cs.AI updates on arXiv.org 10月23日 12:12
CodeCRDT:优化多智能体LLM系统并行速度
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本文提出了一种基于CRDT的协调模式CodeCRDT,通过监控共享状态实现智能体之间的协调,从而解决多智能体LLM系统因高昂的协调成本而无法实现并行加速的问题。实验表明,该方法在某些任务上速度提升可达21.1%,但在其他任务上则可能导致速度降低至39.4%,但最终均能实现100%的收敛,无合并失败。

arXiv:2510.18893v1 Announce Type: cross Abstract: Multi-agent LLM systems fail to realize parallel speedups due to costly coordination. We present CodeCRDT, an observation-driven coordination pattern where agents coordinate by monitoring a shared state with observable updates and deterministic convergence, rather than explicit message passing. Using Conflict-Free Replicated Data Types (CRDTs), CodeCRDT enables lock-free, conflict-free concurrent code generation with strong eventual consistency. Evaluation across 600 trials (6 tasks, 50 runs per mode) shows both benefits and trade-offs: up to 21.1% speedup on some tasks, up to 39.4% slowdown on others, and 100% convergence with zero merge failures. The study formalizes observation-driven coordination for stochastic LLM agents, revealing semantic conflict rates (5-10%) and quality-performance tradeoffs, and provides empirical characterization of when parallel coordination succeeds versus fails based on task structure.

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多智能体LLM系统 CodeCRDT CRDT 并行加速 协调模式
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