arXiv AI

A Study of Belief Revision Postulates in Multi-Agent Systems (Extended Version)

arXiv:2605. 02249v2 Announce Type: replace Abstract: We investigate the belief revision problem in epistemic planning, i.

arXiv AI
Jul 24

Explainable Belief Harmonization under Dynamic Epistemic Partitions

arXiv:2607. 21210v1 Announce Type: cross Abstract: Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assumptions: consensus methods use iterative averaging, logic-based methods resolve conflicting knowledge bases, and epistemic logic analyzes agents' information states.

By Adam Kostka (Warsaw University of Technology), Jaros{\l}aw A. Chudziak (Warsaw University of Technology)
arXiv AI
Jul 1

Belief Contraction in Dynamic Epistemic Logic

arXiv:2606. 31861v1 Announce Type: cross Abstract: Dynamic epistemic logic represents belief change via model transformations induced by epistemic events.

By Gaia Belardinelli (Stanford University), Snow Zhang (University of Berkeley, California)
arXiv AI
Sep 24

Preregistered Belief Revision Contracts

The paper introduces Preregistered Belief Revision Contracts (PBRC), a protocol that separates open communication from admissible epistemic change in deliberative multi-agent systems. PBRC fixes evidence triggers, revision operators, priority rules, and fallback policies, requiring that belief changes cite preregistered triggers and validated evidence tokens. The authors prove that PBRC prevents confidence inflation from conformity, preserves auditability, ensures epistemic accountability, and characterizes enforced belief trajectories under token-invariant contracts.

By Saad Alqithami
arXiv AI
Sep 11

Belief-State Engine: Augmenting LLMs for Principled Planning Under Partial Observability

The paper introduces the Belief-State Engine (BSE), an inference module that supplies a large language model (LLM) with a Bayesian posterior over hidden states in a partially observable Markov decision process (POMDP). By keeping the raw action‑observation log hidden from the LLM, the BSE ensures the agent behaves as a sound Markov policy on the belief MDP, thereby inheriting classical POMDP optimality guarantees. Experiments on the Tiger POMDP and a red‑team attack‑graph task show that BSE‑augmented agents outperform six baselines in task return, belief calibration, and decision consistency.

By Arnab Chattopadhayay, Debdipta Halder
Hugging Face Trending Papers
Jun 2

ZX-Calculus:Trace-Indexed Dependent Types and Epistemic Semantics

We propose ZX-Calculus (Knowledge Evolution Calculus), a conservative extension of Martin-Lof Dependent Type Theory (MLTT) integrating trace-indexed types, presheaf non-monotone semantics, and constructive AGM belief revision. A Coq mechanisation accompanies the paper (34 complete proofs; zero admits for the two central results).

arXiv AI
Sep 25

Epistemic-Probabilistic Model for Guarded Multi-Agent LLM Coordination

The paper introduces Epistemic Probabilistic Language Agents (EPLA), a neuro‑symbolic architecture designed to enable coordination among multi‑agent large language models (LLMs) under uncertainty. EPLA employs a Symbolic Guard that provides structured diagnostic feedback, allowing the LLM to generate typed actions while the Guard controls their execution against an authoritative symbolic state. The authors formalize an epistemic layer using gossip testbeds and epistemic lottery gossip models, combining view‑based call histories with agent‑indexed probability weights to address gaps in social behavior and coordination mechanisms for agentic LLMs.

By Mehdi Nasiri, Mohammad Saeed Arvenaghi, Sadegh Vaezi, Ebrahim Ardeshir-Larijani
arXiv AI
Sep 15

Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

The paper introduces Generalized Agent Iteration (GAI), a formal framework that unifies iterative policy improvement and recursive self‑improvement (RSI) under a single learning paradigm. GAI treats an agent as a configuration of modifiable components and models learning as a cycle of evaluation and improvement, with two key dials: whether the improving mechanism is part of the agent and whether the evaluation standard is external. These dials distinguish between generalized policy iteration (GPI) and RSI, and classify systems as anchored, goal‑drift, or fully self‑referential, allowing existing systems to be mapped and RSI defects to be analyzed systematically.

By Hongyao Tang, Yi Ma, Pengyi Li, Yifu Yuan