Pipeline for Verifying LLM-Generated Mathematical Solutions
On the map: LLM reasoning → Mathematical reasoning
Abstract (arXiv)
With the growing popularity of Large Reasoning Models and their results in solving mathematical problems, it becomes crucial to measure their capabilities. We introduce a pipeline for both automatic and interactive verification as a more accurate alternative to only checking the answer which is currently the most popular approach for benchmarks. The pipeline can also be used as a generator of correct solutions both in formal and informal languages. 3 AI agents, which can be chosen for the benchmark accordingly, are included in the structure. The key idea is the use of prompts to obtain the solution in the specific form which allows for easier verification using proof assistants and possible use of small models (≤ 8B). Experiments on several datasets suggest low probability of False Positives. The open-source implementation with instructions on setting up a server is available at https://github.com/LogicEnj/lean4verificationpipeline.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. Builds on 16 of its 19 references that are in the map. First posted on arXiv 24 Feb 2026.
Builds on
16 of 19 references.
Closest ancestors · 3
Same direction, newest first
- 2025DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning29 May
- 2025DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition30 Apr
- 2025Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning15 Apr
Other references · 13
- 2023Let's Verify Step by Stepcited by 3,598
- 2022Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofscited by 278
- 2024ProcessBench: Identifying Process Errors in Mathematical Reasoningcited by 277
- 2024DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Datacited by 225
- 2024DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Searchcited by 189
- 2023SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoningcited by 157
- 2025Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correctioncited by 140
- 2019The Lean mathematical librarycited by 127
- 2024Lean Copilot: Large Language Models as Copilots for Theorem Proving in Leancited by 52
- 2025Aristotle: IMO-level Automated Theorem Provingcited by 47
- 2019Exploration of Neural Machine Translation in Autoformalization of Mathematics in Mizarcited by 40
- 2022Towards a Mathematics Formalisation Assistant using Large Language Modelscited by 22
1 more
- 2025G"odel's Poetrycited by 1
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Citing papers per year: 2026: 1.
1 paper.
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Close in wording, no citation either way
- 2025FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
- 2025Step-Wise Formal Verification for LLM-Based Mathematical Problem Solving
- 2026LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks
- 2025APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning
- 2025From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs
- 2024Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
- 2026ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning
- 2024Not All Votes Count! Programs as Verifiers Improve Self-Consistency of Language Models for Math Reasoning