Web applications (web apps) have become a key arena for large language models (LLMs) to demonstrate their code generation capabilities and commercial potential. However, building a benchmark for LLM-generated web apps remains challenging due to the need for real-world user requirements, generalizable evaluation metrics without relying on ground-truth implementations or test cases, and interpretable evaluation results. To address these challenges, we introduce WebCoderBench, the first real-world-collected, generalizable, and interpretable benchmark for web app generation. WebCoderBench comprises 1,572 real user requirements, covering diverse modalities and expression styles that reflect realistic user intentions. WebCoderBench provides 24 fine-grained evaluation metrics across 9 perspectives, combining rule-based and LLM-as-a-judge paradigm for fully automated, objective, and general evaluation. Moreover, WebCoderBench adopts human-preference-aligned weights over metrics to yield interpretable overall scores. Experiments across 12 representative LLMs and 2 LLM-based agents show that there exists no dominant model across all evaluation metrics, offering an opportunity for LLM developers to optimize their models in a targeted manner for a more powerful version.
GAUSS: Benchmarking Structured Mathematical Skills for Large Language Models
Hyperbolic Labs；California Institute of Technology（加州理工学院）；University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；Nvidia（英伟达）；University of Washington（华盛顿大学）；University of Hong Kong（香港大学）
An Interpretable and Scalable Framework for Evaluating Large Language Models
Department of Statistics, University of California, Riverside（加州大学河滨分校统计学系）；School of Statistics and Data Science, Southeast University（东南大学统计与数据科学学院）；Department of Statistics and Data Science, Southern University of Science and Technology（南方科技大学统计与数据科学系）
FinDeepIndicator: Benchmarking Deep Research Agents in End-to-End Financial Indicator Construction
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；China Economics and Management Academy, Central University of Finance and Economics（中央财经大学中国经济与管理研究院）；Asian Institute of Digital Finance, National University of Singapore（新加坡国立大学亚洲数字金融研究所）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs
Provable Responsible AI and Data Analytics (PRADA) Lab（可证负责任人工智能与数据 analytics 实验室）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹科学与技术大学）；HKUST（香港科技大学）；MBZUAI（穆罕默德·本·拉希德人工智能研究所）；University of Science and Technology of China（中国科学技术大学）；Kyungpook National University（庆尚国立大学）
FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning
Jilin University（吉林大学）；University of Central Florida（中佛罗里达大学）；Arizona State University（亚利桑那州立大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；City University of Hong Kong（香港城市大学）
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
MemTensor (Shanghai) Technology Co., Ltd.（墨天轮（上海）科技有限公司）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Renmin University of China（中国人民大学）
An Investigation of Robustness of LLMs in Mathematical Reasoning: Benchmarking with Mathematically-Equivalent Transformation of Advanced Mathematical Problems
Department of Computer Science University of Illinois Urbana–Champaign（计算机科学系伊利诺伊大学厄巴纳-香槟分校）；Department of Computer Science Stanford University（计算机科学系斯坦福大学）
LLM-based Evaluation Policy Extraction for Ecological Modeling
University of Pittsburgh（匹兹堡大学）；University of Minnesota - Twin Cities（明尼苏达大学-双城分校）；Lawrence Berkeley National Lab（伯克利国家实验室）；University of Maryland College Park（马里兰大学学院市分校）
