超越個體智慧：基於LLM的多智能體系統中的協作、失敗歸因與自我演化綜述

摘要

基於大型語言模型的自主智能體已在推理、規劃與工具使用方面展現出強大能力，然而在需要角色、工具與環境間持續協調的任務中仍有所局限。多智能體系統透過專業化智能體間的結構化協作來應對此問題，但更緊密的協調也擴大了一個相對未被充分探討的風險：錯誤可能跨智能體與互動回合傳播，產生難以診斷的失敗，且這些失敗極少轉化為結構性的自我改進。現有綜述分別涵蓋個別智能體能力、多智能體協作或智能體自我演化，卻未探討其間的因果依賴關係。本調查報告以四個因果相連的階段為基礎提供統一綜述，我們稱之為LIFE進程：奠定能力基礎（Lay the capability foundation）、透過協作整合智能體（Integrate agents through collaboration）、透過歸因發現故障（Find faults through attribution）、以及透過自主自我改進演化（Evolve through autonomous self-improvement）。針對每個階段，我們提供系統性分類，並正式刻畫相鄰階段間的依賴關係，揭示每個階段如何既依賴又制約下一階段。除綜合現有研究外，我們還識別出階段邊界上的開放性挑戰，並提出一個跨階段的研究議程，目標是實現具備持續診斷失敗、重組結構與優化智能體行為能力的閉環多智能體系統，從而將現有協調框架擴展至更趨向自組織形式的集體智能。透過串聯這些先前分散的研究脈絡，本調查報告旨在提供一份系統性參考，同時為邁向自主且能自我改進的多智能體智能提供概念性路線圖。

English

LLM-based autonomous agents have demonstrated strong capabilities in reasoning, planning, and tool use, yet remain limited when tasks require sustained coordination across roles, tools, and environments. Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination also amplifies a less explored risk: errors can propagate across agents and interaction rounds, producing failures that are difficult to diagnose and rarely translate into structural self-improvement. Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined. This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement. For each stage, we provide systematic taxonomies and formally characterize the dependencies between adjacent stages, revealing how each stage both depends on and constrains the next. Beyond synthesizing existing work, we identify open challenges at stage boundaries and propose a cross-stage research agenda for closed-loop multi-agent systems capable of continuously diagnosing failures, reorganizing structures, and refining agent behaviors, extending current coordination frameworks toward more self-organizing forms of collective intelligence. By bridging these previously fragmented research threads, this survey aims to offer both a systematic reference and a conceptual roadmap toward autonomous, self-improving multi-agent intelligence.