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SorryDB: Can AI Provers Complete Real-World Lean Theorems?
arXiv – CS AI|Austin Letson, Leopoldo Sarra, Auguste Poiroux, Oliver Dressler, Paul Lezeau, Dhyan Aranha, Frederick Pu, Aaron Hill, Miguel Corredera Hidalgo, Julian Berman, George Tsoukalas, Lenny Taelman||4 views
🤖AI Summary
Researchers have introduced SorryDB, a dynamic benchmark for evaluating AI systems' ability to prove mathematical theorems using the Lean proof assistant. The benchmark draws from 78 real-world formalization projects and addresses limitations of static benchmarks by providing continuously updated tasks that better reflect community needs.
Key Takeaways
- →SorryDB is a new dynamic benchmark for testing AI theorem provers on real-world Lean mathematical formalization tasks.
- →The benchmark includes tasks from 78 GitHub projects, offering more practical challenges than traditional competition problems.
- →Current AI approaches including LLMs and symbolic provers show complementary strengths rather than one dominant solution.
- →Agentic approaches using Gemini Flash performed best overall but weren't universally superior to other methods.
- →The dynamic nature helps prevent test-set contamination and provides ongoing evaluation metrics for formal mathematics AI.
#ai#machine-learning#theorem-proving#lean#benchmark#mathematical-formalization#llm#symbolic-ai#research
Read Original →via arXiv – CS AI
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