Papers › Learning Structure-Aware Representations of Dependent Types

Learning Structure-Aware Representations of Dependent Types

3 Feb 2024arXiv:2402.02104archive 2025-07-28

Konstantinos Kogkalidis, Orestis Melkonian, Jean-Philippe Bernardy

Agda is a dependently-typed programming language and a proof assistant, pivotal in proof formalization and programming language theory. This paper extends the Agda ecosystem into machine learning territory, and, vice versa, makes Agda-related resources available to machine learning practitioners. We introduce and release a novel dataset of Agda program-proofs that is elaborate and extensive enough to support various machine learning applications -- the first of its kind. Leveraging the dataset's ultra-high resolution, which details proof states at the sub-type level, we propose a novel neural architecture targeted at faithfully representing dependently-typed programs on the basis of structural rather than nominal principles. We instantiate and evaluate our architecture in a premise selection setup, where it achieves promising initial results, surpassing strong baselines.

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