{
  "id": 4276,
  "url": "https://arxiv.org/abs/2605.21516v1",
  "title": "Harnesses for Inference-Time Alignment over Execution Trajectories",
  "summary": "Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance through task decomposition and guided execution. However, more elaborate harnesses are not uniformly better: increasing decomposition or guidance can sometimes improve execution, but can also reduce final task success. We study harness design through the lens of inference-time trajectory alignment. This perspective separates harness into two mech",
  "authors": "Boyuan Wang, Bochao Li, Minghan Wang, Yuxin Tao, Fang Kong",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-15T12:47:13.000Z",
  "fetched_at": "2026-07-14T16:30:54.919Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4276",
  "original_url": "https://arxiv.org/abs/2605.21516v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}