The paper argues that many failures in large language model–based multi‑agent systems stem from concurrency control issues rather than coordination or communication problems. It highlights how concurrent reads and writes to shared state, coupled with long inference windows, lead to stale data, lost updates, and inconsistent outcomes. The authors propose that MAS frameworks incorporate explicit concurrency controls—such as conflict detection, isolation guarantees, and structured resource access—to make concurrency a primary design concern.
arXiv:2606. 15376v1 Announce Type: cross Abstract: Multi-agent LLM systems -- coding agents, devops agents, document agents -- now routinely run several agents in parallel against the same git tree, Kubernetes cluster, or document.
arXiv:2604. 17612v3 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about.
arXiv:2605.29313v2 Announce Type: replace Abstract: LLM multi-agent systems often coordinate through natural-language dialogue or loosely structured shared memory, making intermediate state difficult...
arXiv:2606. 19616v1 Announce Type: cross Abstract: Autonomous coding agents now open millions of pull requests, yet large-scale studies find their PRs are produced faster but accepted less often - a coordination and trust gap that pull-request-level telemetry cannot explain.
arXiv:2608. 02645v1 Announce Type: cross Abstract: Large Language Model (LLM) agents rely on external tools to perform multistage tasks.
AgentRoom introduces a real‑time collaborative editing protocol that enables concurrent coding by multiple large language model agents within a CRDT‑backed shared workspace. By providing file‑level claim, status, and broadcast tools, it allows agents to coordinate directly rather than relying on serial phase handoffs or independent sampling. Experiments with five frontier coding‑CLI models show that AgentRoom reduces task abandonment and run‑to‑run variation compared to solo or parallel‑merge approaches, highlighting the importance of coordination over mere parallelism.
arXiv:2605. 15207v2 Announce Type: replace Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines.
arXiv:2608. 13900v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation.
arXiv:2607. 10811v1 Announce Type: cross Abstract: AI engineering is shifting from passive text generation by large language models (LLMs) to agent-driven task execution, creating new reliability challenges for long-horizon tasks under resource constraints and environmental uncertainty.
AI agents increasingly execute long-running workflows that externalize effects through independently supplied tools. Under retries, speculative execution, concurrency, and partial failures, the result...
arXiv:2606. 00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored.
arXiv:2609.32192v2 Announce Type: replace Abstract: Multi-agent workflows require task-relevant information to be shared across agents, while irrelevant, stale, unverified, or incompatible informatio...