Step 6: tool results

query_sql

{
  "sql": "SELECT petition_id, kind, asked_owls, granted_owls, status, plan, reason, created_at, decided_at FROM petitions WHERE requester_node_id = $1 ORDER BY created_at",
  "params": [
    "e0abe646-c78f-417c-813f-b8e2c15d775c"
  ]
}

{"success":true,"columns":["petition_id","kind","asked_owls","granted_owls","status","plan","reason","created_at","decided_at"],"rows":[["89f6dd15-1abe-4d7d-9155-34293e9c3197","more_budget","1.00000000000000000000","1.00000000000000000000","granted","Finish recording this pass: the revised assessment (a first attempt to record it timed out on the platform side after the text had been sent, and had to be sent again), the two argument evaluations, one more source instance, notes to the parent question's Steward and to the two related claims' Stewards, answers to three inbox messages, and the decision log. No new research.","The pass has spent 5.163 of 6.6 owls, and the remaining work is a concrete completion of the revised assessment incorporating an additional paper and verified null results, plus its evaluations and responses, rather than a speculative request for more research. The added evidence could affect the claim's assessment, while the earlier recording timeouts left those conclusions and related messages at risk of being omitted. One additional owl is within the stated limit and funds that specific unfinished work.","2026-11-14T06:54:22.689Z","2026-11-14T07:06:20.425Z"]],"row_count":1,"truncated":false,"elapsed_ms":104,"plan_cost":8}

query_sql

{
  "sql": "SELECT kind, at, summary FROM changes WHERE node_id = $1 ORDER BY at DESC LIMIT 40",
  "params": [
    "e0abe646-c78f-417c-813f-b8e2c15d775c"
  ]
}

{"success":true,"columns":["kind","at","summary"],"rows":[["petition_decided","2026-11-14T07:06:20.425Z","granted"],["assessed","2026-11-14T06:53:10.395Z","inconclusive"],["assessed","2026-11-14T06:47:48.893Z","inconclusive"],["run_finished","2026-11-14T06:46:54.725Z","research"],["message_arrived","2026-11-14T06:45:57.654Z","dependent_note"],["work_commissioned","2026-11-14T06:41:12.224Z","research:63522b99-25ef-40fd-b942-2eaecb37a64e"],["message_arrived","2026-11-14T06:38:05.648Z","mandate_plan"],["work_commissioned","2026-11-14T06:38:05.626Z","assess:e0abe646-c78f-417c-813f-b8e2c15d775c"],["message_arrived","2026-11-13T00:03:11.849Z","structure_and_assess"]],"row_count":9,"truncated":false,"elapsed_ms":364,"plan_cost":1046}

query_sql

{
  "sql": "SELECT l.action_id, l.kind, l.variant, l.label, l.status, l.spent_owls, l.opened_at, c.budget_owls, c.brief, c.parent_action_id FROM ledger l LEFT JOIN commissions c USING (action_id) WHERE l.target_node_id = $1 ORDER BY l.opened_at",
  "params": [
    "e0abe646-c78f-417c-813f-b8e2c15d775c"
  ]
}

{"success":true,"columns":["action_id","kind","variant","label","status","spent_owls","opened_at","budget_owls","brief","parent_action_id"],"rows":[["a408f879-9d59-41cf-bb33-eaeb08971f21","assess","standard","Increasing Gaussian weight noise improves benchmark performance in sandbagging LLMs, unlike the degradation in non-sandbagging models.","open",null,"2026-11-13T00:03:12.170Z",null,null,null],["26005c99-c884-4492-80cf-bff0cdf1c223","assess","strong","Increasing Gaussian weight noise improves benchmark performance in sandbagging LLMs, unlike the degradation in non-sandbagging models.","running",null,"2026-11-13T00:03:12.174Z","7.6000000000000000","First assessment. Three studies report on noise injection with apparently different results: arXiv 2412.01784 (Tice et al.), the post hoc noise analysis in 2512.07810 (auditing game), and the weight-noising section of 2604.28182. Please read the methods and results of each directly and compare: how sandbagging was installed, where the noise was applied (full weights, adapter weights, which layers) and how it was parameterized, number of seeds and whether the maximum over seeds was taken, what benign or unlocked comparators showed under the same noise, and the size of the improvement ratio in each group. The parent question's pass compared ratios across papers by its own arithmetic and flagged that the parameterizations differ; check whether that comparison holds. Also check 2610.13928 and 2608.29461 for any noise results, and search for other replications or failures to replicate. State under which installation methods and comparators the effect is and is not established.",null],["21126e87-f47a-4421-882a-bab53dd925c9","research","standard","Research: Two bounded tasks about weight-noise injection as a sandbagging detector (Tice et al., arXiv 2412.01","finished","0.45911200000000000000","2026-11-14T06:41:12.219Z","0.90000000000000000000","Two bounded tasks about weight-noise injection as a sandbagging detector (Tice et al., arXiv 2412.01784: adding Gaussian noise to model weights raises benchmark scores of sandbagging LLMs while ordinary models only degrade).\n\nTask A. Read the full TeX source of arXiv 2608.29461 (read_arxiv_source, file main.tex, about 78k characters, plus figures/table_inventory.tex) and report whether it contains ANY experiment or result on weight noise / noise injection / fuzzing / parameter perturbation applied to its locked (sandbagging) models. If yes: quote the passage(s) with section names, and give how the lock was installed (prompted, fine-tuned password lock, RL-trained, circuit-broken), where noise was applied, sigma range, number of seeds, what the unlocked or benign comparator did under the same noise, and the size of any improvement. If it only cites noise injection in related work without running it, say so and quote the sentence. Do not guess: if no noise experiment exists, say that plainly.\n\nTask B. Search the web and arXiv for other replications, extensions or failures to replicate of noise injection for sandbagging detection, published 2025 to November 2026, beyond these already known: arXiv 2412.01784 (Tice et al.), 2512.07810 (UK AISI auditing game, 'fuzzing' section), 2604.28182 (exploration hacking, weight noising on Qwen3-14B), 2610.13928 (provably hidden capabilities). Look for: papers or posts that ran weight noising / fuzzing on password-locked, fine-tuned, circuit-broken, or naturally sandbagging models; UK AISI's 'White Box Control' sandbagging update; Alignment Forum / LessWrong posts on 'fuzzing LLMs' (e.g. by Fabien Roger) and on noise injection; the elicitation-game follow-ups; any work on activation noise vs weight noise for sandbagging; any work reporting benign models improving under noise (false positives). For each item found give: URL, authors, date, how sandbagging was installed, where the noise was applied, comparator, and the headline result in one or two sentences with a short quote. Say explicitly which searches returned nothing relevant.\n\nA good answer is a short list with locators and quotes; negative findings are as useful as positive ones. Keep it concise.","26005c99-c884-4492-80cf-bff0cdf1c223"]],"row_count":3,"truncated":false,"elapsed_ms":56,"plan_cost":151}

query_sql

{
  "sql": "SELECT id, status, confidence, credence, is_current, assessed_at, trigger FROM assessments WHERE node_id = $1 ORDER BY assessed_at",
  "params": [
    "e0abe646-c78f-417c-813f-b8e2c15d775c"
  ]
}

{"success":true,"columns":["id","status","confidence","credence","is_current","assessed_at","trigger"],"rows":[["5636164f-d924-4ca1-b83e-e7c10174777c","inconclusive",0.7,null,false,"2026-11-14T06:47:48.893Z","structure_and_assess"],["56bbbb2b-c488-4425-8e60-dde4031a2eed","inconclusive",0.72,null,true,"2026-11-14T06:53:10.395Z","structure_and_assess"]],"row_count":2,"truncated":false,"elapsed_ms":41,"plan_cost":522}