From Agents to Edges: Paired Optimization for Multi-Agent Collaboration
We introduce Paired Automated Communication Optimization (PACO), which jointly optimizes the instructions at both ends of each edge and their shared communication convention in systems built on black-box language models.
ProofPaper ↗
Key points
- Strong individual agents do not guarantee effective collaboration.
- Communication must align what one agent conveys with how another interprets and uses it.
- A state-aware discounted bandit selects edges for refinement, while execution feedback guides paired revisions evaluated through staged end-to-end execution.
- On a four-agent workflow evaluated across diverse multi-agent benchmarks, PACO consistently outperforms leading prompt-optimization and communication baselines.
Sources (1)
- [1]From Agents to Edges: Paired Optimization for Multi-Agent CollaborationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 09:53 AM
We introduce Paired Automated Communication Optimization (PACO), which jointly optimizes the instructions at both ends of each edge and their shared communication convention in systems built on black-box language models.
Strong individual agents do not guarantee effective collaboration.
Extractive summary: sentences quoted from the sources.
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