Prompt-based evaluations suggest that large language models (LLMs) perform poorly on time series classification, raising doubts about whether they encode meaningful temporal structure. We show that this conclusion reflects limitations of prompt-based generation rather than the model's representational capacity by directly comparing prompt outputs with linear probes over the same internal representations. While zero-shot prompting performs near chance, linear probes improve average F1 from 0.15-0.26 to 0.61-0.67, often matching or exceeding specialized time series models. Layer-wise analyses further show that class-discriminative time series information emerges in early transformer layers and is amplified by visual and multimodal inputs. Together, these results demonstrate a systematic mismatch between what LLMs internally represent and what prompt-based evaluation reveals, leading current evaluations to underestimate their time series understanding.
AdaRewriter: Unleashing the Power of Prompting-based Conversational Query Reformulation via Test-Time Adaptation
School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University（计算机科学与工程学院、计算机网络与信息集成重点实验室、教育部、东南大学）
Enhancing Document-level Argument Extraction with Definition-augmented Heuristic-driven Prompting for LLMs
School of Engineering and Informatics, University of Sussex（萨塞克斯大学工程与信息学院）；International Business School Suzhou, Xi’an Jiaotong-Liverpool University（西交利物浦大学苏州国际商学院）；Management School, University of Liverpool（利物浦大学管理学院）
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；The Chinese University of Hong Kong（香港中文大学）；Sichuan University（四川大学）
A Sentiment Consolidation Framework for Meta-Review Generation
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）
Large Language Models are biased to overestimate profoundness
Pontificia Universidad Católica de Chile（智利天主教大学）；Centro Nacional de Inteligencia Artificial (CENIA)（国家人工智能中心）；Brown University（布朗大学）；Leiden University（莱顿大学）
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））
RoT: Enhancing Large Language Models with Reflection on Search Trees
School of Information Science and Technology, ShanghaiTech（上海科技大学信息科学与技术学院）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；National University of Singapore（新加坡国立大学）；University of Electronic Science and Technology of China（电子科技大学）；Beijing University of Posts and Telecommunieations（北京邮电大学）；Singapore Management University（新加坡管理大学）
Image-of-Thought Prompting for Visual Reasoning Refinement in Multimodal Large Language Models
School of Engineering, Westlake University（西湖大学工程学院）；City University of Hong Kong（香港城市大学）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；Johns Hopkins University（约翰斯·霍普金斯大学）
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Stanford University（斯坦福大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Northeastern University（东北大学）
