Efficient Code Embeddings from Code Generation Models

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.

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.

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.

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.

Text and code embeddings by contrastive pre-training

SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation

arXiv:2606. 13647v1 Announce Type: cross Abstract: We introduce SkMTEB, the first comprehensive MTEB-style text embedding benchmark for Slovak, a low-resource West Slavic language, comprising 31 datasets across 7 task types -- nearly 4$\times$ the depth of existing multilingual benchmark coverage for Slovak.

CODEBLOCK: Learning to Supervise Code at the Right Granularity

arXiv:2606. 18286v1 Announce Type: new Abstract: Supervised fine-tuning of code LLMs typically applies uniform cross-entropy loss to all response tokens, implicitly assuming that every token provides equally useful learning signal.

DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search

State-of-the-art retrieval models increasingly rely on closed training data, creating a reproducibility gap. We present an open end-to-end recipe for training retrieval models and study how English supervision transfers to multilingual retrieval through translate-train.

Bidirectional Small-Granularity Search between Code and Text

arXiv:2606. 07519v1 Announce Type: cross Abstract: We introduce the novel task of bidirectional small-granularity search between code and text, where the queries are small snippets of text or code and the results are also small fragments of the opposite modality, i.

ARC-Encoder: learning compressed text representations for large language models

arXiv:2510. 20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs.

Beyond the Autoregressive Horizon: A Comprehensive Survey of Diffusion Models, World Modelling, and State Space Models for Code

arXiv:2606. 23690v1 Announce Type: cross Abstract: Autoregressive (AR) language models have driven significant progress in automated software engineering, enabling powerful code generation and assistance systems.

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.