DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths 事件

DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths arXiv:2603.00309v2 Announce Type: replace 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 reduce complexity through predefined workflows or fixed agent roles, the ideal is to support truly autonomous agents capable of emergent col