TacoMAS：基於LLM的多智能體系統中拓撲與能力的測試時共演化

TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systems

May 10, 2026

作者: Chen Xu, Yicheng Hu, Ruizi Wang, Xinyu Lin, Wenjie Wang, Dongrui Liu, Fuli Feng

cs.AI

摘要

多智能體系統（MAS）已成為解決複雜任務的重要典範。近期研究探討了能自動優化智能體能力或通訊拓撲結構的自我演化MAS。然而，現有方法要麼學得的拓撲結構在推理時保持固定，要麼僅在推理過程中調整拓撲或能力。我們通過實驗和理論證明，有效的測試時演化需要同時調整這兩個面向，但需在不同時間尺度上進行：能力應快速更新以應對新出現的子任務，而拓撲結構則應較慢演變以維持協調穩定性。為此，我們提出TacoMAS——一種適用於動態MAS的測試時共同演化框架。TacoMAS將MAS推理視為線上圖自適應任務，其中節點代表具備角色特定能力的智能體，邊則定義其通訊拓撲結構。在推理過程中，快速能力循環利用軌跡層級回饋更新智能體專長，而慢速元大語言模型驅動的拓撲循環則對MAS執行智能體的生滅操作（包括邊編輯、智能體新增與移除）。我們進一步證明，這種快慢設計能推動MAS朝向任務條件下的穩定均衡演化。在四個基準測試上的實驗結果表明，TacoMAS優於近20種多智能體基線方法，較最強基線平均提升13.3%。相關程式碼已公開於 https://github.com/chenxu2-gif/TacoMAS-MultiAgent。

English

Multi-agent systems (MAS) have emerged as a promising paradigm for solving complex tasks. Recent work has explored self-evolving MAS that automatically optimize agent capabilities or communication topologies. However, existing methods either learn a topology that remains fixed at inference time or adapt only the topology or capability during inference. We empirically and theoretically show that effective test-time evolution requires jointly adapting both axes, but on different time scales: capabilities should update rapidly to handle emerging subtasks, while the topology should evolve more slowly to preserve coordination stability. We then introduce TacoMAS, a test-time co-evolution framework for dynamic MAS. TacoMAS formulates MAS inference as a task of online graph adaptation, where nodes represent agents with role-specific capabilities and edges define their communication topology. During inference, a fast capability loop updates agent expertise using trajectory-level feedback, while a slow meta-LLM-driven topology loop performs agents' birth-death operations on MAS, including edge edit, agent addition, and agent removal. We further show that this fast-slow design drives MAS evolution toward a task-conditioned stable equilibrium. Experiments on four benchmarks demonstrate that TacoMAS outperforms nearly 20 multi-agent baselines, achieving an average improvement of 13.3% over the strongest baseline. The codes are released at https://github.com/chenxu2-gif/TacoMAS-MultiAgent.