{
  "id": 3930,
  "url": "https://arxiv.org/abs/2605.22138v1",
  "title": "Efficient Agentic Reasoning Through Self-Regulated Simulative Planning",
  "summary": "How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thought), trained end-to-end expecting planning to emerge implicitly. Without control over the presence, structure, or horizon of planning, these systems dramatically increase reasoning length, yielding inefficient token use without reliable accuracy gains. We argue efficient agentic reasoning benefits from decomposing decision-making into three system",
  "authors": "Mingkai Deng, Jinyu Hou, Lara Sá Neves, Varad Pimpalkhute, Taylor W. Killian, Zhengzhong Liu et al.",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-21T08:11:54.000Z",
  "fetched_at": "2026-07-14T16:30:36.743Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3930",
  "original_url": "https://arxiv.org/abs/2605.22138v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}