arXiv AI

Replicating Belief, Not Bits: Epistemic State Replication for Agentic Systems

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.

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
arXiv AI
Aug 18

MELD: A Protocol for Merging Knowledge Across Distributed Agentic Memories

arXiv:2608. 16357v1 Announce Type: cross Abstract: Autonomous agents share a transport and can call each other's tools, but they cannot share what they know: no protocol lets two agents' memories reconcile a fact phrased two ways, link related facts held apart, or reconcile contradictory knowledge without silently discarding either claim.

By Lauri Lov\'en, Jaakko Sauvola, Jukka Riekki, Sasu Tarkoma
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 Machine Learning
Aug 27

Epistemic Memory: A Validity Layer for Self-Maintaining Intelligent Systems

The paper introduces epistemic memory, a validity-maintenance layer for intelligent systems that tracks when stored knowledge remains applicable. It formalizes a dynamic epistemic quotient and shows that fixed semantic representations inevitably incur error as epistemic boundaries shift. The authors propose Observable Belief Memory (OBM), which combines current epistemic quotients, belief over quotient classes, and within-class provenance, and demonstrate that explicit epistemic tracking improves robustness under changing observation conditions.

By Pin-Han Ho, Limei Peng, Yiming Miao, Yan Jiao