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SAGE: Schema-Guided LLMs for Grant Review

arXiv 2609.20829 ↗First posted 22 Jul 2026cs.CLcited by 0

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Abstract (arXiv)

Grant reviewers must apply detailed criteria to application forms, budgets, and supporting documents while producing assessments that colleagues can inspect. We present SAGE, Schema-Guided Aspect-Based Grant Evaluation, a system that translates a grant rubric into structured checks and links its judgements to evidence from the application package. We evaluate SAGE in two stages on 35 nonprofit grant applications. A post-factum comparison with 105 reviews from the original competition shows fair ordinal agreement (kappa = 0.29). The foundation then conducted a criterion-level re-review after inspecting SAGE, producing 202 assessments. In this assisted round, SAGE reached kappa = 0.58 and outperformed a one-prompt-per-criterion baseline (kappa = 0.33 on the common subset), with higher rank correlation and lower error. A claim-level audit further identifies confirmed, disputed, and unaddressed parts of the structured draft. SAGE operationalizes the review methodology by producing a detailed, evidence-linked, and auditable draft for expert correction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 22 Jul 2026.

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References · 7

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2024A Survey on LLM-as-a-Judgecited by 1,423
  5. 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
  6. 2024Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Modelscited by 446
  7. 2022A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challengescited by 61

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