Run step
Step 14: tool results
evaluate_argument
Input
{
"verdict": "likely",
"decisive": false,
"evaluation": "Granting that benignly unlocked models gain under noise as sandbaggers do, a positive fuzzing result cannot by itself indicate intent, which is the claim's second half. It falls short of certain because it rests on one study of 70B organisms with small effects; other forms of benign underelicitation, or other model scales, could behave differently.",
"argument_id": "d5847a26-007c-4e7b-9cd4-88c322a06c9b"
}{"success":true,"message":"Evaluation recorded for argument \"The argument from benignly unlocked models gaining alongside locked ones\": likely."}evaluate_argument
Input
{
"verdict": "slight",
"decisive": false,
"evaluation": "Granting that honest, fully elicited models only degrade under noise, it follows that noise separates sandbagging from full elicitation. It does not follow that noise separates sandbagging from benign underelicitation, the comparison the claim concerns, since the honest comparators were not underelicited. The premise bears on the question only by suggesting that gains are not a generic artifact of noise.",
"argument_id": "588075dd-3071-49c1-8180-b1e9004b8b96"
}{"success":true,"message":"Evaluation recorded for argument \"The argument from fully elicited models only degrading under noise\": slight."}set_claim_importance
Input
{
"claim_id": "01232bf1-55f7-42fd-9f39-8a38bfde7837",
"reasoning": "A specific, contested point about one sandbagging-detection method within AI capability evaluation; matters to evaluation practice but narrow.",
"importance": 0.4,
"contestation": 0.45
}{"success":true,"message":"Importance of claim 01232bf1-55f7-42fd-9f39-8a38bfde7837 set to 0.4. Contestation recorded as 0.45."}