SC-Arena: A Natural Language Benchmark for Single-Cell Reasoning with Knowledge-Augmented Evaluation
Software College, Northeastern University（东北大学软件学院）；Shenzhen University of Advanced Technology（深圳大学先进技术学院）；Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；University of California, Irvine（加州大学 Irvine 分校）；School of Biomedical Engineering, UNSW Sydney（悉尼大学生物医学工程学院）
Jiahao Zhao, Feng Jiang, Shaowei Qin, Zhonghui Zhang, Junhao Liu, Guibing Guo, Hamid Alinejad-Rokny, Min Yang
Large language models (LLMs) are increasingly applied in scientific research, offering new capabilities for knowledge discovery and reasoning. In single-cell biology, however, evaluation practices for both general and specialized LLMs remain inadequate: existing benchmarks are fragmented across tasks, adopt formats such as multiple-choice classification that diverge from real-world usage, and rely on metrics lacking interpretability and biological grounding. We present SC-ARENA, a natural language evaluation framework tailored to single-cell foundation models. SC-ARENA formalizes a virtual cell abstraction that unifies evaluation targets by representing both intrinsic attributes and gene-level interactions. Within this paradigm, we define five natural language tasks (cell type annotation, captioning, generation, perturbation prediction, and scientific QA) that probe core reasoning capabilities in cellular biology. To overcome the limitations of brittle string-matching metrics, we introduce knowledge-augmented evaluation, which incorporates external ontologies, marker databases, and scientific literature to support biologically faithful and interpretable judgments. Experiments and analysis across both general-purpose and domain-specialized LLMs demonstrate that (i) under the Virtual Cell unified evaluation paradigm, current models achieve uneven performance on biologically complex tasks, particularly those demanding mechanistic or causal understanding; and (ii) our knowledge-augmented evaluation framework ensures biological correctness, provides interpretable, evidence-grounded rationales, and achieves high discriminative capacity, overcoming the brittleness and opacity of conventional metrics. SC-Arena thus provides a unified and interpretable framework for assessing LLMs in single-cell biology, pointing toward the development of biology-aligned, generalizable foundation models.
Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
Graduate School of Data Science, KAIST, Daejeon, South Korea（数据科学研究生院，韩国科学技术院，大田，韩国）；Department of Industrial and Systems Engineering, KAIST, Daejeon, South Korea（工业与系统工程系，韩国科学技术院，大田，韩国）；Department of Artificial Intelligence, Yonsei University, Seoul, South Korea（人工智能系，延世大学，首尔，韩国）
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；Zhongguancun Academy（中关村学院）；China Telecommunications Corporation（中国电信集团有限公司）
Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents
Wuhan University of Technology（武汉理工大学）；Agency for Science, Technology and Research, Singapore（新加坡科技研究局）；Hubei University（湖北大学）；University of Science and Technology of China（中国科学技术大学）
CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
Texas A&M University（德克萨斯A&M大学）；Worcester Polytechnic Institute（沃斯特理工大学）；Tohoku University（东北大学）；Georgia Institute of Technology（佐治亚理工学院）；NVIDIA（英伟达）；UCSB（加州大学圣塔芭芭拉分校）；UC Merced（加州大学默塞德分校）
Topology of Reasoning: Retrieved Cell Complex-Augmented Generation for Textual Graph Question Answering
Academy of Advanced Interdisciplinary Studies, Chongqing University of Posts and Telecommunications（重庆邮电大学先进交叉学科研究院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Intelligent System Department, Zhongxing Telecom Equipment (ZTE)（中兴电信设备智能系统部）
Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoning
The Polytechnic Institute, Zhejiang University（浙江大学Polytechnic学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学Hangzhou全球科技创新中心）；University of Oxford（牛津大学）；Ant Group（蚂蚁集团）；University of Aberdeen（阿伯丁大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学UIUC研究院）
STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics
Hong Kong Institute of AI for Science, City University of Hong Kong（香港人工智能科学研究院，香港城市大学）；Department of Data Science, City University of Hong Kong（数据科学系，香港城市大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Li Auto Inc.（李汽有限公司）；Independent Researcher（独立研究者）
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Georgia（佐治亚大学）；South China University of Technology（华南理工大学）；Technical University of Munich, University of Cologne（慕尼黑技术大学、科隆大学）；SenseTime Research（商汤科技研究院）；Qingyuan Research Institute, Shanghai Jiaotong University（青原研究院，上海交通大学）
