ProofNet-Test

Diffusion-Proof: First framework applying diffusion LLMs to formal theorem proving

Researchers introduce Diffusion-Proof, the first framework to train and apply diffusion language models (dLLMs) for formal theorem proving, addressing limitations of autoregressive models in long-range coherence. The framework includes dLLM-Prover-7B for whole-proof generation and dLLM-Corrector-7B for local proof correction via bidirectional infilling. Diffusion-Proof achieves absolute improvements of 1.61% on ProofNet-Test and 6.14% on MiniF2F-Test over an AR baseline, and solves one IMO problem that DeepSeek-Prover-V2-7B could not. The result suggests dLLMs may have structural advantages over AR models for tasks requiring long-range logical coherence.