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Evidence record 7499 · automatically gathered

Evaluating LLM-Based Grant Proposal Review via Structured Perturbations

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

Record details

Published: 9 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (9 March 2026), “Evaluating LLM-Based Grant Proposal Review via Structured Perturbations,” evidence record 7499, https://ethics.ai/record/7499 (originally published by arXiv).

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