arXiv AI

Learning Adaptive Parallel Execution for Efficient Code Localization

arXiv:2601. 19568v2 Announce Type: replace Abstract: Code localization constitutes a key bottleneck in automated software development pipelines.

arXiv Machine Learning

2d ago

A Configuration-First Framework for Reproducible, Low-Code Localization

arXiv:2510. 25692v4 Announce Type: replace-cross Abstract: As machine learning (ML) increasingly underpins critical applications, credible, comparable, and repeatable experimental results become more important.

By Tim Strnad (Jo\v{z}ef Stefan Institute, Slovenia), Bla\v{z} Bertalani\v{c} (Jo\v{z}ef Stefan Institute, Slovenia), Carolina Fortuna (Jo\v{z}ef Stefan Institute, Slovenia)

arXiv AI

Jun 11

Exploration Structure in LLM Agents for Multi-File Change Localization

arXiv:2606. 11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue.

By Akeela Darryl Fattha, Kia Ying Chua, Lingxiao Jiang, Laura Wynter

llms agents benchmarks

arXiv AI

Jun 3

FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement

arXiv:2606. 03852v1 Announce Type: cross Abstract: Large language models often generate code with bugs.

By Yinsheng Yao, Hongxiang Zhang, Weixi Tong, Tianyi Zhang

llms

arXiv AI

Jul 23

PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

arXiv:2607. 19653v1 Announce Type: cross Abstract: Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases.

By Ryan Deng, Yuanzhe Liu, Bastian Lipka, Yao Ma, Xuhao Chen, Tim Kaler, Jatin Ganhotra

llms agents benchmarks

Hugging Face Trending Papers

Jun 10

Exploration Structure in LLM Agents for Multi-File Change Localization

Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repositories linearly, that is, visiting one directory or file per step.

llms agents benchmarks

arXiv AI

Jun 30

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.

By Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan

llms agents benchmarks data-engineering

arXiv Machine Learning

Aug 6

From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.

By Usha Shrestha, Dmitry Ignatov, Radu Timofte

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 3

BLAgent: Agentic RAG for File-Level Bug Localization

arXiv:2605. 17965v2 Announce Type: replace-cross Abstract: Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause analysis, triage, and automated program repair (APR).

By Md Afif Al Mamun, Gias Uddin

llms rag agents

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

arXiv AI

Jul 7

SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery

arXiv:2607. 02807v1 Announce Type: new Abstract: Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems.

By Yuvraj Virk, Zack Edds, Chunqiu Steven Xia, Lingming Zhang

llms agents benchmarks

arXiv AI

Jul 2

Libra: Training the Environment for Agentic Information Retrieval

arXiv:2607. 00016v1 Announce Type: cross Abstract: Information localization within massive repositories is a cornerstone of agentic LLM systems.

By Xuan Zhao, Andy Chiu, Gengyu Wang

llms agents 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

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