{
  "schema_version": "1.0",
  "id": "s13:https://arxiv.org/abs/2607.13034v1",
  "slug": "2607-13034v1-03g7ghx",
  "url": "https://feed7.dev/p/2607-13034v1-03g7ghx",
  "title": "Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution",
  "why_included": "E3 makes agents estimate task scope, try the minimum viable path, and expand only after verification fails. In a controlled edit benchmark, it preserved task completion while sharply reducing work.",
  "summary": "E3 follows Estimate, Execute, Expand: choose an initial scope, attempt the shortest reliable path, then widen it only if verification fails. On **121 deterministic edits**, it matched the strongest baseline’s **100% task success** while cutting cost 85%, tokens 91%, and inspected files 92%.",
  "practical_implication": "Add scope estimation before retrieval-heavy coding loops, and make failed verification the trigger for reading more files or dependencies. Track redundant inspection alongside correctness so one-line changes do not automatically become repository audits.",
  "agent_context": "E3 follows Estimate, Execute, Expand: choose an initial scope, attempt the shortest reliable path, then widen it only if verification fails. On **121 deterministic edits**, it matched the strongest baseline’s **100% task success** while cutting cost 85%, tokens 91%, and inspected files 92%.\n\nAdd scope estimation before retrieval-heavy coding loops, and make failed verification the trigger for reading more files or dependencies. Track redundant inspection alongside correctness so one-line changes do not automatically become repository audits.\n\nMSE-Bench is a capability-controlled simulator, not a deployed-agent measurement. A gpt-4o case study on a real library showed the same direction at comparable task success, but its evidence is narrower and one run was limited by a provider rate limit.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2607.13034v1",
    "published_at": "2026-07-14T17:59:31.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [
    "coding"
  ],
  "topics": [
    "harness-engineering",
    "agent-reliability",
    "context-engineering"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "MSE-Bench is a capability-controlled simulator, not a deployed-agent measurement. A gpt-4o case study on a real library showed the same direction at comparable task success, but its evidence is narrower and one run was limited by a provider rate limit."
  ],
  "lifecycle": "Current",
  "published_at": "2026-07-14T17:59:31.000Z",
  "modified_at": "2026-07-14T17:59:31.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/2607-13034v1-03g7ghx",
    "json": "https://feed7.dev/p/2607-13034v1-03g7ghx.json",
    "markdown": "https://feed7.dev/p/2607-13034v1-03g7ghx.md"
  }
}