When Should Models Change Their Minds? Contextual Belief Management in Large Language Models
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911728612147200 |
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| author | Xu, Haoming Xu, Weihong Li, Zongrui Wang, Mengru Yao, Yunzhi Wu, Chiyu Shang, Jin Gong, Yu Deng, Shumin |
| author_facet | Xu, Haoming Xu, Weihong Li, Zongrui Wang, Mengru Yao, Yunzhi Wu, Chiyu Shang, Jin Gong, Yu Deng, Shumin |
| contents | 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 BeliefTrack, a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation. BeliefTrack 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\footnote{Code is coming soon at https://github.com/zjunlp/CBM. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_30219 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When Should Models Change Their Minds? Contextual Belief Management in Large Language Models Xu, Haoming Xu, Weihong Li, Zongrui Wang, Mengru Yao, Yunzhi Wu, Chiyu Shang, Jin Gong, Yu Deng, Shumin Artificial Intelligence Computation and Language Machine Learning 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 BeliefTrack, a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation. BeliefTrack 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\footnote{Code is coming soon at https://github.com/zjunlp/CBM. |
| title | When Should Models Change Their Minds? Contextual Belief Management in Large Language Models |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.30219 |