无需标识符的代码嵌入模型，实现源码与反编译代码的双向匹配。
Identifier-Free Code Embedding Models for Scalable Search
- 基于对比学习微调Qwen3-Embedding，实现源码与去标识符反编译代码的双向映射。
- 在所有基准测试中显著优于现有模型，跨算法泛化能力突出。
- 适合大规模二进制逆向工程中的函数关联任务，提升自动化分析效率。
函数关联对二进制逆向工程具有重要价值。现有搜索工具虽可规模化执行关联，但未充分利用AI搜索的全部潜力。先前研究探索了特定代码表示间的嵌入模型，但缺乏对源代码与经标准预处理的去标识符反编译代码之间双向关联的支持。为此，我们形式化该函数关联问题，并评估嵌入模型在此任务上的双向关联能力。为提升性能，我们采用对比学习对Qwen3-Embedding模型进行微调。结果表明，新模型在所有函数关联基准上均显著超越其他模型，并在未显式训练过的常量算法关联任务中表现出良好泛化能力。
原文摘要 · Abstract (English)
Function association is a useful process for binary reverse engineers. Search tools exist to perform association at scale, but they do not utilize the full range of capabilities that AI-enabled search provides. Prior work has explored the development of embedding models for association between certain reverse engineering code representations, but that work does not cover bidirectional association between source code and decompiled, stripped code with standard preprocessing requirements. To bridge this gap, we formalize this function association problem and evaluate the extent to which embedding models can bidirectionally associate between these two representations. To improve model performance at this task, we fine-tune a Qwen3-Embedding model with contrastive learning. We find that our new model outperforms other models on all function association baselines by a substantial margin and generalizes to a constant-algorithm association task it is not explicitly trained on.
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