PulseAugur
EN
LIVE 11:13:55

LLMs evaluated for formal math proofs in Lean 4

By PulseAugur Editorial · [1 sources] ·

A new research paper evaluates the performance of various Large Language Models (LLMs) in generating formal mathematical proofs using the Lean 4 theorem prover. The study employed pass@k and refine@k metrics on subsets of the miniF2F and miniCTX datasets. Gemini 3.1 Pro and Claude Opus 4.7 demonstrated the highest success rates, with Gemini achieving 92% on miniF2F and Opus reaching 86% on miniCTX. For cost-efficiency, NVIDIA Nemotron 3 Super and GPT-OSS 120B offered competitive accuracies at a low cost per proof. AI

IMPACT This research highlights LLM capabilities in formal mathematics, potentially aiding theorem proving and mathematical research.

RANK_REASON The cluster contains an academic paper evaluating LLM performance on a specific task. [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 evaluated for formal math proofs in Lean 4

How we ranked this

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
The cluster contains an academic paper evaluating LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
96 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tyson Klingner, Drew Bladek, Escher Crawford, Bohao Chen, Ariel Fu, Kaira Nair, Jarod Alper, Giovanni Inchiostro, Vasily Ilin ·

    Evaluation of LLMs for Mathematical Formalization in Lean

    arXiv:2606.05632v1 Announce Type: new Abstract: Within the past few years, the ability of Large Language Models (LLMs) to generate formal mathematical proofs has improved drastically. We provide a comparison of various LLMs' effectiveness in producing formal proofs in Lean 4 with…

RELATED ENTITIES

RELATED TOPICS