Code Language Models

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LLM-Based Code Documentation Generation and Multi-Judge Evaluation
May 11, 2026Ikbel Ghrab, Mohamed Dhieb, Ismail Khenissi +1LLM Evaluation LLM-as-a-Judge
University of Carthage, Engineering School of Statistics and Information Analysis (ESSAI), Ariana, Tunisia · Easy Transfer, 72 Rue de la Division Leclerc, 91160 Saulx-les-Chartreux, Paris, France · University of Tunis El Manar, Laboratory of Bioinformatics, bioMathematics, and bioStatistics (LR16IPT09), Institut Pasteur de Tunis, 13 Place Pasteur, B.P. 74, Belv´ed`ere, 1002 Tunis, Tunisia
An Execution-Verified Multi-Language Benchmark for Code Semantic Reasoning
May 10, 2026Yikun Li, Jinfeng Jiang, Ting Zhang +7LLM Evaluation Code Language Models
Singapore Management University · Monash University · Nanjing University of Science and Technology +1
Standing on the Shoulders of Giants: Stabilized Knowledge Distillation for Cross--Language Code Clone Detection
May 4, 2026Mohamad Khajezade, Fatemeh H. Fard, Mohamed Sami ShehataLanguage Model Distillation Knowledge Distillation
University of British Columbia, Okanagan Campus, Kelowna, Canada · University of British Columbia, Okanagan Campus, Kelowna, British Columbia, Canada
Code World Model Preparedness Report
May 1, 2026Daniel Song, Peter Ney, Cristina Menghini +21AI Safety Evaluation LLM Safety Evaluation
MSL Preparedness Team, AI Security Team
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing
Apr 30, 2026Wei Cheng, Yongchang Cao, Chen Shen +4Code Generation LLM Inference Acceleration
State Key Laboratory for Novel Software Technology, Nanjing University, China · Tongyi Lab, Alibaba Group, China
PLMGH: What Matters in PLM-GNN Hybrids for Code Classification and Vulnerability Detection
Apr 28, 2026Mohamed Taoufik Kaouthar El Idrissi, Edward Zulkoski, Mohammad HamdaqaGraph Neural Networks Software Vulnerability Detection
Polytechnique Montréal Montréal, Canada · Quantstamp San Francisco, United States
Large Language Models for Multilingual Code Intelligence: A Survey
Apr 27, 2026Chao Jiang, Dugang Liu, Cheng Wen +6Code Translation Code Generation
College of Computer Science and Software Engineering, Shenzhen University, China · Guangzhou Institute of Technology, Xidian University, China · Guangzhou University of Software, China +2
Query2Diagram: Answering Developer Queries with UML Diagrams
Apr 26, 2026Oleg Baryshnikov, Anton M. Alekseev, Sergey I. NikolenkoSoftware Engineering Software Reverse Engineering
HSE University · St. Petersburg Department of Steklov Mathematical Institute, RAS, St. Petersburg State University
Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL
Apr 22, 2026Zhaofeng Wu, Shiqi Wang, Boya Peng +5Supervised Fine-Tuning RL for Code Generation
Meta Superintelligence Labs · MIT
The Path Not Taken: Duality in Reasoning about Program Execution
Apr 22, 2026Eshgin Hasanov, Md Mahadi Hassan Sibat, Santu Karmaker +1LLM Evaluation Code Language Models
Department of Computer Science, University of Central Florida
Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
Apr 20, 2026Éric JacopinCode Generation Code Generation Evaluation
Cosmic AI
WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models
Apr 20, 2026Xinping Lei, Xinyu Che, Junqi Xiong +16LLM Agent Evaluation Code Language Models
Nanjing University · Kuaishou Technology
CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora
Apr 20, 2026Shangyu Li, Juyong Jiang, Meibo Ren +7Code Translation RL for Language Models
The Hong Kong University of Science and Technology · The Hong Kong University of Science and Technology (Guangzhou) · Monash University +1
Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective
Apr 20, 2026Meifang Chen, Zhe Yang, Huang Nianchen +4Data Leakage Memorization in Language Models
†The Chinese University of Hong Kong · ‡Nanyang Technological University · §University of Southern California +1
SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair
Apr 19, 2026Yifan Zhang, Jieyu Li, Kexin Pei +2Automated Program Repair Neuro-Symbolic AI
Vanderbilt University1 · University of Chicago2
Configuration Over Selection: Hyperparameter Sensitivity Exceeds Model Differences in Open-Source LLMs for RTL Generation
Apr 18, 2026Minghao Shao, Zeng Wang, Weimin Fu +5LLM Evaluation Language Model Generation Evaluation
NYU Tandon School of Engineering, USA · Kansas State University, USA · NYU Abu Dhabi, UAE
Improving LLM Code Reasoning via Semantic Equivalence Self-Play with Formal Verification
Apr 18, 2026Antonio Valerio Miceli Barone, Poon Tsz NokAdversarial Training Self-Play RL
School of Informatics, University of Edinburgh
COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation
Apr 16, 2026Heng Ping, Peiyu Zhang, Shixuan Li +5Multi-Objective Evolutionary Optimization Code Generation
University of Southern California United States
From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion
Apr 2, 2026Liang Zhu, Haolin Chen, Lidong Zhao +1Code Generation LLM Uncertainty Estimation
1Tencent, Shenzhen, China
SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories
Jan 20, 2026Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava +2LLM Post-Training Automated Test Generation
Carnegie Mellon University · Nutanix · Stanford University
AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans
Sep 26, 2025Yangtian Zi, Zixuan Wu, Aleksander Boruch-Gruszecki +2Software Engineering Agents Human-AI Collaboration
Northeastern University Boston, MA, United States
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
Aug 22, 2025Yifan Zhang, Chen Huang, Yueke Zhang +5Code Translation LLM Fine-Tuning
Vanderbilt University1 · National University of Singapore2 · University of Notre Dame3
Are AI Coders Snitches? An Empirical Study of Pretraining Data Detection on Code Large Language Models
Jul 23, 2025Tianlin Li, Yunxiang Wei, Zhiming Li +5LLM Evaluation Code Language Models
Nanyang Technological University Singapore · Zhejiang University China · Peng Cheng Laboratory China +2
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs
Jun 25, 2025Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +5Adversarial Prompt Generation LLM Red Teaming
University of California, Davis · University of Southern California
Is Your Benchmark Still Useful? Dynamic Benchmarking for Code Language Models
Mar 9, 2025Batu Guan, Xiao Wu, Yuanyuan Yuan +1Benchmark Design Data Contamination in Language Models
The Chinese University of Hong Kong · Huazhong University of Science and Technology · ETH Zurich

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LLM-Based Code Documentation Generation and Multi-Judge Evaluation

Ikbel Ghrab, Mohamed Dhieb, Ismail Khenissi, Ines Abdeljaoued-Tej

University of Carthage, Engineering School of Statistics and Information Analysis (ESSAI), Ariana, Tunisia · Easy Transfer, 72 Rue de la Division Leclerc, 91160 Saulx-les-Chartreux, Paris, France · University of Tunis El Manar, Laboratory of Bioinformatics, bioMathematics, and bioStatistics (LR16IPT09), Institut Pasteur de Tunis, 13 Place Pasteur, B.P. 74, Belv´ed`ere, 1002 Tunis, Tunisia

LLM Evaluation LLM-as-a-Judge Code Language Models

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High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainability are essential. We presented an AI powered framework that automates documentation generation from code and repositories using eight state of the art Large Language Models (LLMs), including GPT, Gemini, Qwen, and LLaMA variants. Built on the PocketFlow orchestration framework, the system applies modular pipelines and advanced prompt engineering to produce structured, context aware documentation. To ensure quality and guide model selection, we introduced a MultiLLMasJudges evaluation framework, where four independent LLMs assess outputs across nine criteria, such as Completeness, Clarity, and Faithfulness. Experiments conducted on an open-source medical physics library, demonstrated showed a 42% performance gap between top and bottom models. By combining diverse model outputs, optimized prompting, and rigorous evaluation, our approach enhances documentation quality and reduces manual effort, especially in safety critical healthcare software.

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