Thinking-in-Bets proposed to reduce sycophantic revisions in LLM agents
A new method is proposed to address the issue of LLM agents unduly conforming to user feedback by recording claims and supporting evidence.
Language model agents often abandon correct work when challenged by a user. However, simply telling them to stand firm causes them to ignore real mistakes. Once an agent reads a complaint, it tends to discount its own earlier checks in order to agree with the user.
To address this, a training-free method called Thinking-in-Bets has been introduced. In this method, an agent writes a short private record of its main claims, the checks supporting them, and its confidence before reading user feedback. When challenged, the agent consults this Claim Checklist to keep verified work intact while investigating uncertain or newly challenged parts.
The method was evaluated using five Gemini models: 3.5 Flash-Lite, 3.5 Flash, 3.6 Flash, 3.8 Flash, and 4.0.
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