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.
@article{arxiv.2510.23637,
title = {Combining Textual and Structural Information for Premise Selection in Lean},
author = {Job Petrovčič and David Eliecer Narvaez Denis and Ljupčo Todorovski},
journal= {arXiv preprint arXiv:2510.23637},
year = {2025}
}