代码大模型 / AI 编程

CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation

Xinchen Wang, Pengfei Gao, Chao Peng, Ruida Hu, Cuiyun Gao

机构 * Harbin Institute of Technology（哈尔滨工业大学） ； ByteDance（字节跳动） 纠错

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

Reasoning Distillation and Structural Alignment for Improved Code Generation

Amir Jalilifard, Anderson de Rezende Rocha, Marcos Medeiros Raimundo

机构 * Universidade Estadual de Campinas（坎皮纳斯州立大学） 纠错

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

VeriGRAG: Enhancing LLM-Based Verilog Code Generation with Structure-Aware Soft Prompts

Jiayu Zhao, Song Chen

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

Comments 9 pages, 5 figures

SEER: Enhancing Chain-of-Thought Code Generation through Self-Exploring Deep Reasoning

Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Michael R. Lyu

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

Comments The paper was completed in Feb. 2025, submitted to ICSE 2026 in Mar. 2025, received a major revision in Jun. 2025, and was finally accepted in Oct. 2025

QuanBench: Benchmarking Quantum Code Generation with Large Language Models

Xiaoyu Guo, Minggu Wang, Jianjun Zhao

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

Comments This paper was accepted by ASE2025

Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy

Weixu Zhu, Marco Dorigo, Mary Katherine Heinrich

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

Comments This abstract was accepted to and presented at the "Multi-Agent Cooperative Systems and Swarm Robotics in the Era of Generative AI" (MACRAI) workshop at the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)

Self-planning Code Generation with Large Language Models

Xue Jiang, Yihong Dong, Lecheng Wang, Zheng Fang, Qiwei Shang, Ge Li, Zhi Jin, Wenpin Jiao

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

Comments Accepted by TOSEM

Matricial Free Energy as a Gaussianizing Regularizer: Enhancing Autoencoders for Gaussian Code Generation

Rishi Sonthalia, Raj Rao Nadakuditi

机构 * Department of Mathematics, Boston College（数学系，波士顿学院） ； EECS, University of Michigan（电子工程与计算机科学系，密歇根大学） 纠错

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

LLM-VeriPPA: Power, Performance, and Area Optimization aware Verilog Code Generation with Large Language Models

Kiran Thorat, Jiahui Zhao, Yaotian Liu, Amit Hasan, Hongwu Peng, Xi Xie, Bin Lei, Caiwen Ding

机构 * Arizona State University（亚利桑那州立大学） 纠错

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

ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents

Yilei Jiang, Yaozhi Zheng, Yuxuan Wan, Jiaming Han, Qunzhong Wang, Michael R. Lyu, Xiangyu Yue

机构 * CUHK（香港中文大学） ； MMLab（多模态实验室） ； ARISE Lab（ARISE实验室） 纠错

专题命中 代码生成 ：code generation(title)

Comments ScreenCoder-v2

Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models

Dayan Pan, Zhaoyang Fu, Jingyuan Wang, Xiao Han, Yue Zhu, Xiangyu Zhao

机构 * SCSE, Beihang University（北京航空航天大学系统工程学院） ； ERC of ACAT, MOE（教育部人工智能教育联合体） ； City University of Hong Kong（香港城市大学） ； Huawei Technologies Ltd.（华为技术有限公司） ； Key Lab of DIM, MIIT（信息通信技术重点实验室） ； SEM, Beihang University（北京航空航天大学电子机械学院） ； Zhejiang University of Technology（浙江工业大学） 纠错

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

Comments Accepted by CIKM' 25

Hey Pentti, We Did It Again!: Differentiable vector-symbolic types that prove polynomial termination

Eilene Tomkins-Flanagan, Connor Hanley, Mary A. Kelly

机构 * Department of Cognitive Science, Carleton University（认知科学系，卡尔顿大学） 纠错

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

Journal ref Tomkins Flanagan, E., Hanley, C., & Kelly, M. A. (2025, July). Paper presented at MathPsych / ICCM 2025. Via mathpsych.org/presentation/1997

Declarative Techniques for NL Queries over Heterogeneous Data

Elham Khabiri, Jeffrey O. Kephart, Fenno F. Heath, Srideepika Jayaraman, Fateh A. Tipu, Yingjie Li, Dhruv Shah, Achille Fokoue, Anu Bhamidipaty

机构 * IBM Research（IBM研究院） 纠错

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

Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization

Changsheng Wang, Xin Chen, Sijia Liu, Ke Ding

机构 * The OPTML Lab, Michigan State University（密歇根州立大学OPTML实验室） ； Intel（英特尔） ； IBM Research（IBM研究） 纠错

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

Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?

Xiangyang Li, Xiaopeng Li, Kuicai Dong, Quanhu Zhang, Rongju Ruan, Xinyi Dai, Xiaoshuang Liu, Shengchun Xu, Yasheng Wang, Ruiming Tang

机构 * Huawei Noah’s Ark Lab（华为诺亚实验室） 纠错

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

Comments EMNLP 2025 Findings

STARK: Strategic Team of Agents for Refining Kernels

Juncheng Dong, Yang Yang, Tao Liu, Yang Wang, Feng Qi, Vahid Tarokh, Kaushik Rangadurai, Shuang Yang

机构 * Meta Ranking AI Research（Meta 排名人工智能研究） ； Duke University（杜克大学） 纠错

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

Tutoring LLM into a Better CUDA Optimizer

Matyáš Brabec, Jiří Klepl, Michal Töpfer, Martin Kruliš

机构 * Charles University（查理大学） 纠错

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

Comments This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Euro-Par 2025: Parallel Processing, Part II, and is available online at https://doi.org/10.1007/978-3-031-99857-7_18

Journal ref Euro-Par 2025: Parallel Processing. Euro-Par 2025. Lecture Notes in Computer Science, vol 15901. Springer, Cham

Towards Automated Verification of LLM-Synthesized C Programs

Prasita Mukherjee, Benjamin Delaware

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

Structured Interfaces for Automated Reasoning with 3D Scene Graphs

Aaron Ray, Jacob Arkin, Harel Biggie, Chuchu Fan, Luca Carlone, Nicholas Roy

机构 * Massachusetts Institute of Technology（麻省理工学院） 纠错

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

Comments 25 pages, 3 figures