{
  "id": 3330,
  "url": "https://arxiv.org/abs/2606.01351v1",
  "title": "Recognize Your Orchestrator: An Entropy Dynamics Perspective for LLM Multi-Agent Systems",
  "summary": "The transition from single-turn models to Multi-Agent Systems (MAS) promises enhanced problem-solving capabilities, yet the centralized orchestration topology remains a critical point of fragility. To analyze this, we propose a Mean-Field Entropy Dynamics framework, modeling the orchestration process as a system governed by the competing forces of task resolution and cumulative context loading. To facilitate validation, we introduce Inverse Workflow Generation (IWG), a multi-agent pipeline that ",
  "authors": "Junze Zhu, Weihao Chen, Xuanwang Zhang, Zhen Wu, Xinyu Dai",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-31T17:06:01.000Z",
  "fetched_at": "2026-07-14T16:30:09.962Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3330",
  "original_url": "https://arxiv.org/abs/2606.01351v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}