@inproceedings{ou-etal-2026-browseconf,
title = "{B}rowse{C}onf: Confidence-Guided Test-Time Scaling for Web Agents",
author = "Ou, Litu and
Li, Kuan and
Yin, Huifeng and
Zhang, Liwen and
Zhang, Zhongwang and
Wu, Xixi and
Ye, Rui and
Qiao, Zile and
Jiang, Yong and
Xie, Pengjun and
Huang, Fei and
Zhou, Jingren",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.21/",
pages = "446--465",
ISBN = "979-8-89176-395-1",
abstract = "Confidence in LLMs is a useful indicator of model uncertainty and answer reliability. Existing work mainly focused on single-turn scenarios, while research on confidence in complex multi-turn interactions is limited. In this paper, we investigate whether LLM-based search agents have the ability to communicate their own confidence through verbalized confidence scores after long sequences of actions, a significantly more challenging task compared to outputting confidence in a single interaction. Experimenting on open-source agentic models, we first find that models exhibit much higher task accuracy at high confidence while having near-zero accuracy when confidence is low. Based on this observation, we propose Test-Time Scaling (TTS) methods that use confidence scores to determine answer quality, encourage the model to try again until reaching a satisfactory confidence level. Results show that our proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget TTS methods."
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%0 Conference Proceedings
%T BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents
%A Ou, Litu
%A Li, Kuan
%A Yin, Huifeng
%A Zhang, Liwen
%A Zhang, Zhongwang
%A Wu, Xixi
%A Ye, Rui
%A Qiao, Zile
%A Jiang, Yong
%A Xie, Pengjun
%A Huang, Fei
%A Zhou, Jingren
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ou-etal-2026-browseconf
%X Confidence in LLMs is a useful indicator of model uncertainty and answer reliability. Existing work mainly focused on single-turn scenarios, while research on confidence in complex multi-turn interactions is limited. In this paper, we investigate whether LLM-based search agents have the ability to communicate their own confidence through verbalized confidence scores after long sequences of actions, a significantly more challenging task compared to outputting confidence in a single interaction. Experimenting on open-source agentic models, we first find that models exhibit much higher task accuracy at high confidence while having near-zero accuracy when confidence is low. Based on this observation, we propose Test-Time Scaling (TTS) methods that use confidence scores to determine answer quality, encourage the model to try again until reaching a satisfactory confidence level. Results show that our proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget TTS methods.
%U https://aclanthology.org/2026.findings-acl.21/
%P 446-465
Markdown (Informal)
[BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents](https://aclanthology.org/2026.findings-acl.21/) (Ou et al., Findings 2026)
ACL
- Litu Ou, Kuan Li, Huifeng Yin, Liwen Zhang, Zhongwang Zhang, Xixi Wu, Rui Ye, Zile Qiao, Yong Jiang, Pengjun Xie, Fei Huang, and Jingren Zhou. 2026. BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 446–465, San Diego, California, United States. Association for Computational Linguistics.