arXiv AI

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 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 AI

Jun 8

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

arXiv:2506. 11066v3 Announce Type: replace-cross Abstract: Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging.

By Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray

benchmarks

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.

By Leonardo Venuta, Francesco Tosoni, Paolo Ferragina

llms benchmarks

arXiv AI

Jul 10

ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation

arXiv:2607. 08691v1 Announce Type: cross Abstract: Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conventions.

By QiHong Chen, Aaron Imani, Iftekhar Ahmed

agents

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

Jun 9

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.

By Marco A. Valenzuela-Esc\'arcega, Enrique Noriega-Atala, Gus Hahn-Powell, Clayton T. Morrison, Mihai Surdeanu

llms

arXiv AI

Jul 29

Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation

arXiv:2607. 24884v1 Announce Type: cross Abstract: Repository-level code generation relies on heterogeneous evidence whose relevance, compatibility, and completeness are inherently uncertain.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

llms rag

arXiv AI

Jul 28

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases

arXiv:2408. 03910v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories.

By Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Shieh, Wenmeng Zhou

llms agents benchmarks

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

arXiv AI

Jul 13

Practical Source Code Recovery from Binary Functions Using Anchor-Based Retrieval and LLM Reasoning

arXiv:2607. 09452v1 Announce Type: cross Abstract: We present a practical pipeline for recovering source code from stripped binary functions by combining reverse engineering, anchor-based source code retrieval, and large language model reasoning.

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

llms

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

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

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