This paper proposes a framework combining Neural Ordinary Differential Equations (Neural ODEs) and robust control theory to enhance the interpretability and control of large language models (LLMs). By utilizing Neural ODEs to model the dynamic evolution of input-output relationships and introducing control mechanisms to optimize output quality, we demonstrate the effectiveness of this approach across multiple question-answer datasets. Experimental results show that the integration of Neural ODEs and control theory significantly improves output consistency and model interpretability, advancing the development of explainable AI technologies.
Jailbreak-as-a-Service++: Unveiling Distributed AI-Driven Malicious Information Campaigns Powered by LLM Crowdsourcing
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；China People’s Public Security University（中国人民公安大学）；Tongji University（同济大学）；South China Normal University（华南师范大学）；Tsing Hua University（清华大学）
ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Northwestern University（西北大学）；William & Mary（威廉与玛丽学院）；University of Washington（华盛顿大学）；Stanford University（斯坦福大学）
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement
Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology（大连理工大学社会计算与认知智能重点实验室）；School of Computer Science and Technology, Xinjiang Normal University（新疆师范大学计算机科学与技术学院）；Social AI Studio, Singapore University of Technology and Design（新加坡科技设计大学社会AI工作室）
Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Wenge Technology Co., Ltd（北京文格科技有限公司）
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
Intelligent Game and Decision Lab（智能游戏与决策实验室）；National University of Defense Technology（国防科技大学）；University of Melbourne（墨尔本大学）；University of Sydney（悉尼大学）；Hong Kong Baptist University（香港 Baptist 大学）
Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation
Aligarh Muslim University（阿里格尔穆斯林大学）；VIT Bhopal University（VIT博帕尔大学）；Nanyang Technological University（南洋理工大学）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Edinburgh Napier University（爱丁堡龙比亚大学）
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
Indian Institute of Technology Delhi（印度德里理工学院）；Delhi Technological University（德里理工大学）；Indian Institute of Science Education and Research Kolkata（印度加尔各答科学教育与研究学院）
UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs?
Mohamed Bin Zayed University of Artificial Intelligence（Mohamed Bin Zayed人工智能大学）；IIT Gandhinagar（印度加尔各答理工学院）；Harvard University（哈佛大学）；VinUniversity（文大学）；Universitas Indonesia（印度尼西亚大学）
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-Ant Group Joint Research Center for Knowledge Graphs, Zhejiang University（浙江大学-蚂蚁集团知识图谱联合研究中心）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学杭州国际科技创新中心）；Tencent（腾讯）
Better Aligned with Survey Respondents or Training Data? Unveiling Political Leanings of LLMs on U.S. Supreme Court Cases
Technical University of Munich（慕尼黑技术大学）；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；LMU Munich & Munich Center for Machine Learning (MCML)（慕尼黑大学及慕尼黑机器学习中心）
ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究所）
Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary
The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）；Language and Machine Intelligence Department, Ant Group（蚂蚁集团语言与机器智能部门）
Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）数据科学与分析学域）；Financial Technology Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）金融科技学域）
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
