{
  "id": 12585,
  "url": "https://arxiv.org/abs/2607.19338v1",
  "title": "CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents",
  "summary": "Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer. Existing cost-aware systems typically treat such failures as cascade decisions: try a cheap model first, then escalate hard cases to a stronger and more expensive model. In coding, however, execution feedback can also make further cheap-model recovery worthwhile, raising a budgeted deployment question: when should an agent spend more cheap comp",
  "authors": "Qijia He, Jiayi Cheng, Chenqian Le, Rui Wang, Xunmei Liu, Yixian Chen, Jie Mei, Zhihao Wang, Xupeng Chen, Yuhuan Chen, Tao Wang",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T17:56:49.000Z",
  "fetched_at": "2026-07-22T05:10:49.469Z",
  "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/12585",
  "original_url": "https://arxiv.org/abs/2607.19338v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}