BDLF-Qwen3: Enhanced Cross-Architecture Binary Function Similarity Detection Through Binary Dynamic Layer Fusion
Yuanda Wang, Ji Zhou, Xinhui Han, Chao Zhang
Abstract
Binary code analysis is essential for software security across various instruction set architectures. Cross-architecture binary function similarity detection faces significant challenges due to substantial differences in instruction sets and architectural conventions. Existing approaches struggle to capture relationships between code abstraction levels, and lack comprehensive cross-architecture datasets for effective evaluation. Inspired by human cognitive processes of dynamically integrating multi-level information, we propose Binary Dynamic Layer Fusion (BDLF), a novel neural architecture that enhances cross-architecture similarity detection through adaptive layer-wise feature integration. BDLF leverages Qwen3
BibTeX
@inproceedings{aaai2026_bdlfqwen3enhance,
title = {BDLF-Qwen3: Enhanced Cross-Architecture Binary Function Similarity Detection Through Binary Dynamic Layer Fusion},
author = {Yuanda Wang and Ji Zhou and Xinhui Han and Chao Zhang},
booktitle = {AAAI 2026},
year = {2026}
}