arXiv Machine Learning August 4, 2026 By Samuel Valenzuela, Johannes Kinder

Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context

Read the original on arXiv Machine Learning →

arXiv:2608. 02084v1 Announce Type: cross Abstract: Binary function embedding models are trained to encode the semantics of binary code in such a way that they can be generalized to a variety of reverse engineering tasks, such as binary code search, vulnerability detection, or malware classification.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 13

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

By Charles E. Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms rag agents benchmarks safety

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6d ago

Instruction Alignment for Binary Code Representation Learning

arXiv:2608. 11766v1 Announce Type: cross Abstract: Binary code representation learning is a fundamental problem in software security and reverse engineering.

By Huaijin Wang, Shuai Wang

rag safety

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23h ago

LSem2Vec: A Simple yet Effective Two-Stage Approach for Source Code Embedding

arXiv:2409. 14644v4 Announce Type: replace-cross Abstract: The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embeddings playing a crucial role in tasks such as source code clone detection and source code clustering.

By Zixiang Xian, Chenhui Cui, Rubing Huang, Chunrong Fang, Zhenyu Chen

llms rag fine-tuning benchmarks

arXiv AI

Jul 15

Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs

arXiv:2607. 12273v1 Announce Type: cross Abstract: As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lead to severe functional, security, or safety consequences.

By Xiaoning Ren, Yinxing Xue, Lei Ma, Yuheng Huang

llms rag benchmarks safety

arXiv AI

Jul 3

XSearch: Explainable Code Search via Concept-to-Code Alignment

arXiv:2605. 16046v2 Announce Type: replace-cross Abstract: Semantic code search has been widely adopted in both academia and industry.

By Yiming Liu, Ruofan Liu, Yun Lin, Zicong Zhang, Weiyu Kong, Pengnian Qi, Xiao Cheng, Weinan Zhang, Qianxiang Wang, Linpeng Huang

rag benchmarks safety

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Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs

As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lead to severe functional, security, or safety consequences. Reliable automation, therefore, demands the ability to distinguish between confident, well-supported predictions and stochastic guessing.

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Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

Instruction Alignment for Binary Code Representation Learning

LSem2Vec: A Simple yet Effective Two-Stage Approach for Source Code Embedding

Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs

XSearch: Explainable Code Search via Concept-to-Code Alignment

Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs