{
  "id": 14151,
  "url": "https://arxiv.org/abs/2607.25446v1",
  "title": "Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm",
  "summary": "Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol). IMACS (Intelligent Multi-Agent Collaboration System) separates the three into orthogonal, independently swappable layers. Classic organizational theory (Belbin roles, Mintzberg coordination, RACI accountability) becomes executable, validated configurati",
  "authors": "Huan Chen, Xiang Song, Jian Jin, Pan Ren, Liang-Jie Zhang",
  "category": "research",
  "topics": "agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T08:35:21.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/14151",
  "original_url": "https://arxiv.org/abs/2607.25446v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}