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Belief-State Engine: Augmenting LLMs for Principled Planning Under Partial Observability
Large language model agents produce fluent action sequences across a wide range of tasks, yet they fail in characteristic ways once the environment becomes partially observable. Ambiguous feedback pushes them into premature commitments. A single informative observation can collapse their uncertainty
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