When Should Models Change Their Minds? Contextual Belief Management in Large Language Models

arXiv:2605.30219v1 Announce Type: cross 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 BeliefTrack, a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation.

Source: arXiv cs.LG — read the full report at the original publisher.

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