LeanCat: A Benchmark for Category Theory in Lean
Research Paper#Artificial Intelligence, Formal Verification, Category Theory🔬 Research|Analyzed: Jan 3, 2026 08:41•
Published: Dec 31, 2025 11:33
•1 min read
•ArXivAnalysis
This paper introduces LeanCat, a benchmark suite for formal category theory in Lean, designed to assess the capabilities of Large Language Models (LLMs) in abstract and library-mediated reasoning, which is crucial for modern mathematics. It addresses the limitations of existing benchmarks by focusing on category theory, a unifying language for mathematical structure. The benchmark's focus on structural and interface-level reasoning makes it a valuable tool for evaluating AI progress in formal theorem proving.
Key Takeaways
- •Introduces LeanCat, a new benchmark for formal category theory in Lean.
- •Focuses on abstract and library-mediated reasoning, crucial for modern mathematics.
- •Evaluates LLMs' ability to perform structural and interface-level reasoning.
- •Provides a compact and reusable checkpoint for tracking AI and human progress.
Reference / Citation
View Original"The best model solves 8.25% of tasks at pass@1 (32.50%/4.17%/0.00% by Easy/Medium/High) and 12.00% at pass@4 (50.00%/4.76%/0.00%)."