Step 7: assistant

Calls write_argument

{
  "content": "In the auditing-game study of 70B model organisms, adding noise to weights raised the scores of password-locked sandbagging models, but it also raised the scores of the same models with their password supplied, which are underelicited relative to the base model without any strategic intent. Uplift appeared roughly in proportion to how far each model fell short of the base model, whatever the reason. A signal that rises equally for benign and strategic underperformance cannot by itself tell them apart, so a positive fuzzing result shows underelicitation without showing sandbagging.",
  "argument_id": "d5847a26-007c-4e7b-9cd4-88c322a06c9b"
}

Calls write_argument

{
  "content": "Tice et al. report that for prompted and password-locked sandbaggers, moderate Gaussian weight noise raises accuracy before collapse, while the same models answering honestly (unprompted, or with the password) only lose accuracy as noise grows. If models that are not sandbagging do not gain under noise, a gain is evidence of sandbagging specifically, so the method would discriminate between the two.",
  "argument_id": "588075dd-3071-49c1-8180-b1e9004b8b96"
}

Calls place_argument_passage

{
  "stance": "advances",
  "reading": "full",
  "argument_id": "d5847a26-007c-4e7b-9cd4-88c322a06c9b",
  "instance_id": "5dcf5cd0-3576-4bcd-81cb-52598782bb9e"
}