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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes the Belief-State Engine (BSE), an inference module that augments large language models with explicit Bayesian belief tracking for partially observable environments. The authors prove theoretical soundness under their axioms and demonstrate improved task return, belief calibration, and decision consistency on two synthetic benchmark tasks compared to six baselines, but the work remains unreviewed and evaluation is confined to toy domains.
Unreviewed computational study: controlled comparison of proposed architecture against six baselines on synthetic domains with ablation analysis. Artificial agents (LLM-based and baseline policies); no human subjects. Intervention: Belief-State Engine (BSE): inference module maintaining Bayesian posterior over latent states, with LLM exposed only to posterior, not raw history. Compared with: Six baselines: reactive LLM, Chain-of-Thought, ReAct, natural-language belief tracker, QMDP, POMCP.
BSE-augmented agent improves task return, belief calibration, and decision consistency across both Tiger POMDP and red-team attack-graph domains Ten targeted ablations confirm architectural contribution is not specific to any single model LLM paired with BSE is proven to be a sound Markov policy on the belief MDP induced by the underlying POMDP, inheriting Bellman optimality guarantees
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This is an unreviewed preprint presenting a novel architectural approach with controlled task evaluations; it demonstrates proof-of-concept on synthetic domains but lacks clinical, real-world deployment data or independent validation.
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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 onto the wrong hypothesis. Policies drift as the history grows. We trace these symptoms to a common structural cause. An LLM agent, as commonly deployed, is a history-conditioned policy with no explicit belief over hidden state. We propose an architectural fix. The Belief-State Engine (BSE) is an inference module placed outside the LLM. It maintains a Bayesian posterior over the latent states of a given POMDP (Partially Observable Markov Decision Process) model, and at each decision step it exposes only that posterior to the LLM. The raw action-observation log is not shown. We set out a minimal four-axiom specification of what a belief-consistent internal state must satisfy, and prove that the LLM paired with the BSE is a sound Markov policy on the belief MDP induced by the underlying POMDP. It therefore inherits the Bellman optimality guarantees of classical POMDP theory, provided the LLM is never exposed to the raw history. We evaluate the architecture on the Tiger POMDP and a red-team attack-graph task, against six baselines: a reactive LLM, Chain-of-Thought, ReAct, a natural-language belief tracker, QMDP, and POMCP. Across both domains, the BSE-augmented agent improves task return, belief calibration, and decision consistency. Ten targeted ablations isolate the contribution of each architectural choice confirms that the effect is not specific to any one model. Code, environment specifications, prompt templates, and seed logs accompany this paper.
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