ProofCouncil: An LLM Agent for Solving Open Mathematical Problems

📰 ArXiv cs.AI

Learn how ProofCouncil, an LLM agent, solves open mathematical problems using an author-critic architecture and agentic workflows

advanced Published 13 Jul 2026
Action Steps
  1. Build an author-critic architecture for LLMs to solve mathematical problems
  2. Implement agentic workflows to tailor LLMs to real-world mathematical practice
  3. Configure ProofCouncil to tackle open problems in mathematics
  4. Test ProofCouncil's performance on a challenge like FirstProof
  5. Apply ProofCouncil's approach to other areas of mathematics and science
Who Needs to Know This

Mathematicians, AI researchers, and software engineers can benefit from this approach to tackle complex mathematical problems and improve LLM performance

Key Insight

💡 Agentic workflows and author-critic architecture can improve LLM performance in solving open mathematical problems

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🤖 ProofCouncil: an LLM agent for solving open mathematical problems using author-critic architecture & agentic workflows 📝

Key Takeaways

Learn how ProofCouncil, an LLM agent, solves open mathematical problems using an author-critic architecture and agentic workflows

Full Article

Title: ProofCouncil: An LLM Agent for Solving Open Mathematical Problems

Abstract:
arXiv:2607.09474v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in solving open problems in mathematics. However, their performance can be further improved through agentic workflows tailored to real-world mathematical practice. To this end, we introduce ProofCouncil, a mathematical agent that is designed to tackle open problems using an author-critic architecture. ProofCouncil served as a submission to the second batch of FirstProof, a challenge consist
Read full paper → ← Back to Reads

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