Step 6: tool results

{
  "verdict": "slight",
  "decisive": false,
  "evaluation": "Granting its premises, the argument shows that the levers impose real handicaps and raise catch rates, not that they are enough. The step to \"offsets\" needs the tested attacks to be as strong as those a more capable system would find. The empirical premise, that [[claim:ea57432c-c473-4452-94e1-16102c805446|control evaluations catch most sabotage]], is not yet assessed; the results behind it come from GPT-3.5 overseeing GPT-4 and an 8-billion-parameter model overseeing GPT-4o-mini, against attacks scripted by the researchers, and the resampling result did not carry over when stronger models built their own attacks. The structural premise that the handicaps do not shrink with capability is the weak point: simulating a weaker monitor and choosing when to strike are things extra capability supplies, and both cut measured safety sharply where they were tried.",
  "argument_id": "9c1aa45f-c1de-4343-bc20-94940bcdb95a"
}

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting its premises, the levers as now used would probably not offset a moderate gap; the conclusion would not follow for protocols hardened against these specific weaknesses. The premises hold in part. [[claim:4c2cc0db-a4cc-434e-a72f-21adc0285d07|Monitor-aware evasion]] is supported and carries most weight: it collapsed monitoring and resampling at gaps of one to two model generations, and related results show monitor simulation and attack timing doing the same at adjacent model tiers. Those attackers were given the monitor's prompt or tuned thresholds, more than a memoryless system would have. [[claim:811afc08-da63-4118-9876-858adf917cc7|Recognition of tests]] is supported but uneven. [[claim:2d5868e4-745c-474f-ac6f-cc9cbc25ea8c|Decline with the gap]] is supported as a tendency that monitor design can flatten. The steps on collusion and probes are the least secure: present models coordinate poorly without memory, and some probe designs withstood models trained to evade them.",
  "argument_id": "c6a452f1-c565-4a79-b710-58c915cbbc44"
}