Seed Prover 1.5 - ByteDance's next-generation mathematical reasoning model

What is Seed Prover 1.5?

Seed Prover 1.5 is a next-generation formal mathematical reasoning model launched by ByteDance's Seed team. The model employs an innovative Agentic Prover architecture, trained through large-scale reinforcement learning (Agentic RL), significantly improving mathematical reasoning ability and efficiency. It performs exceptionally well in solving challenging mathematical competition problems such as the IMO and Putnam, achieving gold medal-level results. Seed Prover 1.5 introduces a Sketch Model, transforming natural language proofs into formal lemmas, reducing complexity and increasing reasoning success rate. Seed Prover 1.5 achieves state-of-the-art (SOTA) performance on undergraduate, master's, and doctoral level mathematical problems, laying the foundation for future AI-assisted mathematical research.

Main features of Seed Prover 1.5

• Solving highly difficult mathematical problemsSupports efficient solving of mathematical problems at the International Mathematical Olympiad (IMO), Putnam (North American Undergraduate Mathematics Competition), and graduate levels.
• Generate formal proof codeIt transforms the solution process of mathematical problems into compileable and verifiable Lean proof code, ensuring the rigor and correctness of the proof.
• Improve reasoning efficiencyThrough innovative architecture and reinforcement learning training, it significantly improves inference efficiency and reduces computational resource consumption.
• Bridging natural language and formal languageThe Sketch Model transforms natural language proofs into formal lemmas, reducing the difficulty of complex problems and improving the success rate of reasoning.
• Multi-agent collaborationThrough a hierarchical multi-agent system, efficient collaboration between natural language proof, lemma generation, and formal proof can be achieved.

Technical principles of Seed Prover 1.5

• Agentic Prover ArchitectureThe Lean language is treated as a tool, allowing the model to autonomously invoke Mathlib search tools and Python code execution tools during the proof process to acquire knowledge and verify conjectures. The model breaks down complex problems into multiple lemmas, reusing each lemma after proof to progressively build a complete formal proof. Through interaction with the Lean compiler, the model continuously accumulates experience during training, optimizes its proof strategy, and improves its reasoning ability and efficiency.
• Sketch ModelThis approach transforms natural language proofs into formalized lemma structures, reducing the difficulty of directly generating complete formal code. By combining Lean compiler verification, natural language proof checks, and a Rubric scoring model based on long thought chains, the generated lemma structures are evaluated from multiple perspectives to ensure their quality. Through a multi-agent collaborative system, efficient collaboration between natural language proofs, lemma generation, and formal proofs is achieved, improving the success rate and parallelism of reasoning.
• Multi-agent cooperative system:
  • Natural Language ProverGenerates high-level natural language proofs, providing mathematical intuition.
  • Sketch ModelTransform natural language proofs into formal lemma structures.
  • Agentic ProverEach lemma is tackled in parallel to verify the conjecture and generate the final formal proof.

Project address for Seed Prover 1.5

• GitHub repositoryhttps://github.com/ByteDance-Seed/Seed-Prover
• arXiv technical paperhttps://arxiv.org/pdf/2512.17260

Application scenarios of Seed Prover 1.5

• Mathematics CompetitionIt assists in solving challenging math competition problems such as the IMO and Putnam, quickly generating proof code and improving problem-solving efficiency.
• Mathematics EducationAs a teaching tool in higher education, it helps students understand complex mathematical concepts and proof processes, thus aiding their learning.
• Mathematical researchIt assists mathematicians in verifying conjectures, generating preliminary proof frameworks, and promoting research on cutting-edge mathematical problems.
• Formal Mathematics Library ExtensionGenerate high-quality Lean proof code, enrich formal mathematics libraries (such as Mathlib), and improve resource availability.
• Software verificationUsed in software development to verify the correctness of algorithms and logic, ensuring the reliability and security of the software.