Ivan Ilin, Peter Richtárik · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28286 · open-access PDF
大型言語モデル(LLM)エージェントのチームはますます複雑なタスクに適用されますが、そのコラボレーションプロトコルはほとんど実験的に設計されており、コンバージェンスの保証はありません。私たちはコラボレーションの問題解決をブロック最適化として公式化します:各エージェントは共有ソリューションの一部を改善し、そのローカル目標に基づき、その変更が他の部分にどのように影響するかを修正します。
Machine translation (Meta M2M100); the English original follows.
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. A coordinator checks every proposal against the global objective and merges proposals that do not interfere with each other. We prove linear convergence up to an error floor that vanishes when the correctio
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