Step 20: tool results

add_argument

{
  "name": "The argument from prompted-sandbagging probe results",
  "stance": "for",
  "claim_id": "81696185-3711-415e-a7bd-812a5cd6013d",
  "description": "Off-policy deception probes separate prompted sandbagging from honest runs of the same model at high AUROC and low FPR on chat controls."
}

{"success":true,"message":"Created argument \"The argument from prompted-sandbagging probe results\" (for) on claim 81696185-3711-415e-a7bd-812a5cd6013d. Attach its subclaim edges, then give it a written form with write_argument, and evaluate it with evaluate_argument when you record the claim's assessment.","argument_id":"20d26195-b984-46a4-acde-d84749ecba86"}

add_argument

{
  "name": "The argument from false positives on benign fine-tuned models",
  "stance": "against",
  "claim_id": "81696185-3711-415e-a7bd-812a5cd6013d",
  "description": "When probes are scored against benign fine-tunes, including ones with unusual but harmless habits, they flag many benign examples or fail to transfer across training runs."
}

{"success":true,"message":"Created argument \"The argument from false positives on benign fine-tuned models\" (against) on claim 81696185-3711-415e-a7bd-812a5cd6013d. Attach its subclaim edges, then give it a written form with write_argument, and evaluate it with evaluate_argument when you record the claim's assessment.","argument_id":"efbbc14d-5f1c-40e8-aa03-f891313face1"}