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.

翻译：日益流行的智能体AI范式有望利用多个通用大语言模型（LLM）智能体的协同能力，共同完成复杂任务。尽管许多智能体AI系统通过预设工作流或固定智能体角色来降低复杂度，但理想状态是支持真正自主的智能体在多智能体交互中实现涌现式协作。然而在实践中，这种非结构化交互往往导致冗余工作与级联故障，且难以解释或修正。本研究探究由通用LLM智能体组成的多智能体系统——这些智能体通过涌现式协作解决问题，不依赖预设角色、控制流或通信约束。我们提出动态交互图（DIG），将涌现式协作建模为随时间演化的智能体激活与交互因果网络。DIG首次使涌现式协作变得可观测且可解释，能够直接从智能体的协作路径中实时识别、解释并修正因协作引发的错误模式。因此，DIG填补了理解通用LLM智能体在真正智能体化多智能体系统中如何协同解决问题的关键空白。项目网页详见：https://happyeureka.github.io/dig