{
  "id": 250155,
  "title": "HarnessOpt-Bench: Evaluating LLMs at Harness Optimization",
  "url": "https://urgent.news/2026/08/06/harnessopt-bench-evaluating-llms-at-harness-optimization",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-06T17:21:05.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.06301v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding them. This makes automated harness optimization -- the iterative and evaluation-guided improvement of a harness by an AI system -- both an important route to improving AI systems and…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}