Neural architectures for resolving references in program code
Abstract
Resolving and rewriting references is fundamental in programming languages. Motivated by a real-world decompilation task, we abstract reference rewriting into the problems of direct and indirect indexing by permutation. We create synthetic benchmarks for these tasks and show that well-known sequence-to-sequence machine learning architectures are struggling on these benchmarks. We introduce new sequence-to-sequence architectures for both problems. Our measurements show that our architectures outperform the baselines in both robustness and scalability: our models can handle examples that are ten times longer compared to the best baseline. We measure the impact of our architecture in the real-world task of decompiling switch statements, which has an indexing subtask. According to our measurements, the extended model decreases the error rate by 42%. Multiple ablation studies show that all components of our architectures are essential.
Cite
@article{arxiv.2604.14073,
title = {Neural architectures for resolving references in program code},
author = {Gergő Szalay and Gergely Zsolt Kovács and Sándor Teleki and Balázs Pintér and Tibor Gregorics},
journal= {arXiv preprint arXiv:2604.14073},
year = {2026}
}