mlsanityData to 7 Oct 2026

Position: Multi-Agent Systems Should Prioritize Concurrency Control

arXiv 2608.18092 ↗First posted 6 Jun 2026cs.AIcited by 1

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

LLM-based multi-agent systems (MAS) promise scalable collaboration, yet adding agents often reduces reliability. This position paper argues that many MAS failures are fundamentally concurrency control problems: agents concurrently read and write shared state, and long LLM inference windows amplify the risk of stale reads, lost updates, and inconsistent outcomes. Failure modes commonly attributed to coordination or communication breakdowns can be mapped directly onto classical concurrency anomalies. We contend that MAS frameworks should address these failures through explicit concurrency control mechanisms: conflict detection, isolation guarantees, and structured access to shared resources. Concurrency control should be a first-class design concern, not an afterthought.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 39 of its 68 references that are in the map. First posted on arXiv 6 Jun 2026.

Builds on

39 of 68 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Effective Strategies for Asynchronous Software Engineering Agents23 Mar
  2. 2025Why Do Multi-Agent LLM Systems Fail?17 Mar
  3. 2024MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution26 Mar

Other references · 36

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  12. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
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  5. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
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  12. 2017A Survey of Learning in Multiagent Environments: Dealing with Non-Stationaritycited by 93
  13. 2024CodeR: Issue Resolving with Multi-Agent and Task Graphscited by 89
  14. 2025SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planningcited by 58
  15. 2024A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Applicationcited by 54
  16. 2024VillagerAgent: A Graph-Based Multi-Agent Framework for Coordinating Complex Task Dependencies in Minecraftcited by 34
  17. 2024LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Modelscited by 29
  18. 2026Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systemscited by 16
  19. 2025Git Context Controller: Manage the Context of LLM-based Agents like Gitcited by 13
  20. 2025EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaborationcited by 9
  21. 2016A Conflict-Free Replicated JSON Datatypecited by 6
  22. 2025Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systemscited by 6
  23. 2026Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPOcited by 5
  24. 2025Fast Verification of Strong Database Isolation (Extended Version)cited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CollabFlow: Recursive Self-Improvement of Agent Collaborationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026CoAgent: Concurrency Control for Multi-Agent Systems
  2. 2026MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems
  3. 2025CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation
  4. 2026SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
  5. 2026Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems
  6. 2026Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
  7. 2026DPBench: Structural Determinants of Multi-Agent LLM Coordination Under Simultaneous Resource Contention
  8. 2025LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systems