Recent advancements in code-fluent Large Language Models (LLMs) enabled the research on repository-level code editing. In such tasks, the model navigates and modifies the entire codebase of a project according to request. Hence, such tasks require efficient context retrieval, i.e., navigating vast codebases to gather relevant context. Despite the recognized importance of context retrieval, existing studies tend to approach repository-level coding tasks in an end-to-end manner, rendering the impact of individual components within these complicated systems unclear. In this work, we decouple the task of context retrieval from the other components of the repository-level code editing pipelines. We lay the groundwork to define the strengths and weaknesses of this component and the role that reasoning plays in it by conducting experiments that focus solely on context retrieval. We conclude that while the reasoning helps to improve the precision of the gathered context, it still lacks the ability to identify its sufficiency. We also outline the ultimate role of the specialized tools in the process of context gathering. The code supplementing this paper is available at https://github.com/JetBrains-Research/ai-agents-code-editing.
Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification
International Digital Economy Academy (IDEA)（国际数字经济学院）；School of Electronic and Computer Engineering, Peking University（电子与计算机工程学院，北京大学）；Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）
FastCoder: Accelerating Repository-level Code Generation via Efficient Retrieval and Verification
1 State Key Laboratory of Complex \& Critical Software Environment, School of Computer Science；Engineering Beihang University, China；2 School of Software, Beihang University, China；3 Peking University, China
MedREK: Retrieval-Based Editing for Medical LLMs with Key-Aware Prompts
City University of Hong Kong（香港城市大学）；Harvard Medical School（哈佛医学院）；Institute of Automation, CAS（中国科学院自动化研究所）；Columbia University（哥伦比亚大学）；Ohio State University（俄亥俄州立大学）；Westlake University（西湖大学）；Nanjing University（南京大学）
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Arizona State University（亚利桑那州立大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Université de Montréal（蒙特利尔大学）；National Research Council Canada（加拿大国家研究委员会）
Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures
Yildiz Technical University, Dept. of Computer Eng.（Yildiz技术大学，计算机工程系）；Yildiz Technical University, Department of Computer Engineering（Yildiz技术大学，计算机工程系）
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
Griffith University（格里菲斯大学）；University of Bremen（不来梅大学）；Ho Chi Minh City University of Technology（胡志明市技术大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Hanoi University of Science and Technology（河内科技大学）；The University of Queensland（昆士兰大学）
Hybrid Retrieval-Augmented Generation Agent for Trustworthy Legal Question Answering in Judicial Forensics
Department of Electronic Engineering, City University of Hong Kong(DongGuan)（香港城市大学（东莞）电子工程系）；Department of Electronic Engineering, City University of Hong Kong（香港城市大学电子工程系）
Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. Model editors make local updates to the behavior of base (pre-trained) models to inject updated knowledge or correct undesirable behaviors. Existing model editors have shown promise, but also suffer from insufficient expressiveness: they struggle to accurately model an edit's intended scope (examples affected by the edit), leading to inaccurate predictions for test inputs loosely related to the edit, and they often fail altogether after many edits. As a higher-capacity alternativ…
Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition
State Key Laboratory for Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室，南京大学，中国）；National Institute of Healthcare Data Science, Nanjing University, China（医疗数据科学国家研究院，南京大学，中国）
MILA - Quebec AI Institute（MILA - 魁北克人工智能研究所）；Microsoft（微软公司）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Google DeepMind（谷歌DeepMind）
Context-Robust Knowledge Editing for Language Models
Graduate School of Data Science, Seoul National University（首尔国立大学数据科学研究生院）；School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology（全州科学技术院电子工程与计算机科学学院）
Beyond Repository Boundaries: Cross-Repository Graph Retrieval for Code Generation
Quantum AI & Cyber Security Institute, FPT Corporation（量子人工智能与网络安全研究所，FPT公司）；Hanoi University of Science and Technology（河内科学技术大学）；Rutgers University（罗格斯大学）；The University of Melbourne（墨尔本大学）；Center for AI Research, VinUniversity（VinUniversity人工智能研究中心）
Pull Requests as a Training Signal for Repo-Level Code Editing
King's College London, UK（伦敦国王学院）；Chinese University of Hong Kong, HK（香港中文大学）；National University of Singapore, SG（新加坡国立大学）；Microsoft Research Asia, CN（微软亚洲研究院）；The Alan Turing Institute, UK（艾伦·图灵研究所）
