arXiv Machine Learning

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

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.

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

arXiv AI

Aug 13

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

arXiv AI

1d 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

Hugging Face Trending Papers

Jul 14

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.

llms rag benchmarks safety

arXiv Machine Learning

Jul 14

Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Understanding

arXiv:2505. 13353v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long code context or rely on pattern matching shortcuts remains unclear.

By Adam \v{S}torek, Mukur Gupta, Samira Hajizadeh, Prashast Srivastava, Suman Jana

llms benchmarks

Hugging Face Trending Papers

Aug 5

OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling

Context lengths of language models (LMs) have dramatically increased, driven by the demands for in-context learning, self-improvement, and long-horizon agentic workflows. Existing long-context corpora, however, are dominated by books, academic articles, and code repositories, which are finite resources and often scarce in long-distance dependencies.

llms agents fine-tuning benchmarks

arXiv AI

Aug 6

OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling

arXiv:2608. 05141v1 Announce Type: new Abstract: Context lengths of language models (LMs) have dramatically increased, driven by the demands for in-context learning, self-improvement, and long-horizon agentic workflows.

By Indraneil Paul, Falko Helm, Goran Glava\v{s}, Iryna Gurevych

llms agents fine-tuning benchmarks

arXiv AI

Jul 21

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.

By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro

llms agents benchmarks

arXiv AI

Jun 8

NTILC: Neural Tool Invocation via Learned Compression

arXiv:2606. 06566v1 Announce Type: cross Abstract: Agentic tool-calling language models depend on large registries of callable APIs, functions, and local actions.

By Andrew Krikorian, Yayuan Li, Jason J. Corso

llms rag agents efficiency

arXiv AI

Jul 7

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection

arXiv:2607. 03350v1 Announce Type: cross Abstract: Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories such as PyPI.

By Hang Gao, Xiaoyu Chen, Baoquan Cui, Zhen Tang, Peng Qiao, Fengge Wu, Jian Zhang

llms benchmarks

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