@inproceedings{xu-etal-2026-models,
title = "When Should Models Change Their Minds? Contextual Belief Management in Large Language Models",
author = "Xu, Haoming and
Xu, Weihong and
Li, Zongrui and
Wang, Mengru and
Yao, Yunzhi and
Wu, Chiyu and
Shang, Jin and
Gong, Yu and
Deng, Shumin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.336/",
pages = "7729--7751",
ISBN = "979-8-89176-459-0",
abstract = "Long-horizon interactions require language models to manage accumulating information: when to update their state, when to preserve their state, and what to ignore. We study this challenge as \textbf{Contextual Belief Management (CBM)}: maintaining a predicted belief state aligned with formal evidence while isolating task-irrelevant noise. To make CBM measurable, we introduce , a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation. diagnoses three failures: Failed Stay, Failed Update, and Failed Isolation. Across multiple LLMs, vanilla models exhibit severe CBM failures, while explicit belief-tracking prompts provide limited gains. In contrast, reinforcement learning with belief-state rewards reduces failure rates by 70.9{\%} on average. Further probing reveals latent belief-state dynamics behind these failures, and representation-level steering reduces failure rates by 46.1{\%} across two tasks."
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<abstract>Long-horizon interactions require language models to manage accumulating information: when to update their state, when to preserve their state, and what to ignore. We study this challenge as Contextual Belief Management (CBM): maintaining a predicted belief state aligned with formal evidence while isolating task-irrelevant noise. To make CBM measurable, we introduce , a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation. diagnoses three failures: Failed Stay, Failed Update, and Failed Isolation. Across multiple LLMs, vanilla models exhibit severe CBM failures, while explicit belief-tracking prompts provide limited gains. In contrast, reinforcement learning with belief-state rewards reduces failure rates by 70.9% on average. Further probing reveals latent belief-state dynamics behind these failures, and representation-level steering reduces failure rates by 46.1% across two tasks.</abstract>
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%0 Conference Proceedings
%T When Should Models Change Their Minds? Contextual Belief Management in Large Language Models
%A Xu, Haoming
%A Xu, Weihong
%A Li, Zongrui
%A Wang, Mengru
%A Yao, Yunzhi
%A Wu, Chiyu
%A Shang, Jin
%A Gong, Yu
%A Deng, Shumin
%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-models
%X Long-horizon interactions require language models to manage accumulating information: when to update their state, when to preserve their state, and what to ignore. We study this challenge as Contextual Belief Management (CBM): maintaining a predicted belief state aligned with formal evidence while isolating task-irrelevant noise. To make CBM measurable, we introduce , a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation. diagnoses three failures: Failed Stay, Failed Update, and Failed Isolation. Across multiple LLMs, vanilla models exhibit severe CBM failures, while explicit belief-tracking prompts provide limited gains. In contrast, reinforcement learning with belief-state rewards reduces failure rates by 70.9% on average. Further probing reveals latent belief-state dynamics behind these failures, and representation-level steering reduces failure rates by 46.1% across two tasks.
%U https://aclanthology.org/2026.emnlp-main.336/
%P 7729-7751
Markdown (Informal)
[When Should Models Change Their Minds? Contextual Belief Management in Large Language Models](https://aclanthology.org/2026.emnlp-main.336/) (Xu et al., EMNLP 2026)
ACL
- Haoming Xu, Weihong Xu, Zongrui Li, Mengru Wang, Yunzhi Yao, Chiyu Wu, Jin Shang, Yu Gong, and Shumin Deng. 2026. When Should Models Change Their Minds? Contextual Belief Management in Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 7729–7751, Budapest, Hungary. Association for Computational Linguistics.