{"total":28,"rows":[{"id":"2311.09476","title":"ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems","created":"2023-11-16","cited_by_count":265,"area":"rag"},{"id":"2501.09136","title":"Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG","created":"2025-01-15","cited_by_count":249,"area":"rag"},{"id":"2108.11601","title":"Retrieval Augmented Code Generation and Summarization","created":"2021-08-26","cited_by_count":149,"area":"rag"},{"id":"2203.07722","title":"ReACC: A Retrieval-Augmented Code Completion Framework","created":"2022-03-15","cited_by_count":119,"area":"rag"},{"id":"2402.18150","title":"Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation","created":"2024-02-28","cited_by_count":19,"area":"rag"},{"id":"2501.13742","title":"An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities","created":"2025-01-23","cited_by_count":18,"area":"rag"},{"id":"2504.16429","title":"Give LLMs a Security Course: Securing Retrieval-Augmented Code Generation via Knowledge Injection","created":"2025-04-23","cited_by_count":16,"area":"rag"},{"id":"2504.00573","title":"Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Models","created":"2025-04-01","cited_by_count":13,"area":"rag"},{"id":"2409.15895","title":"Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation","created":"2024-09-24","cited_by_count":10,"area":"rag"},{"id":"2404.01554","title":"FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion","created":"2024-04-02","cited_by_count":9,"area":"rag"},{"id":"2502.03233","title":"Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generation","created":"2025-02-05","cited_by_count":9,"area":"rag"},{"id":"2506.11591","title":"Retrieval-Augmented Code Review Comment Generation","created":"2025-06-13","cited_by_count":6,"area":"rag"},{"id":"2410.16229","title":"Building A Coding Assistant via the Retrieval-Augmented Language Model","created":"2024-10-21","cited_by_count":5,"area":"rag"},{"id":"2410.20056","title":"Multi-Field Adaptive Retrieval","created":"2024-10-26","cited_by_count":4,"area":"rag"},{"id":"2505.18458","title":"A Survey of LLM $\\times$ DATA","created":"2025-05-24","cited_by_count":4,"area":"rag"},{"id":"2506.20081","title":"SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization","created":"2025-06-25","cited_by_count":4,"area":"rag"},{"id":"2512.05411","title":"A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systems","created":"2025-12-05","cited_by_count":3,"area":"rag"},{"id":"2512.21681","title":"Exploring the Security Threats of Retriever Backdoors in Retrieval-Augmented Code Generation","created":"2025-12-25","cited_by_count":1,"area":"rag"},{"id":"2606.28367","title":"Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?","created":"2026-06-14","cited_by_count":1,"area":"rag"},{"id":"2506.22742","title":"RAILS: Retrieval-Augmented Intelligence for Learning Software Development","created":"2025-06-28","cited_by_count":0,"area":"rag"},{"id":"2508.10068","title":"SaraCoder: Orchestrating Semantic and Structural Cues for Resource-Optimized Repository-Level Code Completion","created":"2025-08-13","cited_by_count":0,"area":"rag"},{"id":"2601.07861","title":"EmbeddingRWKV: State-Centric Retrieval with Reusable States","created":"2026-01-10","cited_by_count":0,"area":"rag"},{"id":"2604.15663","title":"CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval","created":"2026-04-17","cited_by_count":0,"area":"rag"},{"id":"2605.04763","title":"How Does Chunking Affect Retrieval-Augmented Code Completion? A Controlled Empirical Study","created":"2026-05-06","cited_by_count":0,"area":"rag"},{"id":"2605.18561","title":"Improving BM25 Code Retrieval Under Fixed Generic Tokenization: Adaptive q-Log Odds as a Drop-In BM25 Fix","created":"2026-05-18","cited_by_count":0,"area":"rag"},{"id":"2606.19988","title":"Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning","created":"2026-06-18","cited_by_count":0,"area":"rag"},{"id":"2609.01865","title":"ExecRetrieval: Measuring the Functional-Correctness Gap in Code-Embedding Retrieval","created":"2026-09-01","cited_by_count":0,"area":"rag"},{"id":"2610.11727","title":"MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning","created":"2026-10-08","cited_by_count":0,"area":"rag"}],"next_cursor":null}