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Google paper: logging checks before feedback cuts agents' wrongful revisions

AIGoogle researchers found that Gemini agents wrongly abandoned correct requirements after a bare "that's wrong" complaint 12.2% of the time, compared with 0.02% when they first recorded their checks and confidence. The method, called Thinking-in-Bets, has the agent privately note its main claims, supporting checks, and confidence before seeing the reply, then re-checks only the disputed claims when challenged.
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New Google paper finds that Gemini AI agents are much less likely to undo correct work after a user wrongly says “that’s wrong” if they first write down what they checked and how sure they are.

When a user says "that's wrong, redo it", agents often rewrite code that already passed its tests. Telling them to stand firm backfires, because they then ignore real bugs.

Thinking-in-Bets has the agent privately note its main claims, the checks behind them, and its confidence before seeing the reply. When challenged, it re-checks only the disputed claims and asks which case failed if the complaint is vague.

In patch review across 5 Gemini models, agents wrongly gave up 12.2% of correct requirements after a bare complaint, against 0.02% with the checklist. On report reviews, writing the checklist after the complaint reached only a 65% pass rate, against 89% when written first.

Agents that take user feedback should log what they verified, and how sure they are, before reading the reply.

– arxiv. org/abs/2610.34654

Title: "Thinking-in-Bets at Test Time Reduces Sycophantic Revisions in LLM Agents"

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Rohan Paul · x.comPublished · added here

Original title: New Google paper finds that Gemini AI agents are much less likely to undo correct work after a user wrongly says “that’s wrong” if they f...