A study by Anthropic researcher Andy Hall found that AI models can identify problematic behavior in other AI agents' logs. GPT-5 detected about 98 percent of reward-hacking cases, but more exhaustive detection settings produced increasing numbers of false positives. The findings suggest automated AI auditing is promising but not yet reliable enough for safety-critical oversight.
The study offers a concrete result and a significant practical limitation for using AI models to monitor autonomous agents.
AI models can often spot problematic behavior by other AI agents in logs, according to a study by Anthropic researcher Andy Hall. GPT-5 found about 98 percent of reward-hacking cases, but the more exhaustively the search is tuned, the more false alarms pile up. That's not yet enough for reliable safety checks.
The article AI auditors catch most agent misbehavior but flood reviewers with false alarms, new study finds appeared first on The Decoder.
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