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    <abstract>Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. However, these agents suffer from severe overconfidence, making their expressed confidence unreliable for user trust and downstream abstention. To address this, we augment the Deep Research pipeline with step confidence elicitation after each retrieval, building on the commonly used post-answer verbalized confidence. Interestingly, we find that Evidence Confidence (E-Conf), elicited after the final retrieval step, provides a stronger uncertainty signal than Answer Confidence (A-Conf), elicited after answer generation, and that A-Conf is largely shaped by E-Conf. Based on these findings, we propose DualStake, a dual-path calibration method that applies margin-clipped, confidence-dependent stake rewards to jointly align E-Conf and A-Conf with answer correctness while limiting extreme confidence optimization. Experiments on Qwen2.5-7B, Qwen2.5-7B-Instruct, and Qwen3-4B across 8 QA benchmarks demonstrate that DualStake consistently improves calibration without sacrificing answer accuracy.</abstract>
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%0 Conference Proceedings
%T DualStake: Dual-Path Confidence Calibration in Deep Research Agent
%A Xu, Yinuo
%A Liang, Yuwei
%A Cheng, Jianjie
%A Wang, Meng
%A Yu, Yongcan
%A Lu, Shuo
%A Liang, Jian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xu-etal-2026-dualstake
%X Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. However, these agents suffer from severe overconfidence, making their expressed confidence unreliable for user trust and downstream abstention. To address this, we augment the Deep Research pipeline with step confidence elicitation after each retrieval, building on the commonly used post-answer verbalized confidence. Interestingly, we find that Evidence Confidence (E-Conf), elicited after the final retrieval step, provides a stronger uncertainty signal than Answer Confidence (A-Conf), elicited after answer generation, and that A-Conf is largely shaped by E-Conf. Based on these findings, we propose DualStake, a dual-path calibration method that applies margin-clipped, confidence-dependent stake rewards to jointly align E-Conf and A-Conf with answer correctness while limiting extreme confidence optimization. Experiments on Qwen2.5-7B, Qwen2.5-7B-Instruct, and Qwen3-4B across 8 QA benchmarks demonstrate that DualStake consistently improves calibration without sacrificing answer accuracy.
%U https://aclanthology.org/2026.emnlp-main.389/
%P 8924-8941

Markdown (Informal)

[DualStake: Dual-Path Confidence Calibration in Deep Research Agent](https://aclanthology.org/2026.emnlp-main.389/) (Xu et al., EMNLP 2026)

• DualStake: Dual-Path Confidence Calibration in Deep Research Agent (Xu et al., EMNLP 2026)

ACL

• Yinuo Xu, Yuwei Liang, Jianjie Cheng, Meng Wang, Yongcan Yu, Shuo Lu, and Jian Liang. 2026. DualStake: Dual-Path Confidence Calibration in Deep Research Agent. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8924–8941, Budapest, Hungary. Association for Computational Linguistics.