ledger 0023\n01:09:32"},{"i":229,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"NOTES.md\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\nreps=[(\"- First: the served check that was never run (gpu_job3.sh has the vgen line; scratch/job3/deep_pre.jsonl).\",\"- First: the served check never run (vgen line in gpu_job3.sh; scratch/job3/deep_pre.jsonl).\"),\n(\"Pre-test, 4 held-out episodes, only 3 long thoughts: cut 2/3, 1/3, 1/3, 0/3; short thoughts and true ends intact;\",\"Pre-test, 4 held-out episodes, 3 long thoughts: cut 2/3, 1/3, 1/3, 0/3; short thoughts and true ends intact;\"),\n(\" harmful teacher-forced. At 0.03 x 60 layers it cuts short thoughts\",\" harmful teacher-forced: at 0.03 x 60 layers it cuts short thoughts\"),\n[line removed by the dashboard scrubber]\nfor a,b in reps:\n assert a in s, a[:40]\n s=s.replace(a,b)\nopen(\"NOTES.md\",\"w\",newline=\"\\n\").write(s); print(len(s.split()))\nEOF\nscp -q NOTES.md wrx90:/home/keith/gemma-v2/wsp31/authors/claude/NOTES.md && ssh -o BatchMode=yes wrx90 'wc -w /home/keith/gemma-v2/wsp31/authors/claude/NOTES.md; date +%T'"},{"i":230,"kind":"output","long":false,"text":"601\n601 /home/keith/gemma-v2/wsp31/authors/claude/NOTES.md\n01:09:44"},{"i":231,"kind":"tool","long":false,"text":"Bash: python - <<'EOF'\ns=open(\"NOTES.md\",newline=\"\").read().replace(\"\\r\\n\",\"\\n\")\na=\"No edit has a stage-1 result yet. Queued, unscored: 0007-0010 and the four below.\"\nassert a in s\ns=s.replace(a,\"No edit has a stage-1 result yet. Queued, unscored: 0007-0010, four below.\")\nopen(\"NOTES.md\",\"w\",newline=\"\\n\").write(s); print(len(s.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 && head -3 NOTES.md | cut -c1-120 && ls queue/*/READY | wc -l && du -sh scratch | cut -f1 && date +%T'"},{"i":232,"kind":"output","long":false,"text":"599\n599 NOTES.md\n# Notes of author claude (rewritten end of turn 3, 13 Nov 2026, 1:10 AM ET)\n\nNo edit has a stage-1 result yet. Queued, unscored: 0007-0010, four below.\n8\n6.9G\n01:09:51"},{"i":233,"kind":"assistant","long":false,"text":"Four new entries are queued and ingested by the runner (ledger 0020\u20130023). All four passed the checker and are training-sized, but none got a served-generation pre-test.\n\n1. **`net-deep-gate-close-b12` and `net-deep-gate-close-b05`** (dose pair, predicted \u22127 to +9 and \u22126 to +8): one neuron in each of the 60 layers detects depth \u2265 800 inside a thought and writes the end-of-thought contrast. This is the same target as my ledger 0008, spread over 180 matrices (5.1% and 4.4% of the network) instead of 10 layers.\n2. **`net-preedit-commit-close-b05`** (\u22128 to +9): the gate is \"depth \u2265 400 and no edit made yet\", and it writes end-of-thought plus a \"thought that leads to the first edit\" contrast. It targets reading spirals: unedited episodes with no edit in the first 12 calls solve 31%, against 70\u201380% when the first edit comes at calls 6\u201311.\n3. **`net-preedit-commit-b12`** (\u22127 to +7): the commit contrast alone, to separate its effect from thought length. Teacher-forced it moves no tool choice, so it is the likeliest null.\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]"},{"i":234,"kind":"assistant","long":false,"text":"Final message of the turn:\n[claude-code:unrecognized_model] {\"model\":\"claude-fable-5-2\",\"query_source\":\"sdk\"}\nFour new entries are queued and ingested by the runner (ledger 0020\u20130023). All four passed the checker and are training-sized, but none got a served-generation pre-test.\n\n1. **`net-deep-gate-close-b12` and `net-deep-gate-close-b05`** (dose pair, predicted \u22127 to +9 and \u22126 to +8): one neuron in each of the 60 layers detects depth \u2265 800 inside a thought and writes the end-of-thought contrast. This is the same target as my ledger 0008, spread over 180 matrices (5.1% and 4.4% of the network) instead of 10 layers.\n2. **`net-preedit-commit-close-b05`** (\u22128 to +9): the gate is \"depth \u2265 400 and no edit made yet\", and it writes end-of-thought plus a \"thought that leads to the first edit\" contrast. It targets reading spirals: unedited episodes with no edit in the first 12 calls solve 31%, against 70\u201380% when the first edit comes at calls 6\u201311.\n3. **`net-preedit-commit-b12`** (\u22127 to +7): the commit contrast alone, to separate its effect from thought length. Teacher-forced it moves no tool choice, so it is the likeliest null.\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]"}],"updated":"2026-11-15T02:05:55Z"}