BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation

Researchers introduced BayesBench, a suite of simulation environments to evaluate how large language models update beliefs under multi-turn evidence accumulation. Testing seven LLMs from 3B to 70B parameters, they found that scaling improves latent inference and evidence accumulation, but these gains do not reliably transfer to downstream prediction tasks.

arXiv:2606.30850v1 Announce Type: new 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.