Large language models (LLMs) have shown promise in proving formal theorems using proof
assistants such as Lean. However, existing methods are difficult to reproduce or build on …

Despite the success of large language models (LLMs), the task of theorem proving still
remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods …

Large language models (LLMs) have shown remarkable proficiency in human-level
reasoning and generation capabilities, which encourages extensive research on their …

We introduce DeepSeek-Prover-V1. 5, an open-source language model designed for
theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both …

Recently, large language models have presented promising results in aiding formal
mathematical reasoning. However, their performance is restricted due to the scarcity of …

Neural Code Intelligence--leveraging deep learning to understand, generate, and optimize
code--holds immense potential for transformative impacts on the whole society. Bridging the …

Large Language Models (LLMs) present an intriguing avenue for exploration in the field of
formal theorem proving. Nevertheless, their full potential, particularly concerning the …

Large Language Models (LLMs) have driven substantial progress in artificial intelligence in
recent years, exhibiting impressive capabilities across a wide range of tasks, including …

Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning
in mathematical language to rigorous derivations in formal systems. In recent years, the …

Autoregressive language models, despite their impressive capabilities, struggle with
complex reasoning and long-term planning tasks. We introduce discrete diffusion models as …