Token Coherence: Adapting MESI Cache Protocols to Minimize Synchronization Overhead in Multi-Agent LLM Systems

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• Synchronization cost explosion in LLM multi-agent systems maps with formal precision onto the cache coherence problem in shared-memory multiprocessors.technical 90%
• The proposed Artifact Coherence System (ACS) and lazy invalidation attenuates synchronization cost by at least S/(n + W(d_i)) when S > n + W(d_i), converting O(n x S x |D|) to O((n + W) x |D|).result 95%
• Simulation across four workload configurations yields token savings of 95.0% +/- 1.3% at V=0.05, 92.3% +/- 1.4% at V=0.10, 88.3% +/- 1.5% at V=0.25, and 84.2% +/- 1.3% at V=0.50.result 95%
• The TLA+-verified protocol enforces single-writer safety, monotonic versioning, and bounded staleness across ~2,400 explored states.method 90%
• Performance gains assume workloads with sufficient artifact reuse (S > n + W(d_i)).limitation 85%
• The protocol provides a formal MESI-to-artifact state mapping as a key contribution.technical 90%
• Integration requires modifying existing multi-agent frameworks which may face adoption resistance.limitation 80%
• The reference Python implementation integrates with LangGraph, CrewAI, and AutoGen via thin adapter layers.method 90%

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