Instruction Alignment Enhances Binary Code Representation Learning

A recent paper published on arXiv (2608.11766) suggests utilizing knowledge of instruction alignment to enhance the learning of binary code representations, which is crucial for software security and reverse engineering. Current techniques mainly focus on function-level embeddings that reflect broad semantic connections between binary functions, neglecting the finer instruction-level details. This oversight overlooks important supervision signals from compiler debug information that could improve the accuracy and interpretability of binary code representations. The findings indicate that models fine-tuned for function-level binary code similarity show significantly improved instruction alignment compared to their pre-trained counterparts, highlighting a strong link between instruction alignment and the quality of function-level embeddings. To build on this insight, the authors propose a training strategy that integrates instruction alignment to boost representation learning. This research fills a vital gap in the field, potentially advancing the development of more accurate and interpretable binary code analysis tools. The paper is classified as a cross-type announcement and can be accessed via the provided arXiv URL.