DualStake Calibrates Deep Research Agent Confidence, Cutting Overconfidence
_reachsumit · x · 2026-09-02
The paper 'DualStake' addresses the severe overconfidence issue in Deep Research agents. It proposes a dual-path calibration method that jointly aligns 'Evidence Confidence' (elicited after retrieval) and 'Answer Confidence' (elicited after generation) with correctness. Experiments on Qwen models across 8 QA benchmarks show that DualStake consistently improves calibration without sacrificing accuracy. The code is open-sourced.
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