{
  "id": 16095,
  "url": "https://arxiv.org/abs/2608.02444v1",
  "title": "ParEvalLayer: When Partial LLM-Agent Evaluations Support a Decision",
  "summary": "LLM-agent evaluations often produce task outcomes long before the full benchmark run is complete. A partial score is tempting to report, but it does not show whether the observed tasks support the same conclusion as the completed evaluation. Early tasks can omit important parts of a benchmark, running cheaper tasks first can distort the observed sample, and a rule that decides only easy pairs can appear accurate while leaving many comparisons unresolved. We introduce ParEvalLayer, a decision lay",
  "authors": "Wei-Jung Huang, Bonan Shen",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T16:22:51.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "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/16095",
  "original_url": "https://arxiv.org/abs/2608.02444v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}