Step 4: tool results

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting its premises, the conclusion follows inductively for the range tested. The weight falls on the representativeness premise: the measured debaters were prompted to argue assigned sides, the capability ranges were narrow, and the judges were often weak mainly in information. Extrapolating to wider gaps or optimised debaters is where the inference is weakest. [[claim:347613ab-67cb-4389-adb3-cc84c430d683|The persuasiveness-accuracy relationship]] itself is well supported for tasks where the judge lacks the source text.",
  "argument_id": "b82339fb-3c98-481c-ae1b-909c8c12ab53"
}

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting that both sides gain equally and that strong false arguments are no easier to accept than strong true ones, flat or rising accuracy follows. The second premise is the fragile one: it fails wherever falsehood can exploit judge biases or hide flaws more cheaply than truth can expose them, which is what the optimisation and obfuscation arguments assert. [[claim:41fbdc50-e72f-456f-9498-b332bbc4e634|The flat debater scaling result]] supports the mechanism for prompted debaters on three of four datasets.",
  "argument_id": "dd65b340-306a-4195-8d99-c23a9af95ec2"
}

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting that [[claim:aeba454c-e38f-4c15-925c-b52a896b1bea|trained debaters exploit their judge's systematic errors]] and that the opponent fails to expose this, the judge's accuracy falls as optimisation proceeds. Two training studies observed exactly this with frozen weaker judges. The argument bears on the claim only insofar as gaining capability by being trained to win counts as \"becoming more capable\". It also depends on unconstrained training, since length caps held accuracy stable in one study.",
  "argument_id": "04d5d688-dfab-4163-8cd8-9c55147d4bbb"
}

{
  "verdict": "slight",
  "decisive": false,
  "evaluation": "Granting that [[claim:1ab6aea0-bea9-470b-a3b1-c9d2564fef77|obfuscated arguments can win]] and that the ability to build them outpaces the ability to find their flaws, accuracy would fall beyond some gap. That second premise carries the argument and is unsupported by measurement: the problem has been shown only in informal human debates and in theory, newer protocols aim to close it, and no language-model experiment has demonstrated it. So it marks a possible failure at wide gaps rather than evidence of one.",
  "argument_id": "29ef0a0c-16a2-4efd-ab0c-97d62f02ddf7"
}

{
  "claim_id": "f02489a9-374a-449f-a645-a9d454a68d58",
  "reasoning": "A central empirical premise for whether debate scales as an oversight method, and the main counterexample to the general decline of oversight with capability gap; actively studied, with inference-time and training results pointing different ways. Narrower than the headline debate-works claim.",
  "importance": 0.45,
  "contestation": 0.5
}