Combining Textual and Structural Information for Premise Selection in Lean
PositiveArtificial Intelligence
A new approach to premise selection in theorem proving has been introduced, addressing a significant challenge in scaling formal libraries. By integrating dense text embeddings with graph neural networks, this method captures the intricate relationships between premises, enhancing the efficiency of theorem proving. This innovation is crucial as it could lead to more effective and scalable solutions in formal verification, ultimately benefiting various fields that rely on rigorous proofs.
— Curated by the World Pulse Now AI Editorial System



