一句话：提出利用源端知识构建AI模型，以辅助人类理解二进制代码并设计理解度评估指标。
二进制代码理解在逆向工程、恶意软件分析与编译优化中至关重要，但二进制缺乏语义信息，人类理解困难。该研究提出构建人工智能模型以辅助人类理解二进制代码，其核心是从大规模源代码语料中引入领域知识（如变量名、注释）来学习可泛化的二进制代码表示。作者还计划研究评估指标，并通过人类理解研究来检验模型性能，从而推动二进制理解从纯人工分析走向AI辅助。
Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary code has limited semantic information, which makes it challenging for human comprehension. At the same time, compiling source to binary code, or transpiling among different programming languages (PLs) can provide a way to introduce external knowledge into binary comprehension. We propose to develop Artificial Intelligence (AI) models that aid human comprehension of binary code. Specifically, we propose to incorporate domain knowledge from large corpora of source code (e.g., variable names, comments) to build AI models that capture a generalizable representation of binary code. Lastly, we will investigate metrics to assess the performance of models that apply to binary code by using human studies of comprehension.