一句话：提出将指令对齐作为辅助训练目标，利用编译器调试信息提升二进制代码表示学习，改善函数级相似性检索与判别性。
现有二进制代码表示学习多学习函数级嵌入，能捕捉函数间粗粒度语义关系，却忽略细粒度指令级对应，也未充分利用编译器调试信息中的监督信号。本文先发现：在函数级二进制代码相似性任务上微调后的模型，其指令对齐能力显著优于预训练模型，说明指令对齐与函数级嵌入质量强相关。受此启发，作者设计一种训练方法，将指令对齐显式作为辅助训练目标，与函数级表示学习联合优化。实验表明，该训练方式提高了检索准确率，并使模型在相似性判断上具有更强的判别性。
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.