Instruction Alignment for Binary Code Representation Learning

2026-08-12

AI Summary

This work addresses a critical limitation in existing binary code representation learning methods, which typically overlook instruction-level alignment information and thus fail to effectively leverage fine-grained supervisory signals from compiler debug information. To overcome this, the paper introduces the first approach that explicitly models instruction alignment as an auxiliary training objective. By employing multi-task learning, the method jointly optimizes function-level embeddings and instruction alignment, using debug information to construct precise alignment supervision signals. Experimental results demonstrate that this approach significantly improves accuracy in binary code similarity retrieval, enhances the model’s discriminative power and semantic understanding, and reveals a strong correlation between instruction alignment and the quality of function representations. Consequently, it establishes a more interpretable and precise framework for binary code representation learning.

Abstract

Binary code representation learning is a fundamental problem in software security and reverse engineering. Existing methods mainly learn function-level embeddings that capture coarse-grained semantic relationships between binary functions, but they largely ignore fine-grained instruction-level correspondences. This limitation misses valuable supervision signals available from compiler debug information, which can support the learning of more accurate and interpretable binary code representations. We propose to leverage instruction alignment knowledge to further improve binary code representation learning. Our preliminary study reveals that models finetuned for function-level binary code similarity exhibit substantially better instruction alignment than their pre-trained model, suggesting a strong correlation between instruction alignment and function-level embedding quality. Motivated by this observation, we design a training approach that explicitly incorporates instruction alignment as an auxiliary training objective. Our experiments show that instruction alignment training improves retrieval accuracy and provides more discriminative signal for the model's similarity judgments.

Problem

• binary code representation learning
• instruction alignment
• function-level embedding
• compiler debug information
• fine-grained correspondence

Innovation

• instruction alignment
• binary code representation learning
• function-level embedding
• debug information
• auxiliary training objective