{
  "id": 10445,
  "url": "https://arxiv.org/abs/2607.13034v1",
  "title": "Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution",
  "summary": "Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path be",
  "authors": "Junjie Yin, Xinyu Feng",
  "category": "research",
  "topics": "agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-14T17:59:31.000Z",
  "fetched_at": "2026-07-15T05:10:55.633Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10445",
  "original_url": "https://arxiv.org/abs/2607.13034v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}