arXiv:2605.22863v1 Announce Type: new Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer model's state and encode at the receiver model. Recent work such as Cache-to-Cache (C2C; Fu et al., 2026) seeks to exchange KV caches by learning adapters that translate sharer KV matrices to the receiver model. However, the adapters are large and expensive to train, and translate individual tokens, which requires the target context to be identical. This is unsuitable for agent communication, where th
The proliferation of LLM agents highlights the inefficiencies of text-based communication, driving research into more direct model-to-model data exchange methods.
Improving inter-model communication efficiency is critical for scaling AI agent ecosystems and reducing the computational overhead and latency associated with complex multi-agent systems.
This research explores a more efficient method for models to directly share their internal states (KV caches) without linguistic mediation, potentially enabling fluid, real-time agent collaboration.
- · AI agent developers
- · Cloud computing providers (reduced inference cost)
- · AI research institutions
- · Inefficient text-based communication paradigms
- · Systems heavily reliant on human-interpretable intermediate steps
AI agents will exhibit faster, more efficient, and more complex collaborative behaviors.
This could lead to breakthroughs in multi-agent systems capable of solving highly complex, interdependent tasks currently intractable.
The development of highly integrated 'super-agents' formed by seamlessly communicating sub-agents could emerge, blurring the lines of individual AI capabilities.
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Read at arXiv cs.LG