Mask-Proof: An LLM-based Automated Data Curation Pipeline on Mathematical Proofs

2026-06-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, AI for Mathematical Reasoning · Depth: Expert, quick

Summary

Mask-Proof is an LLM-based automated data curation pipeline designed to address the challenge of evaluating step-level reasoning in long mathematical proofs. It transforms real proofs into automatically checkable masked-step tasks by obscuring key formula steps and providing surrounding context. An LLM-based equivalence judge, utilizing repeated votes for stability, then evaluates model reconstructions. This pipeline generated Mask-ProofBench, a benchmark comprising 292 curated problems from diverse research areas. Experiments involving 17 different models demonstrated that reasoning-enhanced models achieved a 12% to 27% performance improvement over standard models. The evaluator itself shows high reliability, achieving 96.8% agreement with expert annotators, thereby enabling faithful, reproducible, and comparable measurement of mathematical reasoning at a granular step level.

Key takeaway

For AI Scientists and Machine Learning Engineers developing mathematical reasoning capabilities in LLMs, you should integrate Mask-Proof's methodology for robust evaluation. This pipeline offers a scalable, reproducible way to measure step-level reasoning, moving beyond final answer checks. Consider using the Mask-ProofBench dataset to benchmark your models, as reasoning-enhanced models show significant performance gains, ensuring your research contributes to more trustworthy AI assistance in scientific proof.

Key insights

The Mask-Proof pipeline enables scalable, reproducible evaluation of LLM step-level mathematical reasoning using masked-step tasks and an LLM-based judge.

Principles

• Step-level reasoning requires granular evaluation.
• LLM judges can reliably assess mathematical equivalence.
• Masking key steps reveals reasoning capabilities.

Method

Mask-Proof converts proofs into masked-step tasks, provides context, and uses an LLM-based equivalence judge with repeated voting to evaluate model reconstructions, creating a reproducible benchmark.

In practice

• Use Mask-ProofBench for LLM math evaluation.
• Implement masked-step tasks for reasoning assessment.
• Develop LLM-based judges for equivalence checks.

Topics

• Large Language Models
• Mathematical Proofs
• Reasoning Evaluation
• Mask-ProofBench
• Automated Data Curation

Code references

• weating/Mask-Proof

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.