EvoCodeBench: A Human-Performance Benchmark for Self-Evolving LLM-Driven Coding Systems
Nanyang Technological University（南洋理工大学）；East China University of Science and Technology（东华大学）；Guangdong Ocean University（广东海洋大学）；City University of Hong Kong（香港城市大学）；Stanford University（斯坦福大学）
As large language models (LLMs) continue to advance in programming tasks, LLM-driven coding systems have evolved from one-shot code generation into complex systems capable of iterative improvement during inference. However, existing code benchmarks primarily emphasize static correctness and implicitly assume fixed model capability during inference. As a result, they do not capture inference-time self-evolution, such as whether accuracy and efficiency improve as an agent iteratively refines its solutions. They also provide limited accounting of resource costs and rarely calibrate model performance against that of human programmers. Moreover, many benchmarks are dominated by high-resource languages, leaving cross-language robustness and long-tail language stability underexplored. Therefore, we present EvoCodeBench, a benchmark for evaluating self-evolving LLM-driven coding systems across programming languages with direct comparison to human performance. EvoCodeBench tracks performance dynamics, measuring solution correctness alongside efficiency metrics such as solving time, memory consumption, and improvement algorithmic design over repeated problem-solving attempts. To ground evaluation in a human-centered reference frame, we directly compare model performance with that of human programmers on the same tasks, enabling relative performance assessment within the human ability distribution. Furthermore, EvoCodeBench supports multiple programming languages, enabling systematic cross-language and long-tail stability analyses under a unified protocol. Our results demonstrate that self-evolving systems exhibit measurable gains in efficiency over time, and that human-relative and multi-language analyses provide insights unavailable through accuracy alone. EvoCodeBench establishes a foundation for evaluating coding intelligence in evolving LLM-driven systems.
Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education
R. B. Annis School of Engineering（R. B. Annis 工程学院）；University of Indianapolis（印第安纳大学）；E. S. Witchger School of Engineering（E. S. Witchger 工程学院）；Marian University（玛丽安大学）；University of Maryland Eastern Shore（马里兰大学东部分校）；The Boehm Center for Systems and Software Engineering（Boehm 系统与软件工程中心）
MedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation
The Chinese University of Hong Kong（香港中文大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；South China University of Technology（华南理工大学）；Chinese Academy of Sciences（中国科学院）
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Zhongguancun Academy（中关村学院）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Shenzhen Loop Area Institute（深圳河套学院）；Tsinghua University（清华大学）
El Agente Forjador: Task-Driven Agent Generation for Quantum Simulation
Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Chemistry, University of Toronto（多伦多大学化学系）；Department of Materials Science & Engineering, University of Toronto（多伦多大学材料科学与工程系）；Department of Chemical Engineering & Applied Chemistry, University of Toronto（多伦多大学化学工程与应用化学系）；Acceleration Consortium（加速联盟）；Vector Institute for Artificial Intelligence（人工智能矢量研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究 institute (CIFAR)）；NVIDIA
Generating and Evolving Reward Functions for Highway Driving with Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangdong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things（广东省普适物联网集成通信、感知与计算重点实验室）
DéjàQ: Open-Ended Evolution of Diverse, Learnable and Verifiable Problems
Willem Röpke AI Lab, Vrije Universiteit Brussel Belgium（维尔姆·罗普克人工智能实验室，布鲁塞尔自由大学）；FLAIR, University of Oxford United Kingdom（FLAIR，牛津大学）；University College London United Kingdom（伦敦大学学院）
Specula: Scaling formal specifications for autonomous model checking of system code
Nanjing University（南京大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft Research Asia（微软亚洲研究院）；University of British Columbia（不列颠哥伦比亚大学）
College of Computer Science and Software Engineering, Shenzhen University, China（深圳大学计算机科学与软件工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), China（广东省人工智能与数字经济发展实验室（深圳））；School of Information Technology, Carleton University, Canada（卡尔顿大学信息技术学院）
