mlsanityData to 14 Oct 2026

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

arXiv 2607.05844 ↗First posted 7 Jul 2026cs.AI cs.CL cs.MAcited by 3

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Abstract (arXiv)

Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still collapse disagreement behind overwrite rules that are difficult to inspect or correct. We present StateFuse, a conflict-aware replicated memory contract built on standard OpSet/CRDT merge. StateFuse does not introduce a new join algebra; it defines an agent-facing semantics layer with immutable history, explicit conflict objects, exact and semantic correction handles (claim_id / claim_ref), deterministic predicate contracts, and projection-time resolution that cannot rewrite replicated state. We evaluate StateFuse against flat multi-value, raw-log, provenance-style, and collapsed baselines under matched resolver and verification policies. On a 282-question official conflict-bearing MemoryAgentBench slice, the compared methods tie on answer accuracy, but conflict-preserving surfaces keep contradictions visible while collapsed surfaces do not. In a controlled agent loop with uniform verification, preserving ambiguity enables safer abstention and correction than early collapse. A correction-handle ablation further shows that semantic handles matter when exact prior identifiers are unavailable. The resulting claim is narrow: StateFuse is best supported as a safer public memory contract for contradiction surfacing, abstention, and auditable correction, not as a universal accuracy gain.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 7 Jul 2026.

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14 of 26 references.

References · 14

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  6. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  7. 2023GAIA: a benchmark for General AI Assistantscited by 1,128
  8. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 254
  9. 2024AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?cited by 158
  10. 2025MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agentscited by 84
  11. 2025AgentGit: A Version Control Framework for Reliable and Scalable LLM-Powered Multi-Agent Systemscited by 12
  12. 2016A Conflict-Free Replicated JSON Datatypecited by 6
2 more
  1. 2016Delta State Replicated Data Typescited by 4
  2. 2018OpSets: Sequential Specifications for Replicated Datatypes (Extended Version)cited by 1

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3 papers.