LAMP achieves 96.7% success generating verified Lean proofs for 90 Combinatorics on Words theorems by coordinating Planner, Builder, and Verifier agents with a CoW ontology accessed through Model Context Protocol.
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LEAP enables general-purpose LLMs to reach 100% solve rate on the 2025 Putnam and 70% on a new Lean-IMO-Bench by combining informal reasoning with iterative Lean interaction.
ProofRank benchmark shows substantial differences in LLM proof quality not captured by correctness, with trade-offs between quality metrics and accuracy.
Lean-GAP is a dataset of 430 graduate algebra problems formalized in Lean 4 from Dummit and Foote, with a described pipeline for autoformalization and verification plus analysis of challenges.
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LAMP: Lean-based Agentic framework with MCP and Proof Repair
LAMP achieves 96.7% success generating verified Lean proofs for 90 Combinatorics on Words theorems by coordinating Planner, Builder, and Verifier agents with a CoW ontology accessed through Model Context Protocol.
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LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks
LEAP enables general-purpose LLMs to reach 100% solve rate on the 2025 Putnam and 70% on a new Lean-IMO-Bench by combining informal reasoning with iterative Lean interaction.
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Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctness
ProofRank benchmark shows substantial differences in LLM proof quality not captured by correctness, with trade-offs between quality metrics and accuracy.
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Lean-GAP: A Dataset of Formalized Graduate Algebra Problems
Lean-GAP is a dataset of 430 graduate algebra problems formalized in Lean 4 from Dummit and Foote, with a described pipeline for autoformalization and verification plus analysis of challenges.