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arXiv 2025年

作者： Chen, Qiguang Qin, Libo Liu, Jinhao Peng, Dengyun Guan, Jiannan Wang, Peng Hu, Mengkang Zhou, Yuhang Gao, Te Che, Wanxiang Research Center for Social Computing and Interactive Robotics Harbin Institute of Technology China School of Computer Science and Engineering Central South University China The University of Hong Kong Hong Kong Fudan University China

Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like "overthinking" and "test-time scaling." This survey seeks to fill this gap by offering a unified perspective on Long CoT. (1) We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT. (3) We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and test-time scaling, offering insights into how these processes manifest in practice. (4) Finally, we identify significant research gaps and highlight promising future directions, including the integration of multi-modal reasoning, efficiency improvements, and enhanced knowledge frameworks. By providing a structured overview, this survey aims to inspire future research and further the development of logical reasoning in artificial intelligence1 © 2025, CC BY-SA.

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Gen-CNN: a framework for the automatic generation of CNNs for image classification

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Neural computing and Applications 2025年第1期37卷 149-168页

作者： García-Aguirre, Rogelio Navarro-López, Eva María Torres-Treviño, Luis Facultad de Ingeniería Mecánica y Eléctrica Universidad Autónoma de Nuevo León Ave. Universidad S/N San Nicolás de los Garza Nuevo León66455 Mexico School of Interactive Games and Media Golisano College of Computing and Information Sciences Rochester Institute of Technology 20 Lomb Memorial Drive RochesterNY14623 United States School of Environment Education and Development University of Manchester Oxford Road ManchesterM13 9PL United Kingdom

Convolutional neural networks (CNNs) have become widely adopted for computer vision tasks. However, the vast amount of design choices and the complex interactions among their hyperparameters, which ultimately influence the model’s performance, impede their accessibility to users who are not experts in machine learning (ML). To address this challenge, we present AutoML as a solution, leveraging hyperparameter optimization (HPO) for effective parameter selection. Particularly good at handling non-convex, non-differentiable optimization tasks, genetic algorithms are easy to implement and parallelize, making them well suited for deep learning applications. In this context, we introduce Gen-CNN, an AutoML framework based on a genetic algorithm that generates CNN models for image classification. Our framework incorporates transfer learning and operates in a low-compute regime to accelerate the hyperparameter optimization phase. We test Gen-CNN on four datasets, including Sign Language Digits for convergence assessment and KVASIR-v2, ISIC-2019, and BreakHis for performance evaluation. Our results prove that Gen-CNN automatically generates CNN models with classification performance comparable to state-of-the-art custom models already published in the literature. Moreover, in the recommended testing regime for heuristic optimization techniques, we surpassed other HPO algorithms by achieving better mean categorical accuracy. Gen-CNN code is available at—omitted for anonymous review. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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ProxiCycle: Passively Mapping Cyclist Safety Using Smart Handlebars for Near-Miss Detection 25

ProxiCycle: Passively Mapping Cyclist Safety Using Smart Han...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Joseph Breda Keyu Chen Thomas Plötz Shwetak Patel Paul G. Allen School of Computer Science & Engineering University of Washington Seattle Washington USA Gridware San Fancisco California USA School of Interactive Computing Georgia Institute of Technology Atlanta Georgia USA

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The Goofus & Gallant Story Corpus for Practical Value Alignment

arXiv

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arXiv 2025年

作者： Nahian, Md Sultan Al Tasrin, Tasmia Frazier, Spencer Riedl, Mark Harrison, Brent Institute for Biomedical Informatics University of Kentucky LexingtonKY United States Department of Computer Science University of Kentucky LexingtonKY United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact. © 2025, CC BY.

关键词： Adversarial machine learning

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Escape or D13: Understanding Youth Perspectives of AI through Educational Game Co-design 25

Escape or D13: Understanding Youth Perspectives of AI throug...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Jared Ordoña Lim Grace Barkhuff Jane Awuah Sophie Clyde Riya Sogani Christina Gardner-McCune David Touretzky Judith Odili Uchidiuno School of Interactive Computing Georgia Institute of Technology Atlanta Georgia USA Department of Computer and Information Science and Engineering University of Florida Gainesville Florida USA Computer Science Department Carnegie Mellon University Pittsburgh Pennsylvania USA

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Learner-Centered Design of computing Education 1

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丛书名： Synthesis Lectures on Human-Centered Informatics

1000年

作者： Mark Guzdial

ISBN: (数字)9783031022166

ISBN: (纸本)9783031010880

computing education is in enormous demand. Many students (both children and adult) are realizing that they will need programming in the future. This book presents the argument that they are not all going to use programming in the same way and for the same purposes. What do we mean when we talk about teaching everyone to program? When we target a broad audience, should we have the same goals as computer science education for professional software developers? How do we design computing education that works for everyone? This book proposes use of a learner-centered design approach to create computing education for a broad audience. It considers several reasons for teaching computing to everyone and how the different reasons lead to different choices about learning goals and teaching methods. The book reviews the history of the idea that programming isn't just for the professional software developer. It uses research studies on teaching computing in liberal arts programs, to graphic designers, to high school teachers, in order to explore the idea that computer science for everyone requires us to re-think how we teach and what we teach. The conclusion describes how we might create computing education for everyone.

关键词： User Interfaces and Human Computer Interaction

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ExploreSelf: Fostering User-driven Exploration and Reflection on Personal Challenges with Adaptive Guidance by Large Language Models 25

ExploreSelf: Fostering User-driven Exploration and Reflectio...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Inhwa Song SoHyun Park Sachin R Pendse Jessica Lee Schleider Munmun De Choudhury Young-Ho Kim School of Computing KAIST Daejeon Republic of Korea NAVER Cloud Seongnam Republic of Korea Northwestern University Evanston Illinois USA Medical Social Sciences Pediatrics and Psychology Northwestern University Evanston Illinois USA School of Interactive Computing Georgia Institute of Technology Atlanta Georgia USA NAVER AI Lab Seongnam Gyeonggi Republic of Korea

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Architecting Utopias: How AI in Healthcare Envisions Societal Ideals and Human Flourishing 25

Architecting Utopias: How AI in Healthcare Envisions Societa...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Catherine Wieczorek Heidi Biggs Kamala Payyapilly Thiruvenkatanathan Shaowen Bardzell School of Interactive Computing Georgia Institute of Technology Atlanta Georgia USA School of Literature Media and Communication Georgia Institute of Technology Atlanta Georgia USA College of Information Sciences and Technology The Pennsylvania State University State College Pennsylvania USA

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Enhancing Predictive Imaging Biomarker Discovery Through Treatment Effect Analysis

Enhancing Predictive Imaging Biomarker Discovery Through Tre...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Xiao, Shuhan Klein, Lukas Petersen, Jens Vollmuth, Philipp Jaeger, Paul F. Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Germany Heidelberg University Faculty of Mathematics and Computer Science Germany Dkfz Heidelberg Interactive Machine Learning Group Germany Institute for Machine Learning Eth Zürich Switzerland Dkfz Heidelberg Helmholtz Imaging Germany Germany University of Bonn Medical Faculty Bonn Germany Heidelberg University Hospital Pattern Analysis and Learning Group Department of Radiation Oncology Germany

ISBN: (纸本)9798331510831

Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomarkers, are extracted from pretreatment data, often within randomized controlled trials, and should be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on discovering predictive imaging biomarkers, specific image features, by leveraging pretreatment images to uncover new causal relationships. Unlike laborintensive approaches relying on handcrafted features prone to bias, we present a novel task of directly learning predictive features from images. We propose an evaluation protocol to assess a model's ability to identify predictive imaging biomarkers and differentiate them from purely prognostic ones by employing statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally developed for estimating the conditional average treatment effect (CATE) for this task, which have been assessed primarily for their precision of CATE estimation while overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates the feasibility and potential of our approach in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. Our code is available at https://***/MIC-DKFZ/predictive_image_biomarker_analysis. © 2025 IEEE.

关键词： Deep learning

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Distributed Multi-robot Source Seeking in Unknown Environments with Unknown Number of Sources

arXiv

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arXiv 2025年

作者： Chen, Lingpeng Kailas, Siva Deolasee, Srujan Luo, Wenhao Sycara, Katia Kim, Woojun Shenzhen Institute of Artificial Intelligence and Robotics for Society Chinese University of Hong Kong Shenzhen China Robotics Institute Carnegie Mellon University United States School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science University of Illinois Chicago United States

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function. Copyright © 2025, The Authors. All rights reserved.

关键词： Multipurpose robots

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