Cache Merging Proposed as Convergent State for Multi‑Agent Latent Reasoning

The paper introduces a cache‑merging mechanism for distributed AI agents. It treats the merged cache as a convergent replicated state that all agents can read and write. The authors argue this structure enables latent reasoning across separate models. They formalize the approach using concepts from conflict‑free replicated data types. Experiments demonstrate improved consistency when agents share intermediate results. The method reduces redundant computation by reusing cached inferences. Limitations include scalability concerns for very large caches. The authors propose future work on optimizing merge strategies and extending to heterogeneous agents.