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: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:2606. 02163v1 Announce Type: new Abstract: This article proposes a set-theoretic framework for belief change, called Abstract Worlds Semantics, in which no logical syntax is assumed.
By Daniel Grimaldi, M. Vanina Martinez, Ricardo O. Rodriguez
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
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
arXiv:2607. 09729v1 Announce Type: new Abstract: In his 1996 doctoral thesis, Maurice Pagnucco created the first AGM-like abductive expansion operation.
By Ulisses Franceschi Eliano
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:2607. 09748v1 Announce Type: new Abstract: In distributed systems, the classical State Machine Replication (SMR) model assumes that correct replicas execute deterministic transitions to yield identical bitwise states.
By Jun He, Deying Yu
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:2607. 01988v1 Announce Type: new Abstract: Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity.
By Xue Qin, Simin Luan, Cong Yang, Zhijun Li
arXiv:2609.00455v1 Announce Type: new
Abstract: Large language models (LLMs) are being used as policies for autonomous decision-making and planning in many domains. Despite their strong reasoning cap...
By Shubham Kumar, Harshit Kumar, Narendra Ahuja, Saurabh Jha
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