In the information era, how learners find, evaluate, and effectively use information has become a challenging issue, especially with the added complexity of large language models (LLMs) that have further confused learners in their information retrieval and search activities. This study attempts to unpack this complexity by combining exploratory search strategies with the theories of exploratory learning to form a new theoretical model of exploratory learning from the perspective of students' learning. Our work adapts Kolb's learning model by incorporating high-frequency exploration and feedback loops, aiming to promote deep cognitive and higher-order cognitive skill development in students. Additionally, this paper discusses and suggests how advanced LLMs integrated into information retrieval and information theory can support students in their exploratory searches, contributing theoretically to promoting student-computer interaction and supporting their learning journeys in the new era with LLMs.
Large Language Models as Code Executors: An Exploratory Study
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Xinjiang University（新疆大学）；University of Melbourne（墨尔本大学）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；IBM Research（IBM研究院）；Nantong University（南通大学）；Alibaba（阿里巴巴）
Let it Calm: Exploratory Annealed Decoding for Verifiable Reinforcement Learning
Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）；Department of Statistics, University of Chicago（芝加哥大学统计系）；Toyota Technological Insitute at Chicago（芝加哥丰田技术研究所）；Data Science Institute, University of Chicago（芝加哥大学数据科学研究所）；Meta AI
Enhancing Agentic Autonomous Scientific Discovery with Vision-Language Model Capabilities
Department of Physics, University of Cambridge, Cambridge, United Kingdom（剑桥大学物理系）；Kavli Institute for Cosmology, University of Cambridge, Cambridge, United Kingdom（剑桥大学卡弗利天文研究所）；Department of Physics and Astronomy, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA（哈弗福德学院物理与天文学系）；Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA（加州理工学院物理、数学与天文学系）；Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom（剑桥大学天文研究所）
Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
Huazhong University of Science and Technology（华中科技大学）；Chongqing University（重庆大学）；University of New South Wales（新南威尔士大学）；University of Technology Sydney（悉尼科技大学）
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
University of California, Davis（加利福尼亚大学戴维斯分校）；Bosch Research North America（博世北美研究院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心）；Robert Bosch GmbH（罗伯特·博世有限公司）
Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs
School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院，北京交通大学）；Meituan（美团）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京交通数据挖掘与具身智能重点实验室）
The Nexus of AR/VR, AI, UI/UX, and Robotics Technologies in Enhancing Learning and Social Interaction for Children with Autism Spectrum Disorders: A Systematic Review
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；RIKEN AIP（理化学研究所人工智能项目）；School of Software, Shandong University（山东大学软件学院）；Dept. of Comp. Sci. & Tech., BNRist Center, THU-Bosch ML Center, Tsinghua University（清华大学计算机科学与技术系、BNRist中心、THU-Bosch机器学习中心）
