Thinking-in-Bets reduces sycophantic revisions in LLM agents
Original title: Thinking-in-Bets at Test Time Reduces Sycophantic Revisions in LLM Agents
A pre-feedback claim checklist nearly eliminated false reversals on five Gemini models across eight benchmarks.
Language model agents often drop correct work after a user challenge, while orders to stand firm make them ignore real mistakes. Thinking-in-Bets is training-free: the agent writes a private Claim Checklist of claims, checks, and confidence before reading feedback. On five Gemini models and eight benchmarks, records written before criticism nearly eliminated false reversals in patch review and protected passing drafts; the same record after the complaint recovered little. Gating the record to uncertain cases cut token overhead and fixed more real bugs than the baseline.
Unconfirmed: Reported gains are the authors' evaluations and are not independently confirmed.
- Models tested were Gemini 3.5 Flash-Lite through 4.0 Argon.
- Benchmarks include SWE-Together, DeepSWE, SycEval, and Terminal-Bench.
- Post-complaint claim records recovered very little of the gain.
- Uncertainty-gated records lowered overhead and fixed more real bugs.
- Re-checking the weakest claim also helped non-interactive tasks.
From the original article
1 min read at huggingface.co
Language model agents often abandon correct work when a user challenges it, yet simply telling them to stand firm makes them ignore real mistakes. Once an agent reads a complaint, it tends to discount its own earlier checks in order to agree with the user.
By Nghi D. Q. Bui · huggingface.co
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