{
  "id": 7499,
  "url": "https://arxiv.org/abs/2603.08281v2",
  "title": "Evaluating LLM-Based Grant Proposal Review via Structured Perturbations",
  "summary": "As AI-assisted grant proposals outpace manual review capacity in a kind of ``Malthusian trap'' for the research ecosystem, this paper investigates the capabilities and limitations of LLM-based grant reviewing for high-stakes evaluation. Using six EPSRC proposals, we develop a perturbation-based framework probing LLM sensitivity across six quality axes: funding, timeline, competency, alignment, clarity, and impact. We compare three review architectures: single-pass review, section-by-section anal",
  "authors": "William Thorne, Joseph James, Yang Wang, Chenghua Lin, Diana Maynard",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-09T11:53:50.000Z",
  "fetched_at": "2026-07-14T16:33:16.668Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7499",
  "original_url": "https://arxiv.org/abs/2603.08281v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}