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AI models can spot many cases of reward-hacking by other agents, but searching more exhaustively brings more false alarms. That is the trade-off highlighted by a study from Anthropic researcher Andy Hall, as reported by The Decoder.
Why it mattersThe report says GPT-5 identified about 98 per cent of the reward-hacking cases. That is not enough on its own to make AI auditors reliable safety checks: reviewers still have to sort genuine problems from alerts that may be noise. A strong catch rate sounds reassuring until the false alarms arrive with it.
Discuss: How much extra review should teams accept to catch more agent misbehaviour?
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