KV-Cache Transfer Enables Cross-Model Latent Communication Between Heterogeneous Agents

Rather than text-based inter-agent messaging, this work aligns heterogeneous LLM agents to share KV-cache representations, transferring both perceptual context and reasoning state across different model architectures. Analysis identifies a duality: context-aware transfer relies on sparse reasoning signals while context-unaware transfer requires denser representations.