让多个AI代理自主协作，通过动态图追踪并解释合作过程中的错误。
DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths
- 构建动态交互图（DIG），实时记录代理间的因果互动路径。
- 首次实现对多代理协作中错误模式的可解释与实时纠正。
- 适合研究自主智能体协作、可解释AI系统的学者与开发者。
日益流行的代理式AI范式旨在利用多个通用大语言模型（LLM）代理协同完成复杂任务。尽管许多系统通过预设工作流或固定角色降低复杂性，理想的方案是支持真正自主的代理，在大量交互中实现涌现式协作。然而实践中，无结构的交互常导致重复工作和级联失败，难以解释与修正。本文研究由通用LLM代理组成的多代理系统，通过涌现协作解决问题，无需预设角色、控制流或通信约束。我们提出动态交互图（DIG），将涌现协作建模为随时间演化的代理激活与交互因果网络。DIG首次使涌现协作可观测且可解释，能够实时识别、解释并从协作路径中直接纠正错误模式。这填补了理解通用LLM代理在真正代理式多代理系统中如何协同解题的关键空白。
原文摘要 · Abstract (English)
The increasingly popular agentic AI paradigm promises to harness the power of multiple, general-purpose large language model (LLM) agents to collaboratively complete complex tasks. While many agentic AI systems reduce complexity through predefined workflows or fixed agent roles, the ideal is to support truly autonomous agents capable of emergent collaboration across many interacting agents. Yet in practice, such unstructured interactions often lead to redundant work and cascading failures that are difficult to interpret or correct. In this work, we study multi-agent systems composed of general-purpose LLM agents that solve problems through emergent collaboration, without relying on predefined roles, control flows, or communication constraints. We introduce the Dynamic Interaction Graph (DIG), which captures emergent collaboration as a time-evolving causal network of agent activations and interactions. DIG makes emergent collaboration observable and explainable for the first time, enabling real-time identification, explanation, and correction of collaboration-induced error patterns directly from agents' collaboration paths. Thus, DIG fills a critical gap in understanding how general LLM agents solve problems together in truly agentic multi-agent systems. The project webpage can be found at: https://happyeureka.github.io/dig.
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