Beyond tokens: a unified framework for latent communication in LLM-based multi-agent systems
arXiv:2606.05711v1 Announce Type: new Abstract: Multi-agent systems built on large language models (LLMs) have become a prevailing paradigm for tackling complex reasoning, planning, and tool-use tasks. The dominant communication protocol in such systems is natural language: agents exchange messages token-by-token, verbalising their internal reasoning so that peers can read, verify, and respond. While convenient and interpretable, this protocol suffers from three structural drawbacks -- high inference cost, irreversible information loss during discretization, and ambiguity/redundancy of natural
The rapid development and widespread adoption of LLM-based multi-agent systems necessitate more efficient and nuanced communication protocols to overcome current limitations.
This research introduces a framework that could significantly enhance the performance and reduce the cost of AI agents, making them more scalable and effective for complex tasks.
The dominant natural language communication in multi-agent systems may evolve to include more efficient, latent forms, leading to less resource-intensive and more robust agent interactions.
- · AI agent developers
- · Cloud computing providers (reduced inference cost)
- · Enterprises adopting AI agent workflows
- · Systems heavily reliant on brute-force natural language processing
- · AI frameworks lacking efficient communication layers
Multi-agent systems will become more efficient and capable of tackling harder problems with lower operational costs.
This efficiency gain could accelerate the deployment of autonomous AI agents across various industries, collapsing more workflows.
The enhanced capability of agent systems could lead to new forms of AI-driven automation and decision-making not currently feasible due to communication constraints.
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Read at arXiv cs.CL