Local Work, Global Progress: A Convergence Theory of AI Agent Collaboration
Teams of large language model (LLM) agents are increasingly applied to complex tasks, but their collaboration protocols are mostly designed empirically, without convergence guarantees.
ProofPaper ↗
Key points
- We formalize collaborative problem solving as block optimization: each agent improves one part of a shared solution, guided by its local objective plus a correction for how its changes affect the other parts.
- A coordinator checks every proposal against the global objective and merges proposals that do not interfere with each other.
- With GPT-OSS-120B agents on robot routing, a three-agent team finds collision-free plans in every run, better than those of a single agent that controls all robots.
- We also test the theory on query planning in SQLite and on a synthetic task.
Sources (1)
- [1]Local Work, Global Progress: A Convergence Theory of AI Agent CollaborationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 02:37 PM
Teams of large language model (LLM) agents are increasingly applied to complex tasks, but their collaboration protocols are mostly designed empirically, without convergence guarantees.
We formalize collaborative problem solving as block optimization: each agent improves one part of a shared solution, guided by its local objective plus a correction for how its changes affect the other parts.
Extractive summary: sentences quoted from the sources.
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