代码大模型 / AI 编程

LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QA

Adrián Gude, Roi Santos-Ríos, Francisco Prado-Valiño, Ana Ezquerro, Jesús Vilares

机构 * Universidade da Coruña（科鲁纳大学） ； CITIC Departamento de Ciencias de la Computación y Tecnologías de la Información（CITIC计算机科学与信息技术部门） 纠错

专题命中 代码生成 ：code generation(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted to SemEval 2025. Camera-ready version

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming

Humza Ashraf, Syed Muhammad Danish, Aris Leivadeas, Yazan Otoum, Zeeshan Sattar

机构 * Algoma University（阿尔戈马大学） ； École de Technologie Supérieure (ÉTS)（高等技术学院） ； Ericsson Inc.（爱立信公司） 纠错

专题命中 代码生成 ：code generation(title,abstract)；分类 cs.SE、cs.AI

GUARD:Dual-Agent based Backdoor Defense on Chain-of-Thought in Neural Code Generation

Naizhu Jin, Zhong Li, Tian Zhang, Qingkai Zeng

专题命中 代码生成 ：code generation(title,abstract)；分类 cs.SE

Comments Accepted by SEKE 2025

Synthesizing JSON Schema Transformers

Jack Stanek, Daniel Killough

专题命中 代码生成 ：code generation(abstract)；program synthesis(abstract)；分类 cs.PL

MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time

Peter Phan, Dhruv Agarwal, Kavitha Srinivas, Horst Samulowitz, Pavan Kapanipathi, Andrew McCallum

专题命中 代码生成 ：program synthesis(abstract)；分类 cs.CL、cs.AI、cs.LG

Channel-Wise MLPs Improve the Generalization of Recurrent Convolutional Networks

Nathan Breslow

机构 * Nathan Breslow（独立研究者） 纠错

专题命中 代码生成 ：program synthesis(abstract)；分类 cs.AI、cs.LG

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization

Xinge Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL

Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions

Christophe EL Zeinaty, Wassim Hamidouche, Glenn Herrou, Daniel Menard

机构 * Univ. Rennes, INSA Rennes, CNRS, IETR - UMR 6164（里昂大学） ； KU 6G Research Center, Khalifa University（凯拉特大学6G研究中心） 纠错

专题命中 软件智能体 ：repository(abstract)

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators

Jason Chou, Ao Liu, Yuchi Deng, Zhiying Zeng, Tao Zhang, Haotian Zhu, Jianwei Cai, Yue Mao, Chenchen Zhang, Lingyun Tan, Ziyan Xu, Bohui Zhai, Hengyi Liu, Speed Zhu, Wiggin Zhou, Fengzong Lian

机构 * Hunyuan Team, Tencent（腾讯文心团队） 纠错

专题命中 代码评测 ：code generation(abstract)；分类 cs.SE、cs.CL

Comments Homepage: https://autocodebench.github.io/

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics

Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam

机构 * School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院） 纠错

专题命中 代码评测 ：code generation(abstract)；分类 cs.SE、cs.AI

Comments 8 main pages, 5 figures

A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends

Junjun Jiang, Zengyuan Zuo, Gang Wu, Kui Jiang, Xianming Liu

机构 * School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院，哈尔滨工业大学） 纠错

专题命中 代码评测 ：repository(abstract)

Comments IEEE Transactions on Pattern Analysis and Machine Intelligence

Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code

Muhammad Haseeb

专题命中 仓库级理解 ：code generation(abstract,comments)；分类 cs.SE、cs.AI

Comments 15 pages, 5 figures, research paper on multi-agent LLM systems for code generation

Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience

Vipin Singh, Tianheng Ling, Teodor Chiaburu, Felix Biessmann

机构 * Berlin University of Applied Sciences and Technology（柏林应用科学与技术大学） ； Intelligent Embedded Systems Department, University of Duisburg-Essen（智能嵌入式系统系，杜伊斯堡- Essen大学） ； Einstein Center Digital Future（爱因斯坦数字未来研究中心） 纠错

专题命中 仓库级理解 ：repository(abstract)；分类 cs.AI、cs.LG

Comments 6 pages, 6 figures, accepted at 10th International Conference on Smart and Sustainable Technologies (SpliTech) 2025, GitHub: https://github.com/calgo-lab/resilient-timeseries-evaluation