{
  "id": 3345,
  "url": "https://arxiv.org/abs/2606.01199v1",
  "title": "Can LLM Agents Sustain Long-Horizon Organizational Dynamics?",
  "summary": "Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organizations, where goals must propagate through hierarchy, tasks depend on prior execution, and artifacts accumulate over long horizons. We formulate long-horizon organizational simulation as a memory-centered coordination problem and introduce TaskWeave, a hierarchical agentic framework that maintains planning states through a Formulate-Partition-D",
  "authors": "Xuancheng Zhu, Yang Yue, Shuaibing Wan, Zihan Dou, Xiaohan Zhang, Yongrui Liu et al.",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-31T12:28:42.000Z",
  "fetched_at": "2026-07-14T16:30:09.963Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3345",
  "original_url": "https://arxiv.org/abs/2606.01199v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}