Turn 3 · claude

# wsp31 author brief (read fully at the start of every turn)

You are one of three authors (claude, gpt, gemini) taking turns. Your job: make Gemma 4 31B solve more coding-agent
tasks **by editing its weights, with no training**. You read the model (weights, activations, traces, failures),
decide what to change, build the change as a closed-form edit, predict its effect, and queue it. A separate runner
spends the rollouts and writes the measured result next to your prediction. You are scored on tasks gained over the
champion in the same batch (the unedited model plus the edits that have already passed; see "The champion"), on the
margin over your matched control, and on how well your predictions held.

**Be bold.** Small safe nudges were tried on the 4B model and were lost in the noise (see `PRIOR_E4B.md`). A queue
slot is cheap; a third variation of a null result is a wasted slot. High-risk ideas with a clear mechanism and a
prediction that could be wrong are what this programme wants. Say what would prove you wrong.

## Where you work

- Everything happens on host `wrx90`, reached with `ssh -o BatchMode=yes wrx90 '<command>'`. Write scripts in your
  local working directory, copy them with `scp`, run them with ssh. Keep ssh output small (`tail`, `cut`, `head`).
- Root: `/home/keith/gemma-v2` (below: `$R`). Python: `$R/tii-venv/bin/python` (torch, transformers, vLLM 0.19.1,
  peft, safetensors; no scipy). `. $R/env.sh` sets `R` and `PY`.
- **You write only under `$R/wsp31/authors/<you>/`.** Everything else is read-only for you.
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- An edit that passes both stages is promoted: it becomes part of the champion for everything after it.
- The champion is confirmed on tasks you do not see, against the unedited model, and the final champion gets one
  pre-registered held-out run. A changed edit is a new edit.
- A combination of your own edits is a new queue entry with its own record, prediction and control. Combining with
  edits that already passed needs nothing from you: that is what the champion does.

Single-run agent evaluations are noisy by several tasks. An edit worth having shows up as ten or more tasks.
Aim there.

# Notes of author claude (rewritten end of turn 0, 12 Nov 2026 9:45 PM ET)

## What the unedited model does wrong (b001; code/fail_stats.py, req_stats.py, cf_budget.py)
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- Unedited log p(<channel|>) inside thoughts is about -25: thoughts end only where the model decides; hazard-style
  "expected length" is useless at temperature 0. Use the greedy table (code/think_greedy.py).

## Queued this turn (all READY; edits are bit-identical to the pre-tested adapters)
1. deep-thought-gate-g3-b03: one "set" neuron per layer 30..48 (even) detects depth >= 800 in a thought (diag-LDA
   gate, AUC 0.88-0.91) and writes d_end = mean resid(thought end) - mean resid(other thought positions), dose 0.3.
   Pre-test (5 held-out episodes, 23 short / 6 long thoughts): short kept 1.00, long kept 0.53. Predicted -4..9.
2. end-neurons-x25: weights only. 20 MLP neurons in layers 57-59 whose write lens is <channel|> (z >= 6;
   code/end_neurons.py), down columns x25. Short kept 0.86, long 0.34, logged ends more decisive. Predicted -8..9.
3. deep-gate-b01-end-neurons-x9: gate at dose 0.1 plus the same neurons x9. Short 0.95, long 0.48. Predicted -5..9.
4. early-gate-b01-end-neurons-x9: gate fitted on depth >= 400 (g2), dose 0.1, plus neurons x9: the strong dose.
   Short 0.82, long 0.21. Predicted -7..9.
Controls: sign-flipped write direction (gate) / norm-matched other neurons (N). Controls cut nothing in the pre-test.
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authors/claude/scratch:
hold_end
hold_start
job1
job2
logs
cat: queue/CHAMPION.md: No such file or directory

Thu Nov 12 11:57:01 PM EST 2026
# Ledger index (generated by tools/ledger.py index; do not edit by hand)

Read this before proposing anything. A row marked `null` or `harmful` is not to be retried unless the new
record says what is different and why that difference should matter.

| id | date | author | track | title | set / batch | status | verdict | result |
|---|---|---|---|---|---|---|---|---|
| [0001](ledger/0001-claude-unedited-baseline-noise-floor.md) | 2026-11-12 | claude | baseline | Unedited 31B on the screening set, two passes (base rate and identical-arm noise) | wsp31_screen_v1 / s1_base | done | inconclusive | stopped at 59 of 720 episodes; 54% ended at the session limit at concurrency 16 |
| [0002](ledger/0002-claude-state-of-the-art-survey.md) | 2026-11-12 | claude | survey | Survey of training-free weight editing to November 2026, with 20 ranked edit ideas for 31B | - / - | done | n/a | reports/2026-11-12-weight-editing-survey.md |
| [0003](ledger/0003-claude-identical-arms-batch-noise-floor.md) | 2026-11-12 | claude | baseline | Three identical-weight arms in one batch (base rate, noise floor, adapter-path check, runner shakedown) | wsp31_screen_v1 / b001 | running | - | - |
| [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) |
| [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 |
| [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 |
| [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 | - | - |
| [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 | - | - |
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| [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 | - | - |
| [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 | - | - |
| [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 | - | - |
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wsp31/authors/gpt/queue/:
total 20
drwxrwxr-x 5 keith keith 4096 Nov 12 22:37 .
drwxrwxr-x 5 keith keith 4096 Nov 12 21:44 ..
drwxrwxr-x 4 keith keith 4096 Nov 12 22:35 writer-close-amplify-033
drwxrwxr-x 4 keith keith 4096 Nov 12 22:37 writer-close-amplify-050
drwxrwxr-x 4 keith keith 4096 Nov 12 22:39 writer-close-shift-011

# wsp31 tools for edit authors

Changelog (newest first; times US Eastern):
- Thu 12 Nov, 11:40 PM: first served runs of `dose.py`, `validate.py`, `vgen.py` and first run of `closeprobe.py`:
  all work as documented (section 5, with numbers); **any active adapter slows every stream of its batch** (trap
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- Thu 12 Nov, 10:55 PM: rank 32 on all 420 matrices and stacks of rank 34, 64, 96 are applied as written
  (sections 1, 4); new `vgen.py`. 10:30 PM: **thought-end pre-tests need exact contexts** (section 5;
  `closeprobe.py`, `reqset.py --per-call`); rank limits above 32 are 64 or 128, never 96; b001 probe sets.
- Thu 12 Nov, 10:00 PM: champion support (section 4); network-wide builders, `stack.py`, `rescale.py`, `dose.py`,
  `train_size.py`, `capture.py`, `replay.py`. 8:36 PM: first version. Every earlier command still works unchanged.

Everything here lives in `$R/wsp31/tools` (below `$T`; `$R = /home/keith/gemma-v2`). Each entry ends with
**Verified by** (a measurement that was made) or **NOT VERIFIED YET** (written, first real run still queued; use it,
but do not build a claim on its numbers alone). Architecture facts are in `$T/ARCH.md` (read "Consequences for
weight edits" there before designing an edit); measurements with commands are in `$T/REPORT.md`.

    . /home/keith/gemma-v2/env.sh; T=$R/wsp31/tools
    export WSP31_WORK=$R/wsp31/authors/<you>/scratch     # caches and server logs of the tools go here, not into $T

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## 8. Not available yet

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- Activation atlas and its views; `--champion` runs of `dose.py`, `validate.py`, `closeprobe.py`, `vgen.py`.
- Any single entry beyond rank 32 per matrix, and edits of norms, `layer_scalar` or embeddings (`overlay.py` exists
  as code, has not been served, and the runner does not accept it).
- A probe verdict that predicts rollout outcome: not shown for any tool here.

#!/bin/bash
# runs under the GPU0 lock; executes code/gpu_job.sh as it exists at acquisition time
cd /home/keith/gemma-v2/wsp31/authors/claude
date > scratch/hold_start
[ -f code/gpu_job.sh ] || { echo "no job"; exit 3; }
bash code/gpu_job.sh
date > scratch/hold_end
---
#!/bin/bash
# GPU-0 job of author claude (run only through code/gpu_hold.sh under the gpu0 lock)
. /home/keith/gemma-v2/env.sh
cd /home/keith/gemma-v2/wsp31/authors/claude
export WSP31_WORK=$PWD/scratch CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0
$PY code/think_job.py > scratch/logs/job1.log 2>&1
$PY code/think_job2.py 5 > scratch/logs/job2.log 2>&1
---
keith     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 
keith     893374  0.0  0.0   8372  2176 ?        S    23:26   0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh
keith     957831  0.0  0.0   8372  2172 ?        S    23:38   0:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh
index, memory.used [MiB], utilization.gpu [%]
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eval.pt
snap.pt

authors/claude/scratch/logs:
hold1.log
job1.log
job2.log
1017M	authors/claude/scratch

pid, used_gpu_memory [MiB]
1026421, 35750 MiB
184976, 90492 MiB
    PID     ELAPSED CMD
 184976    04:06:54 VLLM::EngineCore
1026421       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
# Interpretability lane: what is known about the inside of Gemma 4 31B

Lane `wsp31/interp`. Updated Thu 12 Nov 2026, 11:50 PM ET. Everything under "Measured" is a number from this lane's
own runs; anything else is labelled. Pre-registration: `PROTOCOL.md` (sections 0-5 and dated amendments 1, 2, 3, 3a,
3b, 3c, 3d, 3e, 4, 4a, 5, 5a, 5b). Located-and-causally-checked components: `ATLAS.md` (currently none). Lens-only readings:
`ATLAS_lens.md`.

**Forward pass, as measured (this wording replaces two earlier ones: "verified", then "not yet verified";
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re-rendered; agreement on an edited model: not measured beyond the tools lane's rank-4 comparison.
Section 3 uses the unedited model only. The dose check (3.5) will change activations inside the pass through
hooks; that has no served counterpart and is labelled where it is reported.
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permutations), at the "agent" position rule (mean over the episode's sampled decision points). Controls beside each:
the same test at the "early" rule (positions up to 6,000) and the "task" rule (first agent turn only), a 5-fold
estimate over all tasks, and the AUC of two activation-free baselines.

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|---|---|---|---|---|---|---|---|
| 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 |
| 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 |

| 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 |
| 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 |
| 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 |
| 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 |

p-values are per contrast, not corrected over the five contrasts (Holm over five leaves only patch below 0.05);
5-fold p-values use 200 permutations (floor .005). The pre-registered permutation test shuffles episode labels and
ignores that the 46 check episodes come from 15 tasks.

Post hoc, exploratory (amendment 3e, written after the table above was seen, before these were computed):

| 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) |
|---|---|---|---|
| solved | 0.470 (9; p .60) | 0.411 (14; p .81) | 0.312 (0.278, p .035) |
| tests_run | 0.365 (17; p .81) | 0.398 (35; p .85) | 0.392 (0.396, p .051) |
| patch | 0.423 (14; p .67) | 0.454 (28; p .67) | 0.472 (0.384, p .016) |
| finished | 0.443 (17; p .65) | 0.539 (30; p .37) | 0.298 (p .11) |
| brief | 0.408 (11; p .74) | 0.609 (22; p .14) | 0.292 (p .13) |

Result: the criterion is met by all five contrasts at the main rule and by none at the "early" rule; the
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   (19 of 66 against 44 of 70). "Edits before reading" never happens; loops do not predict failure.
4. **Per-layer directions for training-sized edits** (section 3). The honest state: the five episode-level
   contrasts (solved, tests run, patch, finished, brief thoughts) are **weak**. Each separates held-out episodes a
   little better than shuffled labels (AUC 0.62 to 0.80 against a null 95th percentile of 0.59 to 0.68), no better
   than the number of model calls does without any activation, and none of them puts the better of two episodes of
   the **same** task first more often than chance. Four of the five are one shared direction (cosines 0.65 to 0.91).
   They describe which tasks and runs go well, not a decision the agent takes; an edit along them is not expected to
   carry the outcome difference. The two within-episode decision contrasts are strong as read-outs: edit-or-write

title: Coordinate-whitened GeGLU late pre-edit commit gate across 13 high-signal layers (dose 1.20)

## Hypothesis

Across the 210 `b001` episodes, a second major failure bottleneck—completely untouched by all seven queued thought-shortening edits (`0007`--`0013`)—is **indecisive `read_file` spiralling into the 30-tool-call hard cap**:
- 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).
- 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`.

Why this differs from `PRIOR_E4B.md` ("always-on edit sooner: 12 vs 28") and `0007`--`0013`:
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## Exact recipe

base: champion generation 0

Designed 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.

size: whole network 1.055181% over 39 matrices, largest matrix 9.659172%

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finished.pt
patch.pt
post.json
solved.pt
summary.json
tests_run.pt
tool_act.pt
tool_submit.pt

# What was already tried on Gemma 4 E4B (paper track, 4-12 Nov 2026)

These are training-free weight edits on the small model, written by Claude, GPT and Gemini, measured on 170
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only for 31B) and on 130 held-out tasks. Sources: the private paper repository (`gemma-weight-programming`), its
experiment log and result files. Counts are tasks solved of 170 unless stated; "vs" is the same-batch unedited arm.

None of these results transfers to 31B automatically. They are listed so that a 31B attempt of the same kind is made
on purpose, with a reason, and not by accident.

## Method that was used

A frontier LLM diagnosed failures from logged agent decisions, located a direction in the residual stream (difference
of means between decision classes), compiled it into a closed-form MLP edit (always-on, or gated by a "quiet neuron"
that fires only under a condition such as "no file edited yet"), wrote a prediction, and tested against a
norm-matched random control.

## What moved behaviour

- Edits control their targeted decision with a dose-response in replay; matched random controls do not. Submit rate
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212:| 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 |
213:| 18 | **Singular-value rescaling search** on selected matrices (black-box) | Transformer² (SVF learned by RL) | SVDs; dev tasks | High overfit risk |
214:| 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 |
215:| 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 |
224:## D. Tooling recommendations for the LLM-author harness
241:## E. Gaps, open questions, and inaccessible sources
{"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], "<turn|>": [3.965581072407076e-07, 7.914503612482804e-07]}, "up": [["<channel|>", 0.04161743447184563], ["run", 0.009498253464698792], ["---", 0.0052758632227778435], ["<", 0.005135818384587765], ["<tool_call|>", 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], "<turn|>": [1.1040862446520805e-08, 3.992269739683252e-06]}, "up": [["---", 0.04118669033050537], ["<channel|>", 0.031182680279016495], ["thought", 0.023336807265877724], ["(\"", 0.0014389149146154523], ["####", 0.0014357835752889514]], "down": [["<|channel>", -0.14833541214466095], ["set", -6.4170999394264

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close_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
    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)]
close_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
    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)]
close_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
    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)]
close_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
    tool up [('<channel|>', 0.716), ('<tool_call|>', 0.091), ('<|channel>', 0.078), ('---', 0.012)] down [('run', -0.598), ('read', -0.391), ('grep', -0.01), ('edit', -0.001)]
close_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
    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)]
close_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
    tool up [('//', 0.166), ('<channel|>', 0.125), ('C', 0.069), ('\\n', 0.055)] down [('run', -0.547), ('read', -0.389), ('grep', -0.007), ('edit', -0.001)]
close_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
    tool up [('<channel|>', 0.893), ('<|channel>', 0.059), ('1', 0.003), ('\\n', 0.002)] down [('run', -0.598), ('read', -0.391), ('grep', -0.01), ('edit', -0.001)]
{'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']}
2: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
14:Usage: python dose.py --contrasts close_thought,tool_act --sets 0-59,20-59 --alphas 0.01,0.03,0.1,0.3
95:    ap.add_argument("--alphas", default="0.01,0.03,0.1,0.3")
145:    alphas = [float(x) for x in a.alphas.split(",")]
152:            for al in alphas:
close files before this hold: 136

/data:

keith     4096 Nov 12 21:26 .
keith     4096 Nov 12 21:40 ..
keith  1710391 Nov 12 20:53 end_neurons.pt
keith 54909320 Nov 12 21:20 think_stats.pt
keith 54579909 Nov 12 21:26 think_stats_g2.pt
keith 54579909 Nov 12 21:26 think_stats_g3.pt

/extras:

keith 4096 Nov 12 21:41 .
keith 4096 Nov 12 21:40 ..
keith 1598 Nov 12 21:39 common_tail.txt
keith  699 Nov 12 21:39 e1.txt
keith 2039 Nov 12 21:39 e2.txt
keith  742 Nov 12 21:39 e3.txt
keith 1476 Nov 12 21:41 e4.txt

tch/job1:
[... 86 lines cut ...]
`sigma` = sd on the positives, `m` = unit mean `mlp_in` (the model has no bias; `m . x` is `cbar` at every position).
Up row = `m / cbar` (reads 1). Down column = `clipdiv(d_L / |d_L|, w_post_L) * dose * |d_L| * rms_y_L / (gbar_L *
layer_scalar_L)` with `gbar_L` = mean activation of the new neuron on the late positions and `d_L` as in family A
(E minus other thought positions): at an average late position the write moves `resid_post` by `dose * d_L`.

Gate variant g3 (`code/think_refit.py`, CPU refit from the saved MLP inputs): sharpness 12 instead of 3, positives = thought positions at depth >= 800, negatives = thought positions at depth < 200 and positions outside thoughts (threshold = 95% quantile of the negatives). Training-set firing rate, layers 30..48: depth < 100 0.05-0.09, 100..399 0.17-0.23, 400..799 0.28-0.45, >= 800 0.57-0.75, outside thoughts 0.00-0.03, at thought ends 0.00; AUC 0.88-0.91. 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.

Control (`ctrl/`): same code path and statistics; the written unit direction has the sign of every coordinate

 X (13, 6658, 5376) torch.float16
 cls (6658,) torch.int64
 depth (6658,) torch.int64
 ep (6658,) torch.int64
 n (5,) torch.float64
 resid_sum (5, 36, 5376) torch.float64
 mlpin_sum (5, 36, 5376) torch.float64
 act_sum (5, 36, 21504) torch.float64
 absact_sum (36, 21504) torch.float64
 rmsy_sum (5, 36) torch.float64
 rnorm_sum (5, 36) torch.float64
 CL list 36 [24, 25, 26, 27, 28, 29]
 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
 A_proj dict 5
   E dict 2
     mean list 10 [2.8446428775787354, 1.1685497760772705, 10.992581367492676, 1.9841610193252563, 1.4817982912063599, 5.0086140632629395]
     sd list 10 [0.3666640818119049, 0.151480033993721, 1.0210658311843872, 0.26093870401382446, 0.17310023307800293, 0.666898787021637]
   early dict 2
     mean list 10 [2.276689052581787, 1.0085142850875854, 9.492975234985352, 1.397033929824829, 1.4942704439163208, 4.794047832489014]
     sd list 10 [0.6681310534477234, 2.721149206161499, 1.5431852340698242, 0.5406621098518372, 0.5879179835319519, 2.2555582523345947]
[... 242 lines cut ...]
        ("G3B0p1N8", [("B", 0.1, "g3"), ("N", 8.0, "g1")], False), ("G2B0p03", [("B", 0.03, "g2")], False),
        ("G2B0p1c", [("B", 0.1, "g2")], True), ("G3B0p1c", [("B", 0.1, "g3")], True), ("N24p0", [("N", 24.0, "g1")], False)]
out = A_DIR + "/scratch/job2/ad"; os.makedirs(out, exist_ok=True)
for nm, parts, ctrl in ARMS:
    e = EditSet()
[line removed by the dashboard scrubber]
    rep = e.save(f"{out}/{nm}", note=dict(parts=[list(p) for p in parts], ctrl=ctrl)); print("built", nm, rep["r"])
open(A_DIR + "/scratch/job2/arms.txt", "w").write("\n".join(a[0] for a in ARMS))

Fri Nov 13 12:01:28 AM EST 2026
 893373       35:14 /bin/bash ./run_gpu.sh logs/job5.log ./job5.sh
 893374       35:14 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh
 957831       23:27 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh
1026408       10:38 timeout 1740 ./job5.sh
1026409       10:38 /bin/sh ./job5.sh
1026421       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
1030456       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
1054488       03:55 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
authors/claude/code/thinklib.py:11:Controls: same code path, the written unit direction has the sign of every coordinate flipped at random (seeded):
authors/claude/code/thinklib.py:22:LA = list(range(30, 50, 2))
authors/claude/code/thinklib.py:24:GL = LA + LC
authors/claude/code/thinklib.py:25:CL = list(range(24, 60))
authors/claude/code/thinklib.py:41:def signflip(d, seed):
authors/claude/code/thinklib.py:42:    g = torch.Generator().manual_seed(seed)
authors/claude/code/think_job.py:8:import random
authors/claude/code/think_job.py:65:    torch.manual_seed(0)
authors/claude/code/think_job.py:73:    for L in tl.GL:
authors/claude/code/think_job.py:86:        if L == tl.GL[1]:
authors/claude/code/think_job.py:88:    for L in tl.GL[2:]:
authors/claude/code/think_job.py:89:        st["gate"][L] = dict(st["gate"][tl.GL[0]])
authors/claude/code/think_job.py:127:    gidx = [tl.CL.index(L) for L in tl.GL]
authors/claude/code/think_job.py:141:        rng = random.Random(1234 + ei)
authors/claude/code/think_job.py:205:    for gi, L in enumerate(tl.GL):
authors/claude/code/think_job.py:251:        rng = random.Random(99 + ei)
authors/claude/code/think_job.py:265:                    r = m.forward(ids2, out_pos=[n - 1], capture=("mlp_in",), cap_layers=tl.GL,
authors/claude/code/think_job.py:269:                                     for gi, L in enumerate(tl.GL)])
authors/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."""

1794546110
                tot[p[1]] += 1
        print(nm, len(lst), "positions by class", tot, "subs", sum(1 for r in lst for a, b in zip(tl.positions(r, cap)[3], r["ids"]) if a != b))


def pick_episodes(recs):
    inst = sorted({r["instance"] for r in recs})
    held = {x for i, x in enumerate(inst) if i % 4 == 1}

    def nlate(r, cap):
        return sum(1 for p in tl.positions(r, cap)[1] if p[1] == 3)
    best_tr, best_ev = {}, {}
    for r in recs:
        if r["instance"] in held:
            k = nlate(r, NEVAL_CAP)
            if r["instance"] not in best_ev or k > best_ev[r["instance"]][0]:
                best_ev[r["instance"]] = (k, r)
        else:
            k = nlate(r, NTRAIN_CAP)
            if r["instance"] not in best_tr or k > best_tr[r["instance"]][0]:
[... 74 lines cut ...]

def finish_stats(m, st, raw):
    st["w_post"] = torch.stack([m.L[L]["post_feedforward_layernorm"].float().cpu() for L in tl.CL])
    st["ls"] = torch.tensor([float(m.L[L]["layer_scalar"].float()) for L in tl.CL])
    st["w_final"] = m.final_norm.float().cpu()
    st["e101"] = m.emb[101].float().cpu()
    tot = torch.zeros(tl.D, dtype=torch.float64, device=m.emb.device)
    for s in range(0, m.emb.shape[0], 32768):

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]
edited share of thought rows 0.07; episodes with an edit 5 of 22
per 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)]
GL [30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 54, 56, 58]
depth>=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
late 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
late 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
ci>=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
pre-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
pre-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
trouble: 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
trouble3k: 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
troubleD: 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
['run', 'read', 'edit', 'submit', 'write']

00:04:04
import json,sys,glob,collections,statistics as st
R="/home/keith/gemma-v2/results/wsp31"
runs=sys.argv[1:] or [R+"/batches/b001/base",R+"/batches/b001/aa",R+"/batches/b001/zero"]
for run in runs:
    rows=[json.loads(l) for l in open(run+"/rebench/shard0_gpu0/task_results.jsonl")]
    n=len(rows); s=sum(bool(r["resolved"]) for r in rows)
    print(run.split("/")[-1],"n",n,"solved",s)
    for lab,sel in (("solved",[r for r in rows if r["resolved"]]),("failed",[r for r in rows if not r["resolved"]])):
        if not sel: continue
        print(" ",lab,"dur med",round(st.median(r["duration_seconds"] for r in sel)),"p1 med",round(st.median((r.get("phase1_seconds") or 0) for r in sel)),
              "tools med",st.median(r["tool_calls"] for r in sel),"llm med",st.median(r["total_llm_calls"] for r in sel),
              "empty patch",sum(1 for r in sel if not r["patch_chars"]),
aa
aa.launch.log
base
base.launch.log
server.log
zero
zero.launch.log
[... 31 lines cut ...]
   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
   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,
g3 {"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}
  by type {'down_proj': 0.01361, 'gate_proj': 0.05059, 'up_proj': 0.00747}
  by depth (10-layer bands) [None, None, None, 0.00941, 0.03008, None]
n25 {"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}
  by type {'down_proj': 0.19292}
  by depth (10-layer bands) [None, None, None, None, None, 0.19292]

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
fail/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
fail/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
fail/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
fail/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
solved/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
solved/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
solved/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
fail/finished
   [task]                  224s rrrrrTrrTTeeeTeeeererrTrrrTrs
   [task]                     133s rrrTTeTTTTrs
   [task]                  272s rrrTTTeTTTTTrrrrrreTTrrs
   cool-RR__PySnooper.57472b46.lm_rewrite__031xv 419s rrrrrrerrrrrrrrrrrrrrrrrTrs
   [task]                          260s rrrrrrrrrrrrrrrrrrrereerrrrers
   [task]                    159s rrrrrrrrrrrTreeerrrTTTrrs
[line removed by the dashboard scrubber]
   [task]                        403s rrrrrrrTrrrrrrrre-rrrrrrs
   jd__tenacity.0d40e76f.combine_module__jua0mom 241s rrrrrrerrrTerTTTTTrTrTTTrrrrs
solved/finished
   Mimino666__langdetect.a1598f1a.func_basic__rk 181s rrrrrerTrrrerrrrrrrTeeeeeerTrTrrrrs
[... 36 lines cut ...]
   jd__tenacity.0d40e76f.combine_module__jua0mom 298s rrrrrerrrrrrrrrrrrrrrrrrrrrrrrrs
   jd__tenacity.0d40e76f.func_basic__85q611gl    331s rTrrrrrrrrrrrrrrrrrrrrrrrrrrres
00:04:45
 893374       38:31 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh
 957831       26:45 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh
1026421       13:55 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_act --sets 0-59 --al
1054488       07:12 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
1067894       02:57 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/sessH_2350.log ] &&

P(solved | no edit within first k calls), n
0 288 0.54 | edited at exactly k..k+1: 0.00 0
4 272 0.53 | edited at exactly k..k+1: 0.58 48
6 224 0.51 | edited at exactly k..k+1: 0.80 44
8 178 0.45 | edited at exactly k..k+1: 0.70 30
10 147 0.40 | edited at exactly k..k+1: 0.71 34
12 113 0.31 | edited at exactly k..k+1: 0.38 16
15 89 0.29 | edited at exactly k..k+1: 0.30 10
18 71 0.28 | edited at exactly k..k+1: 0.44 9
22 46 0.24 | edited at exactly k..k+1: 0.33 12
solved/finished n 133 thought tok/ep 3957 beyond200 0.54 beyond400 0.35 beyond800 0.16 beyond1500 0.05 | thoughts/ep 19.1 | pre-edit share of thought tok 0.56
fail/finished n 47 thought tok/ep 4323 beyond200 0.54 beyond400 0.35 beyond800 0.16 beyond1500 0.05 | thoughts/ep 21.6 | pre-edit share of thought tok 0.55
fail/limit n 29 thought tok/ep 8515 beyond200 0.69 beyond400 0.54 beyond800 0.35 beyond1500 0.18 | thoughts/ep 23.9 | pre-edit share of thought tok 0.56
fail/limit/empty n 31 thought tok/ep 8628 beyond200 0.73 beyond400 0.59 beyond800 0.40 beyond1500 0.22 | thoughts/ep 21.4 | pre-edit share of thought tok 0.90
fail/cap n 19 thought tok/ep 6460 beyond200 0.52 beyond400 0.33 beyond800 0.15 beyond1500 0.05 | thoughts/ep 32.9 | pre-edit share of thought tok 0.69
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{"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":
{"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
[line removed by the dashboard scrubber]
{"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"]}}}
{"type": "function", "function": {"name": "get_status", "description": "Check active budget details (time, tool calls) and active patch details.", "parameters": {"type": "object", "properties": {}}}}
{"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": {}}}}
[task].jsonl
   think  200  run_command {"command": "grep -rn --include=\"*.py\" \"update_incumbents\" ."}
   think   45  run_command {"command": "grep -rn --include=\"*.py\" \"_rejected_config_ids\" ."}
   think   58  read_file {"filepath": "smac/intensifier/abstract_intensifier.py", "start_line": 450}
   think  353  read_file {"filepath": "smac/intensifier/abstract_intensifier.py", "start_line": 710, "end_line": 730}
   think 3910  read_file {"filepath": "smac/facade/hyperparameter_optimization_facade.py"}
   think   54  read_file {"filepath": "smac/main/smbo.py"}
   think   15  run_command {"command": "grep -n \"def tell\" smac/main/smbo.py"}
   think   25  read_file {"end_line": 200, "filepath": "smac/main/smbo.py", "start_line": 166}
   think   13  read_file {"end_line": 250, "filepath": "smac/main/smbo.py", "start_line": 201}
   think  143  read_file {"end_line": 650, "filepath": "smac/intensifier/abstract_intensifier.py", "start_line": 600}
   think   19  run_command {"command": "grep -n \"def __post_init__\" smac/intensifier/abstract_intensifier.py"}
   think   15  read_file {"end_line": 200, "filepath": "smac/intensifier/abstract_intensifier.py", "start_line": 134}
   think  325  read_file {"filepath": "smac/intensifier/intensifier.py"}
[... 41 lines cut ...]
   think   14  read_file {"end_line": 100, "filepath": "copier/_cli.py", "start_line": 76}
   think 1171  run_command {"command": "grep -n \"def run_copy\" copier/_main.py"}
   think   21  read_file {"end_line": 1700, "filepath": "copier/_main.py", "start_line": 1664}
   think 1713  run_command {"command": "grep -rn \"clone(\" . | grep \".py\""}
   think  657  read_file {"filepath": "copier/_vcs.py", "start_line": 178}
Counter({'run_command': 3730, 'read_file': 2130, 'edit_file': 546, 'submit_patch': 239, 'write_file': 37, 'get_status': 19})
[(('end_line', 'filepath', 'start_line'), 1221), (('filepath',), 663), (('filepath', 'start_line'), 220), (('filepath', 'start_line"'), 13), (('end_line', 'filepath', 'start_line"'), 8)]
span quantiles [16.0, 21.0, 25.0, 31.0, 41.0, 50.0, 59.4, 95.0, 101.0] n 1221

00:06:41
"""thinklib.py (author claude): closed-form "end the thought" edits for Gemma 4 31B.

Three families, all built from class means of activations at logged thought positions (no optimiser anywhere):
  A  always-on (Concept Country form): rank-one down_proj term  c (x) v^T  in layers 30..48 even; v = unit mean
     neuron-activation vector over thought positions, c writes the stream direction d_end = mean(resid at the last
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  B  gated: one neuron per layer 30..48 even is replaced ("set") by a detector of "deep inside a long thought"
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  C  gated, output side: the same kind of detector neuron in layers 54, 56, 58 writes the direction that raises the
     <channel|> logit: (E[101] - mean E) * final_norm_weight.
Controls: same code path, the written unit direction has the sign of every coordinate flipped at random (seeded):
identical per-coordinate magnitudes, so identical norm in the stream, in the raw MLP output and in |dW|_F.
"""
import math
import sys

import torch

sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools")
[... 142 lines cut ...]
                js, cols, f = (s["actrl"], s["actrl_cols"], -dose) if ctrl else (s["anti"], s["anti_cols"], -dose)
            for k, j in enumerate(js.tolist()):
                e.col_add(L, "down_proj", j, f * cols[:, k].float())
            if len(js):
                info[L] = dict(n=len(js), neurons=js.tolist(), rho=0.0)
    else:
        raise ValueError(fam)
    return info

T0 = time.time()
A_DIR = "/home/keith/gemma-v2/wsp31/authors/claude"
sys.path.insert(0, "/home/keith/gemma-v2/wsp31/tools")
sys.path.insert(0, A_DIR + "/code")
import thinklib as tl  # noqa: E402
from edits import EditSet  # noqa: E402

PROBE = "/home/keith/gemma-v2/wsp31/tools/data/probe_screen.jsonl"
MOCK = "--mock" in sys.argv
S = A_DIR + ("/scratch/mock" if MOCK else "/scratch/job1")
STATS = A_DIR + ("/scratch/mock/think_stats.pt" if MOCK else "/code/data/think_stats.pt")
# evaluation order = priority (arms late in the list are the first to be dropped at the deadline)
GRID = [("B", 0.3), ("C", 8.0), ("A", 0.1), ("N", 3.0), ("B", 1.0), ("C", 20.0), ("A", 0.3), ("N", 1.0), ("M", 0.75),
        ("B", 0.1), ("C", 3.0)]
CTRL = [("B", 0.3), ("C", 8.0), ("A", 0.1), ("N", 3.0)]
GRID_TAIL = [("A", 0.03), ("N", 8.0)]
NTRAIN_CAP, NEVAL_CAP = 11000, 8000
DEADLINE_CAPTURE, DEADLINE = 600, 1400
TURN_END = 1794533816 + 90 * 60          # this turn is stopped then; leave 13 minutes after the job for the queue entries
if not ("--mock" in sys.argv or "--dry" in sys.argv):
[... 235 lines cut ...]
        log("d_end", L, "norm %.2f" % float(d.norm()), "resid norm late %.2f" % float(st["rnorm_sum"][3, i] / st["n"][3]))
    names = build_grid(st, S + "/ad")
    log("grid built")
    evaluate(m, st, names, S + "/ad", ev[:8])
    log("done")


if __name__ == "__main__":

00:07:47
[    2.3] model loaded
[   14.2] captured 0 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]
[   35.9] captured 1 HIPS__autograd.ac044f0d.func_p n 11000 pos 345 cls [5, 80, 60, 120, 80]
[   46.7] captured 2 Peter-van-Tol__pydantic-shapel n 11000 pos 346 cls [6, 80, 60, 120, 80]
[  214.4] captured 18 HIPS__autograd.ac044f0d.lm_rew n 11000 pos 226 cls [6, 80, 60, 0, 80]
[  224.6] captured 19 Mimino666__langdetect.a1598f1a n 11000 pos 207 cls [3, 80, 44, 0, 80]
[  235.0] captured 20 PyCQA__flake8.cf1542ce.lm_rewr n 11000 pos 232 cls [12, 80, 60, 0, 80]
[  245.2] captured 21 PyCQA__flake8.cf1542ce.lm_rewr n 11000 pos 234 cls [14, 80, 60, 0, 80]
[  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
[  874.6] evaluated 4 HIPS__autograd.ac044f0d.lm_rew rows 991 arms 18
{'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)}
[{'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}
166 58 in s1 58 len med 14898 max 16000
usage: vgen.py [-h] [--port PORT] [--at {after,close,cont,forced,turn}]
               [--n N] [--per-record PER_RECORD] [--records RECORDS]
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               [--streams STREAMS] [--show SHOW] [--show-chars SHOW_CHARS]
               [--probeset PROBESET] [--champion CHAMPION] [--budget BUDGET]
               [--out OUT] [--selftest]
[... 29 lines cut ...]
307:                caps[name].append(x[cap_pos_t].float().cpu())
344:            if stop_after is not None and l >= stop_after:
357:    def logprobs_from_final(self, hp):
 893374       41:34 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh
 957831       29:47 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh
1026421       16:57 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_
1054488       10:15 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
1067894       05:59 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/

00:09:56
[    0.4] deadline 1120 capture deadline 420 train 43 eval 15
[    0.4] model loaded
[    0.8] captured 0 [task] n 11000 rows 298 groups [6, 252, 56, 196, 70, 120]
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[    1.4] captured 2 cool-RR__PySnooper.57472b46.lm n 11000 rows 378 groups [6, 332, 175, 9, 116, 138]
[    1.8] captured 3 benoitc__gunicorn.bacbf8aa.fun n 11000 rows 383 groups [10, 333, 94, 15, 103, 19]
[    1.8] directions: episodes for d_commit 3 split-half cos (every 6th layer) ['1.00'] cos(end, com) ['0.00']
[    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'}
[    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'}
[    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'}
[    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'}
[    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'}
[    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'}
[    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'}
[    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'}
[    3.9] stats saved; rows 1304 episodes 4
[    3.9] raw saved
[    3.9] built D_E05 r 1 rho min/med/max 0.047 0.049 0.051
[    3.9] built P_C05 r 1 rho min/med/max 0.036 0.039 0.040
[... 7 lines cut ...]
real	0m18.023s
user	0m48.191s
sys	0m24.771s
 893374       44:00 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh
 957831       32:13 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh
1026421       19:23 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_
1054488       12:41 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
1067894       08:25 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/

#!/usr/bin/env python
[line removed by the dashboard scrubber]
[--champion current|DIR] [--probeset data/probe_calls.jsonl] [--out FILE.json] | vgen.py --selftest: what does the
served model WRITE from a decision point, unedited (or champion) next to each edit? Run inside a gpu0 session (a
[line removed by the dashboard scrubber]
agent then produced (a per-call probe set from `reqset.py --per-call`), cut at a labelled point:
  turn    the prompt of the call: the model writes its whole answer (thought, then tool call);
  cont    inside the logged thought, at a place where the agent went on thinking;
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  forced  a cont position with <channel|> appended: the thought is cut there by force, what follows a cut thought;
  after   just after the real <channel|>.
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the tool prints: the outcome classes of dose.py (well-formed tool call, malformed, thinking never closed, empty,
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the share that closes at once; cont / close / turn only), the share choosing the tool the agent chose in the log,
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loaded under a content-hash name (as champion + edit when --champion is given) and unloaded afterwards. These are
single greedy continuations of contexts from unedited trajectories: they show what an edit does to the next turn,
not whether a task gets solved. --selftest runs the bookkeeping on logged answers (CPU, no server)."""
[... 98 lines cut ...]
if [ -n "$MODEL" ]; then
  setsid $T/serve_dir.sh $MODEL 0 $PORT $UTIL $LORAS > $LOG 2>&1 &
else
  setsid /home/keith/gemma-v2/serve_31b.sh 0 $PORT $UTIL $LORAS > $LOG 2>&1 &
fi
SPID=$!
echo $SPID > $W/logs/server_${PORT}.pid
stop() {

00:10:51
{'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)}
[{'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}]
{'start': 3319, 'comp': 166, 'lp': 166}
1325 Counter({'cont': 3946, 'turn': 1325, 'close': 1325, 'think': 1325, 'tool': 1322}) deep cont 70
71:def select(recs, per_trace=4, kinds=("turn", "step", "think", "tool"), max_pos=None, traces=None):
72-    """Deterministic spread of decision points: up to per_trace points of each kind per trace, evenly spaced.
73-    Returns [(trace_index, point_dict)], ordered by trace then position (so prefixes share the server cache)."""
74-    out = []
75-    for ti, r in enumerate(recs if traces is None else recs[:traces]):
76-        chosen = []
77-        for k in kinds:
78-            c = [p for p in r["points"] if p["kind"] == k and (max_pos is None or p["pos"] < max_pos)]
79-            if len(c) > per_trace:
80-                c = [c[round(i * (len(c) - 1) / (per_trace - 1))] for i in range(per_trace)] if per_trace > 1 else c[:1]
81-            chosen += c
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83-    return out
84-
85-
[... 5 lines cut ...]
91-    ap.add_argument("--limit", type=int, default=0)
92-    a = ap.parse_args()
93-    assert "golden" not in a.glob, "golden sets are sealed"
 893374       44:38 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh
 957831       32:51 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh
1026421       20:01 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_
1054488       13:19 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
1067894       09:03 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/

"""finalize.py SLUG SPEC LOW HIGH "TITLE" [--arms EDITARM,CTRLARM] [--extra FILE]
Builds queue/SLUG/{edit,ctrl} with build_think.py, runs the checker, writes build.sh and record.md (no READY).
SPEC like "B:0.3" or "B:0.3,C:8". LOW/HIGH: predicted_gain_120 interval. --extra FILE: text appended to the
prediction section (the behaviour predictions written by the author for this entry)."""
import os
import subprocess
import sys

A = "/home/keith/gemma-v2/wsp31/authors/claude"
PY = "/home/keith/gemma-v2/tii-venv/bin/python"
T = "/home/keith/gemma-v2/wsp31/tools"
slug, spec, low, high, title = sys.argv[1:6]
arms = sys.argv[sys.argv.index("--arms") + 1].split(",") if "--arms" in sys.argv else []
extra = open(sys.argv[sys.argv.index("--extra") + 1]).read() if "--extra" in sys.argv else ""
gv = sys.argv[sys.argv.index("--gate") + 1] if "--gate" in sys.argv else "g1"      # g1 (stage 1), g2 or g3 (think_refit.py)
evf = A + ("/scratch/job1/eval1.pt" if gv == "g1" and "--eval2" not in sys.argv else "/scratch/job2/eval.pt")
sopt = "" if gv == "g1" else f" --stats code/data/think_stats_{gv}.pt"
q = f"{A}/queue/{slug}"
os.makedirs(q, exist_ok=True)
build = f"""#!/bin/bash
[... 141 lines cut ...]
ctrl
edit
record.md

queue/deep-thought-gate-g3-b03/edit:
adapter_config.json
adapter_model.safetensors
edit.json

episodes 2 ['[task]', 'Nikoleta-v3__blackbook'] arms ['base', 'D_E05', 'P_C05', 'P_EC05', 'D_E05c']
rows by class {0: 16, 1: 1070, 2: 1181, 3: 675, 4: 300, 5: 16, 6: 16} | thought rows pre-edit 2356 post-edit 570
== held-out gates (pre-activation g > 0 = fires), layers [0, 59]
 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
 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
 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
 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
[line removed by the dashboard scrubber]
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
base     | 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
D_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
P_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
P_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
D_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
00:14:02
 893374       47:48 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job5.sh
 957831       36:01 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh
1026421       23:12 /home/keith/gemma-v2/tii-venv/bin/python dose.py --contrasts close_thought,tool_
1054488       16:29 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
1094379       02:41 bash -c cd /home/keith/gemma-v2/wsp31/tools; for i in $(seq 1 19); do [ -s logs/

00:18:29
##### A MAXRANK=128 MAXLORAS=6 util=0.90 budget=640 (00:15:03, 1680 s left in hold)
server up after 75s (pid/pgid 1104910, log /home/keith/gemma-v2/wsp31/tools/logs/server_18200_1113_001503.log)
[line removed by the dashboard scrubber]
=== speedbench A_r128_l6_u0.90 (00:16:18, 550 s left)
#!/bin/bash
# sessH.sh: one GPU0 hold for speed by adapter shape (speedbench.py), three server configurations one after another
# (each through gpu0_session.sh, so each server is stopped before the next starts), all at memory share 0.90 like the
# runner's batch server:
#   A  rank limit 128, 6 slots: unedited, rank 32 on 420 matrices, rank 96 on 420; mixed batches (4 + 4 streams)
#      unedited + rank 32 and unedited + rank 96, with the speed of every stream
#   B  rank limit 64, 6 slots: unedited, rank 32 on 420; mixed unedited + rank 32
#   C  rank limit 32, 8 slots: the same
# (The rank and coverage sweep at limit 32 and the zero-padding question were answered by the throughput lane.)
# Queue: nohup flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessH.sh > logs/sessH_$(date +%H%M).log 2>&1 &
# Env: HOLD seconds (1680, never above 1740), CONFIGS ("tag:maxrank:maxloras:util:budget"), OUT, REPS.
T=/home/keith/gemma-v2/wsp31/tools
cd $T
START=$(date +%s); HOLD=${HOLD:-1680}; END=$(( START + HOLD ))
echo "hold start $(date)"; echo $START > logs/sessH_hold_start
[... 5 lines cut ...]
  [ $l -lt 200 ] && { echo "##### skip $tag: $l s left"; continue; }
  [ $bud -gt $(( l - 12 )) ] && bud=$(( l - 12 ))
  echo "##### $tag MAXRANK=$mr MAXLORAS=$ml util=$ut budget=$bud ($(date +%H:%M:%S), $l s left in hold)"
  TAG=$tag UTIL=$ut HOLD_END=$END MAXRANK=$mr MAXLORAS=$ml EXTRA="" BUDGET=$bud ./gpu0_session.sh 18200 $ut -- ./sessH_body.sh
  echo "##### $tag rc=$?"
  sleep 3
done
echo "hold end $(date), used $(( $(date +%s) - START )) s"; date +%s > logs/sessH_hold_end

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("edits", nargs="*")
    ap.add_argument("--port", type=int)
    ap.add_argument("--at", choices=sorted(KIND), default="turn")
    ap.add_argument("--n", type=int, default=16)
    ap.add_argument("--per-record", type=int, default=1)
    ap.add_argument("--records", type=int, default=200)
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    ap.add_argument("--max-pos", type=int, default=6000)
    ap.add_argument("--streams", type=int, default=4)
    ap.add_argument("--show", type=int, default=2)
    ap.add_argument("--show-chars", type=int, default=500)
    ap.add_argument("--probeset", default=os.environ.get("WSP31_CALLSET", os.path.join(T, "data/probe_calls.jsonl")))
    ap.add_argument("--champion", default=None)
    ap.add_argument("--budget", type=float, default=float(os.environ.get("LEFT", 1500)) - 45)
    ap.add_argument("--out", default=None)
    ap.add_argument("--selftest", action="store_true")
    a = ap.parse_args()
    import wlib
[... 47 lines cut ...]
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                  f"{tok.decode(x['ids'])[:a.show_chars]!r}")
    if a.out:
        json.dump(res, open(a.out, "w"), indent=1)


if __name__ == "__main__":
    main()

[    3.9] built P_C05 r 1 rho min/med/max 0.036 0.040 0.041
[    3.9] built P_EC05 r 1 rho min/med/max 0.056 0.065 0.068
[    3.9] built P_EC05c r 1 rho min/med/max 0.056 0.065 0.068
[    3.9] grid built ['D_E05', 'P_C05', 'P_EC05', 'P_EC05c']
[    3.9] raw saved
[   14.5] evaluated 0 [task] rows 1478 arms 9
[   27.5] evaluated 1 [task] rows 1796 arms 9
[   27.5] done
base     | 0.000 0.000 0.000 0.000 |  +0.00 | 1.00 1.00 n 14
D_E05    | 0.000 0.000 0.000 0.000 |  -0.08 | 1.00 1.00 n 14
P_C05    | 0.000 0.000 0.000 0.000 |  +0.05 | 1.00 1.00 n 14
P_EC05   | 0.000 0.000 0.000 0.000 |  -0.07 | 1.00 1.00 n 14
P_EC05c  | 0.000 0.000 0.000 0.000 |  -0.00 | 1.00 1.00 n 14
Q0008    | 0.000 0.000 0.000 0.000 |  -0.17 | 1.00 1.00 n 14
K05      | 0.000 0.000 0.000 0.000 |  -0.03 | 1.00 1.00 n 14
K12      | 0.000 0.000 0.000 0.000 |  -0.12 | 1.00 1.00 n 14
K05c     | 0.000 0.000 0.000 0.000 |  -0.01 | 1.00 1.00 n 14
00:21:40
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=== speedbench A_r128_l6_u0.90 (00:16:18, 550 s left)

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OK edit: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj
OK ctrl: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj
- `edit/adapter_model.safetensors` sha256 `2a24f449360a2d677579615675d7eb19f1869d415589f7bda23334ffb7b71193`: identical to the adapter `K05` evaluated in the hold
- `ctrl/adapter_model.safetensors` sha256 `b70d51d50308fd14440cae72cb4f1edc4db39b77ae290b87b0824371f0a7b59e`: identical to the adapter `K05c` evaluated in the hold
size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%
control size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%
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keith  4096 Nov 13 00:24 .
keith  4096 Nov 13 00:24 ..
keith   481 Nov 13 00:24 build.sh
keith  4096 Nov 13 00:24 ctrl
keith  4096 Nov 13 00:24 edit
keith  6805 Nov 13 00:24 record.md
keith 11924 Nov 13 00:24 size.json

control size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%

[... 6 lines cut ...]
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  = t`.
- `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
  minus mean at the other thought positions, `rms_y_L` = rms of the raw MLP output at depth >= 400, `clipdiv(d, w) =
  d / w`, zero where `|w_post|` is under its 5% quantile. At response 1 the write moves `resid_post[L]` by
  `0.05 * d_end_L`.
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00:24:50

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78:             ("@EPISODES@", ", ".join(st["instances"])), ("@NEP@", str(len(st["instances"])))):
44:check = "\n".join(l for l in (out.stdout + out.stderr).splitlines() if l.startswith(("OK", "FAIL", "saved")) or "rror
62:rep = open(A + "/scratch/job3/report.txt").read().splitlines() if os.path.exists(A + "/scratch/job3/report.txt") else
80:assert txt.startswith("title: ") and "|" not in txt.splitlines()[0]
control size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%
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2
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  `0.05 * d_end_L`.
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```
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 v
L38 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 
00:25:30

120 218 ['[task]', '[task]', '[task]'] ['[task]', '[task]', '[task]']
['HIPS__autograd.ac044f0d.func_pm_op_break_chains__mhdjtwmz', 'HIPS__autograd.ac044f0d.func_pm_ctrl_shuffle__rsn9e29k', '[task]', '[task]', '[task]', '[task]']
HIPS__autograd.ac044f0d.func_pm_op_break_chains__mhdjtwmz -> ['HIPS__autograd.ac044f0d.lm_rewrite__cetfos0u']
HIPS__autograd.ac044f0d.func_pm_ctrl_shuffle__rsn9e29k -> ['HIPS__autograd.ac044f0d.lm_rewrite__cetfos0u']
[task] -> []
[task] -> []
stage1_ids.txt
stage2_exposed_ids.txt
stage2_ids.txt
stage2_ids_240.txt
"""Stage 2 was cut to 218 tasks on 13 Nov 2026 (stage2_exposed_ids.txt removed; the list this script writes is
kept as stage2_ids_240.txt). This script reproduces the original split.

Split wsp31_screen_v1 (360 tasks) into the stage-1 list (120) and the stage-2 list (240), spreading every repo
over both: repos in seeded random order, tasks in seeded random order inside a repo, every third task to stage 1.

    python3 build_stage_ids.py            # writes wsp31/queue/sets/stage{1,2}_ids.txt, refuses to overwrite
"""
import json, random, sys
from collections import defaultdict
[... 27 lines cut ...]
paused_pool_v2_rate16_sft_f2
paused_pool_v2_screen_w5r_p13
pool_v1
pool_v1_rescreen
pool_v2
pool_v21_bubba_b1
pool_v21_bubba_b2
pool_v21_pilot

201:    cls, dep, ed = meta[:, 1], meta[:, 2], meta[:, 5].bool()
235:    cls, dep, ed = meta[:, 1], meta[:, 2], meta[:, 5].bool()
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[    3.8] stats saved; rows 1304 episodes 4
[    3.8] built D_E05 r 1 rho min/med/max 0.048 0.051 0.051
[    3.8] built P_C05 r 1 rho min/med/max 0.038 0.039 0.042
[    3.8] built P_EC05 r 1 rho min/med/max 0.062 0.065 0.065
[    3.8] built K_E03 r 1 rho min/med/max 0.029 0.031 0.031
[    3.8] raw saved
[    3.8] built D_E05c r 1 rho min/med/max 0.048 0.051 0.051
[    3.8] built K_E03c r 1 rho min/med/max 0.029 0.031 0.031
[    3.8] grid built ['D_E05', 'P_C05', 'P_EC05', 'K_E03', 'D_E05c', 'K_E03c']
[   13.2] evaluated 0 [task] rows 1478 arms 8
[   24.5] evaluated 1 [task] rows 1796 arms 8
[   24.6] done
00:28:02

3:## Hypothesis
22:## Exact recipe
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78:Checker and rebuild:
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98:### Pre-test 2: served generation (`tools/vgen.py --at cont`, vLLM, one hold)
103:## Prediction (written before the run)
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11
00:28:45
##### C MAXRANK=32 MAXLORAS=8 util=0.90 budget=500 (00:28:13, 890 s left in hold)

<class 'dict'> ['dir', 'null', 'meta', 'resid.0', 'resid.1', 'resid.2', 'resid.3', 'resid.4', 'resid.5', 'resid.6', 'resid.7', 'resid.8']
dir (60, 5376)
null (60, 5376)
meta ('dict', 6)
resid.0 (5376,)
resid.1 (5376,)
resid.2 (5376,)
resid.3 (5376,)
resid.4 (5376,)
resid.5 (5376,)
resid.6 (5376,)
resid.7 (5376,)
resid.8 (5376,)
resid.9 (5376,)
resid.10 (5376,)
resid.11 (5376,)
resid.12 (5376,)
resid.13 (5376,)
resid.14 (5376,)
resid.15 (5376,)
[... 77 lines cut ...]
def build(D):
    e = EditSet()
    e.write_dir(layers, D)
    if not a.writers_only:
        e.read_dir(layers, D)
    return e

00:30:54

00:33:19
Fri Nov 13 12:33:17 AM EST 2026
code/gpu_job3.sh: line 4: /home/keith/gemma-v2/env.sh
: No such file or directory
code/gpu_job3.sh: line 5: cd: $'/home/keith/gemma-v2/wsp31/authors/claude\r': No such file or directory
code/gpu_job3.sh: line 8: 1
: ambiguous redirect
code/gpu_job3.sh: line 10: 1794547997
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")
code/gpu_job3.sh: line 11: 1794547997
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")
code/gpu_job3.sh: line 13: 1794547997
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")
code/gpu_job3.sh: line 18: syntax error: unexpected end of file from `if' command on line 14
1155059       03:01 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh
##### C rc=0
hold end Fri Nov 13 12:33:17 AM EST 2026, used 1094 s

58:    DEADLINE = min(DEADLINE, TURN_END - 14 * 60 - T0)
gpu_job3.sh: Bourne-Again shell script, ASCII text executable, with very long lines (381)
net_job.py:  Python script, ASCII text executable
syntax_ok
0
00:34:30
1155058       04:12 /bin/bash ./run_gpu.sh logs/job6.log ./job6.sh
1155059       04:12 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh
1161482       01:12 timeout 1740 ./job6.sh
1161483       01:12 /bin/sh ./job6.sh
1161878       01:00 bash -c sleep 893; exec flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessI.sh
1163926       00:01 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
[This command modified 1 file you've previously read: net_job.py. Call Read before editing.]

ARMS = [("D_E05", [("deep", dict(end=0.05))], False),
        ("P_C05", [("pre", dict(com=0.05))], False),
        ("P_EC05", [("pre", dict(end=0.05, com=0.05))], False),
        ("D_E12", [("deep", dict(end=0.12))], False),
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        ("P_C12", [("pre", dict(com=0.12))], False),
        ("D_E05c", [("deep", dict(end=0.05))], True),
        ("P_EC05c", [("pre", dict(end=0.05, com=0.05))], True),
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        ]
if not MOCK:
    DEADLINE = min(DEADLINE, TURN_END - 17 * 60 - T0)
    DEADLINE_CAPTURE = min(DEADLINE_CAPTURE, 0.4 * DEADLINE)


def log(*a):
    print(f"[{time.time() - T0:7.1f}]", *a, flush=True)


[... 5 lines cut ...]
saved scratch/fb/K05t r 1 modules 36 max rel_fro 0.0654
saved scratch/fb/K05tc r 1 modules 36 max rel_fro 0.0654
[    4.1] raw saved
[   14.7] evaluated 0 [task] rows 1478 arms 9
[   27.5] evaluated 1 [task] rows 1796 arms 9
[   27.5] done
installed
00:35:52

OK edit: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj
OK ctrl: 36 modules changed (of 36 present), r=1, max |dW|_F/|W|_F 0.06538 at 53.down_proj
- `edit/adapter_model.safetensors` sha256 `2a24f449360a2d677579615675d7eb19f1869d415589f7bda23334ffb7b71193`: identical to the adapter `K05t` evaluated in the hold
- `ctrl/adapter_model.safetensors` sha256 `b70d51d50308fd14440cae72cb4f1edc4db39b77ae290b87b0824371f0a7b59e`: identical to the adapter `K05tc` evaluated in the hold
size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%
control size: whole network 0.6316% over 36 matrices, largest matrix 6.5384%
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- `ctrl/adapter_model.safetensors` sha256 `825e8bbe156b1fd219afbedeae351f13185b520c6e7762a60912c99f33a7945c`: no evaluated adapter of this name (built after the hold from the same statistics)
size: whole network 1.5158% over 36 matrices, largest matrix 15.6941%
control size: whole network 1.5158% over 36 matrices, largest matrix 15.6941%
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OK ctrl: 36 modules changed (of 36 present), r=17, max |dW|_F/|W|_F 0.2007 at 59.down_proj
- `edit/adapter_model.safetensors` sha256 `4d55c53d2754b12b1123b2672f7a251ce46921492b40b7b36e7b763b5e933e9a`: no evaluated adapter of this name (built after the hold from the same statistics)
- `ctrl/adapter_model.safetensors` sha256 `46b8c7b046afe679a75a4233b9871472d27752838b535e2a29051f771e2c1cc3`: no evaluated adapter of this name (built after the hold from the same statistics)
size: whole network 1.0134% over 36 matrices, largest matrix 20.0706%
control size: whole network 1.0133% over 36 matrices, largest matrix 20.0691%
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00:37:04

1181203 1181202       00:00              \_ ps -eo pid,ppid,etime,cmd --forest
1181206 1181202       00:00              \_ cut -c1-200
1181207 1181202       00:00              \_ head -14
   3086       1    06:41:01 /usr/libexec/colord
   3183       1    06:41:01 /usr/libexec/rtkit-daemon
   3736       1    06:40:55 /usr/sbin/cups-browsed
--
1155058 1106734       08:00  \_ /bin/bash ./run_gpu.sh logs/job6.log ./job6.sh
1155059 1155058       08:00      \_ flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1740 ./job6.sh
1161482 1155059       05:00          \_ timeout 1740 ./job6.sh
1161483 1161482       05:00              \_ /bin/sh ./job6.sh
1161491 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
1160079       1       05:16 /usr/bin/containerd-shim-runc-v2 -namespace moby -id f0d86e2234873f7eb49fe57c4f600ffb82ada31f293795f8dab0ecb3bf725465 -address /run/containerd/containerd.sock
1160119 1160079       05:16  \_ sleep infinity
0, 35768 MiB, 100 %
1, 90512 MiB, 100 %

Traceback (most recent call last):
  File "<stdin>", line 6, in <module>
  File "<stdin>", line 4, in rep
AssertionError: open(q + "/build.sh", "w").write(build)
os.chmod(q + "/build
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rec_de05.txt:136
rec_ke03.txt:121
31:@SIZE@
33:@CTRLSIZE@
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81:@CHECK@
83:@IDENT@
95:@ROWS@
100:@VGEN@
00:38:50

job6.sh
#!/bin/sh
# holds 6+: dose check, version 2 (PROTOCOL amendment 3g): perp control, low doses, per-point values, KL skip rule.
# Resumable: dose2.py skips configurations already in out/dose.json.
cd /home/keith/gemma-v2/wsp31/interp/dirs
PY=/home/keith/gemma-v2/tii-venv/bin/python
T0=$(date +%s)
left() { echo $((1560 - $(date +%s) + T0)); }
while read C S A V; do
  L=$(left)
  if [ "$L" -le 300 ]; then echo "time left $L: stop"; break; fi
  TAG=$(echo "${C}_${S}" | tr "," "_")
  if grep -q "^done" ../logs/dose2_$TAG.out 2>/dev/null; then echo "dose2 $TAG already done"; continue; fi
      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
00:39:25

    extra = [("K05t", A_DIR + "/scratch/fb/K05t", 6)]
    extra = [x for x in extra if os.path.exists(x[1])]
    evaluate(m, st, names, S + "/ad", ev[:4], extra)
    log("done")


if __name__ == "__main__":
    main()

[   13.4] evaluated 0 [task] rows 1478 arms 8
[   24.8] evaluated 1 [task] rows 1796 arms 8
[   24.8] done
0
00:40:07

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
split 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
split 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
split 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
split 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
split 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
split 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
split 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
split 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
split 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
split 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
split 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
00:41:00

334:            if G["fit"]["auc_z"] < (0.0 if MOCK else 0.75):
[    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': '
[    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
[    4.3] stats saved; rows 1304 episodes 4
[    4.3] built D_E05 r 1 rho min/med/max 0.044 0.049 0.050
[    4.3] built P_EC05 r 1 rho min/med/max 0.056 0.062 0.063
[    4.3] built D_E12 r 1 rho min/med/max 0.106 0.117 0.119
[    4.3] built K_E03 r 1 rho min/med/max 0.026 0.029 0.030
[    4.3] raw saved
[    4.3] built D_E05c r 1 rho min/med/max 0.044 0.049 0.050
[    4.3] built K_E03c r 1 rho min/med/max 0.026 0.029 0.030
[    4.3] grid built ['D_E05', 'P_EC05', 'D_E12', 'K_E03', 'D_E05c', 'K_E03c']
[   13.9] evaluated 0 [task] rows 1478 arms 8
[   25.4] evaluated 1 [task] rows 1796 arms 8
[   25.4] done
base     | 0.000 0.000 0.000 0.000 |  +0.00 | 1.00 1.00 n 14/1 | 0 of 
D_E05    | 0.000 0.000 0.000 0.000 |  +0.28 | 1.00 1.00 n 14/1 | 0 of 
P_EC05   | 0.000 0.000 0.000 0.000 |  +0.15 | 1.00 1.00 n 14/1 | 0 of 
D_E12    | 0.000 0.000 0.000 0.000 |  +0.29 | 1.00 1.00 n 14/1 | 0 of 
K_E03    | 0.000 0.000 0.000 0.000 |  +0.19 | 1.00 1.00 n 14/1 | 0 of 
D_E05c   | 0.000 0.000 0.000 0.000 |  +0.17 | 1.00 1.00 n 14/1 | 0 of 
K_E03c   | 0.000 0.000 0.000 0.000 |  +0.19 | 1.00 1.00 n 14/1 | 0 of 
K05t     | 0.000 0.000 0.000 0.000 |  +0.21 | 1.00 1.00 n 14/1 | 0 of 
00:43:45

                com_split_cos=cosf(half[0], half[1]).tolist(), cos_end_com=cosf(d_end, d_com).tolist())
    return dict(end=d_end.float(), com=d_com.float()), info


def finish_stats(m, st, raw):
    st["w_post"] = torch.stack([m.L[L]["post_feedforward_layernorm"].float().cpu() for L in LAYERS])
    st["ls"] = torch.tensor([float(m.L[L]["layer_scalar"].float()) for L in LAYERS])
    st["dirs"], st["dir_info"] = directions(st)
    di = st["dir_info"]
    log("directions: episodes for d_commit", di["n_com"], "split-half cos (every 6th layer)",
        ["%.2f" % x for x in di["com_split_cos"][::6]], "cos(end, com)", ["%.2f" % x for x in di["cos_end_com"][::6]])
    meta = raw["meta"]
    lab = labels(meta)
    cls, dep, ed = meta[:, 1], meta[:, 2], meta[:, 5].bool()
    think = (cls >= 1) & (cls <= 3)
    st["gate"] = {k: {} for k in lab}
[line removed by the dashboard scrubber]
    t = torch.tensor([0, 0, 0.25, 1.0, 0]).double()
[line removed by the dashboard scrubber]
        Am = st["act_sum"][:, li] / st["act_n"][:, None]
[... 29 lines cut ...]
            if L % 4 == 0 or L == 59:
[line removed by the dashboard scrubber]
                    "gbar %.2f" % gbar, {k: "%.2f" % v for k, v in fr.items()})
        del Wg, Wu, Wd, X
    return st


def build_arm(st, parts, ctrl):

01:02:33
32
[    0.4] deadline 899.5705749988556 capture deadline 330 train 43 eval 15
[    3.1] model loaded
[  335.2] capture stops at episode 32 rows 8877
[  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']
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[  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'}
[
[... 513 characters cut ...]

01:03:51
[  361.6] raw saved
[  437.2] evaluated 0 [task] rows 1478 arms 11
episodes 1 ['[task]'] arms ['base', 'D_E05', 'P_EC05', 'D_E12', 'K_E03', 'K_E08', 'P_C12', 'K05t', 'D_E05c', 'P_EC05c', 'K_E03c']
rows by class {0: 8, 1: 467, 2: 700, 3: 137, 4: 150, 5: 8, 6: 8} | thought rows pre-edit 847 post-edit 457
== held-out gates (pre-activation g > 0 = fires), layers [0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 59]
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
[... 13 lines cut ...]
D_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
K_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
K_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
P_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
K05t     | 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
D_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
P_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
K_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

01:06:25
[  514.7] evaluated 1 [task] rows 1796 arms 11
[  592.4] evaluated 2 [task] rows 1500 arms 11
episodes 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']
rows by class {0: 19, 1: 1301, 2: 1497, 3: 1469, 4: 450, 5: 19, 6: 19} | thought rows pre-edit 3697 post-edit 570
== held-out gates (pre-activation g > 0 = fires), layers [0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 59]
[line removed by the dashboard scrubber]
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
base     | 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
D_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
P_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
D_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
K_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
K_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
P_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
K05t     | 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
D_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
P_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
K_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
 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
 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
 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
 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
L0 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
L6 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
L12 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
L24 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
L36 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.
[... 518 characters cut ...]

01:07:20
[  437.2] evaluated 0 [task] rows 1478 arms 11
[  514.7] evaluated 1 [task] rows 1796 arms 11
[  592.4] evaluated 2 [task] rows 1500 arms 11
deep-gate-b01-end-neurons-x9
deep-thought-gate-g3-b03
early-gate-b01-end-neurons-x9
end-neurons-x25
net-deep-gate-close-b05
net-deep-gate-close-b12
net-preedit-commit-b12
net-preedit-commit-close-b05
== net-deep-gate-close-b05
OK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.2218 at 6.down_proj
- `edit/adapter_model.safetensors` sha256 `b9b76229861ff7ab53e5211b216d840753ce2f2fd145da34737d7035eba0784d`: identical to the adapter `D_E05` evaluated in the hold
- `ctrl/adapter_model.safetensors` sha256 `3b3d9e5c36cba18b4ba6252e0932f0c825cdb1a93f69bd0cdb02750aa479b8b9`: identical to the adapter `D_E05c` evaluated in the hold
size: whole network 4.4116% over 180 matrices, largest matrix 22.1827%
control size: whole network 4.4117% over 180 matrices, largest matrix 22.1814%
wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/net-deep-gate-close-b05/record.md 2325 words
== net-deep-gate-close-b12
[... 15 lines cut ...]
- `edit/adapter_model.safetensors` sha256 `1938741b536e0e74d8bbda953ee113a56088d74dc25e4f3c554d4d4c68ebd64c`: identical to the adapter `P_C12` evaluated in the hold
- `ctrl/adapter_model.safetensors` sha256 `0ff75e1c4666fea7adad367f5faed26d0a7110fd68b0c39bf8dfafc03ea9f60c`: no evaluated adapter of this name (built after the hold from the same statistics)
size: whole network 4.4709% over 180 matrices, largest matrix 18.9846%
control size: whole network 4.4709% over 180 matrices, largest matrix 18.9846%
wrote /home/keith/gemma-v2/wsp31/authors/claude/queue/net-preedit-commit-b12/record.md 2323 words
      32:50 flock /home/keith/gemma-v2/wsp31/gpu0.lock timeout 1560 code/gpu_hold.sh
      19:04 flock /home/keith/gemma-v2/wsp31/gpu0.lock ./sessI.sh
      10:49 /home/keith/gemma-v2/tii-venv/bin/python code/net_job.py

01:07:43
[  670.1] evaluated 3 janehmueller__python-json-conf rows 1750 arms 11
[  670.1] done
python part done after 672 s; session budget 229
4
episodes 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']
rows by class {0: 22, 1: 1592, 2: 2157, 3: 2109, 4: 600, 5: 22, 6: 22} | thought rows pre-edit 5288 post-edit 570
== held-out gates (pre-activation g > 0 = fires), layers [0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 59]
[line removed by the dashboard scrubber]
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
base     | 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
D_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
P_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
D_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
K_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
K_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
P_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
K05t     | 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
D_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
P_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
[... 7 lines cut ...]
 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
 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
queue/net-deep-gate-close-b05/record.md:135:@VGEN@
queue/net-deep-gate-close-b12/record.md:135:@VGEN@
queue/net-preedit-commit-b12/record.md:139:@VGEN@
queue/net-preedit-commit-close-b05/record.md:143:@VGEN@
Fri Nov 13 01:07:42 AM EST 2026
01:07:46

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']
net-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']
net-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']
net-preedit-commit-b12 ok 2613 words; ['size: whole network 4.4709% over 180 matrices, largest matrix 18.9846%', 'predicted_gain_120: -7 to 7']
OK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5323 at 6.down_proj
OK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5323 at 6.down_proj
build.sh check.txt ctrl edit record.md size.json 
OK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.2218 at 6.down_proj
OK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.2218 at 6.down_proj
build.sh check.txt ctrl edit record.md size.json 
OK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5287 at 6.down_proj
OK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.5287 at 6.down_proj
build.sh check.txt ctrl edit record.md size.json 
OK edit: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.1898 at 28.gate_proj
OK ctrl: 180 modules changed (of 180 present), r=1, max |dW|_F/|W|_F 0.1898 at 28.gate_proj
build.sh check.txt ctrl edit record.md size.json 
Built by `code/net_job.py` (one GPU-0 hold, forward pass `tools/g31.py` only, no optimiser anywhere) and rebuilt
bit for bit by `build.sh` (`code/net_build.py` from `code/data/net_stats_slim.pt`).

Data. `tools/data/probe_b001.jsonl`: unedited arms `base`, `aa`, `zero` of batch b001, all 58 instances are stage-1
tasks. Instances sorted by name; every fourth (index % 4 == 1, 15 tasks) is held out for the pre-test, the other 43
are fit tasks. One episode per task (the one with most deep, post-edit and first-edit thought positions), truncated
[line removed by the dashboard scrubber]
No task outcome enters any statistic.

73: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.
82:  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
19M	queue/net-deep-gate-close-b05 19M	queue/net-deep-gate-close-b12 19M	queue/net-preedit-commit-b12 19M	queue/net-preedit-commit-close-b05 7.1G	scratch 01:08:35
[This command modified 1 file you've previously read: addnotes.py. Call Read before editing.]

queue/net-deep-gate-close-b12/record.md:1
queue/net-deep-gate-close-b05/record.md:1
1
==> queue/net-deep-gate-close-b05/record.md <==
title: 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)

==> queue/net-deep-gate-close-b12/record.md <==
title: 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)

==> queue/net-preedit-commit-b12/record.md <==
title: 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)

==> queue/net-preedit-commit-close-b05/record.md <==
title: 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)
1
1
1
1
queue/deep-gate-b01-end-neurons-x9/READY
queue/deep-thought-gate-g3-b03/READY
queue/early-gate-b01-end-neurons-x9/READY
queue/end-neurons-x25/READY
queue/net-deep-gate-close-b05/READY
queue/net-deep-gate-close-b12/READY
queue/net-preedit-commit-b12/READY
queue/net-preedit-commit-close-b05/READY
01:08:48

640 NOTES.md
640 NOTES.md
6.9G
deep-gate-b01-end-neurons-x9
deep-thought-gate-g3-b03
early-gate-b01-end-neurons-x9
end-neurons-x25
net-deep-gate-close-b05
net-deep-gate-close-b12
net-preedit-commit-b12
net-preedit-commit-close-b05
READY
build.sh
check.txt
ctrl
edit
record.md
size.json
 Nov 12 23:52 gemini__late-preedit-commit-gate.json
 Nov 12 23:48 gemini__rumination-damp-r2-net.json
 Nov 12 22:35 [task].json
 Nov 12 22:37 [task].json
 Nov 12 22:39 [task].json
4
01:09:16
[This command modified 1 file you've previously read: NOTES.md. Call Read before editing.]