AI RESEARCH
Evaluating LLM-Based Grant Proposal Review via Structured Perturbations
arXiv CS.CL
•
ArXi:2603.08281v1 Announce Type: new 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.