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arXiv · 2510.23637

Combining Textual and Structural Information for Premise Selection in Lean

Job Petrovčič·David Eliecer Narvaez Denis·Ljupčo Todorovski
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Abstract

Premise selection is a key bottleneck for scaling theorem proving in large formal libraries. Yet existing language-based methods often treat premises in isolation, ignoring the web of dependencies that connects them. We present a graph-augmented approach that combines dense text embeddings of Lean formalizations with graph neural networks over a heterogeneous dependency graph capturing both state-premise and premise-premise relations. On the LeanDojo Benchmark, our method outperforms the ReProver language-based baseline by over 25\% across standard retrieval metrics. These results suggest that relational information is beneficial for premise selection.

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Job Petrovčič, David Eliecer Narvaez Denis, Ljupčo Todorovski. 2025-10-24. Combining Textual and Structural Information for Premise Selection in Lean. https://arxiv.org/abs/2510.23637

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