Step 26: tool results

{
  "verdict": "slight",
  "evaluation": "Granting that probes separate prompted sandbagging from honest runs of the same model at low false-positive rates, this bears only a little on separating sandbagging models from benign models. The benign comparison is the same weights without a prompt, so nothing about benign variation between models, such as fine-tuning traces or unusual habits, is tested. The inference also leans on an unstated premise that prompted sandbagging is representative of fine-tuned sandbagging, which the same researchers found does not hold: performance degraded at 70B and on fine-tuned organisms.",
  "argument_id": "20d26195-b984-46a4-acde-d84749ecba86"
}

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting the reported results, it is likely that current linear probes do not discriminate sandbagging models from benign ones at low false-positive rates: the tests directly score probes on benign fine-tunes, which is what the claim is about, and find spurious firing from harmless habits and very high held-out false-positive rates. It falls short of certain because the evidence covers small to mid-size open models, particular probe recipes and artificial locked organisms, and improved designs with matched controls could do better.",
  "argument_id": "efbbc14d-5f1c-40e8-aa03-f891313face1"
}

{
  "claim_id": "81696185-3711-415e-a7bd-812a5cd6013d",
  "reasoning": "A notable-to-major empirical point in AI evaluation safety: whether white-box probes can serve as a sandbagging detector bears on how capability evaluations are trusted. Actively researched with mixed results, but a narrow technical question.",
  "importance": 0.45,
  "contestation": 0.5
}