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Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models

L5 · ResearcherResearcharXiv· 10/12/2026

Novel architecture technique for ML engineers working on multi-agent systems and model optimization.

AI Summary

Researchers introduce CacheFuse, a -free method enabling direct sharing between heterogeneous LLMs for more efficient latent communication than text-based approaches.

Excerpt

Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning, especially by coordinating effectively within multi-agent systems (MAS). However, language agents traditionally communicate via text exchange, which introduces a bottleneck in information transfer and adds prefill latency for downstream agents. Recent work proposes that directly sharing the key-value (KV) cache between language models can enable

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