DALA: Auction-Based Communication for Efficient Multi-Agent LLM Systems
Researchers introduce Dynamic Auction-based Language Agent (DALA), a framework addressing inefficiency in multi-agent systems built on large language models (LLMs). Traditional 'free-for-all' communication leads to exponential token costs and low signal-to-noise ratios. DALA treats communication bandwidth as a scarce, tradable resource via a centralized auction where agents bid to speak based on predicted message value density. This encourages concise, informative exchanges, reducing costs and improving signal quality.
Key facts
- arXiv:2511.13193v2
- Multi-agent systems (MAS) built on LLMs suffer from inefficient communication
- Exponential token costs and low signal-to-noise ratios hinder practical deployment
- DALA treats communication bandwidth as a scarce and tradable resource
- Inter-agent communication is modeled as a centralized auction
- Agents learn to bid for speaking opportunities based on predicted value density
- Framework encourages concise, informative messages
- Aims to reduce costs and improve signal quality
Entities
—