{
  "id": 11746,
  "url": "https://arxiv.org/abs/2607.15524",
  "title": "Recursive Harness Self-Improvement",
  "summary": "Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This motivates harness-in-the-loop learning: optimizing harnesses for both immediate agent performance and the quality of traces used for future model training. However, continually updating provider-built scaffolds is costly and labor-intensive. We therefore investigate whether optimizing user-constructed harnesses in a ta",
  "authors": "Hyunin Lee, Jinglue Xu, Jeffrey Seely, Donghyun Lee, Matei Zaharia, Yujin Tang",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-16T20:00:00.000Z",
  "fetched_at": "2026-07-20T05:10:09.534Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/11746",
  "original_url": "https://arxiv.org/abs/2607.15524",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}