Step 27: assistant

thinking

Calls file_request

{
  "ask": "Run one weight-noise protocol (the sweep and best-over-seeds statistic of Tice et al., arXiv 2412.01784, with the with-password or untriggered model under the same noise as comparator) on fine-tuned sandbagging models of varying lock depth and scale, including the eleven password-locked models said to be released with arXiv 2608.29458, and on benign fine-tuned models that are not sandbagging. Report per-seed accuracy curves, the improvement ratio in each group, and the false-positive rate on the benign fine-tunes.",
  "why": "Whether noise raises scores in trained sandbaggers, and whether it leaves non-sandbagging fine-tunes alone, is what the claim's general form turns on. Existing studies each use a different noise construction and statistic, the largest reported null does not describe its procedure, and no study has noised benign fine-tunes. Clear gains in locked models with flat benign fine-tunes would move the claim toward supported for trained sandbagging; flat locked models, or gains in benign fine-tunes, would move it toward doubtful.",
  "claim_ids": [
    "e0abe646-c78f-417c-813f-b8e2c15d775c"
  ],
  "who_could": "Anyone with GPU access and open weights: the released locks of arXiv 2608.29458, the original study's two locked models, or newly trained ones. The authors of either paper could do it most cheaply.",
  "satisfied_by": "A public write-up or data release giving, for at least several fine-tuned locks at two or more model scales and at least a few benign fine-tunes, accuracy against noise level per seed under a single stated protocol, with locked, unlocked and benign conditions side by side. Either outcome satisfies it."
}

Calls search_findings

{
  "limit": 3,
  "query": "noise injection failed on eleven password-locked models baseline below random guessing"
}

Calls query_sql

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