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Seed Prover 1.5 - ByteDance's New-Generation Mathematical Reasoning Model

Author: AI小集 · Original source: AI工具集

What is Seed Prover 1.5

Seed Prover 1.5 is a new-generation formal mathematical reasoning model launched by ByteDance's Seed team. The model adopts an innovative Agentic Prover architecture and is trained through large-scale reinforcement learning (Agentic RL), significantly improving mathematical reasoning capability and efficiency. The model performs excellently in solving highly difficult mathematical competition problems such as IMO and Putnam, reaching gold medal level. Seed Prover 1.5 introduces the Sketch Model, which transforms natural language proofs into formal lemmas, reducing complexity and improving the reasoning success rate. Seed Prover 1.5 has set new SOTA performance on undergraduate, master's, and doctoral-level mathematical problems, laying the foundation for future AI-assisted mathematical research.

Seed Prover 1.5

Main Features of Seed Prover 1.5

  • Solving Highly Difficult Mathematical Problems: Supports efficiently solving International Mathematical Olympiad (IMO), North American undergraduate mathematics competition (Putnam), and graduate-level mathematical problems.
  • Generating Formal Proof Code: Transforms the problem-solving process of mathematical problems into compilable and verifiable Lean proof code, ensuring the rigor and correctness of proofs.
  • Improving Reasoning Efficiency: Through an innovative architecture and reinforcement learning training, significantly improves reasoning efficiency and reduces computational resource consumption.
  • Bridging Natural Language and Formal Language: Uses the Sketch Model to transform natural language proofs into formal lemmas, reducing the difficulty of complex problems and improving the reasoning success rate.
  • Multi-Agent Collaboration: Through a hierarchical multi-agent system, achieves efficient collaboration among natural language proofs, lemma generation, and formal proofs.

Technical Principles of Seed Prover 1.5

  • Agentic Prover Architecture: Treats the Lean language as a tool. During the proof process, the model can autonomously invoke Mathlib search tools, Python code execution tools, etc., acquiring knowledge and verifying conjectures through tool calls. The model decomposes complex problems into multiple lemmas, retains and reuses each proved lemma, and gradually constructs a complete formal proof. Through interaction with the Lean compiler, the model continuously accumulates experience during training, optimizes proof strategies, and improves reasoning capability and efficiency.
  • Sketch Model: Transforms natural language proofs into formalized lemma structures, reducing the difficulty of directly generating complete formal code. Combined with Lean compiler verification, natural language proof checking, and a Rubric scoring model based on long chain-of-thought, it evaluates the generated lemma structures from multiple perspectives to ensure their quality. Through a multi-agent collaboration system, it achieves efficient coordination among natural language proofs, lemma generation, and formal proofs, improving the success rate and parallelism of reasoning.
  • Multi-Agent Collaboration System：
    • Natural Language Prover: Generates high-level natural language proofs, providing mathematical intuition.
    • Sketch Model: Transforms natural language proofs into formalized lemma structures.
    • Agentic Prover: Tackles each lemma in parallel, verifies conjectures, and generates the final formal proof.

Project Address of Seed Prover 1.5

  • GitHub Repository：https://github.com/ByteDance-Seed/Seed-Prover
  • arXiv Technical Paper：https://arxiv.org/pdf/2512.17260

Application Scenarios of Seed Prover 1.5

  • Mathematical Competitions: Assists in solving highly difficult mathematical competition problems such as IMO and Putnam, quickly generating proof code, and improving problem-solving efficiency.
  • Mathematics Education: As a teaching tool for higher education, helps students understand complex mathematical concepts and proof processes, and assists learning.
  • Mathematical Research: Assisting mathematicians in verifying conjectures, generating preliminary proof frameworks, and advancing research on frontier mathematical problems.
  • Formal Mathematics Library Expansion: Generating high-quality Lean proof code, enriching formal mathematics libraries (such as Mathlib), and improving resource availability.
  • Software Verification: Used in software development to verify the correctness of algorithms and logic, ensuring software reliability and security.