{
  "id": 5500,
  "url": "https://arxiv.org/abs/2604.20938v1",
  "title": "HARBOR: Automated Harness Optimization",
  "summary": "Long-horizon language-model agents are dominated, in lines of code and in operational complexity, not by their underlying model but by the harness that wraps it: context compaction, tool caching, semantic memory, trajectory reuse, speculative tool prediction, and the glue that binds the model to a sandboxed execution environment. We argue that harness design is a first-class machine-learning problem and that automated configuration search dominates manual stacking once the flag space exceeds a h",
  "authors": "Biswa Sengupta, Jinhua Wang",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-22T13:45:12.000Z",
  "fetched_at": "2026-07-14T16:31:48.873Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5500",
  "original_url": "https://arxiv.org/abs/2604.20938v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}