TL;DR
A symbolic-probabilistic framework that unites symbolic epistemic structure with probabilistic belief dynamics and supports a convergence guarantee and provides a diagnostic interpretation of the remaining disagreement is proposed.
Although probabilistic statements are ubiquitous, foundational disagreements persist about their understanding, as exemplified by debates between Bayesians and frequentists; moreover, it is unclear when and why acting on them actually leads to desirable outcomes. Here, we argue that every probability is the output of a...
Bayesian Chronicle Agents is introduced, a minimal belief layer separating what an agent believes from how it speaks, and it is shown that prescribed $\kappa$ remains recoverable after the language round-trip, with perfect rank-order recovery across all four models.
A multi-LLM \emph{council} lets several large language models (LLMs) deliberate on a question and return an answer together with a confidence estimate. As these systems become increasingly used for reasoning, that confidence should represent a calibrated \emph{probability of being correct}, and the decision should rema...
We investigate the structure of interactive beliefs in networks: the epistemic state in which agents hold, revise, and act on their models of the epistemic states of other agents. What a group believes depends on what each member agent takes the others to believe, and on what each takes the others to believe about stil...
DeReLab is introduced, a generative framework that produces multi-turn belief-updating conversations from parameterized graph structures across default and inheritance reasoning, with formally verified ground truth at every turn, enabling controlled measurement of how models respond to confirming and disconfirming evid...
The Predictive Justified Perspective (PJP) model is formally defined and proved that it retains the computational efficiency of state-based planning while ensuring logically sound belief reasoning (satisfying the KD45 axioms).
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