[2603.00309] DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths

Computer Science > Artificial Intelligence arXiv:2603.00309 (cs) [Submitted on 27 Feb 2026] Title:DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths Authors:Hanqing Yang, Hyungwoo Lee, Yuhang Yao, Zhiwei Liu, Kay Liu, Jingdi Chen, Carlee Joe-Wong View a PDF of the paper titled DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths, by Hanqing Yang and 6 other authors View PDF HTML (experimental) Abstract: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 utilize predefined workflows or agent roles in order to reduce complexity, ideally these agents would be truly autonomous, able to achieve emergent collaboration even as the number of collaborating agents increases. Yet in practice, such unstructured interactions can 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 operate without predefined roles, control flow, or communication constraints, relying instead on emergent collaboration to solve problems. 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...