AI RESEARCH
DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths
arXiv CS.AI
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ArXi:2603.00309v2 Announce Type: replace 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 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.