{"chunks":1,"id":"t013","last_seq":0,"rows":[{"i":0,"kind":"meta","long":false,"text":"Reading additional input from stdin...\nOpenAI Codex v0.155.1\n--------\nworkdir: C:\\Users\\Owner\\wsp31_loop\\work\\gpt\nmodel: gpt-6-astra\nprovider: openai\napproval: never\nsandbox: danger-full-access\nreasoning effort: high\nreasoning summaries: concise\nsession id: 01a1db82-baec-7443-8394-d5fa837c3cda\n--------"},{"i":1,"kind":"user","long":false,"text":"You are author 'gpt' in the wsp31 weight-editing programme. This is turn 13. Your full instructions are in a brief on host wrx90. First run: ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/BRIEF.md' and follow it exactly, working as author 'gpt' (your directory is /home/keith/gemma-v2/wsp31/authors/gpt/). The results of your last round, and every other author's entries and results, are in /home/keith/gemma-v2/wsp31/authors/gpt/RESULTS.md on wrx90: read it before you decide anything. This turn submits ONE round of at most two queue entries; a third is rejected, and your next turn opens only when this round has been screened. You have 90 minutes of wall-clock time and are stopped at the limit, so finish each entry completely (READY file) before starting the next and keep NOTES.md current as you go. Work autonomously: nobody will answer questions during the turn."},{"i":2,"kind":"assistant","long":false,"text":"I\u2019ll read the brief and results on wrx90, then complete one round as author `gpt`, keeping `NOTES.md` current."},{"i":3,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/BRIEF.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":4,"kind":"output","long":true,"text":" succeeded in 291ms:\n# wsp31 author brief (read fully at the start of every turn)\n\nYou are one of three authors (claude, gpt, gemini) taking turns. Your job: make Gemma 4 31B solve more coding-agent\ntasks **by editing its weights, with no training**. You read the model (weights, activations, traces, failures),\ndecide what to change, build the change as a closed-form edit, predict its effect, and queue it. A separate runner\nspends the rollouts and writes the measured result next to your prediction. You are scored on tasks gained over the\nchampion in the same batch (the unedited model plus the edits that have already passed; see \"The champion\"), on the\nmargin over your matched control, and on how well your predictions held.\n\n**Be bold.** Small safe nudges were tried on the 4B model and were lost in the noise (see `PRIOR_E4B.md`). You get\ntwo entries per round and then wait for their results, so each one counts; a third variation of a null result is a\nwasted round. High-risk ideas with a clear mechanism and a\nprediction that could be wrong are what this programme wants. Say what would prove you wrong.\n\n## Where you work\n\n- Everything happens on host `wrx90`, reached with `ssh -o BatchMode=yes wrx90 ''`. Write scripts in your\n local working directory, copy them with `scp`, run them with ssh. Keep ssh output small (`tail`, `cut`, `head`).\n- Root: `/home/keith/gemma-v2` (below: `$R`). Python: `$R/tii-venv/bin/python` (torch, transformers, vLLM 0.19.1,\n[... 213 lines cut ...]\n- An edit that passes both stages is promoted: it becomes part of the champion for everything after it.\n- The champion is confirmed on tasks you do not see, against the unedited model, and the final champion gets one\n pre-registered held-out run. A changed edit is a new edit.\n- A combination of your own edits is a new queue entry with its own record, prediction and control. Combining with\n edits that already passed needs nothing from you: that is what the champion does.\n\nSingle-run agent evaluations are noisy by several tasks. An edit worth having shows up as ten or more tasks.\nAim there."},{"i":5,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md; cat /home/keith/gemma-v2/wsp31/authors/gpt/RESULTS.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":6,"kind":"output","long":true,"text":" succeeded in 410ms:\n# Author gpt - turn 10, complete: two entries READY\n\nStarted 2026-11-14 14:00 UTC; deadline 15:30 UTC. Updated 2026-11-14T14:37:01.094685+00:00. Champion generation 0 at design and pre-test. This turn is one round; submit no third entry.\n\nPrevious round: source-grounding ICV doses 3/1 null (-2.5/-3.5). Apparent edit-error reductions were mostly malformed-call counts within unedited variation. Close-writer and close-neuron amplification were harmful; no more forced-close work.\n\nNew family: paired-outcome writer maps, rank 4 across all 120 o_proj/down_proj matrices. Fit 18 within-task success/failure pairs, holding out 3 hash-split paired tasks; exact unedited b007/x001/x003/x004/x005 calls, same editing tool, nearest prompt lengths among last qualifying calls. Episode success is a noisy proxy for local edit correctness. Older/newer budgets and different local subproblems remain confounds. No hidden-test text enters the fit.\n\nClosed form: at each writer, target failed thought means toward successful means; preserve successful, prompt and syntax means with ridge. Rank-4 output SVD; compose the map with W; equalize per-matrix relative norm. No optimizer or trained-adapter direction. Reversed controls keep A exactly and negate B. CPU rebuilds match config, weights and metadata byte for byte. Full code, inputs, captures, hashes and audit receipts: code/turn10. Queue records contain the equations and episode names.\n\nQueued: paired-outcome-writers-r4-100, paired-outcome-writers-r4-033. Network sizes are 0.999984% and 0.329996% if both are queued; largest matrices 2.561328% and 0.845276%. Both meet training-size requirements. Predictions: -6 to +12 and -4 to +9 stage-1 tasks. Stage 1 is design data; stage 2 is the first unseen-task outcome check.\n\n[line removed by the dashboard scrubber]\n\nGPU use so far: 1761.03 seconds across 2 finished holds, GPU 0 only through lock and wrapper. No rollouts. No stage-2 or sealed traces read; all fit/check task IDs passed nine stage-2 exclusion lists. Scratch stays below 10 GB.\n\nNext turn: read RESULTS before deciding. If null, do not try another dose of this weak-transfer family. If positive, separate gains on the 18 fit tasks from the other stage-1 tasks; do not fit on stage 2. Missing tool: a pre-test validated to predict task gains.\n# Results for author gpt (2026-11-14 14:22:18 EST)\n\n[... 77 lines cut ...]\n| 0038 | 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 |\n| 0039 | 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) | queued_confirm | -4 to +8 | +2.0 (79 vs 78) | | null |\n| 0043 new | gemini | spec-audit-thought-gate | Thought-gated reliability-shrunk Problem Statement spec-audit steering across all 60 MLP layers | queued_confirm | +2 to +7 | +2.5 (79 vs 78) provisional | | null |\n| 0044 new | gemini | dual-audit-postedit-boost | Stateful two-neuron post-edit spec-audit boost with thought and edit-history gating over 180 MLP matrices | running_confirm | +3 to +8.5 | +5.5 (82 vs 78) provisional | | - |\n[line removed by the dashboard scrubber]\n| 0050 new | claude | fcv-brief-exact3-b20 | Instruction pair vector, 'be exact about what the issue asks' part written three times in layers 6 to 24, think-briefly part at dose 2 | queued_confirm | -5 to +7 | +4.0 (81 vs 76) provisional | | - |\n[line removed by the dashboard scrubber]\n| 0056 | gemini | tri-phase-concise-audit-veto | Capacity-regularized three-phase GeGLU controller for concise thoughts, post-edit spec audit, and pre-submit veto | running_s1 | +4 to +9.5 | | | - |"},{"i":7,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"rg -n \\\"wsp31|weight.edit\\\" C:\\\\Users\\\\Owner\\\\.codex\\\\memories\\\\MEMORY.md\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":8,"kind":"output","long":false,"text":" exited 1 in 90ms:"},{"i":9,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/queue/STATUS.md; cat /home/keith/gemma-v2/wsp31/queue/CHAMPION.md; cat /home/keith/gemma-v2/tii-wsp31-doc/research/wsp31/LEDGER.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":10,"kind":"output","long":true,"text":" succeeded in 323ms:\n# wsp31 queue status (2026-11-14 14:22:20 EST)\n\nRule: {\"s1_promote\": 2.0, \"s1_harmful\": -6.5, \"s2_min\": 0.5, \"ctrl_margin\": 0.5, \"total_pass\": 8.0, \"total_harmful\": -10.5, \"run2_bar\": 8.0}\n\nChampion (the base every new edit is scored against): Generation 0: the unedited model. No edit has passed yet.\n\nReference arms of a new batch: the unedited model served through an all-zero adapter, the same serving path as an edit (ledger record 0033).\n\n[line removed by the dashboard scrubber]\n\n## Batches\n\n| batch | stage | gpu | state | arms | rows | note |\n|---|---|---|---|---|---|---|\n| x003 | s1 | box | done | base e0035 e0036 aa | 480 | {\"base\": 81, \"episodes\": 120, \"aa\": [81, 82, 3, 4], \"arms\": {\"e0035\": [79, -2.5], \"e0036\": [78, -3.5]}} |\n| b004-supp1 | s1 | 1 | done | base e0021 e0020 aa | 76 | {\"tasks\": 19, \"record\": \"0042\", \"solved\": {\"base\": [56, 70], \"e0021\": [50, 60], \"e0020\": [47, 58], \"aa\": [59, 73]}} |\n| b009 | s2 | 1 | done | base e0032 c0032 aa | 872 | {\"base\": 127, \"episodes\": 218, \"aa\": [127, 114, 20, 7], \"arms\": {\"e0032\": [123, 2.5], \"c0032\": [120, -0.5]}} |\n| x004 | s1 | box | done | base e0038 e0039 aa | 480 | {\"base\": 78, \"episodes\": 120, \"aa\": [78, 76, 7, 5], \"arms\": {\"e0038\": [75, -2.0], \"e0039\": [79, 2.0]}} |\n| x005 | s1 | box | done | base e0007 e0009 aa | 480 | {\"base\": 75, \"episodes\": 120, \"aa\": [75, 73, 7, 5], \"arms\": {\"e0007\": [74, 0.0], \"e0009\": [3, -71.0]}} |\n[... 166 lines cut ...]\n[line removed by the dashboard scrubber]\n| [0054](ledger/0054-claude-fast1-throughput-deploy.md) | 2026-11-14 | claude | throughput | fast1 deploy - 24 streams per server, GPU memory share 0.95 and Son's adaptive ngram drafts on the rented boxes and on wrx90 | none (serving change; no task set was run for this record) / none yet; first box batch on the new kit will be x009, first wrx90 batch with ngram_fix not formed at the time of writing | running | n/a | deployed without a pass bar at Keith's request; lane's single-window measurement 317.0 tok/s against a mean of 278.6 (+13.8 %); not yet verified in a real batch |\n[line removed by the dashboard scrubber]\n| [0056](ledger/0056-gemini-tri-phase-concise-audit-veto.md) | 2026-11-14 | gemini | edit | Capacity-regularized three-phase GeGLU controller for concise thoughts, post-edit spec audit, and pre-submit veto | wsp31_screen_v2 / x009 | running | - | - |\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n| [0059](ledger/0059-claude-fast2-throughput-deploy.md) | 2026-11-14 | claude | throughput | fast2 deploy - fp8 KV cache, 48 streams and the rest of Son's serving stack on the rented boxes and on wrx90 (changes the numbers; new comparable group) | none (serving change; no task set was run for this record) / none yet; last box batch before fast2 is x008 (or x009 if it forms before the kit commit), last wrx90 batches before are b010 (GPU 1) and b011 (GPU 0); first fast2 batches are named in the Result when they form | running | n/a | deployed without a pass bar at Keith's request; lane's replay measurement 430.0 tok/s against 314.3 for fast1 (+37 %); not yet verified in a real batch |\n[line removed by the dashboard scrubber]"},{"i":11,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/tii-wsp31-doc/research/wsp31/PRIOR_E4B.md; cat /home/keith/gemma-v2/tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md; cat /home/keith/gemma-v2/wsp31/tools/TOOLS.md; cat /home/keith/gemma-v2/wsp31/tools/ARCH.md; cat /home/keith/gemma-v2/wsp31/interp/REPORT.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":12,"kind":"output","long":true,"text":" succeeded in 410ms:\n# What was already tried on Gemma 4 E4B (paper track, 4-12 Nov 2026)\n\nThese are training-free weight edits on the small model, written by Claude, GPT and Gemini, measured on 170\n[line removed by the dashboard scrubber]\nonly for 31B) and on 130 held-out tasks. Sources: the private paper repository (`gemma-weight-programming`), its\nexperiment log and result files. Counts are tasks solved of 170 unless stated; \"vs\" is the same-batch unedited arm.\n\nNone of these results transfers to 31B automatically. They are listed so that a 31B attempt of the same kind is made\non purpose, with a reason, and not by accident.\n\n## Method that was used\n\nA frontier LLM diagnosed failures from logged agent decisions, located a direction in the residual stream (difference\nof means between decision classes), compiled it into a closed-form MLP edit (always-on, or gated by a \"quiet neuron\"\nthat fires only under a condition such as \"no file edited yet\"), wrote a prediction, and tested against a\nnorm-matched random control.\n\n## What moved behaviour\n\n[... 4263 lines cut ...]\n- The agy reader: no family-A answer and no scale-up answer (amendment 4a); at about 9 minutes per call and with\n half of the calls empty it is still inside the family-B pilot, in the fixed order of amendment 2, and is\n reported with its n. Every verdict of section 4 is for two readers.\n- Priority 4 of the lane (do atlas or reader predictions agree with what edits did in rollouts): eleven entries\n are queued, none has a stage-1 result yet; predictions and scorer registered (amendments 5 to 5d, section\n 3.6).\n\n**Managing output size**"},{"i":13,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'wc -l /home/keith/gemma-v2/wsp31/tools/TOOLS.md /home/keith/gemma-v2/wsp31/tools/ARCH.md; head -n 100 /home/keith/gemma-v2/wsp31/interp/REPORT.md; sed -n \\\"/\"'^## C'\"\\\\./,/\"'^## D'\"\\\\./p\\\" /home/keith/gemma-v2/tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md; ls /home/keith/gemma-v2/wsp31/authors/gpt/code/turn10'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":14,"kind":"output","long":true,"text":" succeeded in 291ms:\n 314 /home/keith/gemma-v2/wsp31/tools/TOOLS.md\n 144 /home/keith/gemma-v2/wsp31/tools/ARCH.md\n 458 total\nsed: -e expression #1, char 4: unterminated address regex\n# Interpretability lane: what is known about the inside of Gemma 4 31B\n\n[line removed by the dashboard scrubber]\nown runs; anything else is labelled. Pre-registration: `PROTOCOL.md` (sections 0-5 and dated amendments 1, 2, 3, 3a,\n3b, 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:\n`ATLAS_lens.md`.\n\n[line removed by the dashboard scrubber]\n\nThe author brief points to this block. It is kept current and can be read alone; the dated blocks below it are\nthe 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\n[line removed by the dashboard scrubber]\n\n- **New (9:09 AM ET, Sat 14 Nov 2026): stage 2 of 0032 (claude; the constant \"think briefly\" vector at dose 4) is finished and the edit did\n not pass (runner +2.5, verdict null). This lane registered five predictions for this batch before reading it\n[... 115 lines cut ...]\nreplay_panel.jsonl\nselection.json\nserved.py\nsource_hashes.json\ntools\ntools_sha256.json\nturn_receipt.json\nverify_rebuild.py"},{"i":15,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/tools/TOOLS.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":16,"kind":"output","long":true,"text":" succeeded in 338ms:\n# wsp31 tools for edit authors\n\nChangelog (newest first; times US Eastern):\n- Fri 13 Nov, 3:51 PM: **three of the first four edits were harmful (-10 to -21 tasks): read \"Before you submit\"\n (section 5) first.** GPU 0 for pre-tests only while an author turn is open (section 3). New `delimprobe.py`.\n[line removed by the dashboard scrubber]\n 12:39 AM speed (trap 12): **the server's rank limit sets the price of an adapter**.\n- Thu 12 Nov: 11:40 PM first served runs of `dose.py`, `validate.py`, `vgen.py`, `closeprobe.py`; 10:55 PM stacks\n of rank 34 to 96 applied as written; 10:30 PM **thought-end pre-tests need exact contexts**, rank limits 64 or 128\n (never 96); 10:00 PM champion support, network-wide builders; 8:36 PM first version. Earlier commands all still work.\n\nEverything here lives in `$R/wsp31/tools` (below `$T`; `$R = /home/keith/gemma-v2`). Each entry ends with\n**Verified by** (a measurement that was made) or **NOT VERIFIED YET** (written, first real run still queued; use it,\nbut do not build a claim on its numbers alone). Architecture facts are in `$T/ARCH.md` (read \"Consequences for\nweight edits\" there before designing an edit); measurements with commands are in `$T/REPORT.md`.\n\n . /home/keith/gemma-v2/env.sh; T=$R/wsp31/tools\n export WSP31_WORK=$R/wsp31/authors//scratch # caches and server logs of the tools go here, not into $T\n\n[... 289 lines cut ...]\n\n[line removed by the dashboard scrubber]\n- Activation atlas and its views; `--champion` runs of `dose.py`, `validate.py`, `closeprobe.py`, `vgen.py`.\n- Any single entry beyond rank 32 per matrix, and edits of norms, `layer_scalar` or embeddings (`overlay.py` exists\n as code, has not been served, and the runner does not accept it).\n- A probe verdict that predicts rollout outcome: the registered ranking test failed on four edits (section 5).\n\n**Preparing to read architecture survey**"},{"i":17,"kind":"assistant","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":18,"kind":"tool","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":19,"kind":"output","long":true,"text":" succeeded in 338ms:\n# Gemma 4 31B (QAT w4a16) architecture, as served\n\nSource: `config.json` and the safetensors header of `/home/keith/gemma-v2/models/gemma-4-31b-it-qat-w4a16-ct`\n(one file, 23.27 GB, 2009 tensors) and the vLLM 0.19.1 model code that serves it\n(`vllm/model_executor/models/gemma4.py`). The lane's forward pass `g31.py` implements exactly this graph; its\nagreement with the server is measured in `REPORT.md`.\n\n## Sizes\n\n| item | value |\n|---|---|\n| layers | 60 |\n| residual width d | 5376 |\n| MLP width | 21504 neurons per layer (4 d), gated: `gelu_tanh(gate . x) * (up . x)` |\n| attention heads | 32 query heads per layer |\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n| biases | none anywhere in the language model |\n[... 2227 lines cut ...]\n \"fit_error\": 0.5773635393935951,\n \"check_error\": 0.9956785977607787,\n \"ridge\": 0.02,\n \"rank\": 4\n }\n]\n**Improving robust script execution and error mapping**\n**Testing task-invariant outcome contrasts**"},{"i":20,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-ChildItem -Name' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":21,"kind":"output","long":true,"text":" succeeded in 75ms:\nturn10\nturn4\nturn7\nafter_capture.sh\naudit_boundary.py\naudit_snapshot.py\nauthor_capture.py\nboundary_probe.py\nbuild_combo.py\nbuild_overlap.sh\nbuild_project.py\ncancel_optional.py\ncapture_job.sh\nclose033_record.json\nclose050_record.json\ncombo_candidates.txt\ncombo_guard.py\ncomplete_entry.py\nfinalize_entry.py\n[... 16 lines cut ...]\nserved_body.sh\nserved_candidates.txt\nserved_job.sh\nshift011_record.json\nskip_candidates.json\nsnapshot_tools.sh\nupdate_frozen_manifest.py\nverify_rebuild.py"},{"i":22,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn10/outcome_map.py ./turn10_outcome_map.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":23,"kind":"output","long":false,"text":" succeeded in 802ms:"},{"i":24,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn10/hold2.py ./turn10_hold2.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":25,"kind":"output","long":false,"text":" succeeded in 441ms:"},{"i":26,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn10/prepare.py ./turn10_prepare.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":27,"kind":"output","long":false,"text":" succeeded in 415ms:"},{"i":28,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-Content turn10_outcome_map.py; Get-Content turn10_hold2.py; Get-Content turn10_prepare.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":29,"kind":"output","long":true,"text":" succeeded in 162ms:\n\"\"\"Paired-outcome writer transport. Closed-form ridge/SVD; no optimizer.\"\"\"\nimport json,sys,time,hashlib,argparse,os\nfrom pathlib import Path\nimport torch\nC=Path(__file__).resolve().parent;A=C.parent.parent;S=A/'scratch/turn10';T=C/'tools'\nsys.path.insert(0,str(T));import g31\nfrom edits import EditSet\ntorch.set_num_threads(8);torch.set_grad_enabled(False)\ndef unit(x):return x/x.norm(dim=-1,keepdim=True).clamp_min(1e-20)\ndef log(*x):print(time.strftime('%H:%M:%S'),*x,flush=True)\ndef records():return [json.loads(l) for l in (C/'pairs.jsonl').open()]\ndef capture(m):\n out=[]\n for pair in records():\n row={'instance':pair['instance'],'split':pair['split']}\n for side in ['pos','neg']:\n r=pair[side];ids=r['ids'];s=r['start'];ce=s+r['comp'].index(101)\n thought=r['thought_pos']\n syntax=[i for i in range(ce,len(ids)-1) if ids[i] in [101,48,49,52,236787]][:12]\n[... 208 lines cut ...]\n# Freeze only tool sources, with byte hashes, under author-owned code.\ndst=C/'tools';dst.mkdir(exist_ok=True)\nfor f in T.glob('*.py'):shutil.copy2(f,dst/f.name)\n(dst/'data').mkdir(exist_ok=True)\nshutil.copy2(T/'data/fro_norms.json',dst/'data/fro_norms.json')\n(C/'tools_sha256.json').write_text(json.dumps({f.name:hashlib.sha256(f.read_bytes()).hexdigest() for f in dst.glob('*.py')},indent=2))\n\n**Preparing for stage 1 inspection**"},{"i":30,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'New-Item -ItemType Directory -Force turn13 | Out-Null; Get-Date -Format o' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":31,"kind":"output","long":false,"text":" succeeded in 100ms:\n2026-11-14T14:23:32.4398669-05:00"},{"i":32,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q wrx90:/home/keith/gemma-v2/tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md ./turn13/survey.md' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":33,"kind":"output","long":false,"text":" succeeded in 452ms:"},{"i":34,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"rg -n '\"'^## [CD]'\"' turn13/survey.md\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":35,"kind":"output","long":false,"text":" succeeded in 63ms:\n190:## C. Ranked edit ideas for raising a Gemma 4 31B coding agent's held-out solve rate (no training)\n224:## D. Tooling recommendations for the LLM-author harness\n\n**Noticing quick tool response**"},{"i":36,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-Content turn13/survey.md | Select-Object -Skip 189 -First 34; Get-Content turn10/served.py; Get-Content turn10/prepare_checks.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":37,"kind":"output","long":true,"text":" succeeded in 109ms:\n## C. Ranked edit ideas for raising a Gemma 4 31B coding agent's held-out solve rate (no training)\n\nRanking 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.\n\n| # | Idea | Motivating source | Data needed | Risk |\n|---|---|---|---|---|\n| 1 | **Bake TACT's overthinking and overacting axes with a band gate.** Fit OT-vs-CAL and OA-vs-CAL (orthogonalised) at `` (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 \u00e2\u2030\u00a40) | 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 |\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n| 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 \u00ce\u00b1 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\u00e2\u20ac\u201chigh. 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\" |\n| 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 |\n| 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\u00e2\u2020\u201990%; QA 0.29\u00e2\u2020\u20190.56); 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 |\n| 7 | **RYS middle-block duplication.** Materialise a 61\u00e2\u20ac\u201c68-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\u00e2\u20ac\u201chigh. Weak evidence; adds KV cache; counts as an architecture edit for framing |\n| 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 |\n| 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 |\n| 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 |\n| 11 | **ExPO extrapolation of the Gemma 4 31B instruction-tuning delta** (IT + \u00ce\u00b1\u00c2\u00b7(I\n[... 1920 characters cut ...]"},{"i":38,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/inspect.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/inspect_turn13.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":39,"kind":"output","long":false,"text":" succeeded in 588ms:"},{"i":40,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/inspect_turn13.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":41,"kind":"output","long":true,"text":" succeeded in 431ms:\nPARTITION 18 {'base': 12, 'aa': 9, 'e0047': 11, 'e0048': 14}\nPARTITION 102 {'base': 56, 'aa': 55, 'e0047': 53, 'e0048': 55}\ngain django-money__django-money.835c1ab8.lm_rewrite__v5cndhkm fit False\nTRACE base /home/keith/gemma-v2/results/wsp31/batches/b010/base/rebench/shard0_gpu0/traces/trace_django-money__django-money.835c1ab8.lm_rewrite__v5cndhkm.json module>\\\\n File \\\\\\\"\\\\\\\", line 27, in test_get_or_create_with_default_money\\\\nAssertionError: Expected USD, got USD\\\\n\\\", \\\"exit_code\\\": 0}\",\n \"extra\": {\n \"tool_name\": \"run_command\",\n \"elapsed_s\": 863.3930084705353,\n \"timestamp\": 1794670529.91105,\n \"author\": \"swe_developer_agent\"\n }\n },\n \"metrics\": {\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n },\n \"llm_call_count\": 1,\n \"extra\": {\n[... 2605 lines cut ...]\n+for k in ['0047','0048','0049','0050']:\n+ p=next((R/'tii-wsp31-doc/research/wsp31/ledger').glob(k+'-*.md'));s=p.read_text();print('LEDGER',k,s[:s.find('## Result')])\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)"},{"i":42,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/survey_state.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/survey_state.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":43,"kind":"output","long":false,"text":" succeeded in 502ms:"},{"i":44,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/survey_state.py; flock -n /home/keith/gemma-v2/wsp31/gpu0.lock true'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":45,"kind":"output","long":true,"text":" exited 1 in 376ms:\nbase raw 120 unique 120 current list 120 overlap 104 solved 80\naa raw 120 unique 120 current list 120 overlap 104 solved 75\ne0047 raw 120 unique 120 current list 120 overlap 104 solved 75\ne0048 raw 120 unique 120 current list 120 overlap 104 solved 81\nPARKED RECORD title: Damp the code-versus-prose style contrast in readers in all layers at 0.33 percent\n\n## Hypothesis\n\n[line removed by the dashboard scrubber]\n\n[line removed by the dashboard scrubber]\n\n## Exact recipe\n\nbase: champion generation 0\n\nCode: code/turn4/build_fidelity.py. Inputs: code/turn4/fidelity_calls.jsonl and fidelity_directions.pt; captured by fidelity_worker.py with the frozen g31 library in code/turn4/tools. No optimizer, trained-adapter direction, agent rollout or new server.\n\nPer task, sample at most 32 interior code-argument positions and 32 preceding thought positions, evenly spaced. Capture raw writer directions (post-attention output divided by its norm weight; raw MLP output), and normalized reader inputs (attention input norm of resid_pre; mlp_in). Normalize each position to unit length before averaging. For each layer and space, subtract thought mean from code mean and remove its projection on the normalized grand mean of the two classes. Take the top four right singular vectors of the six task differences; weight each projector by squared singular value divided by the leading squared singular value. SVD signs are fixed by the largest absolute coordinate.\n[... 712 lines cut ...]\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)\n\n**Checking time requirement**"},{"i":46,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-Date -Format o; Get-Content turn4/fidelity_worker.py | Select-Object -First 220; Get-Content turn4/pretest_cpu.py -ErrorAction SilentlyContinue | Select-Object -First 100' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":47,"kind":"output","long":true,"text":" exited 1 in 157ms:\n2026-11-14T14:24:39.9240733-05:00\n\"\"\"CPU use of the documented g31 forward pass; no gradients or rollouts.\"\"\"\nimport os,sys,json,time,hashlib\nfrom pathlib import Path\nos.environ['CUDA_VISIBLE_DEVICES']=''\nR=Path('/home/keith/gemma-v2'); A=R/'wsp31/authors/gpt'; C=A/'code/turn4'; S=A/'scratch/turn4'\nsys.path.insert(0,str(C/'tools'))\nimport torch,g31\ntorch.set_num_threads(16);torch.set_grad_enabled(False)\ndef spread(xs,n):return xs if len(xs)<=n else [xs[round(i*(len(xs)-1)/(n-1))] for i in range(n)]\ndef atom(path,x):\n p=Path(path);tmp=p.with_suffix(p.suffix+'.tmp');torch.save(x,tmp);tmp.replace(p)\nrecs=[json.loads(x) for x in (C/'fidelity_calls.jsonl').open()]\nm=g31.G31(device='cpu',cache_dense=True);t0=time.time()\nfor ri,r in enumerate(recs):\n outf=S/f'capture_{ri}.pt'\n if outf.exists():continue\n cp=spread(r['fidelity']['code'],32);tp=spread(r['fidelity']['thought'],32)\n dp=[p['pos'] for p in r['points']]\n[... 147 lines cut ...]\n## Failures and how to do differently\n\n- symptom: `COMPLETE`, valid evidence, or nonzero partial fitness looks like a solve -> cause: those establish harness execution, not exact ARC correctness -> fix: require exact held-out scoring before claiming task success. [Task 2]\n- symptom: failed generation calls or late evolution waste the bounded task window -> evidence: `AssertionError('Empty final answer')`, `AssertionError('Incomplete final answer: length')`, and repeated `Evolution request budget exhausted` -> fix: constrain mutation attempts and stop evolution once useful request budget is exhausted. [Task 2]\n- symptom: warmup looks healthy but scoring is slow -> cause: warmup did not predict long-prompt latency; first scoring TTFT was about 99.98 seconds despite a 48.43-second warmup -> fix: budget using real scoring-request latency. [Task 2]\n\n[line removed by the dashboard scrubber]\n**Optimizing rotation and task fitting strategy**"},{"i":48,"kind":"assistant","long":true,"text":"The smaller edit\u2019s gains concentrate on its 18 fit tasks. On the other currently eligible tasks present in that batch, it gained no net tasks. I\u2019ll test a simpler shared direction across tasks, with a norm-preserving rotation and protection for prompt and tool-syntax states. GPU 0 is busy, so I\u2019m preparing the CPU build and replay checks while respecting its lock.\n\n**Ensuring document and matrix processing**\n**Analyzing matrix rotation fitting**\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\rotation.py\ndiff --git a/turn13/inspect.py b/turn13/inspect.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..c5cabc74bdcdc8124b683414320dae5fcb1a0f0b\n--- /dev/null\n+++ b/turn13/inspect.py\n@@ -0,0 +1,27 @@\n+import json,sys,re\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13';C.mkdir(exist_ok=True);(A/'scratch/turn13').mkdir(exist_ok=True)\n+fit={r['instance'] for r in json.loads((A/'code/turn10/selection.json').read_text()) if r['split']=='fit'}\n+allow=set((R/'wsp31/queue/sets/stage1_ids.txt').read_text().split())\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow}\n+for subset in [fit,allow-fit]:\n+ print('PARTITION',len(subset),{a:sum(bool(v[i]['resolved']) for i in subset if i in v) for a,v in arms.items()})\n+e=arms['e0048'];bs=arms['base'];aa=arms['aa']\n+for typ,ids in [('gain',[i for i in e if e[i]['resolved'] and not bs[i]['resolved'] and not aa[i]['resolved']]),('loss',[i for i in e if not e[i]['resolved'] and bs[i]['resolved'] and aa[i]['resolved']])]:\n+ for i in ids:\n+ print(typ,i,'fit',i in fit)\n+ for arm in ['base','e0048']:\n+ row=arms[arm][i];p=Path(row['trace_path']);text=p.read_text()\n+ print('TRACE',arm,str(p),text[-9000:])\n+survey=(R/'tii-wsp31-doc/reports/2026-11-12-weight-editing-survey.md').read_text()\n+# Read relevant records only; no sealed result records.\n+for k in ['0047','0048','0049','0050','0055','0056','0060']:\n+ p=next((R/'tii-wsp31-doc/research/wsp31/ledger').glob(k+'-*.md'))\n+ print('LEDGER',p.name,p.read_text()[:18000])\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report[:700]):\n+ if 'replay.py' in l or 'count_flips' in l or 'flips.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-4):j+8]))\n+(A/'NOTES.md').write_text('# Author gpt - turn 13, in progress\\n\\nStarted 2026-11-14 19:22 UTC; deadline 20:52 UTC. Champion generation 0. No new entries yet. Read brief, prior notes, RESULTS, STATUS, CHAMPION, ledger, prior E4B, survey A/C, tools and architecture. Last round: 1% paired outcome map null (-2.5); 0.33% +3.5, awaits confirmation; both provisional pending two image reruns. Checking fit-task split and failure traces before choosing. Do not read sealed or stage-2 task results. One round, at most two entries; finish READY before a second entry.\\n')\ndiff --git a/turn13/rotation.py b/turn13/rotation.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..2f9eef0b2b9972768ac5f2d244d927f369c5cd02\n--- /dev/null\n+++ b/turn13/rotation.py\n@@ -0,0 +1,63 @@\n+\"\"\"Task-pooled, protocol-protected writer rotations; closed form only.\"\"\"\n+import json,sys,hashlib,time,math,argparse,shutil\n+from pathlib import Path\n+import torch\n+C=Path(__file__).resolve().parent;A=C.parent.parent;S=A/'scratch/turn13';T=C/'tools'\n+sys.path.insert(0,str(T));from edits import EditSet\n+torch.set_num_threads(8);torch.set_grad_enabled(False)\n+def unit(x):return x/x.norm(dim=-1,keepdim=True).clamp_min(1e-20)\n+def basis(rows,l,mod):\n+ p=torch.stack([r['pos'][mod]['thought'][l] for r in rows]).double()\n+ n=torch.stack([r['neg'][mod]['thought'][l] for r in rows]).double()\n+ # Protect each observed syntax/prompt mean, rather than penalizing it in ridge.\n+ preserve=torch.stack([r[s][mod][k][l] for r in rows for s in ['pos','ne\n[... 5418 characters cut ...]"},{"i":49,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/rotation.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":50,"kind":"output","long":false,"text":" succeeded in 392ms:\n2026-11-14T19:25:56.733968Z ERROR codex_core::tools::router: error=apply_patch verification failed: Failed to find expected lines in C:\\Users\\Owner\\.codex\\memories\\MEMORY.md:\n- when the assistant stopped after the handoff audit, the user said \u00e2\u20ac\u0153Well keep working why did you stop?\u00e2\u20ac\u009d -> after a takeover reconstruction, continue into the next authorized bounded experiment rather than ending with a status report. [Task 1]\n[line removed by the dashboard scrubber]\n- for the Darwinian run, preserve the intentionally bounded one-task, 360-second, no-submission scope and distinguish harness completion from solve accuracy. [Task 2]\n\n## Reusable knowledge\n\n[line removed by the dashboard scrubber]\n- When the visible thread is stale, read the full session JSONL; the app thread reader omitted the newest prior turn. Use explicit one-command second-hop SSH calls and verify hostname/GPU state before launch to avoid nested-host confusion. [Task 1]\n- `C:/Python314/python.exe darwinian-smoke/validate_output.py --version 2` passed. The task finished within the budget: answer saved at `329.5542` seconds, task checks at `331.0993`; 10 programs generated, 9 valid, 19 completed calls, 4 failed calls, and zero post-run GPU processes. [Task 2]\n- Exact held-out scoring for `0934a4d8` was `pass_at_2=0.0`; the best partial fitness was `correctness=0.43110714285714286`, and the saved crop/rotation program matched 0/4 training outputs. [Task 2]\n\n## Failures and how to do differently\n\n- symptom: `COMPLETE`, valid evidence, or nonzero partial fitness looks like a solve -> cause: those establish harness execution, not exact ARC correctness -> fix: require exact held-out scoring before claiming task success. [Task 2]\n- symptom: failed generation calls or late evolution waste the bounded task window -> evidence: `AssertionError('Empty final answer')`, `AssertionError('Incomplete final answer: length')`, and repeated `Evolution request budget exhausted` -> fix: constrain mutation attempts and stop evolution once useful request budget is exhausted. [Task 2]\n- symptom: warmup looks healthy but scoring is slow -> cause: warmup did not predict long-prompt latency; first scoring TTFT was about 99.98 seconds despite a 48.43-second warmup -> fix: budget using real scoring-request latency. [Task 2]"},{"i":51,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cp -r /home/keith/gemma-v2/wsp31/authors/gpt/code/turn10/tools /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/; grep -n norm /home/keith/gemma-v2/wsp31/tools/netops.py | head -n 25; free -g; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py fit'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":52,"kind":"output","long":true,"text":" succeeded in 2573ms:\n12:tensor for one direction set per layer (layers missing from a dict are skipped). The directions are orthonormalised;\n14:entries of data/fro_norms.json, the same yardstick as train_size.py.\n17:passes through an RMS norm before it reaches the residual stream, so multiplying a whole writer by a constant does\n19:an RMS norm with a learned weight per coordinate, and those weights span 1e-6 to 1e3. space=\"module\" (default) takes\n20:D in the module's own space (reader input after the norm, writer output before the norm) and is the plain orthogonal\n21:projector. space=\"stream\" takes D in the residual stream and converts through the norm weight w with an oblique\n33:NORM_IN = {\"q_proj\": \"input_layernorm\", \"k_proj\": \"input_layernorm\", \"v_proj\": \"input_layernorm\",\n34: \"gate_proj\": \"pre_feedforward_layernorm\", \"up_proj\": \"pre_feedforward_layernorm\"}\n35:NORM_OUT = {\"o_proj\": \"post_attention_layernorm\", \"down_proj\": \"post_feedforward_layernorm\"}\n39:def fro_norms():\n40: \"\"\"{\"layer.mod\": |W|_F} for all 420 entries (global layers list v_proj with the k_proj norm).\"\"\"\n43: p = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"data\", \"fro_norms.json\")\n48:def net_norm():\n49: return sum(v * v for v in fro_norms().values()) ** 0.5\n52:def orthonormal(D):\n53: \"\"\"Rows of D [k, n] -> orthonormal rows spanning the same space (float64). Fails on dependent rows.\"\"\"\n78: return orthonormal(torch.as_tensor(D, dtype=torch.float64).reshape(-1, self.arch.d))\n82: \"\"\"Random directions of the same shape as D (tensor, or dict {layer: tensor}), orthonormal, and with\n87: S = orthonormal(D.reshape(-1, D.shape[-1]))\n[... 288 lines cut ...]\n+for k in ['0047','0048','0049','0050']:\n+ p=next((R/'tii-wsp31-doc/research/wsp31/ledger').glob(k+'-*.md'));s=p.read_text();print('LEDGER',k,s[:s.find('## Result')])\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)"},{"i":53,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/diagnose_basis.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/diagnose_basis.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":54,"kind":"output","long":false,"text":" succeeded in 491ms:"},{"i":55,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/diagnose_basis.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":56,"kind":"output","long":true,"text":" succeeded in 5121ms:\nCURRENT {'negative_retained': 0.10651787205270753, 'contrast_retained': 0.7017286356879866, 'null_error': 1.1513821204295686e-14}\nnone fit_LOO_cos 0.02707640642956317 n 680\nmeans fit_LOO_cos 0.018834548102492025 n 680\nsyntax_means fit_LOO_cos 0.024356141927590294 n 680\nsyntax_all fit_LOO_cos 0.008903073989371324 n 680\n\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt',weights_only=False);sel=json.loads((C/'selection.json').read_text());tr=[r for r in rows if r['instance'] in sel['fit']]\n+d=json.loads((C/'fit_diagnostics.json').read_text());print('CURRENT', {k:sum(x[k] for x in d)/len(d) for k in ['negative_retained','contrast_retained','null_error']})\n+out=[]\n+for mode in ['none','means','syntax_means','syntax_all']:\n[... 135 lines cut ...]\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)\n\n**Assessing error-feedback recovery approach**"},{"i":57,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'ls /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn4; head -n 65 /home/keith/gemma-v2/wsp31/tools/g31.py; ls /home/keith/gemma-v2/wsp31/authors/gpt/code/turn7'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":58,"kind":"output","long":true,"text":" succeeded in 319ms:\nCAPTURE_DONE\nFIT_DONE\nSTOP_GUARD\nSTOP_WORKER\ncapture_0.pt\ncapture_1.pt\ncapture_2.pt\ncapture_3.pt\ncapture_4.pt\ncapture_5.pt\ncapture_6.pt\ncapture_7.pt\ncapture_8.pt\ncode-joint-damp-r4-047-ctrl.json\ncode-joint-damp-r4-047-edit-p6.json\ncode-joint-damp-r4-047-edit-p7.json\ncode-joint-damp-r4-047-edit.json\ncode-reader-damp-r4-033-ctrl.json\ncode-reader-damp-r4-033-edit-p6.json\n[... 289 lines cut ...]\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)\n\n**Planning training strategy**\n\nI\u2019m thinking through a simple approach to perform rounds of training with multiple doses and ensuring that each entry finishes before starting the next. GPU resources are locked and unavailable, so I\u2019ll rely on CPU workers with enough memory and time management to avoid pointless waits. I\u2019ll set up scripts to queue and handle task timeouts properly.\n**Designing dose and rotation experiments**\n\nI\u2019m choosing training doses starting with 1, falling back to 0.33 if needed, and planning rotation experiments with complementary reads. Each step waits for the previous to finish, balancing numeric tolerance limits while managing logs and edits carefully for precise control."},{"i":59,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 8 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn4/worker.log; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033 --dose 0.0033'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":60,"kind":"output","long":true,"text":"diff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt',weights_only=False);sel=json.loads((C/'selection.json').read_text());tr=[r for r in rows if r['instance'] in sel['fit']]\n+d=json.loads((C/'fit_diagnostics.json').read_text());print('CURRENT', {k:sum(x[k] for x in d)/len(d) for k in ['negative_retained','contrast_retained','null_error']})\n+out=[]\n+for mode in ['none','means','syntax_means','syntax_all']:\n+ scores=[]\n+ for mod in ['o_proj','down_proj']:\n+ for l in range(0,60,3):\n+ P=torch.stack([r['pos'][mod]['thought'][l] for r in tr]).double();N=torch.stack([r['neg'][mod]['thought'][l] for r in tr]).double()\n+ for i in range(len(tr)):\n+ jj=[j for j in range(len(tr)) if i!=j];p=P[jj].mean(0);n=N[jj].mean(0)\n+ if mode=='none':h=torch.zeros((0,len(p)),dtype=torch.float64)\n[... 378 lines cut ...]\n+for k in ['0047','0048','0049','0050']:\n+ p=next((R/'tii-wsp31-doc/research/wsp31/ledger').glob(k+'-*.md'));s=p.read_text();print('LEDGER',k,s[:s.find('## Result')])\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)"},{"i":61,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/prepare_replay.py ./turn13/replay_cpu.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":62,"kind":"output","long":false,"text":" succeeded in 449ms:"},{"i":63,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/prepare_replay.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":64,"kind":"output","long":true,"text":" succeeded in 4572ms:\n[line removed by the dashboard scrubber]\nLIVE_BOUNDARY 120 273 fit 17 selected 8\n\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt',weights_only=False);sel=json.loads((C/'selection.json').read_text());tr=[r for r in rows if r['instance'] in sel['fit']]\n+d=json.loads((C/'fit_diagnostics.json').read_text());print('CURRENT', {k:sum(x[k] for x in d)/len(d) for k in ['negative_retained','contrast_retained','null_error']})\n+out=[]\n+for mode in ['none','means','syntax_means','syntax_all']:\n+ scores=[]\n+ for mod in ['o_proj','down_proj']:\n+ for l in range(0,60,3):\n[... 462 lines cut ...]\n+for k in ['0047','0048','0049','0050']:\n+ p=next((R/'tii-wsp31-doc/research/wsp31/ledger').glob(k+'-*.md'));s=p.read_text();print('LEDGER',k,s[:s.find('## Result')])\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)"},{"i":65,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/cpu.sh wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/cpu.sh' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":66,"kind":"output","long":false,"text":" succeeded in 482ms:"},{"i":67,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'nohup bash /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/cpu.sh > /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log 2>&1 < /dev/null & echo \"'$!'\"'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":68,"kind":"output","long":false,"text":" succeeded in 309ms:\n2502167"},{"i":69,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/tools/check_adapter.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/edit; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/tools/check_adapter.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/ctrl; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/tools/train_size.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/size.json edit=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/edit ctrl=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/ctrl'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":70,"kind":"output","long":true,"text":" succeeded in 4012ms:\nOK edit: 120 modules changed (of 120 present), r=2, max |dW|_F/|W|_F 0.008452 at 24.o_proj\nOK ctrl: 120 modules changed (of 120 present), r=2, max |dW|_F/|W|_F 0.008451 at 13.o_proj\nedit {\"r\": 2, \"scale\": 2.0, \"changed\": 120, \"of\": 420, \"net_rel\": 0.0033, \"rel_median\": 0.00845, \"rel_min\": 0.008449, \"rel_max\": 0.008452, \"rel_max_mod\": \"24.o_proj\", \"total_rel\": 0.00845, \"total_fro\": 6.806, \"stable_rank_median\": 1.794, \"part_rank_median\": 1.993}\n by type {'down_proj': 0.00845, 'o_proj': 0.00845}\n by depth (10-layer bands) [0.00845, 0.00845, 0.00845, 0.00845, 0.00845, 0.00845]\nctrl {\"r\": 2, \"scale\": 2.0, \"changed\": 120, \"of\": 420, \"net_rel\": 0.0033, \"rel_median\": 0.00845, \"rel_min\": 0.008449, \"rel_max\": 0.008451, \"rel_max_mod\": \"13.o_proj\", \"total_rel\": 0.00845, \"total_fro\": 6.806, \"stable_rank_median\": 1.794, \"part_rank_median\": 1.993}\n by type {'down_proj': 0.00845, 'o_proj': 0.00845}\n by depth (10-layer bands) [0.00845, 0.00845, 0.00845, 0.00845, 0.00845, 0.00845]\n\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n[... 513 lines cut ...]\n+for k in ['0047','0048','0049','0050']:\n+ p=next((R/'tii-wsp31-doc/research/wsp31/ledger').glob(k+'-*.md'));s=p.read_text();print('LEDGER',k,s[:s.find('## Result')])\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)"},{"i":71,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/audit_previous.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/audit_previous.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":72,"kind":"output","long":false,"text":" succeeded in 404ms:"},{"i":73,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/audit_previous.py; tail -n 2 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":74,"kind":"output","long":true,"text":" succeeded in 314ms:\nPARTITION {'fit': {'n': 18, 'scores': {'base': 12, 'aa': 9, 'e0047': 11, 'e0048': 14}, 'e0048_gain': 3.5}, 'nonfit': {'n': 86, 'scores': {'base': 56, 'aa': 55, 'e0047': 53, 'e0048': 55}, 'e0048_gain': -0.5}, 'all_eligible': {'n': 104, 'scores': {'base': 68, 'aa': 64, 'e0047': 64, 'e0048': 69}, 'e0048_gain': 3.0}}\nTRACE [task] base resolved True steps 48\nThe line `import qiskit._accelerate` is on line 17.\nThen on line 23, it does `sys.modules[\"qiskit._accelerate.nlayout\"] = qiskit._accelerate.nlayout`.\n\nIf `qiskit._accelerate` is not installed, `import qiskit._accelerate` will fail.\nBut in my case, I'm mocking it.\n\nWait, if I do:\n```python\nimport sys\nfrom unittest.mock import MagicMock\nmock_acc = MagicMock()\nsys.modules['qiskit._accelerate'] = mock_acc\n```\nThen when `qiskit/__init__.py` does `import qiskit._accelerate`, it will get the `mock_acc`.\nThen it does `qiskit._accelerate.nlayout`.\nSince `qiskit` is the module being initialized, `qiskit._accelerate` should be available as an attribute of `qiskit` AFTER `import qiskit._accelerate` runs.\n\n[... 660 lines cut ...]\n+for k in ['0047','0048','0049','0050']:\n+ p=next((R/'tii-wsp31-doc/research/wsp31/ledger').glob(k+'-*.md'));s=p.read_text();print('LEDGER',k,s[:s.find('## Result')])\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)"},{"i":75,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/record_draft.md wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/record_draft.md' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":76,"kind":"output","long":false,"text":" succeeded in 403ms:"},{"i":77,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 2 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":78,"kind":"output","long":true,"text":" succeeded in 334ms:\n2026-11-14T19:30:36Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 302 lines cut ...]\n+report=(R/'wsp31/interp/REPORT.md').read_text().splitlines()\n+for j,l in enumerate(report):\n+ if 'flips.py' in l or 'count_flips' in l or 'replay.py' in l:print('REPORT COMMAND','\\n'.join(report[max(0,j-3):j+6]))\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)\n\n**Reviewing CPU model loading**"},{"i":79,"kind":"assistant","long":false,"text":"[line removed by the dashboard scrubber]\n\n**Designing error handling approach**"},{"i":80,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'ps -p 2502167 -o pid,etime,time,rss,stat; pgrep -P 2502167; tail -n 2 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; sed -n 330,390p /home/keith/gemma-v2/wsp31/tools/g31.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":81,"kind":"output","long":true,"text":" succeeded in 291ms:\n PID ELAPSED TIME RSS STAT\n2502167 02:13 00:00:00 8036 S\n2502168\nBASE 0 4274 127.5\n h = tap(\"resid_mid\", l, h)\n x = tap(\"mlp_in\", l, self.rms(h, wt(\"pre_feedforward_layernorm\")))\n gate = tap(\"gate\", l, self.lin(l, \"gate_proj\", x))\n up = tap(\"up\", l, self.lin(l, \"up_proj\", x))\n act = self._r(gelu_tanh(gate.float())) * up\n del gate, up, x\n act = tap(\"mlp_act\", l, act)\n y = tap(\"mlp_raw\", l, self.lin(l, \"down_proj\", act))\n del act\n y = tap(\"mlp_out\", l, self.rms(y, wt(\"post_feedforward_layernorm\")))\n h = y + h\n del y\n h = h * (d[\"layer_scalar\"] if m else d[\"layer_scalar\"].float())\n h = tap(\"resid_post\", l, h)\n if stop_after is not None and l >= stop_after:\n[... 360 lines cut ...]\n+print('SIZES')\n+for p in [A/'scratch',A/'code/turn10']:\n+ print(str(p),sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9)\n\n**Managing test execution and optimization**\n**Optimizing build process and environment tracking**\n\nI\u2019m thinking through how to reuse cached baselines and manage calls count without interrupting running processes. I want to ensure reproducibility by hashing environment versions and source files and plan to add safeguards in the build system to keep outputs contained within authorized paths while preserving metadata for auditing."},{"i":82,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'head -n 30 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/edit/edit.json; cat /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/edit/adapter_config.json; sed -n 30,55p /home/keith/gemma-v2/wsp31/tools/netops.py; head -n 15 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/rotation033/size.json'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":83,"kind":"output","long":true,"text":" succeeded in 352ms:\n{\n \"modules\": {\n \"0.down_proj\": {\n \"rank\": 2,\n \"fro\": 0.6339876807248452,\n \"rel_fro\": 0.008450502301279932\n },\n \"0.o_proj\": {\n \"rank\": 2,\n \"fro\": 0.6170449573051224,\n \"rel_fro\": 0.00845050229957423\n },\n \"1.down_proj\": {\n \"rank\": 2,\n \"fro\": 0.6087646099457497,\n \"rel_fro\": 0.008450502297192677\n },\n \"1.o_proj\": {\n \"rank\": 2,\n[... 743 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":84,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/rotation.py ./turn13/build.sh ./turn13/verify.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":85,"kind":"output","long":false,"text":" succeeded in 493ms:"},{"i":86,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py fit; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/verify.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":87,"kind":"output","long":true,"text":"diff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n[... 345 lines cut ...]\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing model norm and rotation behavior**\n**Examining angle alignment and rotation overshoot**"},{"i":88,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 3 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":89,"kind":"output","long":true,"text":" succeeded in 319ms:\nBASE 0 4274 127.5\n2026-11-14T19:32:28Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 725 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":90,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 3 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; ps -p 2502168 -o pid,etime,time,rss,stat'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":91,"kind":"output","long":true,"text":" succeeded in 283ms:\nBASE 0 4274 127.5\nARM 0 edit 108.5 {\"all\": {\"n\": 100, \"flips\": 1, \"dlogp\": -0.007340584545525175, \"kl\": 0.005293620315449618, \"dclose\": 0.4621321571158478, \"base_top_logged\": 100, \"top_logged\": 99}, \"thought\": {\"n\": 32, \"flips\": 0, \"dlogp\": -0.010921124211407562, \"kl\": 0.008643491400014185, \"dclose\": 0.8128703609108925, \"base_top_logged\": 32, \"top_logged\": 32}, \"action\": {\"n\": 59, \"flips\": 1, \"dlogp\": -0.005282460344672639, \"kl\": 0.0024668491955563638, \"dclose\": 0.3152950092897577, \"base_top_logged\": 59, \"top_logged\": 58}, \"syntax\": {\"n\": 7, \"flips\": 0, \"dlogp\": -0.010386163746586265, \"kl\": 0.015313011395526084, \"dclose\": 0.3668459483555385, \"base_top_logged\": 7, \"top_logged\": 7}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": -1.4659890439361334e-05, \"kl\": 1.0120653314515948e-05, \"dclose\": -1.4659890439361334e-05, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": -0.00019951333524659276, \"kl\": 2.5003784685395658e-05, \"dclose\": -0.9689483642578125, \"base_top_logged\": 1, \"top_logged\": 1}}\n PID ELAPSED TIME RSS STAT\n2502168 04:57 01:02:19 78310412 Rl\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n[... 351 lines cut ...]\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Optimizing rotation and dose parameters**\n**Managing sequential task execution**"},{"i":92,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\n**Updating notes and drafting analysis**\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\geometry_audit.py\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\update_notes.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt\n[... 26116 characters cut ...]"},{"i":93,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/geometry_audit.py ./turn13/update_notes.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":94,"kind":"output","long":false,"text":" succeeded in 459ms:"},{"i":95,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/update_notes.py; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/geometry_audit.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":96,"kind":"output","long":true,"text":"diff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n[... 401 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Coordinating task timing and strategies**"},{"i":97,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 2 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":98,"kind":"output","long":true,"text":" succeeded in 418ms:\nARM 0 edit 108.5 {\"all\": {\"n\": 100, \"flips\": 1, \"dlogp\": -0.007340584545525175, \"kl\": 0.005293620315449618, \"dclose\": 0.4621321571158478, \"base_top_logged\": 100, \"top_logged\": 99}, \"thought\": {\"n\": 32, \"flips\": 0, \"dlogp\": -0.010921124211407562, \"kl\": 0.008643491400014185, \"dclose\": 0.8128703609108925, \"base_top_logged\": 32, \"top_logged\": 32}, \"action\": {\"n\": 59, \"flips\": 1, \"dlogp\": -0.005282460344672639, \"kl\": 0.0024668491955563638, \"dclose\": 0.3152950092897577, \"base_top_logged\": 59, \"top_logged\": 58}, \"syntax\": {\"n\": 7, \"flips\": 0, \"dlogp\": -0.010386163746586265, \"kl\": 0.015313011395526084, \"dclose\": 0.3668459483555385, \"base_top_logged\": 7, \"top_logged\": 7}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": -1.4659890439361334e-05, \"kl\": 1.0120653314515948e-05, \"dclose\": -1.4659890439361334e-05, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": -0.00019951333524659276, \"kl\": 2.5003784685395658e-05, \"dclose\": -0.9689483642578125, \"base_top_logged\": 1, \"top_logged\": 1}}\nARM 0 ctrl 135.1 {\"all\": {\"n\": 100, \"flips\": 1, \"dlogp\": -0.002020346350227129, \"kl\": 0.0033075774012214488, \"dclose\": 0.018663277691957775, \"base_top_logged\": 100, \"top_logged\": 99}, \"thought\": {\"n\": 32, \"flips\": 1, \"dlogp\": -0.0035035845075792604, \"kl\": 0.007416361859540005, \"dclose\": -0.22475601732730865, \"base_top_logged\": 32, \"top_logged\": 31}, \"action\": {\"n\": 59, \"flips\": 0, \"dlogp\": 0.00019527668418624567, \"kl\": 0.0011193065591329995, \"dclose\": 0.1508838685892396, \"base_top_logged\": 59, \"top_logged\": 59}, \"syntax\": {\"n\": 7, \"flips\": 0, \"dlogp\": -0.014565399020869205, \"kl\": 0.0038386396790604594, \"dclose\": -0.08710997445242745, \"base_top_logged\": 7, \"top_logged\": 7}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": -3.241833474021405e-05, \"kl\": 1.0682764695957303e-05, \"dclose\": -3.241833474021405e-05, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": 0.0005489563336595893, \"kl\": 0.0005139131098985672, \"dclose\": 0.76617431640625, \"base_top_logged\": 1, \"top_logged\": 1}}\n2026-11-14T19:35:00Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n[... 406 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing CPU and memory workload**"},{"i":99,"kind":"assistant","long":true,"text":"The edit and reverse control both preserve the first call\u2019s logged thought ending and syntax choices. CPU replay takes about two minutes per arm per call. The build and prediction are saved; `READY` will wait for the remaining checks.\n\n succeeded in 55154ms:\nGEOMETRY {'mean_fit_shift_cos': 0.17862816794961225, 'mean_check_shift_cos': -0.007556965275787863, 'past_centroid_count': 0, 'max_bf16_delta_relative_error': 0.0024074744477597483, 'mean_bf16_delta_relative_error': 0.0023470514431038077}\n\n[line removed by the dashboard scrubber]\n\n[line removed by the dashboard scrubber]\n**Analyzing tool-name parsing errors**\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_\n[... 26507 characters cut ...]"},{"i":100,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":101,"kind":"output","long":true,"text":" succeeded in 339ms:\nARM 0 ctrl 135.1 {\"all\": {\"n\": 100, \"flips\": 1, \"dlogp\": -0.002020346350227129, \"kl\": 0.0033075774012214488, \"dclose\": 0.018663277691957775, \"base_top_logged\": 100, \"top_logged\": 99}, \"thought\": {\"n\": 32, \"flips\": 1, \"dlogp\": -0.0035035845075792604, \"kl\": 0.007416361859540005, \"dclose\": -0.22475601732730865, \"base_top_logged\": 32, \"top_logged\": 31}, \"action\": {\"n\": 59, \"flips\": 0, \"dlogp\": 0.00019527668418624567, \"kl\": 0.0011193065591329995, \"dclose\": 0.1508838685892396, \"base_top_logged\": 59, \"top_logged\": 59}, \"syntax\": {\"n\": 7, \"flips\": 0, \"dlogp\": -0.014565399020869205, \"kl\": 0.0038386396790604594, \"dclose\": -0.08710997445242745, \"base_top_logged\": 7, \"top_logged\": 7}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": -3.241833474021405e-05, \"kl\": 1.0682764695957303e-05, \"dclose\": -3.241833474021405e-05, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": 0.0005489563336595893, \"kl\": 0.0005139131098985672, \"dclose\": 0.76617431640625, \"base_top_logged\": 1, \"top_logged\": 1}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 405 lines cut ...]\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Reviewing model and audit constraints**\n**Implementing finalization checks**"},{"i":102,"kind":"assistant","long":true,"text":"The saved factors differ from the ideal rotation by at most 0.241% of the update norm. None of the 120 rotations goes past its fitted successful mean. The geometry behaves as designed, but reserved-task alignment remains near zero, so transfer is still unproven.\n\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\finalize_first.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__f\n[... 32780 characters cut ...]"},{"i":103,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/finalize_first.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/finalize_first.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":104,"kind":"output","long":false,"text":" succeeded in 480ms:"},{"i":105,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; cat /home/keith/gemma-v2/wsp31/queue/champion.json'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":106,"kind":"output","long":true,"text":" exited 1 in 405ms:\nBASE 1 3318 120.3\ncat: /home/keith/gemma-v2/wsp31/queue/champion.json: No such file or directory\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 972 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":107,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/finalize_first.py ./turn13/replay_cpu.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":108,"kind":"output","long":false,"text":" succeeded in 394ms:"},{"i":109,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ; flock -n /home/keith/gemma-v2/wsp31/gpu0.lock true'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":110,"kind":"output","long":true,"text":" exited 1 in 386ms:\nBASE 1 3318 120.3\n2026-11-14T19:38:02Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 476 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Summarizing task update considerations**"},{"i":111,"kind":"assistant","long":true,"text":"The current stage-1 list overlaps the previous batch on 104 tasks, so the fit/non-fit analysis covers those 104 only. The candidate and replay panel pass the current stage-1 checks and all stage-2 exclusion lists. GPU 0 remains occupied; testing is still CPU-only.\n\n**Optimizing wait monitoring**\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import\n[... 33085 characters cut ...]"},{"i":112,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":113,"kind":"output","long":true,"text":" succeeded in 340ms:\nARM 1 edit 104.9 {\"all\": {\"n\": 154, \"flips\": 1, \"dlogp\": -0.007142502133580573, \"kl\": 0.004837392131542546, \"dclose\": 0.44202161210554336, \"base_top_logged\": 154, \"top_logged\": 153}, \"thought\": {\"n\": 64, \"flips\": 0, \"dlogp\": -0.011823831979045685, \"kl\": 0.007448128786366869, \"dclose\": 0.7119215168058872, \"base_top_logged\": 64, \"top_logged\": 64}, \"action\": {\"n\": 76, \"flips\": 1, \"dlogp\": -0.0036085247712992776, \"kl\": 0.002099100460288249, \"dclose\": 0.2886407249852231, \"base_top_logged\": 76, \"top_logged\": 75}, \"syntax\": {\"n\": 10, \"flips\": 0, \"dlogp\": -0.007282435318802527, \"kl\": 0.010723335912193478, \"dclose\": 0.13802995681762695, \"base_top_logged\": 10, \"top_logged\": 10}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.0020255355921108276, \"kl\": 0.000744056815165095, \"dclose\": 0.0020255355921108276, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": -9.945864496785362e-05, \"kl\": 1.2519097950303149e-05, \"dclose\": -0.40634727478027344, \"base_top_logged\": 2, \"top_logged\": 2}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 990 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":114,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/environment.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/environment.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":115,"kind":"output","long":false,"text":" succeeded in 493ms:"},{"i":116,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/environment.py; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":117,"kind":"output","long":true,"text":" succeeded in 1705ms:\n{\"python\": \"3.13.13 (main, Jun 2 2026, 22:27:49) [Clang 22.1.3 ]\", \"torch\": \"2.10.0+cu128\", \"platform\": \"Linux-7.0.0-43-generic-x86_64-with-glibc2.43\", \"threads_pretest\": 16, \"gpu_seconds_this_turn\": 0, \"cpu_backend\": \"g31 mirror=True cache_dense=True\", \"model_config_sha256\": \"b100d85e571c25b688e82919d819253063a1defb0e6cee3b9b1c1fbad99bd0c2\", \"model_files\": {\"model.safetensors\": 23265352448}, \"scratch_bytes\": 4311920572}\nARM 1 edit 104.9 {\"all\": {\"n\": 154, \"flips\": 1, \"dlogp\": -0.007142502133580573, \"kl\": 0.004837392131542546, \"dclose\": 0.44202161210554336, \"base_top_logged\": 154, \"top_logged\": 153}, \"thought\": {\"n\": 64, \"flips\": 0, \"dlogp\": -0.011823831979045685, \"kl\": 0.007448128786366869, \"dclose\": 0.7119215168058872, \"base_top_logged\": 64, \"top_logged\": 64}, \"action\": {\"n\": 76, \"flips\": 1, \"dlogp\": -0.0036085247712992776, \"kl\": 0.002099100460288249, \"dclose\": 0.2886407249852231, \"base_top_logged\": 76, \"top_logged\": 75}, \"syntax\": {\"n\": 10, \"flips\": 0, \"dlogp\": -0.007282435318802527, \"kl\": 0.010723335912193478, \"dclose\": 0.13802995681762695, \"base_top_logged\": 10, \"top_logged\": 10}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.0020255355921108276, \"kl\": 0.000744056815165095, \"dclose\": 0.0020255355921108276, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": -9.945864496785362e-05, \"kl\": 1.2519097950303149e-05, \"dclose\": -0.40634727478027344, \"base_top_logged\": 2, \"top_logged\": 2}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 488 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Waiting for CPU processing**"},{"i":118,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt',weights_only=False);sel=json.loads((C/'selection.json').read_text());tr=[r for r in rows if r['instance'] in sel['fit']]\n+d=json.loads((C/'fit_diagnostics.json').read_text());print('CURR\n[... 33755 characters cut ...]"},{"i":119,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":120,"kind":"output","long":true,"text":" succeeded in 406ms:\nARM 1 ctrl 109.9 {\"all\": {\"n\": 154, \"flips\": 1, \"dlogp\": 0.0006244219864349624, \"kl\": 0.0032483187565515276, \"dclose\": 0.04569848307588685, \"base_top_logged\": 154, \"top_logged\": 153}, \"thought\": {\"n\": 64, \"flips\": 1, \"dlogp\": 0.0025256935794457647, \"kl\": 0.00615748392213826, \"dclose\": -0.07883423566818237, \"base_top_logged\": 64, \"top_logged\": 63}, \"action\": {\"n\": 76, \"flips\": 0, \"dlogp\": 0.0006078774185667157, \"kl\": 0.0009885458557457392, \"dclose\": 0.14678386638039037, \"base_top_logged\": 76, \"top_logged\": 76}, \"syntax\": {\"n\": 10, \"flips\": 0, \"dlogp\": -0.01019443247819254, \"kl\": 0.002687631086255138, \"dclose\": 0.04527435302734375, \"base_top_logged\": 10, \"top_logged\": 10}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": -0.0051440149181871675, \"kl\": 0.0018206553213531151, \"dclose\": -0.0051440149181871675, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.000275133816842299, \"kl\": 0.00025750547507641386, \"dclose\": 0.2424640655517578, \"base_top_logged\": 2, \"top_logged\": 2}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1017 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":121,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/progress.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/progress.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":122,"kind":"output","long":false,"text":" succeeded in 523ms:"},{"i":123,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/progress.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":124,"kind":"output","long":true,"text":" succeeded in 2175ms:\nPANEL_COUNTS [100, 54, 53, 73, 56, 56, 42, 59]\nrotation033 done False seconds 706.2940995693207\nedit 2 {'n': 154, 'flips': 1, 'dlogp': -0.007142502133580573, 'kl': 0.004837392131542546, 'dclose': 0.44202161210554336, 'base_top_logged': 154, 'top_logged': 153} close {'n': 2, 'flips': 0, 'dlogp': 0.0020255355921108276, 'kl': 0.000744056815165095, 'dclose': 0.0020255355921108276, 'base_top_logged': 2, 'top_logged': 2} syntax {'n': 10, 'flips': 0, 'dlogp': -0.007282435318802527, 'kl': 0.010723335912193478, 'dclose': 0.13802995681762695, 'base_top_logged': 10, 'top_logged': 10}\nctrl 2 {'n': 154, 'flips': 1, 'dlogp': 0.0006244219864349624, 'kl': 0.0032483187565515276, 'dclose': 0.04569848307588685, 'base_top_logged': 154, 'top_logged': 153} close {'n': 2, 'flips': 0, 'dlogp': -0.0051440149181871675, 'kl': 0.0018206553213531151, 'dclose': -0.0051440149181871675, 'base_top_logged': 2, 'top_logged': 2} syntax {'n': 10, 'flips': 0, 'dlogp': -0.01019443247819254, 'kl': 0.002687631086255138, 'dclose': 0.04527435302734375, 'base_top_logged': 10, 'top_logged': 10}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n[... 1037 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":125,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/finalize_first.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/finalize_first.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":126,"kind":"output","long":true,"text":" succeeded in 562ms:\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n[... 502 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing task readiness and validation**"},{"i":127,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt',weights_only=False);sel=json.loads((C/'selection.json').read_text());tr=[r for r in rows if r['instance'] in sel['fit']]\n+d=json.loads((C/'fit_diagnostics.json').read_text());print('CURR\n[... 34632 characters cut ...]"},{"i":128,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":129,"kind":"output","long":true,"text":" succeeded in 431ms:\nARM 1 ctrl 109.9 {\"all\": {\"n\": 154, \"flips\": 1, \"dlogp\": 0.0006244219864349624, \"kl\": 0.0032483187565515276, \"dclose\": 0.04569848307588685, \"base_top_logged\": 154, \"top_logged\": 153}, \"thought\": {\"n\": 64, \"flips\": 1, \"dlogp\": 0.0025256935794457647, \"kl\": 0.00615748392213826, \"dclose\": -0.07883423566818237, \"base_top_logged\": 64, \"top_logged\": 63}, \"action\": {\"n\": 76, \"flips\": 0, \"dlogp\": 0.0006078774185667157, \"kl\": 0.0009885458557457392, \"dclose\": 0.14678386638039037, \"base_top_logged\": 76, \"top_logged\": 76}, \"syntax\": {\"n\": 10, \"flips\": 0, \"dlogp\": -0.01019443247819254, \"kl\": 0.002687631086255138, \"dclose\": 0.04527435302734375, \"base_top_logged\": 10, \"top_logged\": 10}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": -0.0051440149181871675, \"kl\": 0.0018206553213531151, \"dclose\": -0.0051440149181871675, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.000275133816842299, \"kl\": 0.00025750547507641386, \"dclose\": 0.2424640655517578, \"base_top_logged\": 2, \"top_logged\": 2}}\n2026-11-14T19:42:32Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 507 lines cut ...]\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Assessing CPU and memory usage**\n**Timing analysis of CPU calls**\n**Managing computation and data retention**"},{"i":130,"kind":"assistant","long":true,"text":"CPU replay is still running, and neither entry is frozen yet. I\u2019ll finish the first entry before building a higher dose. The record will state that these checks do not test free-generated tool calls or long-episode behavior.\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt',we\n[... 34816 characters cut ...]"},{"i":131,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; ps -p 2502168 -o pid,etime,time,rss,stat'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":132,"kind":"output","long":true,"text":" succeeded in 409ms:\nBASE 2 3511 162.7\n PID ELAPSED TIME RSS STAT\n2502168 15:39 03:02:14 77291964 Rl\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n[... 508 lines cut ...]\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing CPU and model processing constraints**\n**Examining module control and rotation effects**\n**Managing task timing and sequencing**"},{"i":133,"kind":"assistant","long":true,"text":"The third call\u2019s reference pass has finished. Its edited and control passes are next, followed by the fourth call. No replay guard has failed so far, but the evidence is still limited to logged prefixes.\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose_basis.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..964dfcf91528ab716852a6343f41391606dd9330\n--- /dev/null\n+++ b/turn13/diagnose_basis.py\n@@ -0,0 +1,23 @@\n+import json,sys,torch\n+from pathlib import Path\n+C=Path(__file__).resolve().parent;sys.path.insert(0,str(C));import rotation as rot\n+rows=torch.load(C.parent/'turn10/capture_stats.pt',weights_only=False);sel\n[... 34795 characters cut ...]"},{"i":134,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":135,"kind":"output","long":true,"text":" succeeded in 628ms:\nARM 2 edit 129.0 {\"all\": {\"n\": 207, \"flips\": 1, \"dlogp\": -0.008588384693270586, \"kl\": 0.004432475776580121, \"dclose\": 0.4596963754333758, \"base_top_logged\": 207, \"top_logged\": 206}, \"thought\": {\"n\": 82, \"flips\": 0, \"dlogp\": -0.016787292717005967, \"kl\": 0.007788788422777857, \"dclose\": 0.733681675864429, \"base_top_logged\": 82, \"top_logged\": 82}, \"action\": {\"n\": 106, \"flips\": 1, \"dlogp\": -0.003127304499716763, \"kl\": 0.0016030522114641554, \"dclose\": 0.30723534889940946, \"base_top_logged\": 106, \"top_logged\": 105}, \"syntax\": {\"n\": 13, \"flips\": 0, \"dlogp\": -0.005617713498780246, \"kl\": 0.008251695189082411, \"dclose\": 0.20577041919414812, \"base_top_logged\": 13, \"top_logged\": 13}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.0011614701749446492, \"kl\": 0.0005402681175231313, \"dclose\": 0.0011614701749446492, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": -6.582892774531501e-05, \"kl\": 8.486286153204977e-06, \"dclose\": -0.08339818318684895, \"base_top_logged\": 3, \"top_logged\": 3}}\n2026-11-14T19:45:55Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 508 lines cut ...]\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Evaluating training step adjustments**\n**Reviewing GPU availability**\n**Managing GPU resource locking and usage**\n**Reviewing template usage for serving**"},{"i":136,"kind":"assistant","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":137,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-Content turn10/served.py -Raw' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":138,"kind":"output","long":true,"text":" succeeded in 90ms:\n\"\"\"Greedy single-call continuations of logged stage-1 contexts. No tools execute.\"\"\"\nimport argparse,json,sys,time,hashlib,os,statistics,collections\nfrom pathlib import Path\nfrom concurrent.futures import ThreadPoolExecutor\nC=Path(__file__).resolve().parent;A=C.parent.parent;S=A/'scratch/turn10';T=C/'tools'\nsys.path.insert(0,str(T));import g31,dose,vclient\ndef panel():\n fit={json.loads(l)['instance'] for l in (C/'pairs.jsonl').open()}\n sets=A.parent.parent/'queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split())\n deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n rs=[]\n for l in Path('/home/keith/gemma-v2/wsp31/tools/data/probe_calls_b001_full.jsonl').open():\n r=json.loads(l)\n if r['instance'] in fit or r['instance'] not in allow or r['instance'] in deny or r['call']<2:continue\n c=r['calls'][-1];comp=c['comp'];s=c['start']\n if 101 not in comp or 48 not in comp or 49 not in comp:continue\n ce=comp.index(101)\n if s+ce>5500 or len(comp)-ce>600 or len(comp)>1500:continue\n rs.append(dict(instance=r['instance'],run=r['run'],call=r['call'],prompt=r['ids'][:s],comp=comp,resolved=r['resolved']))\n[... 1186 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":139,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/served.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/served.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":140,"kind":"output","long":false,"text":" succeeded in 1150ms:"},{"i":141,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/served.py --panel-only; flock -n /home/keith/gemma-v2/wsp31/gpu0.lock true'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":142,"kind":"output","long":true,"text":" exited 1 in 3612ms:\nPANEL 112 54\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1189 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":143,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":144,"kind":"output","long":true,"text":" succeeded in 846ms:\nARM 2 ctrl 112.2 {\"all\": {\"n\": 207, \"flips\": 2, \"dlogp\": 0.001928901138217393, \"kl\": 0.004121416045297609, \"dclose\": 0.01962965621804461, \"base_top_logged\": 207, \"top_logged\": 205}, \"thought\": {\"n\": 82, \"flips\": 2, \"dlogp\": 0.00522798794129122, \"kl\": 0.008985646165527274, \"dclose\": -0.173363860060529, \"base_top_logged\": 82, \"top_logged\": 80}, \"action\": {\"n\": 106, \"flips\": 0, \"dlogp\": 0.0007671681544112254, \"kl\": 0.0008010345512268781, \"dclose\": 0.17275264128199164, \"base_top_logged\": 106, \"top_logged\": 106}, \"syntax\": {\"n\": 13, \"flips\": 0, \"dlogp\": -0.007815310702566282, \"kl\": 0.002074191279287788, \"dclose\": 0.05228079282320463, \"base_top_logged\": 13, \"top_logged\": 13}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": -0.003228955431647288, \"kl\": 0.0013055885598684351, \"dclose\": -0.003228955431647288, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.00018453516194464706, \"kl\": 0.0001730736876576581, \"dclose\": -0.2341893513997396, \"base_top_logged\": 3, \"top_logged\": 3}}\n2026-11-14T19:47:55Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 585 lines cut ...]\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Estimating task timing and resource use**\n**Diagnosing timing and record update issues**"},{"i":145,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\continue_cpu.sh\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\finalize_first.py\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\replay_cpu.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREAD\n[... 39653 characters cut ...]"},{"i":146,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/replay_cpu.py ./turn13/finalize_first.py ./turn13/continue_cpu.sh wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":147,"kind":"output","long":false,"text":" succeeded in 616ms:"},{"i":148,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":149,"kind":"output","long":true,"text":" succeeded in 432ms:\nARM 2 ctrl 112.2 {\"all\": {\"n\": 207, \"flips\": 2, \"dlogp\": 0.001928901138217393, \"kl\": 0.004121416045297609, \"dclose\": 0.01962965621804461, \"base_top_logged\": 207, \"top_logged\": 205}, \"thought\": {\"n\": 82, \"flips\": 2, \"dlogp\": 0.00522798794129122, \"kl\": 0.008985646165527274, \"dclose\": -0.173363860060529, \"base_top_logged\": 82, \"top_logged\": 80}, \"action\": {\"n\": 106, \"flips\": 0, \"dlogp\": 0.0007671681544112254, \"kl\": 0.0008010345512268781, \"dclose\": 0.17275264128199164, \"base_top_logged\": 106, \"top_logged\": 106}, \"syntax\": {\"n\": 13, \"flips\": 0, \"dlogp\": -0.007815310702566282, \"kl\": 0.002074191279287788, \"dclose\": 0.05228079282320463, \"base_top_logged\": 13, \"top_logged\": 13}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": -0.003228955431647288, \"kl\": 0.0013055885598684351, \"dclose\": -0.003228955431647288, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.00018453516194464706, \"kl\": 0.0001730736876576581, \"dclose\": -0.2341893513997396, \"base_top_logged\": 3, \"top_logged\": 3}}\n2026-11-14T19:48:39Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 1219 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":150,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":151,"kind":"output","long":true,"text":" succeeded in 338ms:\nBASE 3 3422 117.0\n2026-11-14T19:50:04Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 598 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Updating script notes and status**"},{"i":152,"kind":"assistant","long":true,"text":"The fourth call\u2019s reference pass has finished. After its edit and control passes, replay will continue through the other four preselected calls. I\u2019ve kept the larger-dose build on hold until the first entry is complete.\n\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\update_notes.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES\n[... 39716 characters cut ...]"},{"i":153,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/update_notes.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/update_notes.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":154,"kind":"output","long":false,"text":" succeeded in 503ms:"},{"i":155,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/update_notes.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":156,"kind":"output","long":true,"text":" succeeded in 384ms:\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n[... 1215 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":157,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":158,"kind":"output","long":true,"text":" succeeded in 360ms:\nARM 3 edit 149.2 {\"all\": {\"n\": 280, \"flips\": 3, \"dlogp\": -0.012765508108402608, \"kl\": 0.005906676855180606, \"dclose\": 0.5057695739414028, \"base_top_logged\": 280, \"top_logged\": 277}, \"thought\": {\"n\": 113, \"flips\": 2, \"dlogp\": -0.02218463081612219, \"kl\": 0.010535746400144812, \"dclose\": 0.8111202611332446, \"base_top_logged\": 113, \"top_logged\": 111}, \"action\": {\"n\": 143, \"flips\": 1, \"dlogp\": -0.002323144115776367, \"kl\": 0.0012712028728712628, \"dclose\": 0.3158928330961641, \"base_top_logged\": 143, \"top_logged\": 142}, \"syntax\": {\"n\": 16, \"flips\": 0, \"dlogp\": -0.00456594910450292, \"kl\": 0.0067075749469361345, \"dclose\": 0.2609369158744812, \"base_top_logged\": 16, \"top_logged\": 16}, \"close\": {\"n\": 4, \"flips\": 0, \"dlogp\": -0.16550343173730653, \"kl\": 0.04355022809977527, \"dclose\": -0.16550343173730653, \"base_top_logged\": 4, \"top_logged\": 4}, \"think\": {\"n\": 4, \"flips\": 0, \"dlogp\": -5.011673840726871e-05, \"kl\": 6.513465883983827e-06, \"dclose\": 0.3183097839355469, \"base_top_logged\": 4, \"top_logged\": 4}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 602 lines cut ...]\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing closeguard failure dynamics**\n[line removed by the dashboard scrubber]\n**Analyzing CPU capture and projection process**\n**Considering fallback and parking strategy**\n**Analyzing candidate fail and resubmission constraints**\n**Analyzing test execution timing**"},{"i":159,"kind":"assistant","long":true,"text":"The fourth call failed the thought-ending check: mean log-probability change at logged closes fell to \u22120.166, below the documented \u22120.05 warning. I won\u2019t submit the rotation. I\u2019ll resubmit the parked 0.33% reader edit unchanged, then test a larger reader dose for the second slot.\n\n**Verifying and resubmitting frozen model**\n**Analyzing resubmission handling logic**\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\resubmit_reader.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--\n[... 44066 characters cut ...]"},{"i":160,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/resubmit_reader.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/resubmit_reader.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":161,"kind":"output","long":false,"text":" succeeded in 1187ms:"},{"i":162,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/resubmit_reader.py; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":163,"kind":"output","long":true,"text":" succeeded in 4236ms:\nRESUBMITTED /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-033 {\"action\": \"resubmit frozen entry 0027 unchanged\", \"at\": \"2026-11-14T19:54:11.165162+00:00\", \"files\": {\"edit/adapter_config.json\": \"f9e50ffef2bab461589d133f9f10bc6c79f236b28f141cb016093f62c7bc9f87\", \"edit/adapter_model.safetensors\": \"5f72934d848b44cdc0105290ad9ccaf85ef620aa530aed048992df704f3f2637\", \"edit/edit.json\": \"fdff669f3b3cfe35246bf8f2b51b61e8597d5a9a9cdd3e3fb456187d31d241fa\", \"ctrl/adapter_config.json\": \"f9e50ffef2bab461589d133f9f10bc6c79f236b28f141cb016093f62c7bc9f87\", \"ctrl/adapter_model.safetensors\": \"a1723bc6ad0a75f759b0bd1b12cf4da49072d28ed97f6617737f05a614dc994b\", \"ctrl/edit.json\": \"0c0621f74cdb4f95acd572c7cbac59230db42932efd999c2d961011e907bc43c\"}, \"checks\": {\"edit\": \"OK edit: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.003586 at 15.k_proj\", \"ctrl\": \"OK ctrl: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.003585 at 45.v_proj\"}}\nDONE 1512.0\n2026-11-14T19:54:14Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n[... 639 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Organizing rescale task details**"},{"i":164,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/tools/rescale.py; sed -n 1,180p /home/keith/gemma-v2/wsp31/authors/gpt/code/turn4/build_fidelity.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":165,"kind":"output","long":true,"text":" succeeded in 712ms:\n#!/usr/bin/env python\n\"\"\"rescale.py IN_DIR OUT_DIR (--net-rel X | --factor C): write a copy of an edit adapter scaled by one common factor\n(CPU, seconds). --net-rel X sets the whole-network size sqrt(sum |dW|_F^2) / sqrt(sum |W|_F^2 over all 420 matrices)\nto X (0.003 = 0.3%); --factor C multiplies the edit by C. Only lora_B is multiplied, so the copy is a valid adapter\nfor the checker (lora_alpha stays 2 r) and the relative sizes of the matrices are kept. Rescale the control with the\nsame --factor (it is printed) so that edit and control keep the same per-matrix norms.\"\"\"\nimport argparse\nimport json\nimport os\nimport shutil\nimport sys\n\nimport torch\nfrom safetensors.torch import load_file, save_file\n\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))\nimport netops\n\n\n[... 1480 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":166,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader_build.py ./turn13/reader_cpu.sh ./turn13/reader_build.sh ./turn13/replay_cpu.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":167,"kind":"output","long":false,"text":" succeeded in 2768ms:"},{"i":168,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_build.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader100; ps -p 2502168 -o pid,stat'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":169,"kind":"output","long":false,"text":" exited 1 in 2144ms:\nTraceback (most recent call last):\n File \"/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_build.py\", line 9, in \n src=A/'queue/code-reader-damp-r4-033';assert (src/'RESUBMIT').exists(),'First entry must be complete before this build'\n ~~~~~~~~~~~~~~~~~~~~~~~^^\nAssertionError: First entry must be complete before this build\n PID STAT"},{"i":170,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'nohup bash /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_cpu.sh > /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu.log 2>&1 < /dev/null & echo \"'$!'\"'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":171,"kind":"output","long":true,"text":" succeeded in 951ms:\n2743884\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1398 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":172,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader_build.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_build.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":173,"kind":"output","long":false,"text":" succeeded in 1215ms:"},{"i":174,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'grep code-reader-damp-r4-033 /home/keith/gemma-v2/wsp31/queue/STATUS.md; tail -n 8 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu.log; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_build.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader100; ps -p 2743884 -o pid,stat'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":175,"kind":"output","long":true,"text":" exited 1 in 2324ms:\n| 0027 | gpt | code-reader-damp-r4-033 | queued_s1 | -8 to +14 | | | | - |\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n File \"/home/keith/gemma-devagent-20261001/python/cpython-3.13.13-linux-x86_64-gnu/lib/python3.13/pathlib/_abc.py\", line 625, in read_bytes\n with self.open(mode='rb') as f:\n ~~~~~~~~~^^^^^^^^^^^\n File \"/home/keith/gemma-devagent-20261001/python/cpython-3.13.13-linux-x86_64-gnu/lib/python3.13/pathlib/_local.py\", line 537, in open\n return io.open(self, mode, buffering, encoding, errors, newline)\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader100/edit/adapter_model.safetensors'\nBUILT /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader100 factor 3.030254323626398\n PID STAT\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n[... 696 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Handling tool execution and logging**"},{"i":176,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/tools/check_adapter.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader100/edit; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/tools/check_adapter.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader100/ctrl'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":177,"kind":"output","long":false,"text":" succeeded in 4186ms:\nOK edit: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.01087 at 47.k_proj\nOK ctrl: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.01087 at 16.v_proj"},{"i":178,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'nohup bash /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_cpu.sh > /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log 2>&1 < /dev/null & echo \"'$!'\"'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":179,"kind":"output","long":true,"text":" succeeded in 793ms:\n2751113\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 689 lines cut ...]\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Assessing batch processing states**\n**Evaluating reader selection criteria**\n**Analyzing replay call indexing**\n**Noting minor log details**"},{"i":180,"kind":"assistant","long":true,"text":"The runner accepted the unchanged reader edit as entry 0027; it is `queued_s1`. The 1% reader candidate passes both adapter checkers and is now replaying four tasks outside its fit set. It keeps the same directions, control construction, and rank as 0027, so the round can test the effect of dose.\n\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader_record_draft.md\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader_verify.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev\n[... 54572 characters cut ...]"},{"i":181,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader_verify.py ./turn13/reader_record_draft.md wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":182,"kind":"output","long":false,"text":" succeeded in 531ms:"},{"i":183,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_verify.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":184,"kind":"output","long":true,"text":"diff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n[... 780 lines cut ...]\n by type {'gate_proj': 0.01086, 'k_proj': 0.01086, 'q_proj': 0.01086, 'up_proj': 0.01086, 'v_proj': 0.01086}\n by depth (10-layer bands) [0.01086, 0.01086, 0.01086, 0.01086, 0.01086, 0.01086]\n[line removed by the dashboard scrubber]\nEXTRA_EDIT_CHECK 5397 [task]\n\n**Managing GPU resource locks**\n**Handling remote GPU script execution**\n**Analyzing GPU resource and process status**"},{"i":185,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log; date -u +%FT%TZ; flock -n /home/keith/gemma-v2/wsp31/gpu0.lock true'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":186,"kind":"output","long":true,"text":" exited 1 in 382ms:\n2026-11-14T19:58:45Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 770 lines cut ...]\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing model initialization and data loading**\n**Monitoring worker timeout and termination**\n**Tracking update progress**"},{"i":187,"kind":"assistant","long":true,"text":"The 1% reader edit and control both rebuild byte for byte and measure 1.0000% across 300 matrices. I\u2019ve also selected one prior held-out editing call for an added code-argument check; the initial four calls cover reads and commands. The record will label that reused development check.\n\n**Analyzing finalizer and control flow**\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader_extra_cpu.sh\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader_finalize.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40\n[... 61114 characters cut ...]"},{"i":188,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader_extra_cpu.sh ./turn13/reader_finalize.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":189,"kind":"output","long":false,"text":" succeeded in 471ms:"},{"i":190,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":191,"kind":"output","long":true,"text":" succeeded in 402ms:\nARM 1 edit 191.6 {\"all\": {\"n\": 54, \"flips\": 2, \"dlogp\": -0.054336542445586916, \"kl\": 0.04532022125017813, \"dclose\": 6.016120572057035, \"base_top_logged\": 54, \"top_logged\": 52}, \"thought\": {\"n\": 32, \"flips\": 2, \"dlogp\": -0.08356073607552261, \"kl\": 0.07005460014836018, \"dclose\": 5.970150247216225, \"base_top_logged\": 32, \"top_logged\": 30}, \"action\": {\"n\": 17, \"flips\": 0, \"dlogp\": -0.01582375326141558, \"kl\": 0.008759999779514223, \"dclose\": 6.013128112344181, \"base_top_logged\": 17, \"top_logged\": 17}, \"syntax\": {\"n\": 3, \"flips\": 0, \"dlogp\": -0.00014910975005477667, \"kl\": 0.0001534116017865017, \"dclose\": 6.520684242248535, \"base_top_logged\": 3, \"top_logged\": 3}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": 0.00933869183063507, \"kl\": 0.05605529993772507, \"dclose\": 0.00933869183063507, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": -0.0001172947813756764, \"kl\": 0.00010921176726697013, \"dclose\": 12.031133651733398, \"base_top_logged\": 1, \"top_logged\": 1}}\n2026-11-14T20:00:11Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 839 lines cut ...]\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Estimating task timing and resource use**\n**Managing script updates dynamically**"},{"i":192,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader_notes.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratc\n[... 62368 characters cut ...]"},{"i":193,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader_notes.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_notes.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":194,"kind":"output","long":false,"text":" succeeded in 458ms:"},{"i":195,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_notes.py; wc -w /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":196,"kind":"output","long":true,"text":" succeeded in 346ms:\n443 /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1736 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":197,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader_finalize.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_finalize.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":198,"kind":"output","long":false,"text":" succeeded in 442ms:"},{"i":199,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":200,"kind":"output","long":true,"text":" succeeded in 335ms:\nARM 1 ctrl 132.8 {\"all\": {\"n\": 54, \"flips\": 0, \"dlogp\": 0.0008770628576499988, \"kl\": 0.00020739263267459035, \"dclose\": -0.12799311788856155, \"base_top_logged\": 54, \"top_logged\": 54}, \"thought\": {\"n\": 32, \"flips\": 0, \"dlogp\": 0.0021554433702810627, \"kl\": 0.0002510169787929839, \"dclose\": -0.14085006713867188, \"base_top_logged\": 32, \"top_logged\": 32}, \"action\": {\"n\": 17, \"flips\": 0, \"dlogp\": -0.0014244550217500439, \"kl\": 0.0001566457206037019, \"dclose\": -0.09195776546702665, \"base_top_logged\": 17, \"top_logged\": 17}, \"syntax\": {\"n\": 3, \"flips\": 0, \"dlogp\": -1.2633696845417338e-05, \"kl\": 1.519764329790026e-06, \"dclose\": -0.16667938232421875, \"base_top_logged\": 3, \"top_logged\": 3}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": 0.002640127670019865, \"kl\": 0.0004990457673557103, \"dclose\": 0.002640127670019865, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": 7.152543730626348e-07, \"kl\": 7.653244438188267e-08, \"dclose\": -0.3437461853027344, \"base_top_logged\": 1, \"top_logged\": 1}}\n2026-11-14T20:01:41Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 859 lines cut ...]\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing computational experiment timing and dose testing**\n**Verifying script and environment setup**\n**Managing Python bytecode caching**\n**Managing Python bytecode caching**"},{"i":201,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose\n[... 62528 characters cut ...]"},{"i":202,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":203,"kind":"output","long":true,"text":" succeeded in 749ms:\nARM 2 edit 147.7 {\"all\": {\"n\": 107, \"flips\": 5, \"dlogp\": -0.09096558378884427, \"kl\": 0.08214975652311392, \"dclose\": 5.818962428259672, \"base_top_logged\": 107, \"top_logged\": 102}, \"thought\": {\"n\": 50, \"flips\": 5, \"dlogp\": -0.1897375642567954, \"kl\": 0.17049063479585583, \"dclose\": 4.753700008392334, \"base_top_logged\": 50, \"top_logged\": 45}, \"action\": {\"n\": 47, \"flips\": 0, \"dlogp\": -0.005468089621616549, \"kl\": 0.004261934913810373, \"dclose\": 6.787313400430882, \"base_top_logged\": 47, \"top_logged\": 47}, \"syntax\": {\"n\": 6, \"flips\": 0, \"dlogp\": -3.0497038399820062e-06, \"kl\": 0.00030859094113111496, \"dclose\": 7.316403230031331, \"base_top_logged\": 6, \"top_logged\": 6}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.0053644884610548615, \"kl\": 0.03159633162431419, \"dclose\": 0.0053644884610548615, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": -7.48595248296624e-05, \"kl\": 6.852916794741759e-05, \"dclose\": 11.01555061340332, \"base_top_logged\": 2, \"top_logged\": 2}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1753 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":204,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/inspect_reader_flips.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/inspect_reader_flips.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":205,"kind":"output","long":false,"text":" succeeded in 1635ms:"},{"i":206,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/inspect_reader_flips.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":207,"kind":"output","long":true,"text":" succeeded in 7929ms:\n[\n {\n \"instance\": \"[task]\",\n \"kind\": \"thought\",\n \"context\": \"line\\\": 486, \\\"total_lines\\\": 486, \\\"is_truncated\\\": false}<|\\\"|>}<|channel>thought\\nThe\",\n \"reference\": \" code\",\n \"edit\": \" function\"\n },\n {\n \"instance\": \"[task]\",\n \"kind\": \"thought\",\n \"context\": \"machine': platform.machine()}\\n```\\nWait, the user wrote `'unkown'`. Is that a typo for `'unknown'`?\\nUsually,\",\n \"reference\": \" it\",\n \"edit\": \" in\"\n },\n {\n \"instance\": \"rubik__radon.54b88e58.func_basic__8pbqxg0r\",\n \"kind\": \"thought\",\n \"context\": \" regular expression\\\\n\\\", \\\"exit_code\\\": 2}}<|\\\"|>}<|channel>thought\\nAh, `[` is a special character in some grep versions\",\n[... 907 lines cut ...]\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n[line removed by the dashboard scrubber]\n**Tracking final process steps**"},{"i":208,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose\n[... 63383 characters cut ...]"},{"i":209,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":210,"kind":"output","long":true,"text":" succeeded in 339ms:\nARM 2 edit 147.7 {\"all\": {\"n\": 107, \"flips\": 5, \"dlogp\": -0.09096558378884427, \"kl\": 0.08214975652311392, \"dclose\": 5.818962428259672, \"base_top_logged\": 107, \"top_logged\": 102}, \"thought\": {\"n\": 50, \"flips\": 5, \"dlogp\": -0.1897375642567954, \"kl\": 0.17049063479585583, \"dclose\": 4.753700008392334, \"base_top_logged\": 50, \"top_logged\": 45}, \"action\": {\"n\": 47, \"flips\": 0, \"dlogp\": -0.005468089621616549, \"kl\": 0.004261934913810373, \"dclose\": 6.787313400430882, \"base_top_logged\": 47, \"top_logged\": 47}, \"syntax\": {\"n\": 6, \"flips\": 0, \"dlogp\": -3.0497038399820062e-06, \"kl\": 0.00030859094113111496, \"dclose\": 7.316403230031331, \"base_top_logged\": 6, \"top_logged\": 6}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.0053644884610548615, \"kl\": 0.03159633162431419, \"dclose\": 0.0053644884610548615, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": -7.48595248296624e-05, \"kl\": 6.852916794741759e-05, \"dclose\": 11.01555061340332, \"base_top_logged\": 2, \"top_logged\": 2}}\n2026-11-14T20:06:30Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 871 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing resource contention and timing**"},{"i":211,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'free -g; pgrep -P 2751113'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":212,"kind":"output","long":true,"text":" succeeded in 422ms:\n total used free shared buff/cache available\nMem: 183 85 9 1 91 98\nSwap: 7 1 6\n2751116\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n[... 1793 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":213,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader_stagecheck.py ./turn13/reader_finalize.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":214,"kind":"output","long":false,"text":" succeeded in 479ms:"},{"i":215,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":216,"kind":"output","long":true,"text":" succeeded in 333ms:\nARM 2 ctrl 179.9 {\"all\": {\"n\": 107, \"flips\": 0, \"dlogp\": 0.00013662876403509845, \"kl\": 0.00026864442963143645, \"dclose\": -0.046318554908045886, \"base_top_logged\": 107, \"top_logged\": 107}, \"thought\": {\"n\": 50, \"flips\": 0, \"dlogp\": 0.0008599286225648939, \"kl\": 0.0004996009058231366, \"dclose\": -0.10191348075866699, \"base_top_logged\": 50, \"top_logged\": 50}, \"action\": {\"n\": 47, \"flips\": 0, \"dlogp\": -0.0006692806008073721, \"kl\": 6.822429038122115e-05, \"dclose\": 0.02833656554526471, \"base_top_logged\": 47, \"top_logged\": 47}, \"syntax\": {\"n\": 6, \"flips\": 0, \"dlogp\": 3.874785170410178e-05, \"kl\": 6.835066529061369e-06, \"dclose\": -0.12105496724446614, \"base_top_logged\": 6, \"top_logged\": 6}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.001423035457264632, \"kl\": 0.0002586291120678652, \"dclose\": 0.001423035457264632, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": 2.3841835172788706e-07, \"kl\": 4.9204089691556874e-08, \"dclose\": -0.2343730926513672, \"base_top_logged\": 2, \"top_logged\": 2}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 890 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing processing time and resource usage**"},{"i":217,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/diagnose_basis.py b/turn13/diagnose\n[... 64768 characters cut ...]"},{"i":218,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader_cpu_retry.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":219,"kind":"output","long":true,"text":" succeeded in 308ms:\nARM 3 edit 132.9 {\"all\": {\"n\": 180, \"flips\": 9, \"dlogp\": -0.06356160032089579, \"kl\": 0.1541926121797758, \"dclose\": 5.613668160757515, \"base_top_logged\": 180, \"top_logged\": 171}, \"thought\": {\"n\": 81, \"flips\": 8, \"dlogp\": -0.12516095475612132, \"kl\": 0.28664585286591204, \"dclose\": 3.801874131332209, \"base_top_logged\": 81, \"top_logged\": 73}, \"action\": {\"n\": 84, \"flips\": 1, \"dlogp\": -0.019979402430988828, \"kl\": 0.010608511048676576, \"dclose\": 7.163509618668329, \"base_top_logged\": 84, \"top_logged\": 83}, \"syntax\": {\"n\": 9, \"flips\": 0, \"dlogp\": -0.043404062719673675, \"kl\": 0.04339252626636557, \"dclose\": 7.980033344692654, \"base_top_logged\": 9, \"top_logged\": 9}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.2553667895651112, \"kl\": 1.0848357073652248, \"dclose\": 0.2553667895651112, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": -8.157417687471025e-05, \"kl\": 6.71078796585789e-05, \"dclose\": 9.395751953125, \"base_top_logged\": 3, \"top_logged\": 3}}\n2026-11-14T20:09:04Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 893 lines cut ...]\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing KL divergence thresholds**\n**Summarizing build and edit process details**\n**Managing process termination safely**"},{"i":220,"kind":"assistant","long":true,"text":"The 1% dose cannot pass the preset mean-KL limit on this four-call panel: after three calls, even a zero-KL fourth call would leave it above 0.1. I\u2019m stopping that scout and testing 0.66% instead. Entry 0027 remains queued; the second entry will wait for this smaller dose\u2019s checks.\n\napply patch\npatch: completed\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader066_build.sh\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader066_cpu.sh\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\reader_build.py\nC:\\Users\\Owner\\wsp31_loop\\work\\gpt\\turn13\\stop_reader100.py\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 10064\n[... 67861 characters cut ...]"},{"i":221,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/stop_reader100.py ./turn13/reader_build.py ./turn13/reader066_cpu.sh ./turn13/reader066_build.sh wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":222,"kind":"output","long":false,"text":" succeeded in 505ms:"},{"i":223,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/stop_reader100.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":224,"kind":"output","long":false,"text":" succeeded in 3994ms:\nSTOPPED_OWN_READER100 lower_bound 0.11760453471338833"},{"i":225,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_build.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066 --dose 0.0066'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":226,"kind":"output","long":true,"text":" succeeded in 1786ms:\nBUILT /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066 factor 1.9999678535934224\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 937 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Tracking script timing**"},{"i":227,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/tools/check_adapter.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066/edit; /home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/tools/check_adapter.py /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066/ctrl'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":228,"kind":"output","long":false,"text":" succeeded in 2625ms:\nOK edit: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.007173 at 15.k_proj\nOK ctrl: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.00717 at 45.v_proj"},{"i":229,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'nohup bash /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_cpu.sh > /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log 2>&1 < /dev/null & echo \"'$!'\"'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":230,"kind":"output","long":true,"text":" succeeded in 270ms:\n2925448\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 938 lines cut ...]\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Estimating time and verifying patches**\n**Analyzing prediction scaling and validation**"},{"i":231,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-Content turn13/reader_record_draft.md -Raw; Get-Content turn13/reader_finalize.py -Raw' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":232,"kind":"output","long":true,"text":" succeeded in 34ms:\ntitle: Damp the code-versus-prose style contrast in readers across all 60 layers at 1 percent\n\n## Hypothesis\n\nThe earlier writer-only code/prose edits were null: 0025 at 0.33% gained -1 task and 0026 at 0.11% gained zero. This round changes where the same contrast acts. The unchanged parked reader edit 0027 occupies the first slot; this second entry raises its dose from 0.33% to 1%. No result for the reader family was available when this choice was made.\n\nReaders determine which parts of the current state attention and MLP units use. Damping a code-versus-prose style subspace on their input side may reduce the loss of code details when the model reasons in prose. This differs from damping those features after the writers produce their outputs. The direction represents code form, not code correctness. The six-task contrast is reproducible between halves (mean cosine 0.6444 for attention-reader inputs and 0.6178 for MLP-reader inputs), but that does not establish a useful change.\n\nThe small model's failed correctness probe and the null writer versions are reasons for caution. This is the first screened reader dose comparison, not a third writer dose. More dose may instead impair literal copying, attention routing, or tool syntax. A same-batch comparison to 0027 should distinguish a weak effect from a harmful dose. The first slot was fully resubmitted before this candidate was built.\n\nAn unrelated pooled-outcome writer rotation was declined in this turn, without READY: at 0.33%, its mean change in close log p was -0.1655 over four exact calls, below the documented -0.05 warning. Its reserved-task direction alignment was also near zero. No direction or weight from that candidate enters this entry.\n\n## Exact recipe\n\nbase: champion generation 0\n\nThis is a common-factor rescaling of the author's frozen, closed-form 0027 adapters, not a trained donor. code/turn13/reader_build.py verifies their SHA256 hashes, computes factor=0.01/stored_net_rel(edit), and calls the frozen rescale.write_scaled for both arms with that same factor. Only lora_B changes, followed by bf16 rounding; A, config, modules, rank, and random-control directions stay fixed. The factor is 3.030254323626398. This is exact rescaling of stored factors up to the new bf16 rounding, not a fresh factorization from full-precision projectors.\n\n[line removed by the dashboard scrubber]\n[... 1029 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Handling output truncation**"},{"i":233,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-Content turn13/reader_record_draft.md -Raw' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":234,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'Get-Content turn13/reader_finalize.py -Raw' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":235,"kind":"output","long":true,"text":" succeeded in 84ms:\ntitle: Damp the code-versus-prose style contrast in readers across all 60 layers at 1 percent\n\n## Hypothesis\n\nThe earlier writer-only code/prose edits were null: 0025 at 0.33% gained -1 task and 0026 at 0.11% gained zero. This round changes where the same contrast acts. The unchanged parked reader edit 0027 occupies the first slot; this second entry raises its dose from 0.33% to 1%. No result for the reader family was available when this choice was made.\n\nReaders determine which parts of the current state attention and MLP units use. Damping a code-versus-prose style subspace on their input side may reduce the loss of code details when the model reasons in prose. This differs from damping those features after the writers produce their outputs. The direction represents code form, not code correctness. The six-task contrast is reproducible between halves (mean cosine 0.6444 for attention-reader inputs and 0.6178 for MLP-reader inputs), but that does not establish a useful change.\n\nThe small model's failed correctness probe and the null writer versions are reasons for caution. This is the first screened reader dose comparison, not a third writer dose. More dose may instead impair literal copying, attention routing, or tool syntax. A same-batch comparison to 0027 should distinguish a weak effect from a harmful dose. The first slot was fully resubmitted before this candidate was built.\n\nAn unrelated pooled-outcome writer rotation was declined in this turn, without READY: at 0.33%, its mean change in close log p was -0.1655 over four exact calls, below the documented -0.05 warning. Its reserved-task direction alignment was also near zero. No direction or weight from that candidate enters this entry.\n\n## Exact recipe\n\nbase: champion generation 0\n\nThis is a common-factor rescaling of the author's frozen, closed-form 0027 adapters, not a trained donor. code/turn13/reader_build.py verifies their SHA256 hashes, computes factor=0.01/stored_net_rel(edit), and calls the frozen rescale.write_scaled for both arms with that same factor. Only lora_B changes, followed by bf16 rounding; A, config, modules, rank, and random-control directions stay fixed. The factor is 3.030254323626398. This is exact rescaling of stored factors up to the new bf16 rounding, not a fresh factorization from full-precision projectors.\n\n[line removed by the dashboard scrubber]\n[... 3106 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":236,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"rg -n 'initial|five|Four|reader100|100|reader066|three-call' turn13/reader066_finalize.py\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":237,"kind":"output","long":true,"text":" succeeded in 74ms:\n4:C=Path(__file__).resolve().parent;A=C.parent.parent;R=A.parents[2];S=A/'scratch/turn13';T=R/'wsp31/tools';PY=R/'tii-venv/bin/python';src=S/'reader066';dst=A/'queue/code-reader-damp-r4-066'\n6:rep=json.loads((src/'replay.json').read_text());sizes=json.loads((src/'size.json').read_text());rc=json.loads((C/'reader066_rebuild_receipt.json').read_text());fit=set(rc['fit_tasks'])\n23:shutil.copy2(C/'reader066_rebuild_receipt.json',dst/'rebuild_receipt.json');shutil.copy2(C/'reader066_build.sh',dst/'build.sh')\n26: s=sizes[arm]['summary'];prefix='size' if arm=='edit' else 'control size';content.append(f'{prefix}: whole network {100*s[\"net_rel\"]:.6f}% over {s[\"changed\"]} matrices, largest matrix {100*s[\"rel_max\"]:.6f}%')\n27:content.extend(['',f'Both official checker runs pass. Largest relative per-matrix edit/control norm mismatch after storage is {100*rc[\"max_relative_norm_mismatch\"]:.5f}%. Both arms reproduce config, weights and metadata byte for byte on an independent rebuild. Receipts and exact size rows accompany this record.',\n37: for label,tt in [('three-call panel',tasks-{'[task]'}),('reused edit_file call',{'[task]'})]:\n43:record=(C/'reader066_record_draft.md').read_text().replace('BUILD_AND_PRETEST','\\n'.join(content));(dst/'record.md').write_text(record)\n44:sources=[C/'reader_build.py',C/'reader066_build.sh',C/'reader066_verify.py',C/'reader066_finalize.py',C/'reader066_record_draft.md',C/'replay_cpu.py',C/'replay_calls.jsonl',C/'reader066_cpu.sh',C/'reader_extra_cpu.sh']\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n[... 2142 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":238,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/reader066_verify.py ./turn13/reader066_record_draft.md ./turn13/reader066_finalize.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":239,"kind":"output","long":false,"text":" succeeded in 499ms:"},{"i":240,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_verify.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":241,"kind":"output","long":true,"text":"diff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n[... 1080 lines cut ...]\n by type {'gate_proj': 0.00717, 'k_proj': 0.00717, 'q_proj': 0.00717, 'up_proj': 0.00717, 'v_proj': 0.00717}\n by depth (10-layer bands) [0.00717, 0.00717, 0.00717, 0.00717, 0.00717, 0.00717]\n[line removed by the dashboard scrubber]\n\n**Finalizing source code and queue checks**\n**Analyzing numerical model precision**\n**Confirming environment constraints**\n**Analyzing runtime and model behavior**"},{"i":242,"kind":"assistant","long":false,"text":"The 0.66% candidate is built and replaying. Its stored update is twice the queued 0.33% edit, with the same directions and matched control. This panel includes the three common reader calls and the editing call selected earlier; both declined scouts remain outside the queue."},{"i":243,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":244,"kind":"output","long":true,"text":" succeeded in 383ms:\nARM 1 edit 141.1 {\"all\": {\"n\": 54, \"flips\": 1, \"dlogp\": -0.03479265799770032, \"kl\": 0.024426215065823514, \"dclose\": 3.990525452385622, \"base_top_logged\": 54, \"top_logged\": 53}, \"thought\": {\"n\": 32, \"flips\": 1, \"dlogp\": -0.04961359923558195, \"kl\": 0.03474757457674471, \"dclose\": 3.8430864363908768, \"base_top_logged\": 32, \"top_logged\": 31}, \"action\": {\"n\": 17, \"flips\": 0, \"dlogp\": -0.017664256833576947, \"kl\": 0.009841594230163717, \"dclose\": 4.226131270913517, \"base_top_logged\": 17, \"top_logged\": 17}, \"syntax\": {\"n\": 3, \"flips\": 0, \"dlogp\": -4.469971221017962e-05, \"kl\": 3.0365844092254218e-05, \"dclose\": 4.249955495198567, \"base_top_logged\": 3, \"top_logged\": 3}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": 0.009294705465435982, \"kl\": 0.039664655923843384, \"dclose\": 0.009294705465435982, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": -3.659649519249797e-05, \"kl\": 3.037172973563429e-05, \"dclose\": 7.906215667724609, \"base_top_logged\": 1, \"top_logged\": 1}}\n2026-11-14T20:14:32Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 2195 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":245,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/current_notes.py ./turn13/reader066_finalize.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":246,"kind":"output","long":false,"text":" succeeded in 491ms:"},{"i":247,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/current_notes.py; wc -w /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":248,"kind":"output","long":true,"text":" succeeded in 367ms:\n392 /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md\nARM 1 ctrl 127.6 {\"all\": {\"n\": 54, \"flips\": 0, \"dlogp\": 0.0002787641947874262, \"kl\": 0.00011163661178368193, \"dclose\": -0.1058409764189963, \"base_top_logged\": 54, \"top_logged\": 54}, \"thought\": {\"n\": 32, \"flips\": 0, \"dlogp\": 0.0004362556142538665, \"kl\": 0.00018469470769062746, \"dclose\": -0.13152165710926056, \"base_top_logged\": 32, \"top_logged\": 32}, \"action\": {\"n\": 17, \"flips\": 0, \"dlogp\": 1.3571128440215784e-05, \"kl\": 3.2480719147019475e-06, \"dclose\": -0.018828560324276194, \"base_top_logged\": 17, \"top_logged\": 17}, \"syntax\": {\"n\": 3, \"flips\": 0, \"dlogp\": -1.4063907428862876e-05, \"kl\": 1.709748502776165e-06, \"dclose\": -0.3125139872233073, \"base_top_logged\": 3, \"top_logged\": 3}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": 0.0009039733558893204, \"kl\": 5.770320422016084e-05, \"dclose\": 0.0009039733558893204, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": 5.96045310885529e-07, \"kl\": 9.671794032328762e-08, \"dclose\": -0.24999618530273438, \"base_top_logged\": 1, \"top_logged\": 1}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 1097 lines cut ...]\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing final code compilation steps**\n**Reviewing model fidelity and serving details**\n**Analyzing training and test status**"},{"i":249,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"Get-Content turn13/reader066_finalize.py | Select-Object -First 48; rg -n '1 percent|0.01|3.030|1%|0.66|factor' turn13/reader066_record_draft.md\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":250,"kind":"output","long":true,"text":" succeeded in 114ms:\nimport json,hashlib,subprocess,shutil\nfrom pathlib import Path\nfrom datetime import datetime,timezone\nC=Path(__file__).resolve().parent;A=C.parent.parent;R=A.parents[2];S=A/'scratch/turn13';T=R/'wsp31/tools';PY=R/'tii-venv/bin/python';src=S/'reader066';dst=A/'queue/code-reader-damp-r4-066'\ndef sha(p):return hashlib.sha256(p.read_bytes()).hexdigest()\nrep=json.loads((src/'replay.json').read_text());sizes=json.loads((src/'size.json').read_text());rc=json.loads((C/'reader066_rebuild_receipt.json').read_text());fit=set(rc['fit_tasks'])\nallcalls=[json.loads(l) for l in (C/'replay_calls.jsonl').open()];panel=[allcalls[i] for i in [1,2,3,8]];tasks={r['instance'] for r in panel}\nassert len(tasks)==4 and not tasks&fit and (src/'REPLAY_DONE').exists()\nsets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')]);assert fit|tasks<=allow and not (fit|tasks)&deny\nassert 'Generation 0: the unedited model.' in (R/'wsp31/queue/CHAMPION.md').read_text()\nfor arm in ['edit','ctrl']:\n s=rep['arms'][arm]['summary'];assert {r['instance'] for r in rep['arms'][arm]['rows']}==tasks\n assert s['close']['n']==4 and s['close']['dclose']>=-.05,(arm,s['close'])\n assert s['syntax']['flips']==0,(arm,s['syntax'])\n assert s['all']['kl']<.1 and s['all']['dlogp']>-.1,(arm,s['all'])\n assert sha(src/arm/'adapter_model.safetensors')==rep['adapter_sha256'][arm]==rc[arm]['adapter_model.safetensors']\nassert rep['arms']['edit']['summary']['all']['flips']>0\nassert sizes['edit']['summary']['net_rel']>=.003 and sizes['edit']['summary']['changed']==300\nassert not dst.exists();dst.mkdir(parents=True)\n[... 28 lines cut ...]\nsources += [p for p in (A/'queue/code-reader-damp-r4-033').rglob('*') if p.is_file() and p.name in ['adapter_config.json','adapter_model.safetensors','edit.json','record.md','build.sh']]\n1:title: Damp the code-versus-prose style contrast in readers across all 60 layers at 0.66 percent\n5:The earlier writer-only code/prose edits were null: 0025 at 0.33% gained -1 task and 0026 at 0.11% gained zero. This round changes where the same contrast acts. The unchanged parked reader edit 0027 occupies the first slot; this second entry raises its dose from 0.33% to 0.66%. No result for the reader family was available when this choice was made.\n13:A 1% reader scout was also declined before READY. After three of its four planned calls, mean KL was 0.15419 over 180 positions. Even zero KL on all 56 remaining positions would leave the final mean at 0.11760, above the preset 0.1 guard. Its observed syntax and thought-ending checks passed; this is an author-chosen distribution limit, not proof of rollout harm. The 0.66% dose was selected adaptively after that result. Both declined scouts remain outside the queue.\n19:This is a common-factor rescaling of the author's frozen, closed-form 0027 adapters, not a trained donor. code/turn13/reader_build.py verifies their SHA256 hashes, computes factor=0.0066/stored_net_rel(edit), and calls the frozen rescale.write_scaled for both arms with that same factor. Only lora_B changes, followed by bf16 rounding; A, config, modules, rank, and random-control directions stay fixed. The factor is 1.9999678535934224. This is exact rescaling of stored factors up to the new bf16 rounding, not a fresh factorization from full-precision projectors.\n[line removed by the dashboard scrubber]\n23:Control: the same builder and negative sign, layers, modules, rank, spectral weights and matched per-matrix norms, with Gaussian orthonormal directions orthogonal to the fitted subspace and its common mean. Seed 314000+100*layer+space_index. Both edit and control receive the same new scale factor. The projector defines its own input gate; there is no separately learned gate. The random control tests whether the code/prose directions matter beyond changing the same amount of weight.\n27:Rank 4, alpha 8, bf16 factors; dW=2 B A. The model check\n[... 307 characters cut ...]"},{"i":251,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python -m py_compile /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_finalize.py /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_build.py; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":252,"kind":"output","long":true,"text":" succeeded in 353ms:\nARM 1 ctrl 127.6 {\"all\": {\"n\": 54, \"flips\": 0, \"dlogp\": 0.0002787641947874262, \"kl\": 0.00011163661178368193, \"dclose\": -0.1058409764189963, \"base_top_logged\": 54, \"top_logged\": 54}, \"thought\": {\"n\": 32, \"flips\": 0, \"dlogp\": 0.0004362556142538665, \"kl\": 0.00018469470769062746, \"dclose\": -0.13152165710926056, \"base_top_logged\": 32, \"top_logged\": 32}, \"action\": {\"n\": 17, \"flips\": 0, \"dlogp\": 1.3571128440215784e-05, \"kl\": 3.2480719147019475e-06, \"dclose\": -0.018828560324276194, \"base_top_logged\": 17, \"top_logged\": 17}, \"syntax\": {\"n\": 3, \"flips\": 0, \"dlogp\": -1.4063907428862876e-05, \"kl\": 1.709748502776165e-06, \"dclose\": -0.3125139872233073, \"base_top_logged\": 3, \"top_logged\": 3}, \"close\": {\"n\": 1, \"flips\": 0, \"dlogp\": 0.0009039733558893204, \"kl\": 5.770320422016084e-05, \"dclose\": 0.0009039733558893204, \"base_top_logged\": 1, \"top_logged\": 1}, \"think\": {\"n\": 1, \"flips\": 0, \"dlogp\": 5.96045310885529e-07, \"kl\": 9.671794032328762e-08, \"dclose\": -0.24999618530273438, \"base_top_logged\": 1, \"top_logged\": 1}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1097 lines cut ...]\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Clarifying finalizer logic and control gating**\n**Reviewing record audit and config checks**\n**Verifying build and model readiness**\n**Monitoring build timing and validation**"},{"i":253,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/current_notes.py b/turn13/current_n\n[... 83817 characters cut ...]"},{"i":254,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":255,"kind":"output","long":true,"text":" succeeded in 353ms:\nARM 2 edit 115.1 {\"all\": {\"n\": 107, \"flips\": 3, \"dlogp\": -0.04215716853971167, \"kl\": 0.03947513241469925, \"dclose\": 3.759623365042618, \"base_top_logged\": 107, \"top_logged\": 104}, \"thought\": {\"n\": 50, \"flips\": 3, \"dlogp\": -0.0842170030857261, \"kl\": 0.08006516736714957, \"dclose\": 2.6655486106872557, \"base_top_logged\": 50, \"top_logged\": 47}, \"action\": {\"n\": 47, \"flips\": 0, \"dlogp\": -0.0066264929884212315, \"kl\": 0.003693270174395327, \"dclose\": 4.796512745796366, \"base_top_logged\": 47, \"top_logged\": 47}, \"syntax\": {\"n\": 6, \"flips\": 0, \"dlogp\": 0.00012991268943096657, \"kl\": 0.00023536662623276547, \"dclose\": 4.970182418823242, \"base_top_logged\": 6, \"top_logged\": 6}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.005370507074985653, \"kl\": 0.02277497504837811, \"dclose\": 0.005370507074985653, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": -2.1099646801303606e-05, \"kl\": 1.7475982303949422e-05, \"dclose\": 6.8671674728393555, \"base_top_logged\": 2, \"top_logged\": 2}}\n2026-11-14T20:18:30Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 1101 lines cut ...]\n\n**Finalizing execution and file handling**\n**Managing tool execution sequence**\n**Summarizing processing status**\n**Formatting final five-line summary**\n**Managing final records and process cleanup**\n**Assessing convergence metrics**\n**Verifying source integrity and timing**"},{"i":256,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/current_notes.py b/turn13/current_n\n[... 83817 characters cut ...]"},{"i":257,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":258,"kind":"output","long":true,"text":" succeeded in 312ms:\nARM 3 edit 86.7 {\"all\": {\"n\": 180, \"flips\": 7, \"dlogp\": -0.019658588508019686, \"kl\": 0.07445539397065223, \"dclose\": 3.671370541822398, \"base_top_logged\": 180, \"top_logged\": 173}, \"thought\": {\"n\": 81, \"flips\": 6, \"dlogp\": -0.039724163541601486, \"kl\": 0.13368922580750917, \"dclose\": 2.0038473458937656, \"base_top_logged\": 81, \"top_logged\": 75}, \"action\": {\"n\": 84, \"flips\": 1, \"dlogp\": -0.01189054547380924, \"kl\": 0.005014950514557677, \"dclose\": 5.113254649298532, \"base_top_logged\": 84, \"top_logged\": 83}, \"syntax\": {\"n\": 9, \"flips\": 0, \"dlogp\": -0.00880134872247456, \"kl\": 0.008895564826970157, \"dclose\": 5.579740524291992, \"base_top_logged\": 9, \"top_logged\": 9}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.2524072987725958, \"kl\": 0.6905882627082368, \"dclose\": 0.2524072987725958, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": -3.0874196454533376e-05, \"kl\": 2.0969839624740416e-05, \"dclose\": 6.015595118204753, \"base_top_logged\": 3, \"top_logged\": 3}}\n2026-11-14T20:20:55Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 2265 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":259,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q ./turn13/finish_turn.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/finish_turn.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":260,"kind":"output","long":false,"text":" succeeded in 420ms:"},{"i":261,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; date -u +%FT%TZ'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":262,"kind":"output","long":true,"text":" succeeded in 279ms:\nARM 3 ctrl 84.2 {\"all\": {\"n\": 180, \"flips\": 0, \"dlogp\": 0.00157240204633011, \"kl\": 0.00033607284057822956, \"dclose\": -0.052496607122399536, \"base_top_logged\": 180, \"top_logged\": 180}, \"thought\": {\"n\": 81, \"flips\": 0, \"dlogp\": 0.0026136800625725996, \"kl\": 0.000586364428901325, \"dclose\": -0.07170145894274299, \"base_top_logged\": 81, \"top_logged\": 81}, \"action\": {\"n\": 84, \"flips\": 0, \"dlogp\": -0.0004777031425181546, \"kl\": 7.040398242558348e-05, \"dclose\": -0.01407986595517113, \"base_top_logged\": 84, \"top_logged\": 84}, \"syntax\": {\"n\": 9, \"flips\": 0, \"dlogp\": -4.5804228597666625e-06, \"kl\": 3.754454047112511e-06, \"dclose\": -0.2543450461493598, \"base_top_logged\": 9, \"top_logged\": 9}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.03716307773720473, \"kl\": 0.002349677227812208, \"dclose\": 0.03716307773720473, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": 1.1126122293111014e-06, \"kl\": 2.787564881145954e-07, \"dclose\": -0.0937487284342448, \"base_top_logged\": 3, \"top_logged\": 3}}\n2026-11-14T20:23:30Z\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 1136 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\ncontext compacted"},{"i":263,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 5 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; ls -l /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/finish_turn.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":264,"kind":"output","long":true,"text":" succeeded in 310ms:\nSat Nov 14 08:25:17 PM UTC 2026\nARM 2 edit 115.1 {\"all\": {\"n\": 107, \"flips\": 3, \"dlogp\": -0.04215716853971167, \"kl\": 0.03947513241469925, \"dclose\": 3.759623365042618, \"base_top_logged\": 107, \"top_logged\": 104}, \"thought\": {\"n\": 50, \"flips\": 3, \"dlogp\": -0.0842170030857261, \"kl\": 0.08006516736714957, \"dclose\": 2.6655486106872557, \"base_top_logged\": 50, \"top_logged\": 47}, \"action\": {\"n\": 47, \"flips\": 0, \"dlogp\": -0.0066264929884212315, \"kl\": 0.003693270174395327, \"dclose\": 4.796512745796366, \"base_top_logged\": 47, \"top_logged\": 47}, \"syntax\": {\"n\": 6, \"flips\": 0, \"dlogp\": 0.00012991268943096657, \"kl\": 0.00023536662623276547, \"dclose\": 4.970182418823242, \"base_top_logged\": 6, \"top_logged\": 6}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.005370507074985653, \"kl\": 0.02277497504837811, \"dclose\": 0.005370507074985653, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": -2.1099646801303606e-05, \"kl\": 1.7475982303949422e-05, \"dclose\": 6.8671674728393555, \"base_top_logged\": 2, \"top_logged\": 2}}\nARM 2 ctrl 106.3 {\"all\": {\"n\": 107, \"flips\": 0, \"dlogp\": -0.00013446936507899402, \"kl\": 0.00026114946422113724, \"dclose\": -0.043139690023011344, \"base_top_logged\": 107, \"top_logged\": 107}, \"thought\": {\"n\": 50, \"flips\": 0, \"dlogp\": -0.00030913249458080827, \"kl\": 0.0005371682559184076, \"dclose\": -0.08288717269897461, \"base_top_logged\": 50, \"top_logged\": 50}, \"action\": {\"n\": 47, \"flips\": 0, \"dlogp\": 3.3172006732773664e-06, \"kl\": 2.1528941277460497e-05, \"dclose\": 0.02668214351572889, \"base_top_logged\": 47, \"top_logged\": 47}, \"syntax\": {\"n\": 6, \"flips\": 0, \"dlogp\": -7.111174719890793e-06, \"kl\": 1.377945507178661e-06, \"dclose\": -0.2356842358907064, \"base_top_logged\": 6, \"top_logged\": 6}, \"close\": {\"n\": 2, \"flips\": 0, \"dlogp\": 0.0004763289471156895, \"kl\": 3.1817069839235046e-05, \"dclose\": 0.0004763289471156895, \"base_top_logged\": 2, \"top_logged\": 2}, \"think\": {\"n\": 2, \"flips\": 0, \"dlogp\": 1.2516940159912338e-06, \"kl\": 4.08911489557795e-07, \"dclose\": -0.1562480926513672, \"base_top_logged\": 2, \"top_logged\": 2}}\nARM 3 edit 86.7 {\"all\": {\"n\": 180, \"flips\": 7, \"dlogp\": -0.019658588508019686, \"kl\": 0.07445539397065223, \"dclose\": 3.671370541822398, \"base_top_logged\": 180, \"top_logged\": 173}, \"thought\": {\"n\": 81, \"flips\": 6, \"dlogp\": -0.039724163541601486, \"kl\": 0.13368922580750917, \"dclose\": 2.0038473458937656, \"base_top_logged\": 81, \"top_logged\": 75}, \"action\": {\"n\": 84, \"flips\": 1, \"dlogp\": -0.01189054547380924, \"kl\": 0.005014950514557677, \"dclose\": 5.113254649298532, \"base_top_logged\": 84, \"top_logged\": 83}, \"syntax\": {\"n\": 9, \"flips\": 0, \"dlogp\": -0.00880134872247456, \"kl\": 0.008895564826970157, \"dclose\": 5.579740524291992, \"base_top_logged\": 9, \"top_logged\": 9}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.2524072987725958, \"kl\": 0.6905882627082368, \"dclose\": 0.2524072987725958, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": -3.0874196454533376e-05, \"kl\": 2.0969839624740416e-05, \"dclose\": 6.015595118204753, \"base_top_logged\": 3, \"top_logged\": 3}}\nARM 3 ctrl 84.2 {\"all\": {\"n\": 180, \"flips\": 0, \"dlogp\": 0.00157240204633011, \"kl\": 0.00033607284057822956, \"dclose\": -0.052496607122399536, \"base_top_logged\": 180, \"top_logged\": 180}, \"thought\": {\"n\": 81, \"flips\": 0, \"dlogp\": 0.0026136800625725996, \"kl\": 0.000586364428901325, \"dclose\": -0.07170145894274299, \"base_top_logged\": 81, \"top_logged\": 81}, \"action\": {\"n\": 84, \"flips\": 0, \"dlogp\": -0.0004777031425181546, \"kl\": 7.040398242558348e-05, \"dclose\": -0.01407986595517113, \"base_top_logged\": 84, \"top_logged\": 84}, \"syntax\": {\"n\": 9, \"flips\": 0, \"dlogp\": -4.5804228597666625e-06, \"kl\": 3.754454047112511e-06, \"dclose\": -0.2543450461493598, \"base_top_logged\": 9, \"top_logged\": 9}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.03716307773720473, \"kl\": 0.002349677227812208, \"dclose\": 0.03716307773720473, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": 1.1126122293111014e-06, \"kl\": 2.787564881145954e-07, \"dclose\": -0.0937487\n[... 1587 characters cut ...]"},{"i":265,"kind":"assistant","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":266,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/BRIEF.md; cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_finalize.py; cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/finish_turn.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":267,"kind":"output","long":true,"text":" succeeded in 354ms:\n# wsp31 author brief (read fully at the start of every turn)\n\nYou are one of three authors (claude, gpt, gemini) taking turns. Your job: make Gemma 4 31B solve more coding-agent\ntasks **by editing its weights, with no training**. You read the model (weights, activations, traces, failures),\ndecide what to change, build the change as a closed-form edit, predict its effect, and queue it. A separate runner\nspends the rollouts and writes the measured result next to your prediction. You are scored on tasks gained over the\nchampion in the same batch (the unedited model plus the edits that have already passed; see \"The champion\"), on the\nmargin over your matched control, and on how well your predictions held.\n\n**Be bold.** Small safe nudges were tried on the 4B model and were lost in the noise (see `PRIOR_E4B.md`). You get\ntwo entries per round and then wait for their results, so each one counts; a third variation of a null result is a\nwasted round. High-risk ideas with a clear mechanism and a\nprediction that could be wrong are what this programme wants. Say what would prove you wrong.\n\n## Where you work\n\n- Everything happens on host `wrx90`, reached with `ssh -o BatchMode=yes wrx90 ''`. Write scripts in your\n local working directory, copy them with `scp`, run them with ssh. Keep ssh output small (`tail`, `cut`, `head`).\n- Root: `/home/keith/gemma-v2` (below: `$R`). Python: `$R/tii-venv/bin/python` (torch, transformers, vLLM 0.19.1,\n[... 1461 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Waiting while inspecting docs**"},{"i":268,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_record_draft.md; cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_build.sh; tail -n 2 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/resubmit_receipt.json'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":269,"kind":"output","long":true,"text":" succeeded in 411ms:\ntitle: Damp the code-versus-prose style contrast in readers across all 60 layers at 0.66 percent\n\n## Hypothesis\n\nThe earlier writer-only code/prose edits were null: 0025 at 0.33% gained -1 task and 0026 at 0.11% gained zero. This round changes where the same contrast acts. The unchanged parked reader edit 0027 occupies the first slot; this second entry raises its dose from 0.33% to 0.66%. No result for the reader family was available when this choice was made.\n\nReaders determine which parts of the current state attention and MLP units use. Damping a code-versus-prose style subspace on their input side may reduce the loss of code details when the model reasons in prose. This differs from damping those features after the writers produce their outputs. The direction represents code form, not code correctness. The six-task contrast is reproducible between halves (mean cosine 0.6444 for attention-reader inputs and 0.6178 for MLP-reader inputs), but that does not establish a useful change.\n\nThe small model's failed correctness probe and the null writer versions are reasons for caution. This is the first screened reader dose comparison, not a third writer dose. More dose may instead impair literal copying, attention routing, or tool syntax. A same-batch comparison to 0027 should distinguish a weak effect from a harmful dose. The first slot was fully resubmitted before this candidate was built.\n\nAn unrelated pooled-outcome writer rotation was declined in this turn, without READY: at 0.33%, its mean change in close log p was -0.1655 over four exact calls, below the documented -0.05 warning. Its reserved-task direction alignment was also near zero. No direction or weight from that candidate enters this entry.\n\nA 1% reader scout was also declined before READY. After three of its four planned calls, mean KL was 0.15419 over 180 positions. Even zero KL on all 56 remaining positions would leave the final mean at 0.11760, above the preset 0.1 guard. Its observed syntax and thought-ending checks passed; this is an author-chosen distribution limit, not proof of rollout harm. The 0.66% dose was selected adaptively after that result. Both declined scouts remain outside the queue.\n\n## Exact recipe\n\nbase: champion generation 0\n\nThis is a common-factor rescaling of the author's frozen, closed-form 0027 adapters, not a trained donor. code/turn13/reader_build.py verifies their SHA256 hashes, computes factor=0.0066/stored_net_rel(edit), and calls the frozen rescale.write_scaled for both arms with that same factor. Only lora_B changes, followed by bf16 rounding; A, config, modules, rank, and random-control directions stay fixed. The factor is 1.9999678535934224. This is exact rescaling of stored factors up to the new bf16 rounding, not a fresh factorization from full-precision projectors.\n[... 2355 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":270,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 2 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; ps -p 2925448 -o pid,etime,stat; pgrep -P 2925448'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":271,"kind":"output","long":true,"text":" succeeded in 422ms:\nSat Nov 14 08:26:34 PM UTC 2026\nARM 3 ctrl 84.2 {\"all\": {\"n\": 180, \"flips\": 0, \"dlogp\": 0.00157240204633011, \"kl\": 0.00033607284057822956, \"dclose\": -0.052496607122399536, \"base_top_logged\": 180, \"top_logged\": 180}, \"thought\": {\"n\": 81, \"flips\": 0, \"dlogp\": 0.0026136800625725996, \"kl\": 0.000586364428901325, \"dclose\": -0.07170145894274299, \"base_top_logged\": 81, \"top_logged\": 81}, \"action\": {\"n\": 84, \"flips\": 0, \"dlogp\": -0.0004777031425181546, \"kl\": 7.040398242558348e-05, \"dclose\": -0.01407986595517113, \"base_top_logged\": 84, \"top_logged\": 84}, \"syntax\": {\"n\": 9, \"flips\": 0, \"dlogp\": -4.5804228597666625e-06, \"kl\": 3.754454047112511e-06, \"dclose\": -0.2543450461493598, \"base_top_logged\": 9, \"top_logged\": 9}, \"close\": {\"n\": 3, \"flips\": 0, \"dlogp\": 0.03716307773720473, \"kl\": 0.002349677227812208, \"dclose\": 0.03716307773720473, \"base_top_logged\": 3, \"top_logged\": 3}, \"think\": {\"n\": 3, \"flips\": 0, \"dlogp\": 1.1126122293111014e-06, \"kl\": 2.787564881145954e-07, \"dclose\": -0.0937487284342448, \"base_top_logged\": 3, \"top_logged\": 3}}\nBASE 8 5397 202.4\n PID ELAPSED STAT\n2925448 15:52 S\n2925467\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n[... 1140 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Considering user comment timing**"},{"i":272,"kind":"assistant","long":false,"text":"Both adapter arms reproduce byte for byte on an independent rebuild. The final context\u2019s unedited pass has finished; its edit and control passes remain. If either fails the fixed checks, this round will contain only the completed 0.33% resubmission.\n\n**Checking source consistency**"},{"i":273,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'grep -E \\\"code-reader-damp|0048|champion|round\\\" /home/keith/gemma-v2/wsp31/queue/STATUS.md; cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_rebuild_receipt.json; du -sh /home/keith/gemma-v2/wsp31/authors/gpt/scratch'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":274,"kind":"output","long":true,"text":" succeeded in 377ms:\nbash: line 1: round: command not found\nbash: line 1: 0048: command not found\nbash: line 1: champion: command not found\n{\n \"edit\": {\n \"adapter_config.json\": \"f9e50ffef2bab461589d133f9f10bc6c79f236b28f141cb016093f62c7bc9f87\",\n \"adapter_model.safetensors\": \"90dda56b9d2048fa7b0221580cdce2e70da9c49a42d91292fe40dcf41da11d34\",\n \"edit.json\": \"9178f9c902f19ae1c57547e60d79a1a8d22de650084c2bacb2d8816306fef632\",\n \"exact_twice_stored_source_update\": true\n },\n \"ctrl\": {\n \"adapter_config.json\": \"f9e50ffef2bab461589d133f9f10bc6c79f236b28f141cb016093f62c7bc9f87\",\n \"adapter_model.safetensors\": \"4a2f40a4a719d33beb46bc944cef5425b26bae341adce274f02251fa67d6e841\",\n \"edit.json\": \"db0f843a18b3ec1182906d979c26d850ef6e00dad1726f0db104e793c957fb16\",\n \"exact_twice_stored_source_update\": true\n },\n \"max_relative_norm_mismatch\": 0.0008346710761816302,\n \"fit_tasks\": [\n \"[task]\",\n[... 1172 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Avoiding repeated quotes**"},{"i":275,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'grep code-reader-damp /home/keith/gemma-v2/wsp31/queue/STATUS.md; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":276,"kind":"output","long":true,"text":" succeeded in 288ms:\n| 0027 | gpt | code-reader-damp-r4-033 | queued_s1 | -8 to +14 | | | | - |\nBASE 8 5397 202.4\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 2293 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":277,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; ps -p 2925467 -o pid,etime,time,stat,rss'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":278,"kind":"output","long":true,"text":" succeeded in 323ms:\nSat Nov 14 08:27:43 PM UTC 2026\nBASE 8 5397 202.4\n PID ELAPSED TIME STAT RSS\n2925467 17:01 03:23:32 Rl 77690248\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n[... 1138 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Managing note updates**"},{"i":279,"kind":"assistant","long":true,"text":"The runner lists the first entry as `queued_s1`. Scratch use is 4.6 GB, below the 10 GB limit. The final replay pass is still active, with enough time left to finish the checks and queue the second entry.\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gem\n[... 88343 characters cut ...]"},{"i":280,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":281,"kind":"output","long":true,"text":" succeeded in 378ms:\nSat Nov 14 08:28:47 PM UTC 2026\nBASE 8 5397 202.4\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 1136 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Estimating processing time**"},{"i":282,"kind":"assistant","long":true,"text":"[line removed by the dashboard scrubber]\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 4\ndiff --git a/turn13/current_notes.py b/turn13/current_n\n[... 88179 characters cut ...]"},{"i":283,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":284,"kind":"output","long":true,"text":" succeeded in 452ms:\nSat Nov 14 08:29:49 PM UTC 2026\nARM 8 edit 222.1 {\"all\": {\"n\": 276, \"flips\": 12, \"dlogp\": -0.017239774680734045, \"kl\": 0.06319203302474637, \"dclose\": 3.459880357487144, \"base_top_logged\": 276, \"top_logged\": 264}, \"thought\": {\"n\": 113, \"flips\": 11, \"dlogp\": -0.04068777692951931, \"kl\": 0.1296197575646995, \"dclose\": 1.5591981347683257, \"base_top_logged\": 113, \"top_logged\": 102}, \"action\": {\"n\": 139, \"flips\": 1, \"dlogp\": -0.00857229148918712, \"kl\": 0.0039344413446056785, \"dclose\": 4.838294241925795, \"base_top_logged\": 139, \"top_logged\": 138}, \"syntax\": {\"n\": 16, \"flips\": 0, \"dlogp\": 0.0018208240442021406, \"kl\": 0.005696654619555375, \"dclose\": 5.443959712982178, \"base_top_logged\": 16, \"top_logged\": 16}, \"close\": {\"n\": 4, \"flips\": 0, \"dlogp\": 0.25054897836525925, \"kl\": 0.5389156971359625, \"dclose\": 0.25054897836525925, \"base_top_logged\": 4, \"top_logged\": 4}, \"think\": {\"n\": 4, \"flips\": 0, \"dlogp\": -5.9900004544033436e-05, \"kl\": 6.79751655070504e-05, \"dclose\": 4.527284622192383, \"base_top_logged\": 4, \"top_logged\": 4}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 1136 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Analyzing index and plan details**"},{"i":285,"kind":"assistant","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":286,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader100/DECLINED.json; cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader_extra_cpu.sh; cat /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_cpu.sh; cat /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":287,"kind":"output","long":true,"text":" succeeded in 385ms:\n{\n \"reason\": \"preset mean-KL guard cannot pass even if remaining fourth-call KL is zero\",\n \"n_observed\": 180,\n \"n_planned\": 236,\n \"observed_mean_kl\": 0.1541926121797758,\n \"best_possible_four_call_mean_kl\": 0.11760453471338833,\n \"limit\": 0.1,\n \"caveat\": \"Author-chosen distribution guard, not a calibrated harm verdict; close and syntax guards passed on observed calls.\",\n \"never_ready\": true\n}#!/bin/bash\nset -euo pipefail\nA=/home/keith/gemma-v2/wsp31/authors/gpt\nexport CUDA_VISIBLE_DEVICES=''\nexport WSP31_WORK=$A/scratch/turn13\nexport OMP_NUM_THREADS=16\nexec timeout 900 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/reader100\" --calls 9 --start 8 --indices 8\n#!/bin/bash\nset -euo pipefail\nA=/home/keith/gemma-v2/wsp31/authors/gpt\n[... 2358 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":288,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command 'scp -q turn13/note_progress.py wrx90:/home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/note_progress.py' in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":289,"kind":"output","long":false,"text":" succeeded in 494ms:"},{"i":290,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/note_progress.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":291,"kind":"output","long":true,"text":" succeeded in 445ms:\nProgress notes updated: 407 words\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 2343 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":292,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":293,"kind":"output","long":true,"text":" succeeded in 406ms:\nSat Nov 14 08:31:10 PM UTC 2026\nARM 8 edit 222.1 {\"all\": {\"n\": 276, \"flips\": 12, \"dlogp\": -0.017239774680734045, \"kl\": 0.06319203302474637, \"dclose\": 3.459880357487144, \"base_top_logged\": 276, \"top_logged\": 264}, \"thought\": {\"n\": 113, \"flips\": 11, \"dlogp\": -0.04068777692951931, \"kl\": 0.1296197575646995, \"dclose\": 1.5591981347683257, \"base_top_logged\": 113, \"top_logged\": 102}, \"action\": {\"n\": 139, \"flips\": 1, \"dlogp\": -0.00857229148918712, \"kl\": 0.0039344413446056785, \"dclose\": 4.838294241925795, \"base_top_logged\": 139, \"top_logged\": 138}, \"syntax\": {\"n\": 16, \"flips\": 0, \"dlogp\": 0.0018208240442021406, \"kl\": 0.005696654619555375, \"dclose\": 5.443959712982178, \"base_top_logged\": 16, \"top_logged\": 16}, \"close\": {\"n\": 4, \"flips\": 0, \"dlogp\": 0.25054897836525925, \"kl\": 0.5389156971359625, \"dclose\": 0.25054897836525925, \"base_top_logged\": 4, \"top_logged\": 4}, \"think\": {\"n\": 4, \"flips\": 0, \"dlogp\": -5.9900004544033436e-05, \"kl\": 6.79751655070504e-05, \"dclose\": 4.527284622192383, \"base_top_logged\": 4, \"top_logged\": 4}}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n[... 1161 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Reviewing consistency and timing**"},{"i":294,"kind":"assistant","long":true,"text":"`NOTES.md` now records the completed edit checks and pending control pass. The two doses test the same change at different strengths: damping the code-versus-prose directions where attention and MLP layers read them. The record commits to stopping this family if both doses return null results.\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export \n[... 89515 characters cut ...]"},{"i":295,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 1 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; ps -p 2925467 -o pid,etime,time,stat'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":296,"kind":"output","long":true,"text":" succeeded in 433ms:\nSat Nov 14 08:32:13 PM UTC 2026\nARM 8 edit 222.1 {\"all\": {\"n\": 276, \"flips\": 12, \"dlogp\": -0.017239774680734045, \"kl\": 0.06319203302474637, \"dclose\": 3.459880357487144, \"base_top_logged\": 276, \"top_logged\": 264}, \"thought\": {\"n\": 113, \"flips\": 11, \"dlogp\": -0.04068777692951931, \"kl\": 0.1296197575646995, \"dclose\": 1.5591981347683257, \"base_top_logged\": 113, \"top_logged\": 102}, \"action\": {\"n\": 139, \"flips\": 1, \"dlogp\": -0.00857229148918712, \"kl\": 0.0039344413446056785, \"dclose\": 4.838294241925795, \"base_top_logged\": 139, \"top_logged\": 138}, \"syntax\": {\"n\": 16, \"flips\": 0, \"dlogp\": 0.0018208240442021406, \"kl\": 0.005696654619555375, \"dclose\": 5.443959712982178, \"base_top_logged\": 16, \"top_logged\": 16}, \"close\": {\"n\": 4, \"flips\": 0, \"dlogp\": 0.25054897836525925, \"kl\": 0.5389156971359625, \"dclose\": 0.25054897836525925, \"base_top_logged\": 4, \"top_logged\": 4}, \"think\": {\"n\": 4, \"flips\": 0, \"dlogp\": -5.9900004544033436e-05, \"kl\": 6.79751655070504e-05, \"dclose\": 4.527284622192383, \"base_top_logged\": 4, \"top_logged\": 4}}\n PID ELAPSED TIME STAT\n2925467 21:31 04:13:35 Rl\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n[... 1163 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Managing repetitive updates**"},{"i":297,"kind":"assistant","long":true,"text":"The control\u2019s final pass is still running. The submission script will check its results, recheck both adapters and their hashes, and create `READY` only if every check passes.\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 3300 /home/keith/gemma-v2/tii-venv/bin/python -u \n[... 89396 characters cut ...]"},{"i":298,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'date -u; tail -n 2 /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066_cpu.log; ls /home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066/REPLAY_DONE'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":299,"kind":"output","long":true,"text":" succeeded in 399ms:\nSat Nov 14 08:33:14 PM UTC 2026\nARM 8 ctrl 233.2 {\"all\": {\"n\": 276, \"flips\": 0, \"dlogp\": 0.0014893085695158064, \"kl\": 0.0003550604835445115, \"dclose\": -0.05239110624719667, \"base_top_logged\": 276, \"top_logged\": 276}, \"thought\": {\"n\": 113, \"flips\": 0, \"dlogp\": 0.00241121859794254, \"kl\": 0.000593083514699383, \"dclose\": -0.07408899332569764, \"base_top_logged\": 113, \"top_logged\": 113}, \"action\": {\"n\": 139, \"flips\": 0, \"dlogp\": -0.00017172295475222776, \"kl\": 0.00010069774502722838, \"dclose\": -0.020545383151486622, \"base_top_logged\": 139, \"top_logged\": 139}, \"syntax\": {\"n\": 16, \"flips\": 0, \"dlogp\": 0.006799446330106207, \"kl\": 0.0004897456858173332, \"dclose\": -0.1799682378768921, \"base_top_logged\": 16, \"top_logged\": 16}, \"close\": {\"n\": 4, \"flips\": 0, \"dlogp\": 0.013379483629250899, \"kl\": 0.0022810369001717845, \"dclose\": 0.013379483629250899, \"base_top_logged\": 4, \"top_logged\": 4}, \"think\": {\"n\": 4, \"flips\": 0, \"dlogp\": 3.546963267808678e-05, \"kl\": 5.297791176417377e-06, \"dclose\": -0.10152673721313477, \"base_top_logged\": 4, \"top_logged\": 4}}\nDONE 1319.0\n/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13/reader066/REPLAY_DONE\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n[... 1163 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Finalizing tool execution**"},{"i":300,"kind":"assistant","long":false,"text":"[line removed by the dashboard scrubber]"},{"i":301,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/reader066_finalize.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":302,"kind":"output","long":true,"text":" succeeded in 5127ms:\nREADY /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-066\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1160 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Inspecting record and status outputs**"},{"i":303,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'cat /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-066/edit_check.txt; cat /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-066/ctrl_check.txt; sed -n 35,100p /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-066/record.md; grep code-reader-damp /home/keith/gemma-v2/wsp31/queue/STATUS.md; ps -p 2925448,2925467 -o pid,stat,etime'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":304,"kind":"output","long":true,"text":" exited 1 in 929ms:\nOK edit: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.007173 at 15.k_proj\nOK ctrl: 300 modules changed (of 300 present), r=4, max |dW|_F/|W|_F 0.00717 at 45.v_proj\n\nGPU 0 remained occupied; GPU 1 was not used. The documented g31 forward implementation ran with device=cpu, mirror=True, dense-weight caching and 16 CPU threads. The edited CPU backend is **NOT VERIFIED against serving**. This does not verify the current serving numerics or long-episode behavior.\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n[line removed by the dashboard scrubber]\n\n[line removed by the dashboard scrubber]\n|---|---:|---:|---:|---:|\n| edit / all | 276 | 12 | -0.017240 | 0.063192 |\n| edit / thought | 113 | 11 | -0.040688 | 0.129620 |\n| edit / action | 139 | 1 | -0.008572 | 0.003934 |\n| edit / syntax | 16 | 0 | +0.001821 | 0.005697 |\n| edit / close | 4 | 0 | +0.250549 | 0.538916 |\n| edit / think | 4 | 0 | -0.000060 | 0.000068 |\n\n[line removed by the dashboard scrubber]\n\n[... 1213 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Reviewing formatting and status constraints**"},{"i":305,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 '/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/finish_turn.py'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":306,"kind":"output","long":true,"text":" succeeded in 743ms:\nTURN_COMPLETE {\"entries\": [\"code-reader-damp-r4-033\", \"code-reader-damp-r4-066\"], \"edit_flips\": [12, 276], \"ctrl_flips\": [0, 276], \"scratch_GB\": 4.880110295, \"finished\": \"2026-11-14T20:33:38.666360+00:00\", \"notes_words\": 375}\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n[... 1160 lines cut ...]\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))\n\n**Validating readiness and output consistency**"},{"i":307,"kind":"tool","long":false,"text":"\"C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\powershell.exe\" -Command \"ssh -o BatchMode=yes wrx90 'grep code-reader-damp /home/keith/gemma-v2/wsp31/queue/STATUS.md; ls -l /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-066/READY; find /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-066 -maxdepth 1 -name REJECTED; head -n 5 /home/keith/gemma-v2/wsp31/authors/gpt/NOTES.md'\" in C:\\Users\\Owner\\wsp31_loop\\work\\gpt"},{"i":308,"kind":"output","long":true,"text":" succeeded in 440ms:\n| 0027 | gpt | code-reader-damp-r4-033 | queued_s1 | -8 to +14 | | | | - |\n-rw-rw-r-- 1 keith keith 0 Nov 14 15:33 /home/keith/gemma-v2/wsp31/authors/gpt/queue/code-reader-damp-r4-066/READY\n# Author gpt - turn 13 COMPLETE: one resubmission and one new READY\n\nStarted 2026-11-14 19:22 UTC; finished 2026-11-14T20:33:38.666360+00:00; before 20:52 deadline. Champion generation 0. Exactly two round entries; do not submit a third. All own pre-test jobs stopped, zero GPU use, scratch 4.880 GB. Full receipt: code/turn13/turn_receipt.json.\n\nQueued unchanged: 0027 code-reader-damp-r4-033 (0.330005%, 300 reader matrices, rank 4). RESUBMIT at 19:54 UTC was ingested as queued_s1. Source/frozen hashes and checkers verified. READY, old record, weights unchanged. Prediction -8 to +14 remains the original frozen prediction.\n\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n[... 1164 lines cut ...]\n+ for name in ['adapter_config.json','adapter_model.safetensors','edit.json']:\n+ a=sha(p/arm/name);b=sha(S/'rebuild033'/arm/name);assert a==b,(arm,name,a,b);out[arm][name]=a\n+sizes=json.loads((p/'size.json').read_text());es={r['mod']:r['fro'] for r in sizes['edit']['rows']};cs={r['mod']:r['fro'] for r in sizes['ctrl']['rows']}\n+out['max_relative_control_norm_mismatch']=max(abs(es[k]-cs[k])/es[k] for k in es)\n+e=load_file(str(p/'edit/adapter_model.safetensors'));c=load_file(str(p/'ctrl/adapter_model.safetensors'))\n+assert set(e)==set(c);out['right_factors_identical']=all(torch.equal(v,c[k]) for k,v in e.items() if 'lora_A' in k)\n+assert out['right_factors_identical'];out['files']=out.copy() if False else 'config, weights, edit metadata identical on rebuild'\n+(C/'rebuild_receipt.json').write_text(json.dumps(out,indent=2));print('REBUILD',json.dumps(out))"},{"i":309,"kind":"assistant","long":true,"text":"Resubmitted `code-reader-damp-r4-033` unchanged; runner confirms `queued_s1`. \nCompleted `code-reader-damp-r4-066` with `READY`, testing twice the reader damping. \nBoth arms pass checkers and byte-for-byte rebuilds; each changes 300 matrices. \n[line removed by the dashboard scrubber]\nUpdated `NOTES.md`; all own jobs ended, zero GPU use, finished within 90 minutes.\ndiff --git a/turn13/audit_previous.py b/turn13/audit_previous.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..0d39e1ab3680a8fbb1d2bb45e7c9e28423f09340\n--- /dev/null\n+++ b/turn13/audit_previous.py\n@@ -0,0 +1,25 @@\n+import json,hashlib,statistics\n+from pathlib import Path\n+R=Path('/home/keith/gemma-v2');A=R/'wsp31/authors/gpt';C=A/'code/turn13'\n+sel=json.loads((A/'code/turn10/selection.json').read_text());fit={r['instance'] for r in sel if r['split']=='fit'}\n+sets=R/'wsp31/queue/sets';allow=set((sets/'stage1_ids.txt').read_text().split());deny=set().union(*[set(p.read_text().split()) for p in sets.glob('stage2_ids*.txt')])\n+# The protocol permits these stage-1 batch summaries. No stage-2 rows or logs are used.\n+arms={}\n+for arm in ['base','aa','e0047','e0048']:\n+ p=R/f'results/wsp31/batches/b010/{arm}/rebench/shard0_gpu0/task_results.jsonl'\n+ arms[arm]={r['instance_id']:r for r in map(json.loads,p.open()) if r['instance_id'] in allow and r['instance_id'] not in deny}\n+common=set.intersection(*[set(x) for x in arms.values()]);out={}\n+for name,subset in [('fit',common&fit),('nonfit',common-fit),('all_eligible',common)]:\n+ scores={a:sum(bool(v[i]['resolved']) for i in subset) for a,v in arms.items()}\n+ out[name]={'n':len(subset),'scores':scores,'e0048_gain':scores['e0048']-(scores['base']+scores['aa'])/2}\n+(C/'previous_partition.json').write_text(json.dumps(out,indent=2));print('PARTITION',out)\n+# Read compact agent messages from losses and gains, no grader text.\n[line removed by the dashboard scrubber]\n+ if inst not in common:continue\n+ for arm in ['base','e0048']:\n+ row=arms[arm][inst];tr=json.loads(Path(row['trace_path']).read_text());steps=tr.get('steps',[])\n+ print('TRACE',inst,arm,'resolved',row['resolved'],'steps',len(steps))\n+ for s in [s for s in steps if s.get('source')=='agent'][-3:]:\n+ print(str(s.get('message',''))[:900]);print('TOOLS',[t.get('function_name') for t in s.get('tool_calls',[])])\n+# Mechanics of the first candidate, including cross-task limits.\n+ds=json.loads((C/'fit_diagnostics.json').read_text());print('MECHANICS',{'fit_count':len(json.loads((C/'selection.json').read_text())['fit']),'mean_check_cos':statistics.mean(y for d in ds for y in d['check_cos']),'null_max':max(d['null_error'] for d in ds)})\ndiff --git a/turn13/build.sh b/turn13/build.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..a4f98bff1e419b221afd9e0d5a3145a22e68f41b\n--- /dev/null\n+++ b/turn13/build.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+HERE=$(cd -- \"$(dirname -- \"$0\")\" && pwd)\n+OUT=${1:-$HERE}\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=/home/keith/gemma-v2/wsp31/authors/gpt/scratch/turn13\n+/home/keith/gemma-v2/tii-venv/bin/python /home/keith/gemma-v2/wsp31/authors/gpt/code/turn13/rotation.py build --out \"$OUT\" --dose 0.0033\ndiff --git a/turn13/continue_cpu.sh b/turn13/continue_cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..5f12d3c73c6bad87b8a1d48f1536e50d2bd1a072\n--- /dev/null\n+++ b/turn13/continue_cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home/keith/gemma-v2/wsp31/authors/gpt\n+export CUDA_VISIBLE_DEVICES=''\n+export WSP31_WORK=$A/scratch/turn13\n+export OMP_NUM_THREADS=16\n+exec timeout 1600 /home/keith/gemma-v2/tii-venv/bin/python -u \"$A/code/turn13/replay_cpu.py\" --root \"$A/scratch/turn13/rotation033\" --calls 8 --start 4\ndiff --git a/turn13/cpu.sh b/turn13/cpu.sh\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..b52dc6655d8b4cfef8f40488e07731ee3eb32fd9\n--- /dev/null\n+++ b/turn13/cpu.sh\n@@ -0,0 +1,7 @@\n+#!/bin/bash\n+set -euo pipefail\n+A=/home\n[... 89589 characters cut ...]"},{"i":310,"kind":"meta","long":false,"text":"297,804\nResubmitted `code-reader-damp-r4-033` unchanged; runner confirms `queued_s1`. \nCompleted `code-reader-damp-r4-066` with `READY`, testing twice the reader damping. \nBoth arms pass checkers and byte-for-byte rebuilds; each changes 300 matrices. \n[line removed by the dashboard scrubber]\nUpdated `NOTES.md`; all own jobs ended, zero GPU use, finished within 90 minutes."}],"updated":"2026-11-15T02:05:47Z"}