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LLMs show promise in grading math proofs, with new framework outperforming SOTA

By PulseAugur Editorial · [1 sources] ·

Researchers have explored the capability of Large Language Models (LLMs) to grade mathematical proofs, a task currently reliant on human experts. A new study introduces a dataset of USAMO 2025 candidate solutions with expert grading and evaluates various LLMs for proof verification. The proposed MABGrader framework, which uses a Multi-Armed Bandit approach, demonstrated a significant improvement over existing methods, outperforming the state-of-the-art ProofGrader by 26.4% in Quadratic Weighted Kappa. AI

IMPACT This research could lead to more scalable and objective grading of complex mathematical reasoning, potentially impacting AI's role in education and competitive programming.

RANK_REASON The cluster contains an academic paper detailing a new methodology and dataset for LLM-based mathematical proof verification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show promise in grading math proofs, with new framework outperforming SOTA

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14 / 100
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The cluster contains an academic paper detailing a new methodology and dataset for LLM-based mathematical proof verification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Azim Ospanov, Zijin Feng, Ding Ding, Chengwu Liu, Haoli Bai, Jiacheng Sun, Lifeng Shang, Farzan Farnia ·

    Can LLMs Reliably Grade Olympiad Proofs? A Study of Mathematical Verification with LLMs

    arXiv:2610.36767v1 Announce Type: new Abstract: Large Language Models (LLMs) have made remarkable progress on solving competition-level mathematics, yet their ability to verify natural-language mathematical proofs remains relatively underexplored. This gap is especially pronounce…

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