title: MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective
    Search and Data Curation
summary: Automated Theorem Proving (ATP) in formal languages remains a formidable
  challenge in AI, demanding rigorous logical deduction and navigating vast
  search spaces. While large language models (LLMs) have shown promising
  performance, existing stepwise provers often suffer from biased search
  guidance, leading to inefficiencies and suboptimal proof strategies. This paper
  introduces the Multi-Perspective Search Prover (MPS-Prover), a novel stepwise
  ATP system designed to overcome these limitations. MPS-Prover incorporates two
  key innovations: a highly effective post-training data curation strategy that
  prunes approximately 40% of redundant training data without sacrificing
  performance, and a multi-perspective tree search mechanism. This search
  integrates a learned critic model with strategically designed heuristic rules
  to diversify tactic selection, prevent getting trapped in unproductive states,
  and enhance search robustness. Extensive evaluations demonstrate that
  MPS-Prover achieves state-of-the-art performance on multiple challenging
  benchmarks, including miniF2F and ProofNet, outperforming prior 7B parameter
  models. Furthermore, our analyses reveal that MPS-Prover generates
  significantly shorter and more diverse proofs compared to existing stepwise and
  whole-proof methods, highlighting its efficiency and efficacy. Our work
  advances the capabilities of LLM-based formal reasoning and offers a robust
  framework and a comprehensive analysis for developing more powerful theorem
  provers.
paper_authors._id: 682ab4fe7a9f1a7ec9779dd6
paper_authors.name: Zhenwen Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:24:26.692Z
paper_authors._id: 62ffa3f8311cad266f9af236
paper_authors.avatarUrl: /avatars/4c88cb518e000a475f8381573f21aa7f.svg
paper_authors.fullname: Zhenwen Liang
paper_authors.type: user
paper_authors.user: invokerliang
paper_authors._id: 682ab4fe7a9f1a7ec9779dd7
paper_authors.name: Linfeng Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:24:45.999Z
paper_authors._id: 64c94eddcb2f1bf0e7db5a4d
paper_authors.avatarUrl: /avatars/f7e2532d3c85d5e5b5a02c579ea68c3a.svg
paper_authors.fullname: Linfeng Song
paper_authors.type: user
paper_authors.user: freesunshine0316
paper_authors._id: 682ab4fe7a9f1a7ec9779dd8
paper_authors.name: Yang Li
paper_authors._id: 682ab4fe7a9f1a7ec9779dd9
paper_authors.name: Tao Yang
paper_authors._id: 682ab4fe7a9f1a7ec9779dda
paper_authors.name: Feng Zhang
paper_authors._id: 682ab4fe7a9f1a7ec9779ddb
paper_authors.name: Haitao Mi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T07:24:56.781Z
paper_authors._id: 65147a1426fbd558dbd08f1b
paper_authors.avatarUrl: /avatars/86574ee2d5c22e940be1c4e50be88675.svg
paper_authors.fullname: Haitao Mi
paper_authors.type: user
paper_authors.user: haitaominlp
paper_authors._id: 682ab4fe7a9f1a7ec9779ddc
paper_authors.name: Dong Yu
paper_title: MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective
    Search and Data Curation
paper_summary: Automated Theorem Proving (ATP) in formal languages remains a formidable
  challenge in AI, demanding rigorous logical deduction and navigating vast
  search spaces. While large language models (LLMs) have shown promising
  performance, existing stepwise provers often suffer from biased search
  guidance, leading to inefficiencies and suboptimal proof strategies. This paper
  introduces the Multi-Perspective Search Prover (MPS-Prover), a novel stepwise
  ATP system designed to overcome these limitations. MPS-Prover incorporates two
  key innovations: a highly effective post-training data curation strategy that
  prunes approximately 40% of redundant training data without sacrificing
  performance, and a multi-perspective tree search mechanism. This search
  integrates a learned critic model with strategically designed heuristic rules
  to diversify tactic selection, prevent getting trapped in unproductive states,
  and enhance search robustness. Extensive evaluations demonstrate that
  MPS-Prover achieves state-of-the-art performance on multiple challenging
  benchmarks, including miniF2F and ProofNet, outperforming prior 7B parameter
  models. Furthermore, our analyses reveal that MPS-Prover generates
  significantly shorter and more diverse proofs compared to existing stepwise and
  whole-proof methods, highlighting its efficiency and efficacy. Our work
  advances the capabilities of LLM-based formal reasoning and offers a robust
  framework and a comprehensive analysis for developing more powerful theorem
  provers.
paper_ai_summary: MPS-Prover, a novel stepwise ATP system, improves proof efficiency and quality through data curation and a multi-perspective tree search mechanism combining learned critics and heuristic rules.
paper_ai_keywords: Automated Theorem Proving Large Language Models stepwise provers post-training data curation multi-perspective tree search learned critic model heuristic rules miniF2F ProofNet