arXiv AI August 19, 2026 By Zixiang Xian, Chenhui Cui, Rubing Huang, Chunrong Fang, Zhenyu Chen

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.

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

arXiv Machine Learning

Aug 11

LLM-Based Embeddings for Program Analysis and Optimization

arXiv:2608. 07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs.

By Calvin Higgins, Marco Alvarez

llms rag benchmarks

arXiv AI

1d ago

Efficient Code Embeddings from Code Generation Models

arXiv:2508. 21290v2 Announce Type: replace-cross Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages.

By Daria Kryvosheieva, Saba Sturua, Michael G\"unther, Han Xiao

rag benchmarks

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

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

arXiv Machine Learning

Aug 4

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.

By Samuel Valenzuela, Johannes Kinder

rag benchmarks

arXiv AI

Jul 24

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

arXiv:2512. 10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data.

By Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

llms rag

Related stories

LLM-Based Embeddings for Program Analysis and Optimization

Efficient Code Embeddings from Code Generation Models

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

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

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

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit