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

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2604.15558v1 Announce Type: new Abstract: Deliberative multi-agent systems allow agents to exchange messages and revise beliefs over time. While this interaction is meant to

arXiv:2604.00555v1 Announce Type: new Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce

arXiv:2607.05844v1 Announce Type: new Abstract: Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still

arXiv:2604.27707v1 Announce Type: new Abstract: Current agentic memory systems (vector stores, retrieval-augmented generation, scratchpads, and context-window management) do not

arXiv:2604.08601v1 Announce Type: new Abstract: The rise of autonomous AI agents exposes a fundamental flaw in API-centric architectures: probabilistic systems directly execute

arXiv:2604.05229v1 Announce Type: new Abstract: Agentic AI systems plan, use tools, maintain state, and produce multi-step trajectories with external effects. Those properties

arXiv:2605.15871v1 Announce Type: new Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.