AI auditors catch most agent misbehavior but flood reviewers with false alarms, new study finds

A study by Anthropic researcher Andy Hall found that AI models can often detect problematic behavior by other AI agents in logs, with GPT-5 catching about 98 percent of reward-hacking cases. However, the more exhaustively the search is tuned, the more false alarms accumulate, leaving the approach short of what is needed for reliable safety checks.

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 https://the-decoder.com/ai-auditors-catch-most-agent-misbehavior-but-flood-reviewers-with-false-alarms-new-study-finds/ appeared first on The Decoder https://the-decoder.com .