CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation
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arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909863104217088 |
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| author | Pugachev, Sergey |
| author_facet | Pugachev, Sergey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18893 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation Pugachev, Sergey Distributed, Parallel, and Cluster Computing Artificial Intelligence Software Engineering I.2.11; D.2.11 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. |
| title | CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Software Engineering I.2.11; D.2.11 |
| url | https://arxiv.org/abs/2510.18893 |