BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation
Large language models (LLMs) in multi-turn conversations should update beliefs as evidence accumulates, yet evaluations often ignore this process. BayesBench introduces a suite of simulation environments with three progressively complex tasks (Bayesian estimation, prediction, and latent-framed prediction) to assess how closely LLM belief updates match a rational Bayesian reasoner. Across seven LLMs (3B-70B), scaling improves latent inference and evidence accumulation, but gains do not reliably transfer to downstream prediction, revealing a gap between inferring latent structure and rationally updating beliefs.
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[Submitted on 29 Jun 2026]
Title:BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation
View a PDF of the paper titled BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation, by Ankur Samanta and 9 other authors
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Abstract:Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment. Acting rationally then requires inferring the unobserved quantities that govern it and updating beliefs about them as evidence accumulates. Yet most evaluations only score the model's final-turn answer in a single-turn format, leaving this process unexamined. We ask how closely LLMs' belief updates match those of a rational Bayesian reasoner in multi-turn settings, and introduce BayesBench, a suite of simulation environments that probe this across three progressively complex tasks: (i) Bayesian estimation, where the model infers an unknown parameter from sequential evidence; (ii) Bayesian prediction, where the model turns inferred beliefs about a latent variable into outcome forecasts; and (iii) latent-framed Bayesian prediction, where observations are filtered through a user-persona framing, requiring joint inference over the latent state and the persona. Across seven LLMs (3B--70B), scaling improves latent inference and evidence accumulation, with updates occasionally matching the Bayesian posterior. However, these gains do not reliably carry over to downstream prediction, exposing a gap between inferring latent structure and using it to rationally update beliefs about the target outcome.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.30850 [cs.AI]
(or arXiv:2606.30850v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.30850
arXiv-issued DOI via DataCite (pending registration)
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From: Ankur Samanta [view email] [v1] Mon, 29 Jun 2026 19:30:32 UTC (852 KB)
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