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New LeanContext benchmark evaluates neural theorem provers

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced LeanContext, a new benchmark designed to evaluate neural theorem proving systems. This benchmark consists of over 129,000 Lean whole-proof generation problems, which are used to disentangle the impact of mathematical resources versus local formal context on proof generation. Experiments with diffusion- and autoregressive-based models, including LLaDA and Llama, indicate that training with mathematical context significantly improves performance. Prioritizing omitted declarations referenced by the target further enhances success rates. AI

IMPACT This benchmark could accelerate research into AI systems capable of formal mathematical reasoning and theorem proving.

RANK_REASON The cluster describes a new benchmark and dataset for evaluating AI models in the domain of theorem proving, presented in an academic paper. [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 →

New LeanContext benchmark evaluates neural theorem provers

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The cluster describes a new benchmark and dataset for evaluating AI models in the domain of theorem proving, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hang Zhang, Qihao Ye, Litong Liu, Yunbei Zhang, Yugyung Lee, Yue Zhou, Bowen Liu, Emily First, Cheng Han, Qiao Zhuang ·

    LeanContext: Disentangling Mathematical Resources from Local Formal Context in Lean Whole-Proof Generation

    arXiv:2611.08277v1 Announce Type: new Abstract: Neural theorem proving increasingly uses retrieved premises and project context to support proof generation. Evaluating these resources requires measuring the advantage of target-selected declarations over comparable alternatives an…

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