StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems

arXiv:2608.08236v1 Announce Type: new Abstract: Multi-agent LLM systems often fail not for lack of candidate answers, but because they have no persistent mechanism for deciding

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

arXiv:2603.23231v1 Announce Type: new Abstract: Empowering large language models with long-term memory is crucial for building agents that adapt to users' evolving needs. However,

arXiv:2604.01007v1 Announce Type: new Abstract: AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal

arXiv:2606.26806v1 Announce Type: new Abstract: Long-running language agents need more than memory access. Retrieval systems can fetch past facts at query time, but they do not

arXiv:2608.04289v1 Announce Type: new Abstract: Long-horizon agents increasingly use persistent memory and tools to take actions with external side effects. A central failure mode

arXiv:2607.09748v1 Announce Type: new Abstract: In distributed systems, the classical State Machine Replication (SMR) model assumes that correct replicas execute deterministic

arXiv:2609.03340v1 Announce Type: new Abstract: Distributed LLM-agent teams can read the latest shared facts and still act on an obsolete plan. A planner may derive an action from