<ul><li>Neural methods are being used to enhance automated reasoning in proof assistants, but integrating these advancements into practical verification workflows remains a challenge.</li><li>LeanHammer is introduced as the first domain-general hammer for Lean, leveraging a novel neural premise selection system for a hammer in dependent type theory.</li><li>LeanHammer dynamically adapts to user-specific contexts and combines symbolic proof search and reconstruction, aiming to improve productivity in the Lean proof assistant.</li><li>Comprehensive evaluations show that LeanHammer with its premise selector can solve 21% more goals compared to existing premise selectors, bridging the gap between neural retrieval and symbolic reasoning in formal verification.</li></ul>

Premise Selection for a Lean Hammer

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