Turn 10 · gpt

 succeeded in 249ms:
# 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`). You get
two entries per round and then wait for their results, so each one counts; a third variation of a null result is a
wasted round. 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,
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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.

 succeeded in 261ms:
# Author gpt — turn 7, queued-s1-0035-and-0036

Updated 2026-11-14T05:18:54.043693+00:00. Started 03:54 UTC; hard turn deadline 05:24 UTC, 14 Nov 2026. Champion generation 0 at design. One round, at most two entries.

Previous results: 0011/0012 harmful; 0025/0026 code-style writer damping null (-1 and 0 tasks against mean reference). Do not repeat close amplification. Parked 0013/0027 remain parked. Prior notes: code/turn7/previous_notes.md.

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CPU audit: 208 b007 reference episodes from the 104 tasks shared by both stage-1 lists. Edit replies: 244 OK, 32 error, 57 missing; missing is not success. Only 9 immediate identical retries after 488 error replies, so retry suppression alone has a small ceiling. The main target is fewer incorrect edits and better use of tool evidence. The borgbackup example reconstructs source in thought then fails old_string matching.

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Submitted this turn: source-grounding-icv-b3, source-grounding-icv-b1.
GPU usage: zero seconds; never acquired the lock.

[... 68 lines cut ...]
| 0023 | claude | net-preedit-commit-close-b05 | 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 do | parked | -8 to +9 |  |  | - |
| 0028 new | gemini | nullspace-close-r4-midlate | Protocol-nullspace rank-4 exact thought-close writer subspace across L10-59 (0.48%) | done | +1 to +13 | -10.5 (62 vs 73) |  | harmful |
| 0031 new | claude | icv-brief-b20 | Instruction vector, think briefly: an always-on neuron per layer writes the stream shift of a 'keep your thinking short' paragraph (dose 2) | done | -6 to +6 | +9.0 (77 vs 72) | +10.5 (control +6.5) provisional | null |
| 0032 new | claude | icv-brief-b40 | Instruction vector, think briefly: an always-on neuron per layer writes the stream shift of a 'keep your thinking short' paragraph (dose 4) | running_s2 | -9 to +6 | +3.0 (71 vs 72) |  | - |
| 0038 new | claude | fcv-brief-b20 | Think-briefly instruction vector, self-consistent compile (dose 2): each layer writes only what the paragraph's mean shift still lacks | done | -2 to +8 | -2.0 (75 vs 78) |  | null |
| 0039 new | claude | fcv-brief-exact-b20 | Instruction pair vector: think briefly + be exact about what the issue asks, compiled self-consistently into one neuron per layer (dose 2) | done | -4 to +8 | +2.0 (79 vs 78) |  | null |
| 0043 | gemini | spec-audit-thought-gate | Thought-gated reliability-shrunk Problem Statement spec-audit steering across all 60 MLP layers | queued_s1 | +2 to +7 |  |  | - |
| 0044 | gemini | dual-audit-postedit-boost | Stateful two-neuron post-edit spec-audit boost with thought and edit-history gating over 180 MLP matrices | queued_s1 | +3 to +8.5 |  |  | - |

 succeeded in 243ms:
# wsp31 queue status (2026-11-14 09:00:17 EST)

Rule: {"s1_promote": 3.0, "s1_harmful": -6.0, "s2_min": 4.0, "ctrl_margin": 5.0, "total_pass": 10.0, "total_harmful": -10.0}

Champion (the base every new edit is scored against): Generation 0: the unedited model. No edit has passed yet.

Reference arms of a new batch: the unedited model served through an all-zero adapter, the same serving path as an edit (ledger record 0033).

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## Batches

| batch | stage | gpu | state | arms | rows | note |
|---|---|---|---|---|---|---|
| b001-supp1 | s1 | 1 | done | base aa zero | 93 | {"tasks": 31, "record": "0030", "solved": {"base": [60, 74], "aa": [60, 74], "zero": [65, 81]}} |
| b006 | s2 | 1 | done | base e0015 c0015 aa | 872 | {"base": 121, "episodes": 218, "aa": [121, 110, 24, 13], "arms": {"e0015": [119, 3.5], "c0015": [112, -3.5]}} |
| b007 | s1 | 0 | done | base e0025 e0026 aa | 480 | {"base": 65, "episodes": 120, "aa": [65, 73, 4, 12], "arms": {"e0025": [68, -1.0], "e0026": [69, 0.0]}} |
| x001 | s1 | box | done | base e0031 e0032 aa |  | {"base": 72, "episodes": 120, "aa": [72, 64, 13, 5], "arms": {"e0031": [77, 9.0], "e0032": [71, 3.0]}} |
| x002 | s1 | box | done | base e0028 e0016 aa | 480 | {"base": 73, "episodes": 120, "aa": [73, 72, 6, 5], "arms": {"e0028": [62, -10.5], "e0016": [73, 0.5]}} |
[... 165 lines cut ...]
0038-claude-fcv-brief-b20.md
0039-claude-fcv-brief-exact-b20.md
0040-claude-episode-budget-20000.md
0041-claude-correction-b002-supp1.md
0042-claude-correction-b004-supp1.md
0043-gemini-spec-audit-thought-gate.md
0044-gemini-dual-audit-postedit-boost.md
TEMPLATE.md

 succeeded in 266ms:
# 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

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|---|---|---|
| `turn` | newline after `<|turn>model` | think first (`<|channel>`) or call a tool at once |
| `step` | `<tool_response|>` that closes a tool result | think, call another tool, or answer in text |
| `think` | `<channel|>` that closes a thought | call a tool or write text |
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**Fetching files locally**

 succeeded in 232ms:
# wsp31 tools for edit authors

Changelog (newest first; times US Eastern):
- Fri 13 Nov, 3:51 PM: **three of the first four edits were harmful (-10 to -21 tasks): read "Before you submit"
  (section 5) first.** GPU 0 for pre-tests only while an author turn is open (section 3). New `delimprobe.py`.
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  12:39 AM speed (trap 12): **the server's rank limit sets the price of an adapter**.
- Thu 12 Nov: 11:40 PM first served runs of `dose.py`, `validate.py`, `vgen.py`, `closeprobe.py`; 10:55 PM stacks
  of rank 34 to 96 applied as written; 10:30 PM **thought-end pre-tests need exact contexts**, rank limits 64 or 128
  (never 96); 10:00 PM champion support, network-wide builders; 8:36 PM first version. Earlier commands all still work.

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: the registered ranking test failed on four edits (section 5).

 succeeded in 231ms:
sed: -e expression #1, char 4: unterminated address regex
# Interpretability lane: what is known about the inside of Gemma 4 31B

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own runs; anything else is labelled. Pre-registration: `PROTOCOL.md` (sections 0-5 and dated amendments 1, 2, 3, 3a,
3b, 3c, 3d, 3e, 3f, 3g, 3h, 3i, 3j, 3k, 4, 4a, 4b, 5, 5a, 5b, 5c, 5d, 5e). Located-and-causally-checked components: `ATLAS.md` (one entry since 1:06 AM ET, Fri 13 Nov: the close-the-thought direction). Lens-only readings:
`ATLAS_lens.md`.

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The author brief points to this block. It is kept current and can be read alone; the dated blocks below it are
the record. All screening numbers here are stage 1 unless stage 2 is named, one rollout per task and arm: 120 tasks in b002 and b003, 102
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- **New (8:57 AM ET, Sat 14 Nov 2026): 0009 (claude; the model's own end-of-thought neurons at 25 times their write) is harmful: 3 of 120
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  stage 1, the same 120 tasks, budget 20,000, one rollout per task). Both carried predictions this lane registered in
  advance (amendment 5): 1 of 4 mechanism predictions hit, and the outcome prediction for 0007 missed by one task.**
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    against both unedited arms, and for solved tasks it does not. Stage 2 is queued for both.
  - Twice the dose removed more cut-offs and did not solve more: 0032 against 0031 lost 10 and gained 4 (p 0.18,
    not a shown difference). In the 35 tasks cut off in both unedited arms, 0031 is not cut off in 10 and 0032 in
    23, and each solves 12 (the unedited arms 9 and 9). Of the 59 tasks both unedited arms solve, 0031 solves 56
    and 0032 53. Episodes with an empty patch: 21 | 18 | 13 | 8.
  - No sign of broken tool-call syntax in the traces: `read_file` calls that fail on the path 5 | 12 | 2 | 3 (74

**Considering improved quantization and testing**

 succeeded in 257ms:
sed: -e expression #1, char 4: unterminated address regex
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bash: line 1: minutes: command not found
bash: line 1: seconds: command not found
/home/keith/gemma-v2/wsp31/authors/gpt/code/close033_record.json
/home/keith/gemma-v2/wsp31/authors/gpt/code/audit_boundary.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/author_capture.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/build_project.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/audit_snapshot.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/non_split.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/probe_job.sh
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/environment.json
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/prepare_closecheck.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/cpu_ground_stats.pt
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/write_record.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/gpu_dispatch.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/hold2.sh
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/provenance.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/turn7/audit_calls.py
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/home/keith/gemma-v2/wsp31/authors/gpt/code/cancel_optional.py
/home/keith/gemma-v2/wsp31/authors/gpt/code/capture_job.sh
3.6G	/home/keith/gemma-v2/wsp31/authors/gpt/scratch
               total        used        free      shared  buff/cache   available
Mem:             183          17          88           0          80         166
Swap:              7           1           6

**Managing script quoting and patching**

 succeeded in 40ms:

verify_rebuild.py
update_frozen_manifest.py
turn7\write_record.py
turn7\update_notes.py
turn7\trace_schema.py
turn7\thinklib.py
turn7\stop_own.py
turn7\setup_remote.py
turn7\select_final.py
turn7\replay_cpu.py
turn7\replay_after_capture.sh
turn7\repeat_audit.py
turn7\recovery_audit.py
turn7\read_ledgers.py
turn7\ready.py
turn7\provenance.py
turn7\progress.py
turn7\prepare_cpu.py
[... 83 lines cut ...]
audit_snapshot.py
audit_boundary.py
after_capture.sh
Path                              
----                              
C:\Users\Owner\wsp31_loop\work\gpt

 succeeded in 237ms:
"""icv_job.py (author gpt, turn 7; adapted from claude turn 6): one GPU-0 hold with tools/g31.py (forward pass only, no server).

Instruction vectors compiled into the weights as an always-on MLP neuron in every layer.
  1. capture: for stage-1 calls of the unedited b001 arms, run the logged call (prompt + the completion the agent
     wrote) once as logged and once with an instruction paragraph inserted at the end of the first user message.
     d_L[I] = mean over calls of mean over completion positions of resid_post_L(with I) - resid_post_L(without).
  2. per layer the part layer L adds itself: delta_L = d_L - layer_scalar_L * d_{L-1}  (d_{-1} = 0).
  3. in every layer one quiet MLP neuron is replaced ("set") by an always-on neuron: gate row K w, up row w, with
     w . x = 1 on average over completion positions (x = MLP input), so its activation is about K at every position;
     its down column writes beta * delta_L into the stream (through the post-feedforward norm and layer_scalar).
  4. teacher-forced evaluation on held-out tasks: base, prompted with I, and each arm, on the logged completion.
`--mock` runs the whole script on CPU with random activations (plumbing test).
"""
import json
import math
import os
import random
import sys
import time
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+print('LATEST RESULTS SCHEMA')
+for arm in ['base','aa']:
+ ps=list((R/f'results/wsp31/batches/x004/{arm}').rglob('task_results.jsonl'))
+ for p in ps[:1]:
+  rows=[json.loads(l) for l in p.open()];print(str(p),len(rows),rows[0])
+  fail=[x for x in rows if not x.get('resolved') and not x.get('agent_error')]
+  print('FAIL',[(x.get('instance_id'),x.get('patch_chars'),x.get('tool_calls')) for x in fail[:10]])
+(A/'NOTES.md').write_text('# Author gpt - turn 10 in progress\n\nStarted 2026-11-14 14:00 UTC; deadline 15:30 UTC. Champion generation 0. Prior source-grounding ICV doses 3 and 1 were null (-2.5/-3.5). Do not repeat closing-thought or constant source-grounding edits. Reading latest stage-1 failures and choosing a new mechanism. No entries submitted yet; maximum two. GPU budget 2700 seconds, none used.\n')

 succeeded in 263ms:
diff --git a/turn10/inspect.py b/turn10/inspect.py
new file mode 100644
index 0000000000000000000000000000000000000000..af7e4eaaf226ec36a85c1bf73e83173e545ec8f4
--- /dev/null
+++ b/turn10/inspect.py
@@ -0,0 +1,26 @@
+from pathlib import Path
+import json,re
+R=Path('/home/keith/gemma-v2'); A=R/'wsp31/authors/gpt'; C=A/'code/turn10'; S=A/'scratch/turn10'
+C.mkdir(exist_ok=True); S.mkdir(exist_ok=True)
+survey=(R/'tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md').read_text()
+print(survey[survey.index('## C.'):survey.index('## D.')])
+rep=(R/'wsp31/interp/REPORT.md').read_text().splitlines()
+for i,l in enumerate(rep):
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+  if i<400: print('\n'.join(rep[max(0,i-2):i+12]))
+print('LEDGER RECIPES')
+for p in sorted((R/'tii-wsp31-doc/research/wsp31/ledger').glob('*.md')):
+ if p.name[:4] in ['0025','0031','0035','0038','0039','0043','0044']:
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+print('LATEST RESULTS SCHEMA')
+for arm in ['base','aa']:
+ ps=list((R/f'results/wsp31/batches/x004/{arm}').rglob('task_results.jsonl'))
+ for p in ps[:1]:
+  rows=[json.loads(l) for l in p.open()];print(str(p),len(rows),rows[0])
+  fail=[x for x in rows if not x.get('resolved') and not x.get('agent_error')]
+  print('FAIL',[(x.get('instance_id'),x.get('patch_chars'),x.get('tool_calls')) for x in fail[:10]])
+(A/'NOTES.md').write_text('# Author gpt - turn 10 in progress\n\nStarted 2026-11-14 14:00 UTC; deadline 15:30 UTC. Champion generation 0. Prior source-grounding ICV doses 3 and 1 were null (-2.5/-3.5). Do not repeat closing-thought or constant source-grounding edits. Reading latest stage-1 failures and choosing a new mechanism. No entries submitted yet; maximum two. GPU budget 2700 seconds, none used.\n')

 succeeded in 50ms:
## C. Ranked edit ideas for raising a Gemma 4 31B coding agent's held-out solve rate (no training)

Ranking is by my estimate of expected held-out gain times probability of a clean confirmation. That estimate is mine, not a sourced figure. "Bake" means compile into your rank-32 LoRA-shaped adapter or a gated quiet-neuron write.

| # | Idea | Motivating source | Data needed | Risk |
|---|---|---|---|---|
| 1 | **Bake TACT's overthinking and overacting axes with a band gate.** Fit OT-vs-CAL and OA-vs-CAL (orthogonalised) at `<channel\|>` (end of thought) on judge-labelled steps. Implement TACT-Gate's "subtract excess beyond calibrated band" as a one-sided gated neuron per top-AUC layer (a GeGLU gate gives a soft threshold). Verify hook-vs-baked equivalence first | TACT (+4.8 avg on Gemma-4-26B-A4B; random and shuffled controls ≤0) | Your failure traces, chunk-labelled by a frontier judge (TACT spent ~$600 for 2k trajectories); activations at end-of-thought | Medium. Only TACT's own evidence; ~3.2% harmful flips; a soft gate is an approximation of the hook |
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| 4 | **Spectrally filtered donor delta.** Take community gemma-4-31B-it agentic-coding LoRAs. Keep each matrix's dominant singular directions, Frobenius-match magnitude, and DARE/TIES-merge several. Treat the dose α as a hyperparameter | Selective-RL (controls beat magnitude-only and random low-rank); fine-tuning transfer; DARE/TIES | Donor LoRAs (verify licence, data, contamination); none are RL-trained | Medium–high. Unknown donor quality. Note: this imports someone else's training, so it is "training-free for us" and may need to be reported separately from "LLM-authored" |
| 5 | **Head-level cognitive edits (CREST/ThinkEdit style).** Find heads whose outputs separate productive from unproductive verification and backtracking (or premature "submit"); remove or add the component in those heads' o_proj | CREST (up to +17.5%); ThinkEdit (0.2% of params, +3.34%) | Per-head activations at labelled step types | Medium. Shown on math reasoning, not agents |
| 6 | **Verification / tool-use direction.** One residual direction raising the rate of "run tests / inspect output" actions, dosed in the agent loop | Tool-call rates (0→90%; QA 0.29→0.56); TACT's overacting axis as the counterweight | Contrast of states before tool-call vs no-tool turns | Medium. Too much dose means more steps or timeouts. Pair with idea 1's OA axis |
| 7 | **RYS middle-block duplication.** Materialise a 61–68-layer checkpoint that repeats a contiguous mid block; search (i, j) on dev agent tasks or a proxy (teacher-forced NLL on successful trajectories, plus LCB) | RYS on Qwen3.5-27B | Proxy sweep (cheap), then agent evals | Medium–high. Weak evidence; adds KV cache; counts as an architecture edit for framing |
| 8 | **Agency-axis nudge at prefill**, so the agent acts rather than defers or stalls (relevant if traces show hedging or asking for confirmation) | Agency axis (d=1.01; decode-only had no effect) | Contrast "acts" vs "asks or stalls" turns | Medium. Risk of reckless actions; side-effect check needed |
| 9 | **Behaviour-basis composition with black-box coefficient search.** Build a basis (persistence, verification, brevity, tool-use, format) and let TPE or CMA-ES search coefficients on dev, constrained by KL on held-out chat | Steer2Adapt (+8.2%); Heretic's TPE + KL; Evo-L2S | Basis vectors; dev split; KL reference set | High overfitting risk. Requires strict dev/test isolation |
| 10 | **Projection ablation of a "give-up / premature-finish" direction** (Arditi-style orthogonalisation of writer matrices), searched Heretic-style | Arditi et al.; Heretic | Contrast of states before early-submit failures vs successful continuations | Medium. Ablation is global, so may over-suppress legitimate finishing |
| 11 | **ExPO extrapolation of the Gemma 4 31B instruction-tuning delta** (IT + α·(IT
[... 1668 characters cut ...]

 succeeded in 236ms:
#!/usr/bin/env python
"""replay.py [--adapter DIR] [--episodes 10] [--calls 3] [--max-pos 6000] [--max-comp 600] [--probeset FILE]
[--out FILE.json]: exact replay of logged model calls, and the likelihood of stored trajectories, without a server
(this lane's forward pass on GPU0: run under the gpu0 lock; about 5 s per call and arm, the base arm is cached). A
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unedited agent then produced at temperature 0 (`comp`). The model, with the adapter if one is given, is run on
prompt + comp (teacher forcing) and reports, for solved and failed episodes and separately for the thinking part
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name would change. For the base arm it also prints the difference to the log-probs the rollout server logged (a
check of the forward pass against the served model). Calls are spread evenly over each episode; completions longer
than --max-comp are cut (the kept prefix is still exact). An edit that lowers the likelihood of solved trajectories
much more than that of failed ones is suspect; a higher likelihood is not evidence of more solved tasks."""
import argparse
import hashlib
import json
import os
import sys
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    for part in s.split(","):
        if "-" in part:
            a, b = part.split("-")
            out += list(range(int(a), int(b) + 1))
        else:
            out.append(int(part))
    assert all(0 <= x < n for x in out)
    return out

 succeeded in 38ms:
"""Closed-form rank-4 code/prose subspace edits. CPU, no optimizer."""
import sys,json,argparse,hashlib
from pathlib import Path
import torch
A=Path('/home/keith/gemma-v2/wsp31/authors/gpt');C=A/'code/turn4';S=A/'scratch/turn4'
sys.path.insert(0,str(C/'tools'))
from edits import EditSet
torch.set_num_threads(8);torch.set_grad_enabled(False)
def unit(x):return x/x.norm(dim=-1,keepdim=True).clamp_min(1e-20)
def fit():
    data=[torch.load(S/f'capture_{i}.pt',weights_only=False) for i in range(6)]
    out={};diag={}
    for space in ['attn_write','mlp_write','attn_read','mlp_read']:
        p=torch.stack([x['means'][space]['code'] for x in data]).double()
        n=torch.stack([x['means'][space]['thought'] for x in data]).double()
        common=unit((p+n).mean(0));diff=p-n
        diff=diff-(diff*common[None]).sum(-1,keepdim=True)*common[None]
        vec=[];weight=[];kept=[];cos=[]
        for l in range(60):
[... 213 lines cut ...]
+print('SELECTED',len(fit),len(check),json.dumps(summary,indent=1))
+for p in (fit+check)[:2]:
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+# Freeze only tool sources, with byte hashes, under author-owned code.
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

diff --git a/turn10/inspect.py b/turn10/inspect.py
new file mode 100644
index 0000000000000000000000000000000000000000..af7e4eaaf226ec36a85c1bf73e83173e545ec8f4
--- /dev/null
+++ b/turn10/inspect.py
@@ -0,0 +1,26 @@
+from pathlib import Path
+import json,re
+R=Path('/home/keith/gemma-v2'); A=R/'wsp31/authors/gpt'; C=A/'code/turn10'; S=A/'scratch/turn10'
+C.mkdir(exist_ok=True); S.mkdir(exist_ok=True)
+survey=(R/'tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md').read_text()
+print(survey[survey.index('## C.'):survey.index('## D.')])
+rep=(R/'wsp31/interp/REPORT.md').read_text().splitlines()
+for i,l in enumerate(rep):
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+  if i<400: print('\n'.join(rep[max(0,i-2):i+12]))
+print('LEDGER RECIPES')
+for p in sorted((R/'tii-wsp31-doc/research/wsp31/ledger').glob('*.md')):
+ if p.name[:4] in ['0025','0031','0035','0038','0039','0043','0044']:
+  txt=p.read_text(); print(p.name,txt[:10000])
[... 70 lines cut ...]
[line removed by the dashboard scrubber]
+# Freeze only tool sources, with byte hashes, under author-owned code.
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

**Checking specific time reference**
 succeeded in 15036ms:

 succeeded in 217ms:
                    self.filepath = file_path
                    return True
        return False
<|"|>,old_string:<|"|>        if path in self._mtimes:
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                if file_path not in current_files:
                    del self._mtimes[path][file_path]
                    self.filepath = file_path
                    return True
        return False
        return False
<|"|>}<tool_call|><|tool_response>
"""netops.py: network-wide operations of EditSet (edits.py), for edits of the size real training makes: many layers,
readers and writers, a few directions per matrix, 0.3% to 3% of the whole network. CPU only. Use through EditSet:

    e = EditSet()
    e.write_dir(range(60), D, alpha=1.0)      # every o_proj and down_proj: dW = alpha * P W   (P projects on D)
    e.read_dir(range(60), D, alpha=1.0)       # every q/k/v/gate/up_proj:   dW = alpha * W P
    e.scale_to(per_matrix=1.0, net_rel=0.003) # every changed matrix the same relative size, whole network 0.3%
[... 304 lines cut ...]
[line removed by the dashboard scrubber]
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+# Freeze only tool sources, with byte hashes, under author-owned code.
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

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 succeeded in 208ms:
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REQUEST_SCALAR {'finish_reason': 'STOP', 'is_agent_call': True, 'policy_version': None}
SELECTED 8 1 [
 {
  "instance": "r1chardj0n3s__parse.30da9e4f.func_basic__83wal9im",
  "split": "fit",
  "pos": {
   "batch": "x004",
   "arm": "base",
   "call": 2,
   "tool": "edit_file",
   "start": 4922,
   "request_sha256": "e475c67e3580dda32813bc1f8d6e613f3a3ed13a71e3fe098e9ce17a96333206"
  },
  "neg": {
   "batch": "x004",
   "arm": "aa",
   "call": 2,
   "tool": "edit_file",
[... 168 lines cut ...]
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                if file_path not in current_files:
                    del self._mtimes[path][file_path]
                    self.filepath = file_path
                    return True
        return False
        return False
<|"|>}<tool_call|><|tool_response>

 succeeded in 69ms:

0025-gpt-code-writer-damp-r4-033.md ---
## Exact recipe
## Prediction (written before the run)
0031-claude-icv-brief-b20.md ---
## Exact recipe
0035-gpt-source-grounding-icv-b3.md ---
## Exact recipe
0038-claude-fcv-brief-b20.md ---
0032's regime (+3.0, wrong patches up). That stack is not intended; on a champion that holds 0031 this entry should
## Exact recipe
0039-claude-fcv-brief-exact-b20.md ---
0031 `icv-brief-b20` and 0032 `icv-brief-b40` write the mean stream shift of a "keep your thinking very short"
## Exact recipe
0043-gemini-spec-audit-thought-gate.md ---
## Exact recipe
0044-gemini-dual-audit-postedit-boost.md ---
## Exact recipe
## Prediction (written before the run)
[... 196 lines cut ...]
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+# Freeze only tool sources, with byte hashes, under author-owned code.
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(dst/'data').mkdir(exist_ok=True)
+shutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

diff --git a/turn10/inspect.py b/turn10/inspect.py
new file mode 100644
index 0000000000000000000000000000000000000000..af7e4eaaf226ec36a85c1bf73e83173e545ec8f4
--- /dev/null
+++ b/turn10/inspect.py
@@ -0,0 +1,26 @@
+from pathlib import Path
+import json,re
+R=Path('/home/keith/gemma-v2'); A=R/'wsp31/authors/gpt'; C=A/'code/turn10'; S=A/'scratch/turn10'
+C.mkdir(exist_ok=True); S.mkdir(exist_ok=True)
+survey=(R/'tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md').read_text()
+print(survey[survey.index('## C.'):survey.index('## D.')])
+rep=(R/'wsp31/interp/REPORT.md').read_text().splitlines()
+for i,l in enumerate(rep):
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+  if i<400: print('\n'.join(rep[max(0,i-2):i+12]))
+print('LEDGER RECIPES')
+for p in sorted((R/'tii-wsp31-doc/research/wsp31/ledger').glob('*.md')):
+ if p.name[:4] in ['0025','0031','0035','0038','0039','0043','0044']:
+  txt=p.read_text(); print(p.name,txt[:10000])
[... 76 lines cut ...]
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(dst/'data').mkdir(exist_ok=True)
+shutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

 succeeded in 18291ms:
**Reviewing ledger reading and normalization**
**Applying ridge regression with low-rank SVD**

 succeeded in 224ms:
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REQUEST_SCALAR {'finish_reason': 'STOP', 'is_agent_call': True, 'policy_version': None}
SELECTED 18 3 [
 {
"""edits.py: write closed-form weight edits of Gemma 4 31B in Python and pack them as a LoRA adapter (CPU only).

    from edits import EditSet
    e = EditSet()
    e.row_add(30, "gate_proj", 123, delta)        # dW[123, :] += delta      (what neuron 123 of layer 30 reads)
    e.col_add(30, "down_proj", 123, delta)        # dW[:, 123] += delta      (what it writes)
    e.neuron(30, 123, up=vec, down=vec2, mode="set")   # replace the neuron's up row and down column outright
    e.neuron_scale(30, 123, down=0.0)             # ablate the neuron (scale its write by 0)
    e.add(41, "o_proj", U, V)                     # any low-rank term: dW += U @ V, U [out, k], V [k, in]
    e.save("adapters/my_edit")                    # -> adapter_model.safetensors, adapter_config.json, edit.json

A module's adapter rank is (#edited rows) + (#edited columns) + (sum of k of the low-rank terms); the adapter rank
is the largest over modules and must not exceed the server's --max-lora-rank (32 by default). Row and column edits
are exact up to bf16 storage of the delta (8 significant bits per element): the selector factor is a one-hot.
Modules: q_proj k_proj v_proj o_proj gate_proj up_proj down_proj of layers 0..59 (shapes in ARCH.md). The ten global
[... 455 lines cut ...]
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+# Freeze only tool sources, with byte hashes, under author-owned code.
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(dst/'data').mkdir(exist_ok=True)
+shutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

 succeeded in 229ms:
382083

diff --git a/turn10/hold1.sh b/turn10/hold1.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c1c26191499ad267857281209e963fe377ec229d
--- /dev/null
+++ b/turn10/hold1.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+set -euo pipefail
+R=/home/keith/gemma-v2
+A=$R/wsp31/authors/gpt
+export WSP31_WORK=$A/scratch/turn10
+export CUDA_VISIBLE_DEVICES=0
+export OMP_NUM_THREADS=8
+export MKL_NUM_THREADS=8
+export PYTHONUNBUFFERED=1
+cd "$A"
+exec flock "$R/wsp31/gpu0.lock" timeout 1620 "$R/tii-venv/bin/python" "$A/code/turn10/outcome_map.py" hold
[... 253 lines cut ...]
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(dst/'data').mkdir(exist_ok=True)
+shutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

**Analyzing data handling and GPU constraints**
**Evaluating completion criteria**

 succeeded in 78ms:
- attn_write: top four directions retain 91.20% of task-contrast squared energy on average over layers; mean split-half direction cosine 0.6960.
- mlp_write: top four directions retain 88.93% of task-contrast squared energy on average over layers; mean split-half direction cosine 0.6335.

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Audit motivation: b001 has 700 edit_file calls, 541 captured status=ok responses, 49 captured status=error responses, and 110 without a captured response; 34 errors mention string matching. The first loose text scan also matched source-code strings, so only parsed tool status counts are used (code/turn4/edit_error_audit.json). Two failure examples: Mimino666__langdetect.a1598f1a.lm_rewrite__dk9wx21q (base call 19) and PyCQA__flake8.cf1542ce.lm_rewrite__yh5du3ns (base call 14): old_string not found. These observations motivate the experiment; they do not identify the cause of every wrong patch.

Edit weights SHA256: cc61e0e3e4b7346c081f7bc0ae3f9d86e49d5788d919b62fee7e66b62190810c

Control weights SHA256: 926273ebb4316d6a8a71b0c0e3432b02a186393146318638c5fddd1527fe9e8e

Runner facts (2026-11-13 08:17:17 EST): source `wsp31/authors/gpt/queue/code-writer-damp-r4-033/`; served copy `wsp31/queue/frozen/[task]/`; sha256 over files, edit `7af6609c85c90616da21231a223e534ab8b0933ba9c492ee1126b9b0e69e6a95`, control `21d25e1a07a9adb9c238e67293939e7e241ed31d4960074798b336433bc7778c`; both passed the checker.

## Prediction (written before the run)

predicted_gain_120: -6 to 12

[... 427 lines cut ...]
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(dst/'data').mkdir(exist_ok=True)
+shutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

**Analyzing tensor output reliability**
**Reviewing multi-stage evaluation process**

 succeeded in 242ms:
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592:correlational, three runs per task, greedy decoding with serving noise); why finished patches are wrong (the
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907:`dirs/out/replay_agree.json`; `cd wsp31/interp/dirs && python replay_agree.py --out out/replay_agree.json`. Top
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983:Limits: correlational; three greedy runs per task under serving noise; snapshot of a batch that was still
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1922:  `dirs.py`, `post.py`, `dose.py`, `dose2.py`, `dose_eval.py`, `replay_agree.py`, `render_check.py`, `synth.py`;
[... 18 lines cut ...]
09:06:46 CAPTURE [task] neg 6982 6.12
09:06:54 CAPTURE [task] pos 9585 8.31
# Champion

Generation 0: the unedited model. No edit has passed yet.

Every batch scores an entry against this base: the reference arm is the champion and your edit and control are served as champion + your adapter (exact sum, `wsp31/tools/stack_exact.py`). Your entry is still only your own change (rank 32 or less). An edit that passes both screening stages becomes the next generation.

 succeeded in 77ms:
Positive paragraph (I1):
> Important: ground every change in current source text and observed tool results. Read the relevant implementation before editing. Copy old_string exactly from the latest file contents. After a tool error, use its message to correct the next action instead of repeating the failed call. Check the resulting diff against the requested behavior before submitting.

Generic paragraph (I0):
> Important: work on the user's coding task with the tools provided in this session. The repository contains source files and supporting files. Tool calls take arguments and return results. Follow the task description while deciding what action to take next. Continue working on the task using the available context and provide your final response when finished.

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For each layer L, d_L is the task-weighted mean of resid_post(I1) minus resid_post(I0) at matched continuation positions. Set delta_L = d_L - layer_scalar_L * d_(L-1), with d_(-1)=0. Split-half direction cosine, min/median/max over layers: -0.001, 0.478, 0.914.

Choose the neuron with the third-smallest mean absolute activation in each layer on these captures. Fit w so w dot mlp_in is about 1: compare the mean-input read with a dual ridge solve (regularizer N times the mean squared input coordinate); use ridge only if its standard deviation on calibration calls is below 0.7 of the mean read's and its calibration mean is between 0.9 and 1.1. Calibration calls have capture index modulo 3 equal to zero; the others fit w. If the absolute BOS score exceeds 3, remove that BOS direction and rescale. Set the gate row to 8w and the up row to w. The new activation is approximately 8(w dot x)^2, not a true constant. Its calibration coefficient of variation has min/median/max 0.057, 0.116, 0.582.

The new down column is clipdiv(beta * delta_L, post_feedforward_norm_weight) times mean_raw_MLP_RMS / (mean_new_activation * layer_scalar_L). clipdiv zeroes the lowest 5% of absolute post-norm weights before inversion. Save row/column differences against the unedited weights. This is a first-order write approximation: it ignores the new column's change to RMS and the response of later layers. It also averages away the position-dependent part of the instruction and does not reproduce its attention patterns. It acts at prompt positions too. Bf16 factors add rounding.

Control: identical gate/up changes, neuron, layers, rank and dose. First flip the signs of the intended NEW down column c with seed 710000 + L. Let u be the unit old column, a = (c dot u)u, and z be the randomized column projected off u. Set the new control column to a + z * norm(c-a)/norm(z), then subtract the old column to form the update. This preserves the norm of the new write and its projection onto the old column, so the total column-update norm also matches before rounding. The remaining direction is random. The largest stored per-matrix norm mismatch is 0.00066. Stored new-column raw-norm ratio (control/edit), min/median/max: [0.998871023544941, 0.9999547802018586, 1.0015328220316937]. The corresponding post-norm-weighted ratio: [0.99786242540014, 0.9999814337979892, 1.0303488934770586]. This controls update direction while holding the read edits fixed. An initial whole-update sign control was discarded before edited replay because it could leave a large old-column write under the new gate; CONTROL_AMENDMENT.md records the correction. Those discarded controls are not this entry's ctrl/.

Rebuild: bash build.sh OUT_DIR invokes code/turn7/build_ground.py from frozen code/turn7/ground_stats.pt. ground_cpu.py captured and solved the statistics on CPU with 16 threads and dense-weight caching. ground_job.py supplies the compiler used for rebuilds. Both use a frozen copy of the documented tools in code/turn7/tools. The compiler adapts claude's turn-6 icv_job.py; ground_job.py and the small thinklib.py contain the complete changed code path. No optimizer, grad
[... 1091 characters cut ...]

 succeeded in 332ms:
BOUNDARY PASS 21 tasks 42 episodes 9 stage2 exclusion lists

LEDGER 0007-claude-deep-gate-b01-end-neurons-x9.md 
 ## Hypothesis

In the unedited batch b001 (three identical arms, 61 episodes finished when read) 14 of the 28 failures end at the
7.5-minute session limit, at least 10 of them with an empty patch and 8 of those without a single file edit (at 110
episodes, 9:40 PM ET: solved 44%, at the limit 35%, empty patch 25%). At about
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"Wait, ..." restarts. Making long thoughts end sooner should return that wall-clock to tool calls, so that episodes
which now time out without a patch get to edit, test and submit.

Different from the E4B "think briefly" edits (28 / 28 / 25 vs 29, PRIOR_E4B.md): there the session limit was not
reported as binding; here it ends 23% to 35% of unedited episodes, and the edit is built from the state at the logged ends of
thoughts (and, in families B and C, acts only deep inside a long thought), not from a short-vs-long contrast.


[... 654 lines cut ...]
+dst=C/'tools';dst.mkdir(exist_ok=True)
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(dst/'data').mkdir(exist_ok=True)
+shutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

**Reviewing memory and code inspection details**
**Analyzing build process and artifact consistency**

 succeeded in 233ms:
162:    def base_weight(self, l, mod):
163-        """Dequantised checkpoint weight, bf16 [out, in] (k_proj for v_proj on global layers)."""
164-        if mod == "v_proj" and not self.arch.geom(l)["has_v"]:
165-            mod = "k_proj"
166-        if (l, mod) in self._dense:
167-            return self._dense[(l, mod)]
168-        w = dequant(*self.L[l][mod])
169-        if self.cache_dense:
170-            self._dense[(l, mod)] = w
171-        return w
172-
173-    def weight(self, l, mod):
174-        """Weight the forward pass uses for the base path: an override if one is set, else the checkpoint."""
09:08:19 CAPTURE pallets__jinja.ada0a9a6.func_basic__2vdao9be neg 9130 7.94
09:08:29 CAPTURE r1chardj0n3s__parse.30da9e4f.func_pm_remove_cond__k4df18dk pos 11576 10.02
09:08:36 CAPTURE r1chardj0n3s__parse.30da9e4f.func_pm_remove_cond__k4df18dk neg 8166 7.09
09:08:45 CAPTURE [task] pos 10582 9.19
09:08:54 CAPTURE [task] neg 10555 9.17
09:09:00 CAPTURE [task] pos 6763 5.89
[... 378 lines cut ...]
+for f in T.glob('*.py'):shutil.copy2(f,dst/f.name)
+(dst/'data').mkdir(exist_ok=True)
+shutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')
+(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))

**Estimating processing and throughput times**
**Reviewing testing parameters and performance**
**Estimating resource feasibility**

 succeeded in 245ms:
#!/usr/bin/env python
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[--tol 0.10] [--save-largest DIR] [--champion current|DIR] [--out FILE.json] | dose.py --selftest: how large can this edit be made before the
agent stops producing well-formed turns? Run inside a gpu0 session (a running server with runtime adapter loading).
For each dose (whole-network size, 0.003 = 0.3%) the tool writes a copy of the edit scaled by one common factor
(rescale.py: every matrix keeps its share), loads it under a content-hash name, measures, unloads and deletes the
copy. Measured per dose, always next to the unedited model on the same prompts: (1) at labelled decision points of
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well-formed tool call (<|tool_call>call:NAME{...}<tool_call|> with a tool name the harness has, outside thinking),
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is coherent when its share of well-formed tool calls is within --tol of the unedited model's and its shares of
malformed, never-closed, empty and repetitive turns are no more than --tol above it. The last line names the largest
coherent dose (all smaller doses coherent too). This says the model still works as an agent at that size; it says
nothing about whether the edit helps. --mode alpha scales through lora_alpha instead of rewriting the file (dose
tests only: such a directory is not a valid queue entry). With --champion (or WSP31_CHAMPION) the reference is the
champion and every dose is served as champion + scaled edit; doses are still sizes of your edit alone. --selftest classifies the logged answers (CPU)."""
[... 1015 lines cut ...]
+   rr=[r for r in rows if r['at']==at]
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+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

 succeeded in 1914ms:
PANEL 112 52
09:10:39 CAPTURE [task] neg 7545 6.5
09:10:49 CAPTURE [task] pos 10426 9.05
09:10:59 CAPTURE [task] neg 11173 9.7
09:11:06 CAPTURE mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm pos 8771 7.65
09:11:15 CAPTURE mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm neg 9411 8.13

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
[... 830 lines cut ...]
+   rr=[r for r in rows if r['at']==at]
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+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

 succeeded in 1029ms:
09:10:33 CAPTURE [task] pos 9065 7.84
09:10:39 CAPTURE [task] neg 7545 6.5
09:10:49 CAPTURE [task] pos 10426 9.05
09:10:59 CAPTURE [task] neg 11173 9.7
09:11:06 CAPTURE mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm pos 8771 7.65
09:11:15 CAPTURE mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm neg 9411 8.13
09:11:21 CAPTURE lepture__mistune.bf54ef67.combine_module__dalnbilu pos 7198 6.25
09:11:28 CAPTURE lepture__mistune.bf54ef67.combine_module__dalnbilu neg 7651 6.62
09:11:34 CAPTURE prettytable__prettytable.ca90b055.combine_file__2mwa4qpg pos 6917 6.07

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
[... 421 lines cut ...]
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Processing attention weights and outcome labels**
**Evaluating computation strategies on CPU and GPU**
**Managing race conditions**

 succeeded in 959ms:
09:11:40 CAPTURE prettytable__prettytable.ca90b055.combine_file__2mwa4qpg neg 6692 5.93
09:11:40 FIT 18 3 mean check cosine 0.07760504063112345 retained 0.5427474183864531
09:11:41 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E033 0.003300000090131365 120
09:11:41 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E100 0.009999999793127694 120
09:11:42 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E300 0.03000000082936939 120
09:11:43 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/C100 0.009999999793127694 120
DIAGNOSTICS o_proj
0 20 {'retained': 0.6057944316894114, 'fit_error': 0.6923969871067123, 'check_error': 1.0078041087721195} cos 0.06856088848230853
20 40 {'retained': 0.5355620455581478, 'fit_error': 0.6936781239290354, 'check_error': 1.0057906440552125} cos 0.1018633714965034
40 60 {'retained': 0.5716482943441719, 'fit_error': 0.715048528518079, 'check_error': 1.0051635305975537} cos 0.07635008654283564
DIAGNOSTICS down_proj
0 20 {'retained': 0.5052666207433784, 'fit_error': 0.7725324637919686, 'check_error': 1.0034682774506956} cos 0.05213358997401563
20 40 {'retained': 0.49642569434498973, 'fit_error': 0.7298701313842395, 'check_error': 0.998999522814173} cos 0.09174956637672978
40 60 {'retained': 0.54178742363862, 'fit_error': 0.7236737932622003, 'check_error': 1.0041875509296694} cos 0.07497274091434769

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
[... 425 lines cut ...]
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Analyzing high-dimensional mapping challenges**
**Evaluating test reliability and metric sensitivity**

 succeeded in 940ms:
09:11:41 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E033 0.003300000090131365 120
09:11:41 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E100 0.009999999793127694 120
09:11:42 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E300 0.03000000082936939 120
09:11:43 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/C100 0.009999999793127694 120
09:12:22 EVAL 0 mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm 67 ['base', 'E033', 'E100', 'E300', 'C100']
DIAGNOSTICS o_proj
0 20 {'retained': 0.6057944316894114, 'fit_error': 0.6923969871067123, 'check_error': 1.0078041087721195} cos 0.06856088848230853
20 40 {'retained': 0.5355620455581478, 'fit_error': 0.6936781239290354, 'check_error': 1.0057906440552125} cos 0.1018633714965034
40 60 {'retained': 0.5716482943441719, 'fit_error': 0.715048528518079, 'check_error': 1.0051635305975537} cos 0.07635008654283564
DIAGNOSTICS down_proj
0 20 {'retained': 0.5052666207433784, 'fit_error': 0.7725324637919686, 'check_error': 1.0034682774506956} cos 0.05213358997401563
20 40 {'retained': 0.49642569434498973, 'fit_error': 0.7298701313842395, 'check_error': 0.998999522814173} cos 0.09174956637672978
40 60 {'retained': 0.54178742363862, 'fit_error': 0.7236737932622003, 'check_error': 1.0041875509296694} cos 0.07497274091434769
EVAL records 1
mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm [('E033', 0, -0.00033891317434608936), ('E100', 2, -0.01906244270503521), ('E300', 2, -0.11239234358072281), ('C100', 1, -0.02401219680905342)]

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
[... 425 lines cut ...]
+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Optimizing learning and computation**

 succeeded in 228ms:
#!/usr/bin/env python
"""fastprobe.py EDIT_DIR --port PORT [--traces 12] [--per-trace 8] [--max-pos 8000] [--keep] [--champion current|DIR] [--out FILE.json]:
fast behavioural probe of an edit on a running vLLM server (no rollouts; the server must have been started with
runtime adapter loading, as gpu0_session.sh does). It loads the adapter under a name derived from the hash of its
[line removed by the dashboard scrubber]
labelled decision points of stored agent traces for the base model and for the edit, unloads the adapter, and
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
(think), <|tool_call> and <turn|>. Base answers are cached per server start in data/cache. Because only the top 20
[line removed by the dashboard scrubber]
the same measurement over the full vocabulary without a server. With --champion (or WSP31_CHAMPION) the reference
arm is the champion adapter and the edit is served stacked on it (champ.py), as the runner serves an entry.
"""
import argparse
import hashlib
import json
import os
import sys
import time
[... 1003 lines cut ...]
+   rr=[r for r in rows if r['at']==at]
[line removed by the dashboard scrubber]
+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

 succeeded in 1225ms:
09:11:41 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E100 0.009999999793127694 120
09:11:42 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/E300 0.03000000082936939 120
09:11:43 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/scouts/C100 0.009999999793127694 120
09:12:22 EVAL 0 mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm 67 ['base', 'E033', 'E100', 'E300', 'C100']
09:13:02 EVAL 1 mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm 92 ['base', 'E033', 'E100', 'E300', 'C100']
09:13:22 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/cpu100/edit 0.0099999997806608 120
DIAGNOSTICS o_proj
0 20 {'retained': 0.6057944316894114, 'fit_error': 0.6923969871067123, 'check_error': 1.0078041087721195} cos 0.06856088848230853
20 40 {'retained': 0.5355620455581478, 'fit_error': 0.6936781239290354, 'check_error': 1.0057906440552125} cos 0.1018633714965034
40 60 {'retained': 0.5716482943441719, 'fit_error': 0.715048528518079, 'check_error': 1.0051635305975537} cos 0.07635008654283564
DIAGNOSTICS down_proj
0 20 {'retained': 0.5052666207433784, 'fit_error': 0.7725324637919686, 'check_error': 1.0034682774506956} cos 0.05213358997401563
20 40 {'retained': 0.49642569434498973, 'fit_error': 0.7298701313842395, 'check_error': 0.998999522814173} cos 0.09174956637672978
40 60 {'retained': 0.54178742363862, 'fit_error': 0.7236737932622003, 'check_error': 1.0041875509296694} cos 0.07497274091434769
EVAL records 2
mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm [('E033', 0, -0.00033891317434608936), ('E100', 2, -0.01906244270503521), ('E300', 2, -0.11239234358072281), ('C100', 1, -0.02401219680905342)]
close [('base', [101], 0.0), ('E033', [101], 0.1054084300994873), ('E100', [101], 0.38440945744514465), ('E300', [101], 0.48356178402900696), ('C100', [109], -1.593332290649414)]
mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm [('E033', 1, 0.00035774181014858186), ('E100', 1, 0.002461430849507451), ('E300', 2, -0.03195725008845329), ('C100', 1, -0.0042394534684717655)]
close [('base', [101], 0.0), ('E033', [101], -3.6954406823497266e-06), ('E100', [101], -2.658302582858596e-05), ('E300', [101], -0.0004880375927314162), ('C100', [101], 4.649122274713591e-06)]
[... 432 lines cut ...]
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Analyzing control states and prediction margins**
**Evaluating dose selection and control strategies**
**Estimating computational resource needs**

 succeeded in 224ms:
401410

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
+ assert p['instance'] in allow
+ assert all(p['instance'] not in ids for ids in denies.values()),p['instance']
+ assert p['pos']['resolved'] and not p['neg']['resolved']
+ assert p['pos']['tool']==p['neg']['tool']
[... 413 lines cut ...]
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Reviewing model training and validation details**
**Verifying matrix factorization stability**

 succeeded in 221ms:
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
160-        c = float(c)
161-        for store in (self.rows, self.cols):
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
164-                    for j in d:
165-                        d[j] = d[j] * c
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
169-
170:    def scale_to(self, net_rel=None, per_matrix=None):
171-        """per_matrix=x: rescale every changed matrix to |dW|_F / |W|_F = x (training moves every matrix by nearly
172-        the same relative amount). net_rel=y: then one common factor so the whole-network size is y. With both, the
173-        matrices are equalised first and the common factor follows (x is then only a ratio). Returns net_rel."""
174-        if per_matrix is not None:
175-            for k, s in self.sizes().items():
176-                assert s["fro"] > 0, f"{k} has a zero edit"
[... 479 lines cut ...]
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Verifying computation consistency**
**Reviewing build script execution**
**Managing complex timeout budget**

 succeeded in 9249ms:
09:14:52 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/cpu033/edit 0.0033000000860908046 120
09:15:20 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/cpu033/ctrl 0.0033000000860908046 120
09:14:52 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/cpu300/edit 0.03000000079058293 120
09:15:19 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/cpu300/ctrl 0.03000000079058293 120

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
+ assert p['instance'] in allow
[... 497 lines cut ...]
+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Analyzing budget constraints and timing**

 succeeded in 231ms:
09:14:37 EVAL 4 prettytable__prettytable.ca90b055.combine_file__2mwa4qpg 60 ['base', 'E033', 'E100', 'E300', 'C100']
09:15:07 EVAL 5 prettytable__prettytable.ca90b055.combine_file__2mwa4qpg 50 ['base', 'E033', 'E100', 'E300', 'C100']
09:15:31 EVAL 6 [task] 41 ['base', 'E033', 'E100', 'E300', 'C100']
09:15:49 EVAL 7 [task] 36 ['base', 'E033', 'E100', 'E300', 'C100']
09:16:12 EVAL 8 [task] 42 ['base', 'E033', 'E100', 'E300', 'C100']
edit {"r": 4, "scale": 2.0, "changed": 120, "of": 420, "net_rel": 0.03, "rel_median": 0.07682, "rel_min": 0.0768, "rel_max": 0.07687, "rel_max_mod": "38.down_proj", "total_rel": 0.07682, "total_fro": 61.87, "stable_rank_median": 2.22, "part_rank_median": 3.783}
  by type {'down_proj': 0.07682, 'o_proj': 0.07682}
  by depth (10-layer bands) [0.07682, 0.07682, 0.07682, 0.07682, 0.07682, 0.07683]
ctrl {"r": 4, "scale": 2.0, "changed": 120, "of": 420, "net_rel": 0.03, "rel_median": 0.07682, "rel_min": 0.0768, "rel_max": 0.07687, "rel_max_mod": "38.down_proj", "total_rel": 0.07682, "total_fro": 61.87, "stable_rank_median": 2.22, "part_rank_median": 3.783}
  by type {'down_proj': 0.07682, 'o_proj': 0.07682}
  by depth (10-layer bands) [0.07682, 0.07682, 0.07682, 0.07682, 0.07682, 0.07683]

GEOMETRY {"033": {"control_exact_B_sign_reverse": true, "cpu_vs_gpu_scout_different_stored_elements": 2289, "total_stored_elements": 10035200, "edit_sha256": "5f1bb2d83aad63ce853009ce0f0841871b065edbad8e6b1bf02de1d5ff785579"}, "100": {"control_exact_B_sign_reverse": true, "cpu_vs_gpu_scout_different_stored_elements": 2290, "total_stored_elements": 10035200, "edit_sha256": "aae8b4fc48aa0aebbba7cab48fa95b8835e78a09e330df4a7b6452bb67a1d6a8"}, "300": {"control_exact_B_sign_reverse": true, "cpu_vs_gpu_scout_different_stored_elements": 2289, "total_stored_elements": 10035200, "edit_sha256": "6e1f4148d4ef77b31e16757aaa4c37dc2638902c4e1bc26d4172b086c4835bf2"}}
4.1G	/home/keith/gemma-v2/wsp31/authors/gpt/scratch

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
[... 507 lines cut ...]
+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Assessing scientific error and code diagnostics**

 succeeded in 1031ms:
DIAGNOSTICS o_proj
0 20 {'retained': 0.6057944316894114, 'fit_error': 0.6923969871067123, 'check_error': 1.0078041087721195} cos 0.06856088848230853
20 40 {'retained': 0.5355620455581478, 'fit_error': 0.6936781239290354, 'check_error': 1.0057906440552125} cos 0.1018633714965034
40 60 {'retained': 0.5716482943441719, 'fit_error': 0.715048528518079, 'check_error': 1.0051635305975537} cos 0.07635008654283564
DIAGNOSTICS down_proj
0 20 {'retained': 0.5052666207433784, 'fit_error': 0.7725324637919686, 'check_error': 1.0034682774506956} cos 0.05213358997401563
20 40 {'retained': 0.49642569434498973, 'fit_error': 0.7298701313842395, 'check_error': 0.998999522814173} cos 0.09174956637672978
40 60 {'retained': 0.54178742363862, 'fit_error': 0.7236737932622003, 'check_error': 1.0041875509296694} cos 0.07497274091434769
EVAL records 10
mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm [('E033', 0, -0.00033891317434608936), ('E100', 2, -0.01906244270503521), ('E300', 2, -0.11239234358072281), ('C100', 1, -0.02401219680905342)]
close [('base', [101], 0.0), ('E033', [101], 0.1054084300994873), ('E100', [101], 0.38440945744514465), ('E300', [101], 0.48356178402900696), ('C100', [109], -1.593332290649414)]
mozilla__bleach.73871d76.func_pm_remove_cond__dfyej8pm [('E033', 1, 0.00035774181014858186), ('E100', 1, 0.002461430849507451), ('E300', 2, -0.03195725008845329), ('C100', 1, -0.0042394534684717655)]
close [('base', [101], 0.0), ('E033', [101], -3.6954406823497266e-06), ('E100', [101], -2.658302582858596e-05), ('E300', [101], -0.0004880375927314162), ('C100', [101], 4.649122274713591e-06)]
lepture__mistune.bf54ef67.combine_module__dalnbilu [('E033', 0, 0.0015495496336370707), ('E100', 1, 0.003362029790878296), ('E300', 1, -0.02503914013504982), ('C100', 2, -0.021451076492667198)]
close [('base', [101], 0.0), ('E033', [101], -0.0002344941021874547), ('E100', [101], -0.004803928080946207), ('E300', [101], -0.03728201612830162), ('C100', [101], -0.00022497738245874643)]
lepture__mistune.bf54ef67.combine_module__dalnbilu [('E033', 0, -0.003126013558357954), ('E100', 1, -0.008546711876988411), ('E300', 1, -0.03113718517124653), ('C100', 0, -0.004122549202293158)]
close [('base', [101], 0.0), ('E033', [101], -0.35080480575561523), ('E100', [7280], -0.8132667541503906), ('E300', [7280], -2.0825448036193848), ('C100', [101], 0.014455974102020264)]
prettytable__prettytable.ca90b055.combine_file__2mwa4qpg [('E033', 3, -0.0032761506736278534), ('E100', 3, -0.007753655314445496), ('E300', 6, -0.14585791528224945), ('C100', 5, -0.1131465882062912)]
close [('base', [101], 0.0), ('E033', [101], -0.0015242259250953794), ('E100', [101], -0.005253578536212444), ('E300', [101], -0.25553980469703674), ('C100', [101], 0.0010672344360500574)]
[... 1046 lines cut ...]
+   rr=[r for r in rows if r['at']==at]
[line removed by the dashboard scrubber]
+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

 exited 1 in 235ms:
09:16:12 EVAL 8 [task] 42 ['base', 'E033', 'E100', 'E300', 'C100']
09:16:33 EVAL 9 prettytable__prettytable.ca90b055.func_basic__1mmuxqev 67 ['base', 'E033', 'E100', 'E300', 'C100']
09:16:56 EVAL 10 [task] 44 ['base', 'E033', 'E100', 'E300', 'C100']
09:17:15 EVAL 11 [task] 32 ['base', 'E033', 'E100', 'E300', 'C100']
09:17:33 EVAL 12 Mimino666__langdetect.a1598f1a.lm_rewrite__dk9wx21q 38 ['base', 'E033', 'E100', 'E300', 'C100']
cat: /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/hold1_finished.json: No such file or directory

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
[... 501 lines cut ...]
+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Noting time update**

 exited 1 in 225ms:
09:17:15 EVAL 11 [task] 32 ['base', 'E033', 'E100', 'E300', 'C100']
09:17:33 EVAL 12 Mimino666__langdetect.a1598f1a.lm_rewrite__dk9wx21q 38 ['base', 'E033', 'E100', 'E300', 'C100']
cat: /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/hold1_finished.json: No such file or directory

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
+ assert p['instance'] in allow
+ assert all(p['instance'] not in ids for ids in denies.values()),p['instance']
[... 500 lines cut ...]
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':rows,'summary':summary})
+  (S/'generation.json').write_text(json.dumps(out));print('ARM',name,json.dumps(summary),'seconds',round(prev,1),flush=True)
+ print('DONE',round(time.time()-start,1),flush=True)
+if __name__=='__main__':main()

**Managing model and data captures**
**Optimizing audit and cleanup**
**Managing cache files safely**

 succeeded in 203ms:
{"start": 1794665179.3569732, "end": 1794665877.3871129, "seconds": 698.0301401615143}09:17:57 SUMMARY {"C100": {"all": {"n": 772, "flips": 18, "dlogp": -0.028410632298400365, "kl": 0.017361596188088156, "top101": 12, "dclose": -2.16575953132748}, "thought": {"n": 403, "flips": 15, "dlogp": -0.03459661653630491, "kl": 0.02311431030481684, "top101": 0, "dclose": -1.2701146691372023}, "close": {"n": 14, "flips": 2, "dlogp": -0.27832514952595894, "kl": 0.11389414092627703, "top101": 12, "dclose": -0.27832514952595894}, "action": {"n": 305, "flips": 1, "dlogp": -0.01196418921087764, "kl": 0.007688858980733041, "top101": 0, "dclose": -3.5120652058085455}, "delimiter": {"n": 50, "flips": 0, "dlogp": -0.00889883019041008, "kl": 0.0029693087957275567, "top101": 0, "dclose": -1.7006758499145507}, "records": 14}, "E033": {"all": {"n": 772, "flips": 10, "dlogp": 0.00022059869880117284, "kl": 0.0015974341749353577, "top101": 14, "dclose": 1.4345653304164274}, "thought": {"n": 403, "flips": 10, "dlogp": 0.0005749486636361177, "kl": 0.0025078605481471967, "top101": 0, "dclose": 0.9670938558081539}, "close": {"n": 14, "flips": 0, "dlogp": -0.0030747721606790685, "kl": 0.007384444726714686, "top101": 14, "dclose": -0.0030747721606790685}, "action": {"n": 305, "flips": 0, "dlogp": -0.00028492837426129567, "kl": 0.00036514690315224933, "top101": 0, "dclose": 2.2102262778360338}, "delimiter": {"n": 50, "flips": 0, "dlogp": 0.0013709583837044192, "kl": 0.00015598793763274443, "top101": 0, "dclose": 0.8733926963806152}, "records": 14}, "E100": {"all": {"n": 772, "flips": 15, "dlogp": -0.0027365098258605894, "kl": 0.010917183436414738, "top101": 13, "dclose": 4.229122434255372}, "thought": {"n": 403, "flips": 13, "dlogp": -0.0009619827490882306, "kl": 0.016394366567986776, "top101": 0, "dclose": 2.7708114311653684}, "close": {"n": 14, "flips": 1, "dlogp": -0.012476601228497333, "kl": 0.04054957357772894, "top101": 13, "dclose": -0.012476601228497333}, "action": {"n": 305, "flips": 1, "dlogp": -0.005511732649494184, "kl": 0.003991734373457653, "top101": 0, "dclose": 6.669067970651095}, "delimiter": {"n": 50, "flips": 0, "dlogp": 0.0026168836014403497, "kl": 0.0007192587123972771, "top101": 0, "dclose": 2.2870898056030273}, "records": 14}, "E300": {"all": {"n": 772, "flips": 40, "dlogp": -0.06396451131127041, "kl": 0.09193375289748987, "top101": 11, "dclose": 8.893344721028225}, "thought": {"n": 403, "flips": 34, "dlogp": -0.08510340193041575, "kl": 0.13916953680828842, "top101": 0, "dclose": 6.002988862636367}, "close": {"n": 14, "flips": 3, "dlogp": -0.1826402531868163, "kl": 0.22620151743779257, "top101": 11, "dclose": -0.1826402531868163}, "action": {"n": 305, "flips": 3, "dlogp": -0.04147759101918486, "kl": 0.03656130604690216, "top101": 0, "dclose": 13.606609375750432}, "delimiter": {"n": 50, "flips": 0, "dlogp": 0.002473958153714193, "kl": 0.011390284267254174, "top101": 0, "dclose": 5.9799740028381345}, "records": 14}}

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
+ assert p['instance'] in allow
+ assert all(p['instance'] not in ids for ids in denies.values()),p['instance']
+ assert p['pos']['resolved'] and not p['neg']['resolved']
+ assert p['pos']['tool']==p['neg']['tool']
[... 496 lines cut ...]
+  if out['arms']:
+   summary['changed']=sum(r['ids']!=b['ids'] for r,b in zip(rows,out['arms'][0]['rows']))
+  out['arms'].append({'name':name,'adapter':path,'seconds':prev,'rows':
[... 272 characters cut ...]

 succeeded in 224ms:
{"edit": {"summary": {"r": 4, "scale": 2.0, "changed": 120, "of": 420, "net_rel": 0.009999844519289671, "rel_median": 0.025607448442845897, "rel_min": 0.02559721074468111, "rel_max": 0.02561327673272679, "rel_max_mod": "38.o_proj", "total_rel": 0.025607184565155733, "total_fro": 20.622587976191692, "stable_rank_median": 2.2202454013444086, "part_rank_median": 3.7828769789818555, "by_type": {"down_proj": 0.02560740646650373, "o_proj": 0.025607560862767595}, "by_depth": [0.025606885395601055, 0.02560754473392491, 0.025607945766715233, 0.025606286380594225, 0.02560762327694732, 0.025607708441354397]}, "rows": [{"mod": "0.down_proj", "fro": 1.9214502447005881, "rel": 0.02561125430082965, "top_rel": 0.018126480157039573, "stable_rank": 1.996341049002564, "part_rank": 3.6672122472063093}, {"mod": "0.o_proj", "fro": 1.8697198831381114, "rel": 0.025606030784245268, "top_rel": 0.017688863565767713, "stable_rank": 2.095485509961068, "part_rank": 3.604288775336221}, {"mod": "1.down_proj", "fro": 1.8446747349716832, "rel": 0.02560665950479722, "top_rel": 0.014506494594108882, "stable_rank": 3.115881210172308, "part_rank": 3.9629948147760223}, {"mod": "1.o_proj", "fro": 1.9395622295319224, "rel": 0.025607799745979, "top_rel": 0.019009506962363525, "stable_rank": 1.814691579062183, "part_rank": 3.5782988660376533}, {"mod": "2.down_proj", "fro": 1.9547699889840289, "rel": 0.025606254992937373, "top_rel": 0.02091320710370737, "stable_rank": 1.499169904819231, "part_rank": 2.96199592536152}, {"mod": "2.o_proj", "fro": 1.8958382607713657, "rel": 0.025606870977538186, "top_rel": 0.016199744537660807, "stable_rank": 2.498599763591573, "part_rank": 3.869678788554157}, {"mod": "3.down_proj", "fro": 1.9382673985982455, "rel": 0.025606790860165308, "top_rel": 0.016774259406020623, "stable_rank": 2.3303631228354065, "part_rank": 3.785160353700936}, {"mod": "3.o_proj", "fro": 1.8834793676624164, "rel": 0.025607576587855153, "top_rel": 0.015733272941019515, "stable_rank": 2.649102770449005, "part_rank": 3.8721007461246426}, {"mod": "4.down_proj", "fro": 1.9257752769108865, "rel": 0.02560818613241731, "top_rel": 0.01506742418337074, "stable_rank": 2.888548114053621, "part_rank": 3.9471582856375877}, {"mod": "4.o_proj", "fro": 1.8440984155741553, "rel": 0.025607404226460136, "top_rel": 0.015494609929832584, "stable_rank": 2.731302606280005, "part_rank": 3.9239503732089434}, {"mod": "5.down_proj", "fro": 1.8278188655536778, "rel": 0.025607405690314205, "top_rel": 0.014847313720295914, "stable_rank": 2.9746467117673934, "part_rank": 3.9342736924335524}, {"mod": "5.o_proj", "fro": 1.8536385707513106, "rel": 0.025607634307733818, "top_rel": 0.01837626629638468, "stable_rank": 1.941888844756251, "part_rank": 3.6690096258905815}, {"mod": "6.down_proj", "fro": 1.8560157912831226, "rel": 0.02560756628991897, "top_rel": 0.01737403067323283, "stable_rank": 2.1723782375436107, "part_rank": 3.735829739464619}, {"mod": "6.o_proj", "fro": 1.8943604566268943, "rel": 0.025605696808745627, "top_rel": 0.017667341719963845, "stable_rank": 2.100539144396381, "part_rank": 3.759517702543781}, {"mod": "7.down_proj", "fro": 1.8874655806465181, "rel": 0.025604277321852775, "top_rel": 0.01827075783204416, "stable_rank": 1.963866320299273, "part_rank": 3.600054486914287}, {"mod": "7.o_proj", "fro": 1.9122850209913909, "rel": 0.025608085957775147, "top_rel": 0.018884866335719088, "stable_rank": 1.8387657564409592, "part_rank": 3.5688279207051825}, {"mod": "8.down_proj", "fro": 1.9550740529823813, "rel": 0.025606899813663923, "top_rel": 0.019174816202523814, "stable_rank": 1.7834115915706732, "part_rank": 3.582282005892869}, {"mod": "8.o_proj", "fro": 1.8754435141968124, "rel": 0.025606842853567208, "top_rel": 0.019027412645799515, "stable_rank": 1.811142404819007, "part_rank": 3.610124009796471}, {"mod": "9.down_proj", "fro": 1.9669966651755557, "rel": 0.02560586919528515, "top_rel": 0.01707827522189498, "stable_rank": 2.2479726811059124, "part_rank": 3.82193503
[... 85714 characters cut ...]

 succeeded in 221ms:
REPLAY cpu100_edit

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
+ assert p['instance'] in allow
+ assert all(p['instance'] not in ids for ids in denies.values()),p['instance']
+ assert p['pos']['resolved'] and not p['neg']['resolved']
+ assert p['pos']['tool']==p['neg']['tool']
[... 514 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Estimating runtime and resources**

 succeeded in 208ms:
 },
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
}
{"start": 1794665918.5473034, "limit": 1500, "pid": 419674}
diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
+ assert p['instance'] in allow
[... 1084 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 1715ms:
FROZEN 99 files capture_bytes 325211847 maps ddf8637741b3f16b75c10c6b826e3fa9d3f13a0f05b16408791b959648838811
REPLAY cpu100_edit
REPLAY cpu033_edit
REPLAY cpu100_ctrl
09:20:07 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/rebuilt100/edit 0.0099999997806608 120
09:20:07 BUILD /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/rebuilt033/edit 0.0033000000860908046 120

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
[... 1132 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 243ms:
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
BIT_EXACT 100 6
BIT_EXACT 033 6
SIZE 100 {'edit': {'net_rel': 0.009999844519289671, 'changed': 120, 'rel_max': 0.02561327673272679, 'stable_rank_median': 2.2202454013444086}, 'ctrl': {'net_rel': 0.009999844519289671, 'changed': 120, 'rel_max': 0.02561327673272679, 'stable_rank_median': 2.2202454013444086}}
SIZE 033 {'edit': {'net_rel': 0.0032999601021971347, 'changed': 120, 'rel_max': 0.00845275934312434, 'stable_rank_median': 2.2199279514647148}, 'ctrl': {'net_rel': 0.0032999601021971347, 'changed': 120, 'rel_max': 0.00845275934312434, 'stable_rank_median': 2.2199279514647148}}
REPLAY cpu100_edit
REPLAY cpu033_edit
REPLAY cpu100_ctrl

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
[... 574 lines cut ...]
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Analyzing log and validation details**
**Reviewing tool usage and script policies**
**Analyzing CPU process and data integrity**
**Verifying program classification syntax**
**Analyzing tool schema inconsistencies**
**Verifying initial fit examples**

 succeeded in 244ms:
REPLAY cpu100_edit
REPLAY cpu033_edit
REPLAY cpu100_ctrl
REPLAY cpu033_ctrl
SERVER_BUDGET 1281
[line removed by the dashboard scrubber]
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[line removed by the dashboard scrubber]
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BIT_EXACT 100 6
BIT_EXACT 033 6
SIZE 100 {'edit': {'net_rel': 0.009999844519289671, 'changed': 120, 'rel_max': 0.02561327673272679, 'stable_rank_median': 2.2202454013444086}, 'ctrl': {'net_rel': 0.009999844519289671, 'changed': 120, 'rel_max': 0.02561327673272679, 'stable_rank_median': 2.2202454013444086}}
SIZE 033 {'edit': {'net_rel': 0.0032999601021971347, 'changed': 120, 'rel_max': 0.00845275934312434, 'stable_rank_median': 2.2199279514647148}, 'ctrl': {'net_rel': 0.0032999601021971347, 'changed': 120, 'rel_max': 0.00845275934312434, 'stable_rank_median': 2.2199279514647148}}

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
[... 1178 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 283ms:
{
  "eval_calls.jsonl": {
    "records": 14,
    "tasks": 11,
    "boundary_violations": []
  },
  "generation_panel.jsonl": {
    "records": 112,
    "tasks": 52,
    "boundary_violations": []
  },
  "replay_panel.jsonl": {
    "records": 8,
    "tasks": 8,
    "boundary_violations": []
  }
}
REPLAY cpu100_ctrl
REPLAY cpu033_ctrl
[... 597 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Reviewing code audit and update timing**

 succeeded in 216ms:
REPLAY cpu100_ctrl
REPLAY cpu033_ctrl
SERVER_BUDGET 1281
server up after 72s (pid/pgid 425976, log /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn10/logs/server_18410_1114_092136.log)
PANEL 112 52
344 /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
[... 583 lines cut ...]
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Designing finalization script workflow**

I’m organizing a script that monitors task completion across multiple arms, ensuring the first entry finishes fully before the second starts, with a controlled queue handling readiness states. The finalizer loads generation data, runs syntax checks with error handling, and writes atomic snapshots to ensure stable updates.

 succeeded in 235ms:
    return dict(episodes=len(eps), answers=sum(len(e) for e in eps), tool_calls=n, unpaired=k, eps_unpaired=ek,
                multi=any(len(e) > 1 for e in eps), long_thoughts=long_n, loops=loops)


def tail(k, n, p):
    """P(X >= k) for X binomial(n, p)."""
    if k <= 0:
        return 1.0
    lg = math.lgamma
    return min(1.0, sum(math.exp(lg(n + 1) - lg(i + 1) - lg(n - i + 1) + i * math.log(p) + (n - i) * math.log(1 - p))
                        for i in range(k, n + 1)))


def verdict(m, hi, ep_hi):
    n, k = m["tool_calls"], m["unpaired"]
    if n and k / n >= MARK and tail(k, n, hi) <= TAIL and \
            (not m["multi"] or tail(m["eps_unpaired"], m["episodes"], ep_hi) <= TAIL):
        return "BROKEN"
    if n >= NMIN and k / n < MARK:
[... 1409 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 205ms:
PANEL 112 52
PROGRESS reference 25 60.0
PROGRESS reference 50 75.9
PROGRESS reference 75 105.5
PROGRESS reference 100 128.7
[line removed by the dashboard scrubber]
PROGRESS cpu100_edit 25 62.4

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
[... 711 lines cut ...]
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Analyzing model output diagnostics**
**Reviewing prediction range discrepancies**
**Verifying pretest and finalizer processes**
**Analyzing generation and testing limitations**

 succeeded in 72ms:

source_names=['outcome_map.py','rebuild.py','prepare.py','pairs.jsonl','maps.pt','capture_stats.pt','selection.json','f
it_diagnostics.json','generation_panel.jsonl','replay_panel.jsonl','eval_calls.jsonl','environment.json','tools_sha256.
json']
hs={n:sha(C/n) for n in source_names};hs.update({'tools/'+str(p.relative_to(C/'tools')):sha(p) for p in 
(C/'tools').rglob('*') if p.is_file() and '__pycache__' not in p.parts})
Data selection (`code/turn10/prepare.py`): unedited base/aa request logs from b007, x001, x003, x004 and x005. Keep 
tasks in both stage-1 lists and exclude all stage-2 list overlaps; audits checked nine lists. In each episode take the 
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
length, then batch/arm names. Reserve tasks with SHA256(task) modulo 4 equal to zero; sort by hash. This yielded 18 
[line removed by the dashboard scrubber]
are in `pairs.jsonl` and `selection.json`.
These checks can exclude a large local syntax break; they do not test full-episode looping, total tool use or patch 
correctness. The dose choice uses these panels, so they are development checks. The custom transport capture/scout 
code is new; g31 and the official replay backend are documented as verified, but the transport-to-solve-rate link is 
unvalidated.

 succeeded in 243ms:
PROGRESS reference 75 105.5
PROGRESS reference 100 128.7
[line removed by the dashboard scrubber]
PROGRESS cpu100_edit 25 62.4
PROGRESS cpu100_edit 50 74.5
PROGRESS cpu100_edit 75 106.0
PROGRESS cpu100_edit 100 132.0
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
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BIT_EXACT 100 6
BIT_EXACT 033 6
SIZE 100 {'edit': {'net_rel': 0.009999844519289671, 'changed': 120, 'rel_max': 0.02561327673272679, 'stable_rank_median': 2.2202454013444086}, 'ctrl': {'net_rel': 0.009999844519289671, 'changed': 120, 'rel_max': 0.02561327673272679, 'stable_rank_median': 2.2202454013444086}}
SIZE 033 {'edit': {'net_rel': 0.0032999601021971347, 'changed': 120, 'rel_max': 0.00845275934312434, 'stable_rank_median': 2.2199279514647148}, 'ctrl': {'net_rel': 0.0032999601021971347, 'changed': 120, 'rel_max': 0.00845275934312434, 'stable_rank_median': 2.2199279514647148}}

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
[... 1445 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 217ms:
{
 "probeset": "/home/keith/gemma-v2/wsp31/authors/gpt/code/turn10/replay_panel.jsonl",
 "calls": 8,
 "episodes": 8,
[line removed by the dashboard scrubber]
 "base_seconds": 34.1,
 "base": {
  "solved": {
   "calls": 4,
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
[line removed by the dashboard scrubber]
   "mean_logprob_think": -0.07958,
   "mean_logprob_action": -0.07637,
   "mean_logprob_all": -0.07763,
   "top_is_logged": 0.99288,
   "calls_without_divergence": 0.5,
   "first_divergence_in_think": 0,
   "first_divergence_in_action": 2,
[... 784 lines cut ...]
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Analyzing tool run and submission consistency**
**Analyzing model divergence causes**

 succeeded in 3620ms:
ARM cpu100_edit tool_name_changes 3
CONTEXT [task] 26 after 4/30 used). Finalize your edits and call submit_patch soon."}<|"|>}<tool_response|><|channel>thought
The diff is there. I don't know why `submit_patch` said `files_changed: 1`, but maybe it's just a summary.

I'll call `submit_patch` again just to be sure.

<channel|><|tool_call>call:submit_patch{}<tool_call|><|tool_response>response:submit_patch{value:<|"|>{"status": "ok", "patch_size": 444, "files_changed": 1}<|"|>}<tool_response|><|channel>thought
Still says 1. I suspect it's because it's ignoring the `tests/` directory in its count.

I'm confident in the fix.

<channel|>
BASE <|tool_call>call:run_command{command:<|"|>git checkout -- tests/test_env.py<|"|>}<tool_call|><|tool_response>
EDIT <|tool_call>call:submit_patch{}<tool_call|><|tool_response>
CONTEXT [task] 2 turn _char(line, char):\n            return self._lines[line - 1][char - 1]\n\n        # Find the semicolon\n        while current_char(line, char) != \";\":\n            assert self.WHITESPACE_RE.match(current_char(line, char))\n            line, char = self._previous_char(line, char)\n        # Skip it\n        line, char = self._previous_char(line, char)\n        # Skip the whitespace and table/feature name", "start_line": 370, "end_line": 420, "total_lines": 681, "is_truncated": false}<|"|>}<tool_response|>
BASE <|channel>thought
The traceback said:
`File "/Users/cjdunn/env_gtools/lib/python3.7/site-packages/glyphsLib/builder/features.py", line 396, in _build_end_locations_rec`
`previous_in_block = st.statements[-1]`
[... 1591 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 213ms:
[line removed by the dashboard scrubber]
PROGRESS cpu033_edit 25 61.5
PROGRESS cpu033_edit 50 78.5
PROGRESS cpu033_edit 75 106.5
PROGRESS cpu033_edit 100 131.3

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
[... 790 lines cut ...]
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Monitoring generation progress**
**Configuring computation environment**

 succeeded in 219ms:
PROGRESS cpu033_edit 25 61.5
PROGRESS cpu033_edit 50 78.5
PROGRESS cpu033_edit 75 106.5
PROGRESS cpu033_edit 100 131.3
[line removed by the dashboard scrubber]
PROGRESS cpu100_ctrl 25 62.4
PROGRESS cpu100_ctrl 50 73.2
# Champion

Generation 0: the unedited model. No edit has passed yet.

Every batch scores an entry against this base: the reference arm is the champion and your edit and control are served as champion + your adapter (exact sum, `wsp31/tools/stack_exact.py`). Your entry is still only your own change (rank 32 or less). An edit that passes both screening stages becomes the next generation.


diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
[... 799 lines cut ...]
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Analyzing script compilation and timing**
**Reviewing parked tasks and stable records**
**Noting script wrapping and build details**

 succeeded in 231ms:
[line removed by the dashboard scrubber]
PROGRESS cpu100_ctrl 25 62.4
PROGRESS cpu100_ctrl 50 73.2
PROGRESS cpu100_ctrl 75 104.5

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
+ assert p['instance'] in allow
[... 788 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Wrapping up final edits**

 succeeded in 210ms:
PROGRESS cpu100_ctrl 25 62.4
PROGRESS cpu100_ctrl 50 73.2
PROGRESS cpu100_ctrl 75 104.5
PROGRESS cpu100_ctrl 100 132.8
[line removed by the dashboard scrubber]

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
+denies={p.name:set(p.read_text().split()) for p in (R/'wsp31/queue/sets').glob('stage2_ids*.txt')}
+for p in pairs:
[... 789 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Finalizing parity run check**

 succeeded in 225ms:
title: Paired outcome writer map across all 60 layers at 1 percent

## Hypothesis

[line removed by the dashboard scrubber]

This differs from 0025/0026's code-versus-prose projection and 0035/0036's constant instruction writes. It uses outcome-labelled, within-task pairs, separate input and output factors, and a ridge solve with preserve samples. It neither projects onto a thought-close direction nor replaces a neuron. The E4B patch-correctness direction failed; this version tests paired tasks and a conditional map across both writers in every layer. That difference warrants a test, not a claim that correctness is readable.

[line removed by the dashboard scrubber]

## Exact recipe

base: champion generation 0
size: whole network 0.999984% over 120 matrices, largest matrix 2.561328%
control size: whole network 0.999984% over 120 matrices, largest matrix 2.561328%

Rank 4, alpha 8, bf16 LoRA factors, o_proj and down_proj in layers 0 through 59. Median stable rank 2.2202. The norm uses all 420 editable matrices as its denominator. No champion overlap exists at design. A future writer edit could add to or oppose this map; no stack benefit is assumed.

[line removed by the dashboard scrubber]
[... 847 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Preparing technical review notes**

 succeeded in 1968ms:
READY paired-outcome-writers-r4-100 2026-11-14T14:34:07.922255+00:00
{
  "utc": "2026-11-14T14:34:07.987046+00:00",
  "ready": [
    "paired-outcome-writers-r4-100"
  ],
  "gpu_seconds": 698.0301401615143,
  "gpu_holds": [
    {
      "start": 1794665179.3569732,
      "end": 1794665877.3871129,
      "seconds": 698.0301401615143
    }
  ],
  "sizes": {
    "100": {
      "r": 4,
      "scale": 2.0,
      "changed": 120,
[... 829 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Analyzing generation readiness and control flow**

 succeeded in 190ms:
PROGRESS cpu100_ctrl 50 73.2
PROGRESS cpu100_ctrl 75 104.5
PROGRESS cpu100_ctrl 100 132.8
[line removed by the dashboard scrubber]
PROGRESS cpu033_ctrl 25 62.7
PROGRESS cpu033_ctrl 50 77.6
READY
build.sh
checks.json
ctrl
edit
rebuild_receipt.json
record.md
size.json
source_hashes.json

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
[... 1612 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 234ms:
PROGRESS cpu033_ctrl 50 77.6
PROGRESS cpu033_ctrl 75 106.9
PROGRESS cpu033_ctrl 100 131.4
[line removed by the dashboard scrubber]
DONE 806.7
cmd rc=0
server 425976 stopped at 09:36:21, hold 885s
{"start": 1794665918.5473034, "end": 1794666981.5439312, "seconds": 1062.9966280460358, "returncode": 0}
diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
--- /dev/null
+++ b/turn10/audit.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+import json,hashlib,sys
+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn10';S=A/'scratch/turn10'
+pairs=[json.loads(l) for l in (C/'pairs.jsonl').open()]
+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())
[... 791 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Confirming task progress**

 succeeded in 224ms:
title: Paired outcome writer map across all 60 layers at 0.33 percent

## Hypothesis

[line removed by the dashboard scrubber]

This differs from 0025/0026's code-versus-prose projection and 0035/0036's constant instruction writes. It uses outcome-labelled, within-task pairs, separate input and output factors, and a ridge solve with preserve samples. It neither projects onto a thought-close direction nor replaces a neuron. The E4B patch-correctness direction failed; this version tests paired tasks and a conditional map across both writers in every layer. That difference warrants a test, not a claim that correctness is readable.

[line removed by the dashboard scrubber]

## Exact recipe

base: champion generation 0
size: whole network 0.329996% over 120 matrices, largest matrix 0.845276%
control size: whole network 0.329996% over 120 matrices, largest matrix 0.845276%

Rank 4, alpha 8, bf16 LoRA factors, o_proj and down_proj in layers 0 through 59. Median stable rank 2.2199. The norm uses all 420 editable matrices as its denominator. No champion overlap exists at design. A future writer edit could add to or oppose this map; no stack benefit is assumed.

## Prediction (written before the run)
[... 817 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Verifying record consistency**

 succeeded in 2612ms:
READY paired-outcome-writers-r4-033 2026-11-14T14:37:00.491436+00:00
{
  "utc": "2026-11-14T14:37:01.094848+00:00",
  "ready": [
    "paired-outcome-writers-r4-100",
    "paired-outcome-writers-r4-033"
  ],
  "gpu_seconds": 1761.02676820755,
  "gpu_holds": [
    {
      "start": 1794665179.3569732,
      "end": 1794665877.3871129,
      "seconds": 698.0301401615143
    },
    {
      "start": 1794665918.5473034,
      "end": 1794666981.5439312,
      "seconds": 1062.9966280460358,
      "returncode": 0
[... 1701 lines cut ...]
+t=time.time();subprocess.run([str(PY),str(C/'rebuild.py'),str(dst),'--dose',str(d)],check=True)
+out={'seconds':time.time()-t,'files':{}}
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

 succeeded in 764ms:
{
  "paired-outcome-writers-r4-100": {
    "READY_utc": "2026-11-14T14:34:07.920860+00:00",
    "pinned_sources_checked": 99
  },
  "paired-outcome-writers-r4-033": {
    "READY_utc": "2026-11-14T14:37:00.489544+00:00",
    "pinned_sources_checked": 99
  },
  "new_entries": 2,
  "gpu_seconds": 1761.02676820755,
  "owned_gpu_processes_active": [],
  "scratch_GiB": 3.8276606192812324,
  "notes_words": 421
}

diff --git a/turn10/audit.py b/turn10/audit.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d6f06286cc28d41a68c29ac217f43ef92f0cfd
[... 824 lines cut ...]
+for arm in ['edit','ctrl']:
+ for fn in ['adapter_config.json','adapter_model.safetensors','edit.json']:
+  h1=hashlib.sha256((src/arm/fn).read_bytes()).hexdigest();h2=hashlib.sha256((dst/arm/fn).read_bytes()).hexdigest()
+  assert h1==h2,(arm,fn,h1,h2)
+  out['files'][arm+'/'+fn]=h1
+(C/('rebuild_receipt_'+nm+'.json')).write_text(json.dumps(out,indent=2));print('REBUILD BIT EXACT',nm,round(time.time()-t,2),flush=True)

**Summarizing test results concisely**