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.
Identifier-Free Code Embedding Models for Scalable Search
The paper introduces Identifier-Free Code Embedding Models designed to improve function association for binary reverse engineering. It formalizes the bidirectional association problem between source code and decompiled, stripped code, and evaluates embedding models on this task. By fine‑tuning a Qwen3‑Embedding model with contrastive learning, the authors achieve significant performance gains over existing baselines and demonstrate generalization to a constant‑algorithm association task.
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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.
arXiv AI
Aug 19
LSem2Vec: A Simple yet Effective Two-Stage Approach for Source Code Embedding
The paper introduces LSem2Vec, a two‑stage method that first uses a large language model to extract source code semantics and then applies a sentence embedding model to produce vector representations. This approach removes the need for task‑specific training or fine‑tuning, addressing errors in LLM outputs. Experiments on three datasets across multiple programming languages show that LSem2Vec outperforms five state‑of‑the‑art unsupervised methods.
arXiv AI
Sep 4
Synthetic Semantic Supervision for Contrastive Code Representation Learning in Small Transformers: An Empirical Study
The paper investigates using synthetic natural-language descriptions to contrastively pretrain small transformer encoders for code representation. By pairing generated descriptions with code in a dual-encoder setup during training and discarding them at inference, the authors achieve significant improvements over traditional pretraining baselines on most evaluated tasks. When fine‑tuned, these models match or surpass much larger zero‑shot models and remain competitive with execution‑aware supervision, indicating a scalable alternative for code embeddings.
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.
arXiv AI
Aug 18
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.
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.
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.
arXiv Machine Learning
Jun 29
Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval
arXiv:2606. 27401v1 Announce Type: cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse.
arXiv AI
Jun 16
AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion
arXiv:2601. 19697v2 Announce Type: replace-cross Abstract: Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context and domain knowledge.
arXiv Machine Learning
Sep 16
Type-IV Code Clone Detection via Layer-Wise Non-Contrastive Representation Learning
The paper introduces LWVIC4Code, a non‑contrastive, layer‑wise representation learning method for detecting Type‑IV code clones—semantically equivalent fragments that differ syntactically. It builds on the VICReg framework, adding cross‑layer consistency regularization and depth‑dependent weighting to refine semantic information across transformer layers. Experiments on Python and multi‑language datasets show that LWVIC4Code matches or outperforms contrastive baselines and zero‑shot large language models, generalizing well to Java and C# without requiring negative samples.
arXiv Computation and Language
Oct 16
Enhancing Knowledge Density via Hierarchical Context Pruning for Retrieval-Augmented Repository-Level Code Completion
The paper introduces SaraCoder, a retrieval‑augmented framework that improves knowledge density for repository‑level code completion by pruning redundant context and selectively injecting cross‑file dependency definitions within a fixed token budget. It employs a Hierarchical Feature Optimization module to reduce an over‑complete candidate pool in four stages, freeing tokens for an External‑Aware Identifier Disambiguator. Experiments on CrossCodeEval and RepoEval‑Updated demonstrate that SaraCoder outperforms strong RAG baselines such as GraphCoder and Repocoder across multiple code LLMs without increasing token usage, and achieves synergistic gains when combined with other cross‑file methods.