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.
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.
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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.
arXiv AI
Sep 2
Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation
arXiv:2609.01601v1 Announce Type: cross Abstract: The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repo...
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
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.
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
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.
arXiv AI
Sep 15
CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback
CHAI for LLMs is a framework that improves large language models’ performance on code‑mixed translation tasks by using LLMs as annotators to create preference data, applying reinforcement learning from AI feedback, incorporating LLM‑generated domain knowledge for iterative refinement, and evaluating on real‑world datasets. The approach yields a 68.45% average win rate over state‑of‑the‑art open‑source models in human‑adjudicated tests. It demonstrates a scalable method to enhance code‑mixed language understanding in open‑source LLMs.
arXiv Machine Learning
Sep 1
SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
SpIDER is a dense retrieval method that combines LLM reasoning with graph-based exploration of codebases to locate relevant functions, classes, or files for user queries. It introduces a graph-structured benchmark, SpIDER-Bench, covering multiple programming languages and demonstrates significant recall improvements over traditional BM25 and dense approaches. The method’s graph-based candidate expansion provides auditable structural reasons for each retrieved item while keeping the retrieval budget fixed.
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.
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.
arXiv AI
6d ago
ORCA: Evaluating LLMs on Data Science Code Translation
ORCA is a new benchmark for evaluating large language models on Data Science Code Translation (DSCT), comprising two settings: ORCA-MAIN with 1,600 grounding-level tasks across data querying, manipulation, and deep learning, and ORCA-PROJECT with 200 full-project translation tasks across seven data‑science task types. Each task includes reference translations and test cases to verify functional equivalence, and a multi‑stage quality verification process ensures task correctness. Experiments show that even state‑of‑the‑art LLMs perform poorly on DSCT, with Claude‑Opus‑4.6 achieving only 56.92% success on ORCA‑MAIN and 33.67% on ORCA‑PROJECT, while an intent‑augmented approach improves success rates by 4.80% and 5.33% respectively.