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“ydy0615” 17d211bf93 feat: add web search block functionality and integrate with existing plugins 
- Introduced a new web search block plugin to handle web search queries and results.
- Updated copilot, doc block, and pro block plugins to include web search context in AI completions.
- Implemented utility functions for parsing and building web search markdown.
- Enhanced API to support web search requests and responses.
- Added configuration for web search URL and timeout settings.
- Updated size limit checks to account for web search content.
2026-06-09 19:18:14 +08:00

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"content": "数据为人工智能提供“原材料”，计算智能取决于数据而不是知识；神经计算、进化计算等都是以数据为基础而发展起来。 人工智能“新基建”是强国工程 蔡自兴表示，要从目标、基础、投入、产业、人才等五个方面，推动人工智能“新基建”这一强国工程的建设发展。",
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"content": "直通服贸会｜服贸会上的“科技范儿”人工智能服务 5G、人工智能等一系列新技术赋能新兴服务业，既增强了服务的可贸易性，也成为拉动经济增长和贸易发展的新动力。 无人驾驶对应用规则提出挑战 重庆邮电大学校长 高新波：目前我国自动驾驶还是以人驾驶为主，无人驾驶主要是做辅助驾驶。",
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"title": "OpenAI Gym",
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"title": "The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming",
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"content": "Recent advances in artificial intelligence have been driven by an exponential growth in digitised data. Natural language processing, in particular, has been transformed by machine learning models such as OpenAI’s GPT-3 which generates human-like text so realistic that its developers have warned of the dangers of its misuse. In recent months OpenAI released Codex, a new deep learning model trained on Python code from more than 50 million GitHub repositories. Provided with a natural language description of a programming problem as input, Codex generates solution code as output. It can also explain (in English) input code, translate code between programming languages, and more. In this work, we explore how Codex performs on typical introductory programming problems. We report its performance on real questions taken from introductory programming exams and compare it to results from students who took these same exams under normal conditions, demonstrating that Codex outscores most students. We then explore how Codex handles subtle variations in problem wording using several published variants of the well-known “Rainfall Problem” along with one unpublished variant we have used in our teaching. We find the model passes many test cases for all variants. We also explore how much variation there is in the Codex generated solutions, observing that an identical input prompt frequently leads to very different solutions in terms of algorithmic approach and code length. Finally, we discuss the implications that such technology will have for computing education as it continues to evolve, including both challenges and opportunities.",
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"content": "Recently, we have seen a boom of attempts to improve the operation of networking protocols using machine learning techniques. The proposed reinforcement learning (RL) based control solutions very often overtake traditionally designed ones in terms of performance and efficiency. However, in order to reach such a superb level, an RL control agent requires a lot of interactions with an environment to learn the best policies. Similarly, the recent advancements in image recognition area were enabled by the rise of large labeled datasets (e.g. ImageNet). This paper presents the ns3-gym - the first framework for RL research in networking. It is based on OpenAI Gym, a toolkit for RL research and ns-3 network simulator. Specifically, it allows representing an ns-3 simulation as an environment in Gym framework and exposing state and control knobs of entities from the simulation for the agent's learning purposes. Our framework is generic and can be used in various networking problems. Here, we present an illustrative example from the cognitive radio area, where a wireless node learns the channel access pattern of a periodic interferer in order to avoid collisions with it. The toolkit is provided to the community as open-source under a GPL license.",
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"content": "ChatGPT is a product of AI that is currently being widely discussed on Twitter. This research reviews how ChatGPT writes English essays. This research is descriptive qualitative. The analysis shows that we can access ChatGPT on openai.com or chat.openai.com on the browser. If we do not have an account, we register via email, Google, or Microsoft account. After login, enter a question or statement in the conversation column provided. Send it and ChatGPT will respond and the answer appear quickly. The researcher tries ChatGPT “Can you help me in doing my English assignment?\", and the ChatBot replies \"Of course! I'd be happy to help you with your English assignment. What do you need help with? Do you have a specific question or task that you're working on, or is there a broader topic that you'd like help with? It would be helpful to have some more information so that I can better understand how I can assist you\". Based on several tries, ChatGPT can answer all questions on various topics such as English essays including a descriptive text about Solo and My Family, recount text about personal experience and unforgettable moments, resolution in 2023, and future career. ChatGPT considers the event orders and writing order, including using main, explanatory sentences, and a conclusion. It uses two voices both active and passive voice. Besides, it considers tenses use related to the given topic essay. However, from examples of English essays produced by ChatGPT, it certainly requires further research to find out that the essay results are grammatically accurate.",
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"title": "Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo",
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"title": "A Study on the Utilization of OpenAI ChatGPT as a Second Language Learning Tool",
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"content": "In November 2022, ChatGPT was introduced and caused a sensation, gathering 100 million users only within two months. ChatGPT’s capability of generating high-quality sentences demonstrated potential to be applied in a wide range of fields. In this paper, we explore the utilization of ChatGPT as a second language learning tool and scrutinize its suitability. Specifically, we analyzed technical aspects of ChatGPT including its principles, history, and features. We then assessed its ability from two different perspectives: designing course contents and teaching these contents based on Task-Based Language Teaching (TBLT) method. Firstly, we built up a Korean ESL learner persona who is an intermediate level and directed ChatGPT to construct a course for business English writing. Second, we asked ChatGPT to teach business English writing by TBLT method. Although there were areas for improvement, the results were promising, indicating that ChatGPT was able to execute given prompts to act as a language learning tool. By highlighting opportunities and concerns of ChatGPT in the language learning context, this paper implied its potential to create enhanced learning experience by facilitating a supportive learning environment. Further research is expected to investigate its effectiveness in diverse contexts and purposes.",
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"title": "Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports",
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"title": "ns3-gym: Extending OpenAI Gym for Networking Research",
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"content": "OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes a large number of well-known problems that expose a common interface allowing to directly compare the performance results of different RL algorithms. Since many years, the ns-3 network simulation tool is the de-facto standard for academic and industry research into networking protocols and communications technology. Numerous scientific papers were written reporting results obtained using ns-3, and hundreds of models and modules were written and contributed to the ns-3 code base. Today as a major trend in network research we see the use of machine learning tools like RL. What is missing is the integration of a RL framework like OpenAI Gym into the network simulator ns-3. This paper presents the ns3-gym framework. First, we discuss design decisions that went into the software. Second, two illustrative examples implemented using ns3-gym are presented. Our software package is provided to the community as open source under a GPL license and hence can be easily extended.",
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"title": "人工智能赋能教育的研究进展、热点与趋势",
"url": "https://doi.org/10.63887/jerp.2025.1.8.109",
"content": "人工智能的快速发展不断拓展其在教育领域的应用边界，已成为推动教育高质量发展的关键力量。文章运用CiteSpace对2015—2025年间1229篇国内核心文献进行计量分析，从发文趋势、关键词和突现词等维度探讨人工智能教育研究现状。结果显示，研究主要聚焦于教育应用、新教育模式和生成式人工智能等领域，研究重心逐渐转向技术驱动的教育改革、教育治理及生成式人工智能的优化与创新。文章最后对未来研究提出展望，认为应当将研究覆盖更多教育阶段；综合评估人工智能教育应用的影响以及推动跨学科合作研究。",
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"title": "Physical artificial intelligence (PAI): the next-generation artificial intelligence",
"url": "https://doi.org/10.1631/fitee.2200675",
"content": "人工智能(AI)已经成为各领域创新和社会进步的驱动力。然而, 其大多数工业应用集中在信号处理领域, 这依赖于不同传感器产生和收集的数据。最近, 一些研究人员提出将数字人工智能和物理人工智能结合, 这可能带来人工智能理论基础的重大进步。在本文中, 我们探讨了物理人工智能的概念并提出两个子领域: 集成式物理人工智能和分布式物理人工智能。我们还讨论了物理人工智能可持续发展和治理所面临的挑战和机遇。由于物理人工智能需要连续处理来自边缘、雾和物联网的分布式信号, 它可以被看作分布式计算连续系统在人工智能领域的延伸。",
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"title": "生成式人工智能犯罪的刑事责任分配探究",
"url": "https://doi.org/10.54254/3050-2160/2025.22194",
"content": "新一轮科技革命与产业变革曙光可见，在数字经济不断推进的大背景下，人工智能技术蒸蒸日上，并与多种应用场景深度融合，正成为推动人类进入智能时代的决定性力量。然而，人工智能的快速发展影响着刑法适用，生成式人工智能的刑事责任主体资格问题一直是学界争议的焦点。笔者认为，无论人工智能发展到何种程度，本质上仍是作为人类犯罪的工具，不具备刑事责任主体特征。对涉及人工智能犯罪做好相应预案，需要对生成式人工智能犯罪的刑事责任深度探究并正确分配以进行刑法规制。对于生成式人工智能犯罪，仍需坚持自然人主义，在责任分配的考量中，应当从三个核心层面出发，即设计环节的责任归属、生产过程的责任承担以及最终使用者的责任界定，从而确保责任的合理分摊和有效落实。",
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"title": "State-of-art of Compliant Mechanisms and Their Applications",
"url": "https://doi.org/10.3901/jme.2015.13.053",
"content": "摘要： 柔性机构自20世纪80年代提出以来迅猛发展，已成为现代机构学一个重要分支。短短不足30年间，柔性机构设计理论的构建与发展，为柔性机构成功应用奠定了坚实基础。随着对柔性及柔性机构认识不断深入，柔性机构得到了广泛应用，不断涌现新的成功实例。继5年前综述了柔性机构设计方法研究进展之后，尝试从应用的视角鸟瞰一下柔性机构的最新进展。通过将柔性机构的主体应用划分为精密工程、仿生机器人、智能材料结构三大主阵地，概述柔性机构在每个阵地中的应用进展及研究热点情况，并对其发展做了展望。对四种最具发展潜力和应用前景的新型柔性机构(胞元式柔性机构、辅助接触式柔性机构、平面折展机构、柔性静平衡机构)进行简单描述。",
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"title": "Research Progress and Trend of Key Technology of Intelligent Spraying Robot",
"url": "https://doi.org/10.3901/jme.2022.07.053",
"content": "摘要： 喷涂作为现代产品制造工艺中的一个重要环节，不仅起到美观、防护以及其他特殊作用，也日益成为产品价值的重要组成部分，在家具、航空航天、军工等领域中占据着重要地位。智能喷涂机器人是由计算机、传感、视觉、智能控制等多学科技术交叉综合而构成的复杂机电系统。智能喷涂机器人作为智能喷涂技术的核心，其发展与新材料、新设计和新方法的应用密不可分。针对智能喷涂机器人关键技术研究的共性问题，从喷涂机器人机构设计、喷涂系统动态性能监控、喷涂轨迹自动规划、喷涂质量检测方面综述了当前取得的研究成果；而后针对喷涂系统智能化进程中在机器人机构设计和柔性喷涂系统集成研究面临的挑战进行了分析和讨论；最后，对智能喷涂机器人关键技术研究方面未来的发展方向进行了展望和总结，为喷涂机器人发展方向与关键技术性能提升提供参考，推动喷涂技术全面进入智能化。",
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"title": "Human-centric Smart Manufacturing for Industry 5.0",
"url": "https://doi.org/10.3901/jme.2022.18.088",
"content": "摘要： 工业4.0是技术驱动型的工业模式，注重生产流程的优化、效率和生产力的提高，而忽视了“人”这一最重要的主体。因此，作为一种价值驱动型的新工业模式——工业5.0的概念逐渐引起人们的重视，将工业重心由技术转向对人身心健康的关怀、自然的可持续发展及工业的弹性等方面，而人机智能协作是走向未来以人为本的智能制造的关键。这种价值观的转变预示着人本智能制造会越来越受到重视，因而很有必要对如此新兴的智能制造模式开展详细研究，旨在为工业5.0理念下的人本智造发展提供有益的借鉴参考。为此，首先分析工业革命及制造范式的演化并指出目前制造模式存在的典型问题；然后给出工业5.0的定义，并分析其主要特征以及“人-社会-自然-技术”视角下与工业4.0的区别和联系；接着对工业5.0背景下人机交互方式及人机共生关系进行详细论述；最后探讨元宇宙背景下的人本智造演化及其面临的问题进行展望。",
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"title": "生物特征识别学科发展报告",
"url": "https://doi.org/10.11834/jig.210078",
"content": "从手机解锁、小区门禁到餐厅吃饭、超市收银，再到高铁进站、机场安检以及医院看病，人脸、虹膜和指纹等生物特征已成为人们进入万物互联世界的数字身份证。生物特征识别赋予机器自动探测、捕获、处理、分析和识别数字化生理或行为信号的高级智能，是一个典型而又复杂的模式识别问题，一直处于人工智能技术发展前沿，在新一代人工智能规划、“互联网+”行动计划等国家战略中具有重要地位。由于生物特征识别涉及公众利益攸关的隐私、道德和法律等问题，近期也引起了广泛的社会关注。本文系统综述了生物特征识别学科发展现状、新兴方向、存在问题和可行思路，深入梳理了人脸、虹膜、指纹、掌纹、静脉、声纹、步态、行人重识别以及多模态融合识别的研究进展，以人脸为例重点介绍了生物特征识别领域近些年受到关注的新方向——对抗攻击和防御、深度伪造和反伪造，最后剖析总结了生物特征识别领域存在的3大挑战问题——“感知盲区”、“决策误区”和“安全红区”。本文认为必须变革和创新生物特征的传感、认知和安全机制，才有可能取得复杂场景生物识别学术研究和技术应用的根本性突破，破除现有生物识别技术的弊端，朝着“可感”、“可知”和“可信”的新一代生物特征识别总体目标发展。",
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"title": "Service-oriented Smart Manufacturing",
"url": "https://doi.org/10.3901/jme.2018.16.011",
"content": "摘要： 新一代信息技术（如物联网、大数据、云计算、数字孪生等）与制造的融合发展，促使各制造强国纷纷出台各自的先进制造发展战略，如美国的“工业互联网”和德国的“工业4.0”等。同时，在“制造强国”和“网络强国”大战略背景下，我国也先后出台“中国制造2025”和“互联网+”等制造业国家发展实施战略。其共同主题之一是结合和使用新一代信息技术和人工智能技术，实现制造的物理世界和信息世界互联互通与融合，最终实现智能制造。同时，服务也被各制造强国共同列为实现智能制造的关键技术内容之一。智能服务已成为产业模式变革的核心，制造业的服务化趋势日益凸显。在分析总结智能制造的发展趋势和典型特征基础上，结合新一代信息技术与服务的思想，探索提出了面向服务的智能制造（Service-orientedsmart manufacturing，SoSM），设计了SoSM的实施架构，讨论了SoSM的内涵、关键实施技术与未来研究方向。",
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"title": "Human-centric Smart Manufacturing: Evolution and Outlook",
"url": "https://doi.org/10.3901/jme.2022.18.002",
"content": "摘要： 新一轮科技与产业革命正推动制造业向更高层次发展，促进新一代信息技术与先进制造技术深度融合，也促进智能制造向自主智能方向发展，但生产的目的是更好地满足人类的需求，还需要考虑生产对社会的作用和贡献，因而以人为本的智能制造日益受到关注和重视。人仍然是一个制造系统最为重要的生产要素，需要以人为中心探讨智能制造问题。为此，首先从工业革命进程中人机交互与企业创新的演进发展、生产模式与人类需求的递进关联关系两个方面论述智能制造面临的人本问题，阐明在智能制造中引入“以人为本”理念的必要性；接着从首次工业革命的机器化大规模生产到当今包容性长尾制造的制造业发展历史长河之中，归纳总结出人本制造演进脉络，并以智能包容性长尾制造为例说明人本智能制造理念实现，最后对人本智能制造的未来发展趋势做出展望。",
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"title": "文档智能分析与识别前沿：回顾与展望",
"url": "https://doi.org/10.11834/jig.221112",
"content": "文档分析与识别（简称文档识别）技术将各种非结构化文档数据（图像、联机笔迹）转化为结构化数据，便于计算机处理和理解，应用场景十分广阔。20世纪60年代以来，文档识别方法研究与应用受到广泛关注并取得巨大进展。得益于深度学习技术的发展和应用，文档识别的性能快速提升，相关技术在文档数字化、票据处理、笔迹录入、智能交通、文档检索与信息抽取等领域得到广泛应用。首先介绍文档识别的背景和技术范畴，回顾该领域发展历史，然后重点对深度学习方法兴起以来的研究进行综述，分析当前技术存在的不足，并建议未来值得重视的研究方向。研究现状综述部分，按文档分析与识别的几个主要技术环节（文档图像预处理、版面分析、场景文本检测、文本识别、结构化符号和图形识别、文档检索与信息抽取）分别进行介绍，简述传统方法研究的代表性工作，重点介绍深度学习方法研究的新进展。总体上，当前研究对象向深度、广度扩展，处理方法全面转向深度神经网络模型和深度学习方法，识别性能大幅提升且应用场景不断扩展。在现状分析基础上，指出当前技术在识别精度和可靠性、可解释性、学习能力和自适应性等方面还有明显不足。最后从提升性能、应用扩展、提升学习能力几个角度提出一些研究方向。从提升性能角度，研究问题包括文本识别可靠性、可解释性、全要素识别、长尾问题、多语言、复杂版面分割与理解、变形文档分析与识别等。应用扩展包括新应用（如机器人流程自动化（robotic process automation，RPA）、文字信息抄录、考古）和新技术问题（语义信息抽取、跨模态融合、面向应用的推理决策等）两方面。从提升学习能力角度，相关问题包括小样本学习、迁移学习、多任务学习、领域自适应、结构化预测、弱监督学习、自监督学习、开放集学习和跨模态学习等。;Document analysis and recognition（called document recognition in brief）is aimed to covert non-structured documents（typically，document images and online handwriting）into structured texts for facilitating computer processing and understanding. It is needed in wide applications due to the pervasive communication and usage of documents. The field of document recognition has attracted intensive attention and produced enormous progress in research and applications since 1960s. Particularly，the recent development of deep learning technology has boosted the performance of document recognition remarkably compared to traditional methods，and the technology has been applied successfully to document digitization，form processing，handwriting input，intelligent transportation，document retrieval and information extraction. In this article，we first introduce the background and involved techniques of document recognition，give an overview of the history of research（divided into four periods according to the objects of research，the methods and applications），and then review the main research progress with emphasis on deep learning based methods developed in recent years. After identifying the insufficiency of current technology，we finally suggest some important issues for future research. The review of recent progress is divided into sections corresponding to main processing steps，namely image pre-processing，layout analysis，scene text detection，text recognition，structured symbol and graphics recognition，document retrieval and information extraction. The review of recent progress is divided into sections corresponding to the main processing steps，namely image pre-processing，layout analysis，scene text detection，text recognition，structured symbol and graphics recognition，document retrieval and information extraction. 1）Due to the popularity of camera-captured document images，the current main task in image pre-processing is the rectification of distorted image while the task of binarization is still concerned. Recent methods are mostly end-to-end deep learning based transformation methods. 2）Layout analysis is dichotomized into physical layout analysis（page segmentation）and logical layout analysis（semantic region segmentation and reading order prediction）. Recent page segmentation methods based on fully convolutional network（FCN）or graph neural network（GNN） have shown promises. Logical layout analysis has been addressed by deep neural networks fusing multi-modal information. Table structure analysis is a special task of layout analysis and has been studied intensively in recent years. 3）Scene text detection is a hot topic in document analysis and computer vision fields. Deep learning based methods for text methods can be divided into regression-based methods，segmentation-based methods and hybrid methods. FCN is prevalently used for extracting visual features，based on which models are built to predict text regions. 4）Text recognition is the core task in document analysis. We review recent works for handwritten text recognition and scene text recognition，which share some common strategies but also show different preferences. There are two main streams of methods：segmentation-based and sequence-to-sequence learning methods. The convolutional recurrent neural network（CRNN）model has received high attention in recent years and is being extended in respect of encoding，decoding or learning strategies，while segmentationbased methods combining deep learning are still performing competitively. A noteworthy tendency is the extension of text line recognition to page-level recognition. Following text recognition，we also review the works of end-to-end scene text recognition（also called as text spotting），for which text detection and recognition models are learned jointly. 5）Among symbol and graphics in documents，mathematical expressions and flowcharts have received increasing attention. Recent methods for mathematical expression recognition are mostly image-to-markup generation methods using encoder-decoder models，while graph-based methods promise in generating both recognition and segmentation results. Flowchart recognition is addressed using structured prediction models such as GNN. 6）Document retrieval concerned mainly keyword spotting in pre-deep learning era，while recent works focus on information extraction（spotting semantic entities）by fusing layout and language information. Pre-trained layout and multi-modal language models are showing promises，while visual information is not considered adequately. Overall，the recent progress shows that the objects of recognition are expanded in breadth and depth，the methods are getting closer to deep neural networks and deep learning，the recognition performance is improved constantly，and the technology is applied to extensive scenes. The review also reveals the insufficiencies of the current technology in accuracy and reliability on various tasks，the interpretability，the learning ability and adaptability. Future works are suggested in respect of performance promotion，application extension，and improved learning. Issues of performance promotion include the reliability of recognition，interpretability，omni-element recognition，long-tailed recognition，multilingual documents，complex layout analysis and understanding，recognition of distorted documents. Issues related to applications include new applications（such as robotic process automation（RPA），text scription in natural scenes，archeology），new technical problems involved in applications（such as semantic information extraction，cross-modal fusion，reasoning and decision related to application scenes）. Aiming to improve the automatic system design，learning ability and adaptability，the involved learning problems/methods include small sample learning，transfer learning，multi-task learning，domain adaptation，structured prediction，weakly-supervised learning，self-supervised learning，open set learning， and cross-modal learning.",
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"url": "http://arxiv.org/abs/1808.00177v5",
"content": "We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM",
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{
"title": "OpenAI o1 System Card",
"url": "http://arxiv.org/abs/2412.16720v2",
"content": "The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.",
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{
"title": "A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education",
"url": "http://arxiv.org/abs/2410.21287v1",
"content": "As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world.",
"engine": "arxiv",
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{
"title": "Dota 2 with Large Scale Deep Reinforcement Learning",
"url": "http://arxiv.org/abs/1912.06680v1",
"content": "On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.",
"engine": "arxiv",
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{
"title": "Competitive Programming with Large Reasoning Models",
"url": "http://arxiv.org/abs/2502.06807v2",
"content": "We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered inference strategies designed for competing in the 2024 International Olympiad in Informatics (IOI). We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile. Under relaxed competition constraints, o1-ioi achieved a gold medal. However, when evaluating later models such as o3, we find that o3 achieves gold without hand-crafted domain-specific strategies or relaxed constraints. Our findings show that although specialized pipelines such as o1-ioi yield solid improvements, the scaled-up, general-purpose o3 model surpasses those results without relying on hand-crafted inference heuristics. Notably, o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors. Overall, these results indicate that scaling general-purpose reinforcement learning, rather than relying on domain-specific techniques, offers a robust path toward state-of-the-art AI in reasoning domains, such as competitive programming.",
"engine": "arxiv",
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{
"title": "OpenAI Gym",
"url": "http://arxiv.org/abs/1606.01540v1",
"content": "OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.",
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{
"title": "An Empirical Study of OpenAI API Discussions on Stack Overflow",
"url": "http://arxiv.org/abs/2505.04084v1",
"content": "The rapid advancement of large language models (LLMs), represented by OpenAI's GPT series, has significantly impacted various domains such as natural language processing, software development, education, healthcare, finance, and scientific research. However, OpenAI APIs introduce unique challenges that differ from traditional APIs, such as the complexities of prompt engineering, token-based cost management, non-deterministic outputs, and operation as black boxes. To the best of our knowledge, the challenges developers encounter when using OpenAI APIs have not been explored in previous empirical studies. To fill this gap, we conduct the first comprehensive empirical study by analyzing 2,874 OpenAI API-related discussions from the popular Q&A forum Stack Overflow. We first examine the popularity and difficulty of these posts. After manually categorizing them into nine OpenAI API-related categories, we identify specific challenges associated with each category through topic modeling analysis. Based on our empirical findings, we finally propose actionable implications for developers, LLM vendors, and researchers.",
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{
"title": "ORRB -- OpenAI Remote Rendering Backend",
"url": "http://arxiv.org/abs/1906.11633v1",
"content": "We present the OpenAI Remote Rendering Backend (ORRB), a system that allows fast and customizable rendering of robotics environments. It is based on the Unity3d game engine and interfaces with the MuJoCo physics simulation library. ORRB was designed with visual domain randomization in mind. It is optimized for cloud deployment and high throughput operation. We are releasing it to the public under a liberal MIT license: https://github.com/openai/orrb .",
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{
"title": "MDP environments for the OpenAI Gym",
"url": "http://arxiv.org/abs/1709.09069v1",
"content": "The OpenAI Gym provides researchers and enthusiasts with simple to use environments for reinforcement learning. Even the simplest environment have a level of complexity that can obfuscate the inner workings of RL approaches and make debugging difficult. This whitepaper describes a Python framework that makes it very easy to create simple Markov-Decision-Process environments programmatically by specifying state transitions and rewards of deterministic and non-deterministic MDPs in a domain-specific language in Python. It then presents results and visualizations created with this MDP framework.",
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{
"title": "Voices from the Frontier: A Comprehensive Analysis of the OpenAI Developer Forum",
"url": "http://arxiv.org/abs/2408.01687v1",
"content": "OpenAI's advanced large language models (LLMs) have revolutionized natural language processing and enabled developers to create innovative applications. As adoption grows, understanding the experiences and challenges of developers working with these technologies is crucial. This paper presents a comprehensive analysis of the OpenAI Developer Forum, focusing on (1) popularity trends and user engagement patterns, and (2) a taxonomy of challenges and concerns faced by developers. We first employ a quantitative analysis of the metadata from 29,576 forum topics, investigating temporal trends in topic creation, the popularity of topics across different categories, and user contributions at various trust levels. We then qualitatively analyze content from 9,301 recently active topics on developer concerns. From a sample of 886 topics, we construct a taxonomy of concerns in the OpenAI Developer Forum. Our findings uncover critical concerns raised by developers in creating AI-powered applications and offer targeted recommendations to address them. This work not only advances AI-assisted software engineering but also empowers developer communities to shape the responsible evolution and integration of AI technology in society.",
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"title": "How to Dynamically Create and Populate Word Document Templates with Form Data?",
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"title": "Lung Complications after Allogeneic Hematopoietic Cell Transplant and Outcomes: Implications Beyond Bronchiolitis Obliterans Syndrome.",
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"content": "Lung chronic graft-versus-host disease (cGVHD) after allogeneic hematopoietic cell transplantation (HCT) comprises heterogeneous pulmonary phenotypes known as lung complications after transplantation (LCAT). While bronchiolitis obliterans syndrome (BOS) is well recognized and is associated with poor survival, restrictive phenotypes-including HCT-associated organizing pneumonia (HCT-OP) and truncal sclerosis (TS)- remain poorly defined. Prior studies often grouped restrictive phenotypes, potentially obscuring phenotype-specific risk profiles and introducing survival bias by not taking into account the variable timing of LCAT onset. Direct comparison between specific LCAT phenotypes and patients with cGVHD without lung involvement is limited, leaving uncertainty regarding the relative prognostic impact of individual LCAT phenotypes.",
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"title": "Simulated Reasoning and Self-Verification for Psychiatric Diagnosis in Generalist Large Language Models: Comparative Evaluation.",
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"content": "Large language models (LLMs) and, more recently, large reasoning models (LRMs) have rapidly garnered significant interest for application in psychiatry and behavioral health. However, recent studies have identified significant shortcomings and potential risks in the performance of LLM-based systems, complicating their application to psychiatric diagnosis. Two promising approaches to addressing these challenges and improving the efficacy of these models are simulated reasoning (SR) and self-verification (SV), in which additional \"reasoning tokens\" are used to guide model output, either during or after inference.",
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"title": "Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial.",
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"content": "Contrast agents are widely used in medical imaging to improve tissue differentiation, but they can cause serious inflammatory responses, including contrast-induced nephropathy (CIN). Dexpanthenol (DEX) is known for its antioxidant, anti-inflammatory, and anti-apoptotic properties. This study aimed to investigate the potential protective effects of dexpanthenol in a rat model of diatrizoate-induced CIN. In this study, 32 Wistar albino rats were randomly divided into four groups: control, Urografin (URO; 10 mL/kg, intraperitoneal [i.p.]), URO + DEX (500 mg/kg, i.p. for 3 days), and DEX alone. Renal function markers (serum urea and creatinine), total oxidant status (TOS), total antioxidant status (TAS), and oxidative stress index (OSI) were measured. Histopathological evaluation and immunohistochemical analysis of tumor necrosis factor-alpha (TNF-α) and caspase-3 (Cas-3) were performed. Additionally, SIRT1, Bcl-2, Bax, and p53 mRNA expression levels were assessed. URO administration increased TOS and OSI values and caused significant renal histopathological damage. TNF-α and Cas-3 immunoreactivity, along with Bax and p53 gene expression, were significantly elevated, while Bcl-2 and SIRT1 expression was suppressed. DEX treatment provided partial improvement in these changes, contributed to the preservation of renal architecture, and was associated with improvements in biochemical and molecular parameters approaching those of the control levels. In conclusion, DEX may exert nephroprotective effects against CIN by reducing oxidative stress, inhibiting pro-inflammatory and apoptotic pathways, and increasing anti-apoptotic gene expression. These findings provide a preclinical basis supporting the idea that dexpanthenol is a promising candidate for further translational and clinical studies in contrast-induced renal injury.",
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"title": "Learning Dexterous In-Hand Manipulation",
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"content": "As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world.",
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"content": "On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.",
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"title": "Competitive Programming with Large Reasoning Models",
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"content": "We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered inference strategies designed for competing in the 2024 International Olympiad in Informatics (IOI). We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile. Under relaxed competition constraints, o1-ioi achieved a gold medal. However, when evaluating later models such as o3, we find that o3 achieves gold without hand-crafted domain-specific strategies or relaxed constraints. Our findings show that although specialized pipelines such as o1-ioi yield solid improvements, the scaled-up, general-purpose o3 model surpasses those results without relying on hand-crafted inference heuristics. Notably, o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors. Overall, these results indicate that scaling general-purpose reinforcement learning, rather than relying on domain-specific techniques, offers a robust path toward state-of-the-art AI in reasoning domains, such as competitive programming.",
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"content": "The rapid advancement of large language models (LLMs), represented by OpenAI's GPT series, has significantly impacted various domains such as natural language processing, software development, education, healthcare, finance, and scientific research. However, OpenAI APIs introduce unique challenges that differ from traditional APIs, such as the complexities of prompt engineering, token-based cost management, non-deterministic outputs, and operation as black boxes. To the best of our knowledge, the challenges developers encounter when using OpenAI APIs have not been explored in previous empirical studies. To fill this gap, we conduct the first comprehensive empirical study by analyzing 2,874 OpenAI API-related discussions from the popular Q&A forum Stack Overflow. We first examine the popularity and difficulty of these posts. After manually categorizing them into nine OpenAI API-related categories, we identify specific challenges associated with each category through topic modeling analysis. Based on our empirical findings, we finally propose actionable implications for developers, LLM vendors, and researchers.",
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"content": "The OpenAI Gym provides researchers and enthusiasts with simple to use environments for reinforcement learning. Even the simplest environment have a level of complexity that can obfuscate the inner workings of RL approaches and make debugging difficult. This whitepaper describes a Python framework that makes it very easy to create simple Markov-Decision-Process environments programmatically by specifying state transitions and rewards of deterministic and non-deterministic MDPs in a domain-specific language in Python. It then presents results and visualizations created with this MDP framework.",
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"title": "Voices from the Frontier: A Comprehensive Analysis of the OpenAI Developer Forum",
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"content": "OpenAI's advanced large language models (LLMs) have revolutionized natural language processing and enabled developers to create innovative applications. As adoption grows, understanding the experiences and challenges of developers working with these technologies is crucial. This paper presents a comprehensive analysis of the OpenAI Developer Forum, focusing on (1) popularity trends and user engagement patterns, and (2) a taxonomy of challenges and concerns faced by developers. We first employ a quantitative analysis of the metadata from 29,576 forum topics, investigating temporal trends in topic creation, the popularity of topics across different categories, and user contributions at various trust levels. We then qualitatively analyze content from 9,301 recently active topics on developer concerns. From a sample of 886 topics, we construct a taxonomy of concerns in the OpenAI Developer Forum. Our findings uncover critical concerns raised by developers in creating AI-powered applications and offer targeted recommendations to address them. This work not only advances AI-assisted software engineering but also empowers developer communities to shape the responsible evolution and integration of AI technology in society.",
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"title": "OpenAI Gym",
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"content": "OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.",
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"title": "OpenAI Gym",
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"content": "OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.",
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"title": "The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming",
"url": "https://doi.org/10.1145/3511861.3511863",
"content": "Recent advances in artificial intelligence have been driven by an exponential growth in digitised data. Natural language processing, in particular, has been transformed by machine learning models such as OpenAI’s GPT-3 which generates human-like text so realistic that its developers have warned of the dangers of its misuse. In recent months OpenAI released Codex, a new deep learning model trained on Python code from more than 50 million GitHub repositories. Provided with a natural language description of a programming problem as input, Codex generates solution code as output. It can also explain (in English) input code, translate code between programming languages, and more. In this work, we explore how Codex performs on typical introductory programming problems. We report its performance on real questions taken from introductory programming exams and compare it to results from students who took these same exams under normal conditions, demonstrating that Codex outscores most students. We then explore how Codex handles subtle variations in problem wording using several published variants of the well-known “Rainfall Problem” along with one unpublished variant we have used in our teaching. We find the model passes many test cases for all variants. We also explore how much variation there is in the Codex generated solutions, observing that an identical input prompt frequently leads to very different solutions in terms of algorithmic approach and code length. Finally, we discuss the implications that such technology will have for computing education as it continues to evolve, including both challenges and opportunities.",
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"title": "OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare",
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"title": "ns-3 meets OpenAI Gym",
"url": "https://doi.org/10.1145/3345768.3355908",
"content": "Recently, we have seen a boom of attempts to improve the operation of networking protocols using machine learning techniques. The proposed reinforcement learning (RL) based control solutions very often overtake traditionally designed ones in terms of performance and efficiency. However, in order to reach such a superb level, an RL control agent requires a lot of interactions with an environment to learn the best policies. Similarly, the recent advancements in image recognition area were enabled by the rise of large labeled datasets (e.g. ImageNet). This paper presents the ns3-gym - the first framework for RL research in networking. It is based on OpenAI Gym, a toolkit for RL research and ns-3 network simulator. Specifically, it allows representing an ns-3 simulation as an environment in Gym framework and exposing state and control knobs of entities from the simulation for the agent's learning purposes. Our framework is generic and can be used in various networking problems. Here, we present an illustrative example from the cognitive radio area, where a wireless node learns the channel access pattern of a periodic interferer in order to avoid collisions with it. The toolkit is provided to the community as open-source under a GPL license.",
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"title": "Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay",
"url": "https://doi.org/10.15294/elt.v12i1.64069",
"content": "ChatGPT is a product of AI that is currently being widely discussed on Twitter. This research reviews how ChatGPT writes English essays. This research is descriptive qualitative. The analysis shows that we can access ChatGPT on openai.com or chat.openai.com on the browser. If we do not have an account, we register via email, Google, or Microsoft account. After login, enter a question or statement in the conversation column provided. Send it and ChatGPT will respond and the answer appear quickly. The researcher tries ChatGPT “Can you help me in doing my English assignment?\", and the ChatBot replies \"Of course! I'd be happy to help you with your English assignment. What do you need help with? Do you have a specific question or task that you're working on, or is there a broader topic that you'd like help with? It would be helpful to have some more information so that I can better understand how I can assist you\". Based on several tries, ChatGPT can answer all questions on various topics such as English essays including a descriptive text about Solo and My Family, recount text about personal experience and unforgettable moments, resolution in 2023, and future career. ChatGPT considers the event orders and writing order, including using main, explanatory sentences, and a conclusion. It uses two voices both active and passive voice. Besides, it considers tenses use related to the given topic essay. However, from examples of English essays produced by ChatGPT, it certainly requires further research to find out that the essay results are grammatically accurate.",
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"title": "Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo",
"url": "http://arxiv.org/abs/1608.05742",
"content": "This paper presents an extension of the OpenAI Gym for robotics using the Robot Operating System (ROS) and the Gazebo simulator. The content discusses the software architecture proposed and the results obtained by using two Reinforcement Learning techniques: Q-Learning and Sarsa. Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions.",
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"title": "A Study on the Utilization of OpenAI ChatGPT as a Second Language Learning Tool",
"url": "https://doi.org/10.33851/jmis.2023.10.1.79",
"content": "In November 2022, ChatGPT was introduced and caused a sensation, gathering 100 million users only within two months. ChatGPT’s capability of generating high-quality sentences demonstrated potential to be applied in a wide range of fields. In this paper, we explore the utilization of ChatGPT as a second language learning tool and scrutinize its suitability. Specifically, we analyzed technical aspects of ChatGPT including its principles, history, and features. We then assessed its ability from two different perspectives: designing course contents and teaching these contents based on Task-Based Language Teaching (TBLT) method. Firstly, we built up a Korean ESL learner persona who is an intermediate level and directed ChatGPT to construct a course for business English writing. Second, we asked ChatGPT to teach business English writing by TBLT method. Although there were areas for improvement, the results were promising, indicating that ChatGPT was able to execute given prompts to act as a language learning tool. By highlighting opportunities and concerns of ChatGPT in the language learning context, this paper implied its potential to create enhanced learning experience by facilitating a supportive learning environment. Further research is expected to investigate its effectiveness in diverse contexts and purposes.",
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"title": "Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports",
"url": "https://doi.org/10.1016/j.clinimag.2023.06.008",
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"title": "ns3-gym: Extending OpenAI Gym for Networking Research",
"url": "http://arxiv.org/abs/1810.03943",
"content": "OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes a large number of well-known problems that expose a common interface allowing to directly compare the performance results of different RL algorithms. Since many years, the ns-3 network simulation tool is the de-facto standard for academic and industry research into networking protocols and communications technology. Numerous scientific papers were written reporting results obtained using ns-3, and hundreds of models and modules were written and contributed to the ns-3 code base. Today as a major trend in network research we see the use of machine learning tools like RL. What is missing is the integration of a RL framework like OpenAI Gym into the network simulator ns-3. This paper presents the ns3-gym framework. First, we discuss design decisions that went into the software. Second, two illustrative examples implemented using ns3-gym are presented. Our software package is provided to the community as open source under a GPL license and hence can be easily extended.",
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"title": "人工智能赋能教育的研究进展、热点与趋势",
"url": "https://doi.org/10.63887/jerp.2025.1.8.109",
"content": "人工智能的快速发展不断拓展其在教育领域的应用边界，已成为推动教育高质量发展的关键力量。文章运用CiteSpace对2015—2025年间1229篇国内核心文献进行计量分析，从发文趋势、关键词和突现词等维度探讨人工智能教育研究现状。结果显示，研究主要聚焦于教育应用、新教育模式和生成式人工智能等领域，研究重心逐渐转向技术驱动的教育改革、教育治理及生成式人工智能的优化与创新。文章最后对未来研究提出展望，认为应当将研究覆盖更多教育阶段；综合评估人工智能教育应用的影响以及推动跨学科合作研究。",
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"title": "Physical artificial intelligence (PAI): the next-generation artificial intelligence",
"url": "https://doi.org/10.1631/fitee.2200675",
"content": "人工智能(AI)已经成为各领域创新和社会进步的驱动力。然而, 其大多数工业应用集中在信号处理领域, 这依赖于不同传感器产生和收集的数据。最近, 一些研究人员提出将数字人工智能和物理人工智能结合, 这可能带来人工智能理论基础的重大进步。在本文中, 我们探讨了物理人工智能的概念并提出两个子领域: 集成式物理人工智能和分布式物理人工智能。我们还讨论了物理人工智能可持续发展和治理所面临的挑战和机遇。由于物理人工智能需要连续处理来自边缘、雾和物联网的分布式信号, 它可以被看作分布式计算连续系统在人工智能领域的延伸。",
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"title": "生成式人工智能犯罪的刑事责任分配探究",
"url": "https://doi.org/10.54254/3050-2160/2025.22194",
"content": "新一轮科技革命与产业变革曙光可见，在数字经济不断推进的大背景下，人工智能技术蒸蒸日上，并与多种应用场景深度融合，正成为推动人类进入智能时代的决定性力量。然而，人工智能的快速发展影响着刑法适用，生成式人工智能的刑事责任主体资格问题一直是学界争议的焦点。笔者认为，无论人工智能发展到何种程度，本质上仍是作为人类犯罪的工具，不具备刑事责任主体特征。对涉及人工智能犯罪做好相应预案，需要对生成式人工智能犯罪的刑事责任深度探究并正确分配以进行刑法规制。对于生成式人工智能犯罪，仍需坚持自然人主义，在责任分配的考量中，应当从三个核心层面出发，即设计环节的责任归属、生产过程的责任承担以及最终使用者的责任界定，从而确保责任的合理分摊和有效落实。",
"engine": "openalex",
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},
{
"title": "State-of-art of Compliant Mechanisms and Their Applications",
"url": "https://doi.org/10.3901/jme.2015.13.053",
"content": "摘要： 柔性机构自20世纪80年代提出以来迅猛发展，已成为现代机构学一个重要分支。短短不足30年间，柔性机构设计理论的构建与发展，为柔性机构成功应用奠定了坚实基础。随着对柔性及柔性机构认识不断深入，柔性机构得到了广泛应用，不断涌现新的成功实例。继5年前综述了柔性机构设计方法研究进展之后，尝试从应用的视角鸟瞰一下柔性机构的最新进展。通过将柔性机构的主体应用划分为精密工程、仿生机器人、智能材料结构三大主阵地，概述柔性机构在每个阵地中的应用进展及研究热点情况，并对其发展做了展望。对四种最具发展潜力和应用前景的新型柔性机构(胞元式柔性机构、辅助接触式柔性机构、平面折展机构、柔性静平衡机构)进行简单描述。",
"engine": "openalex",
"category": "science"
},
{
"title": "Research Progress and Trend of Key Technology of Intelligent Spraying Robot",
"url": "https://doi.org/10.3901/jme.2022.07.053",
"content": "摘要： 喷涂作为现代产品制造工艺中的一个重要环节，不仅起到美观、防护以及其他特殊作用，也日益成为产品价值的重要组成部分，在家具、航空航天、军工等领域中占据着重要地位。智能喷涂机器人是由计算机、传感、视觉、智能控制等多学科技术交叉综合而构成的复杂机电系统。智能喷涂机器人作为智能喷涂技术的核心，其发展与新材料、新设计和新方法的应用密不可分。针对智能喷涂机器人关键技术研究的共性问题，从喷涂机器人机构设计、喷涂系统动态性能监控、喷涂轨迹自动规划、喷涂质量检测方面综述了当前取得的研究成果；而后针对喷涂系统智能化进程中在机器人机构设计和柔性喷涂系统集成研究面临的挑战进行了分析和讨论；最后，对智能喷涂机器人关键技术研究方面未来的发展方向进行了展望和总结，为喷涂机器人发展方向与关键技术性能提升提供参考，推动喷涂技术全面进入智能化。",
"engine": "openalex",
"category": "science"
},
{
"title": "Human-centric Smart Manufacturing for Industry 5.0",
"url": "https://doi.org/10.3901/jme.2022.18.088",
"content": "摘要： 工业4.0是技术驱动型的工业模式，注重生产流程的优化、效率和生产力的提高，而忽视了“人”这一最重要的主体。因此，作为一种价值驱动型的新工业模式——工业5.0的概念逐渐引起人们的重视，将工业重心由技术转向对人身心健康的关怀、自然的可持续发展及工业的弹性等方面，而人机智能协作是走向未来以人为本的智能制造的关键。这种价值观的转变预示着人本智能制造会越来越受到重视，因而很有必要对如此新兴的智能制造模式开展详细研究，旨在为工业5.0理念下的人本智造发展提供有益的借鉴参考。为此，首先分析工业革命及制造范式的演化并指出目前制造模式存在的典型问题；然后给出工业5.0的定义，并分析其主要特征以及“人-社会-自然-技术”视角下与工业4.0的区别和联系；接着对工业5.0背景下人机交互方式及人机共生关系进行详细论述；最后探讨元宇宙背景下的人本智造演化及其面临的问题进行展望。",
"engine": "openalex",
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},
{
"title": "生物特征识别学科发展报告",
"url": "https://doi.org/10.11834/jig.210078",
"content": "从手机解锁、小区门禁到餐厅吃饭、超市收银，再到高铁进站、机场安检以及医院看病，人脸、虹膜和指纹等生物特征已成为人们进入万物互联世界的数字身份证。生物特征识别赋予机器自动探测、捕获、处理、分析和识别数字化生理或行为信号的高级智能，是一个典型而又复杂的模式识别问题，一直处于人工智能技术发展前沿，在新一代人工智能规划、“互联网+”行动计划等国家战略中具有重要地位。由于生物特征识别涉及公众利益攸关的隐私、道德和法律等问题，近期也引起了广泛的社会关注。本文系统综述了生物特征识别学科发展现状、新兴方向、存在问题和可行思路，深入梳理了人脸、虹膜、指纹、掌纹、静脉、声纹、步态、行人重识别以及多模态融合识别的研究进展，以人脸为例重点介绍了生物特征识别领域近些年受到关注的新方向——对抗攻击和防御、深度伪造和反伪造，最后剖析总结了生物特征识别领域存在的3大挑战问题——“感知盲区”、“决策误区”和“安全红区”。本文认为必须变革和创新生物特征的传感、认知和安全机制，才有可能取得复杂场景生物识别学术研究和技术应用的根本性突破，破除现有生物识别技术的弊端，朝着“可感”、“可知”和“可信”的新一代生物特征识别总体目标发展。",
"engine": "openalex",
"category": "science"
},
{
"title": "Service-oriented Smart Manufacturing",
"url": "https://doi.org/10.3901/jme.2018.16.011",
"content": "摘要： 新一代信息技术（如物联网、大数据、云计算、数字孪生等）与制造的融合发展，促使各制造强国纷纷出台各自的先进制造发展战略，如美国的“工业互联网”和德国的“工业4.0”等。同时，在“制造强国”和“网络强国”大战略背景下，我国也先后出台“中国制造2025”和“互联网+”等制造业国家发展实施战略。其共同主题之一是结合和使用新一代信息技术和人工智能技术，实现制造的物理世界和信息世界互联互通与融合，最终实现智能制造。同时，服务也被各制造强国共同列为实现智能制造的关键技术内容之一。智能服务已成为产业模式变革的核心，制造业的服务化趋势日益凸显。在分析总结智能制造的发展趋势和典型特征基础上，结合新一代信息技术与服务的思想，探索提出了面向服务的智能制造（Service-orientedsmart manufacturing，SoSM），设计了SoSM的实施架构，讨论了SoSM的内涵、关键实施技术与未来研究方向。",
"engine": "openalex",
"category": "science"
},
{
"title": "Human-centric Smart Manufacturing: Evolution and Outlook",
"url": "https://doi.org/10.3901/jme.2022.18.002",
"content": "摘要： 新一轮科技与产业革命正推动制造业向更高层次发展，促进新一代信息技术与先进制造技术深度融合，也促进智能制造向自主智能方向发展，但生产的目的是更好地满足人类的需求，还需要考虑生产对社会的作用和贡献，因而以人为本的智能制造日益受到关注和重视。人仍然是一个制造系统最为重要的生产要素，需要以人为中心探讨智能制造问题。为此，首先从工业革命进程中人机交互与企业创新的演进发展、生产模式与人类需求的递进关联关系两个方面论述智能制造面临的人本问题，阐明在智能制造中引入“以人为本”理念的必要性；接着从首次工业革命的机器化大规模生产到当今包容性长尾制造的制造业发展历史长河之中，归纳总结出人本制造演进脉络，并以智能包容性长尾制造为例说明人本智能制造理念实现，最后对人本智能制造的未来发展趋势做出展望。",
"engine": "openalex",
"category": "science"
},
{
"title": "文档智能分析与识别前沿：回顾与展望",
"url": "https://doi.org/10.11834/jig.221112",
"content": "文档分析与识别（简称文档识别）技术将各种非结构化文档数据（图像、联机笔迹）转化为结构化数据，便于计算机处理和理解，应用场景十分广阔。20世纪60年代以来，文档识别方法研究与应用受到广泛关注并取得巨大进展。得益于深度学习技术的发展和应用，文档识别的性能快速提升，相关技术在文档数字化、票据处理、笔迹录入、智能交通、文档检索与信息抽取等领域得到广泛应用。首先介绍文档识别的背景和技术范畴，回顾该领域发展历史，然后重点对深度学习方法兴起以来的研究进行综述，分析当前技术存在的不足，并建议未来值得重视的研究方向。研究现状综述部分，按文档分析与识别的几个主要技术环节（文档图像预处理、版面分析、场景文本检测、文本识别、结构化符号和图形识别、文档检索与信息抽取）分别进行介绍，简述传统方法研究的代表性工作，重点介绍深度学习方法研究的新进展。总体上，当前研究对象向深度、广度扩展，处理方法全面转向深度神经网络模型和深度学习方法，识别性能大幅提升且应用场景不断扩展。在现状分析基础上，指出当前技术在识别精度和可靠性、可解释性、学习能力和自适应性等方面还有明显不足。最后从提升性能、应用扩展、提升学习能力几个角度提出一些研究方向。从提升性能角度，研究问题包括文本识别可靠性、可解释性、全要素识别、长尾问题、多语言、复杂版面分割与理解、变形文档分析与识别等。应用扩展包括新应用（如机器人流程自动化（robotic process automation，RPA）、文字信息抄录、考古）和新技术问题（语义信息抽取、跨模态融合、面向应用的推理决策等）两方面。从提升学习能力角度，相关问题包括小样本学习、迁移学习、多任务学习、领域自适应、结构化预测、弱监督学习、自监督学习、开放集学习和跨模态学习等。;Document analysis and recognition（called document recognition in brief）is aimed to covert non-structured documents（typically，document images and online handwriting）into structured texts for facilitating computer processing and understanding. It is needed in wide applications due to the pervasive communication and usage of documents. The field of document recognition has attracted intensive attention and produced enormous progress in research and applications since 1960s. Particularly，the recent development of deep learning technology has boosted the performance of document recognition remarkably compared to traditional methods，and the technology has been applied successfully to document digitization，form processing，handwriting input，intelligent transportation，document retrieval and information extraction. In this article，we first introduce the background and involved techniques of document recognition，give an overview of the history of research（divided into four periods according to the objects of research，the methods and applications），and then review the main research progress with emphasis on deep learning based methods developed in recent years. After identifying the insufficiency of current technology，we finally suggest some important issues for future research. The review of recent progress is divided into sections corresponding to main processing steps，namely image pre-processing，layout analysis，scene text detection，text recognition，structured symbol and graphics recognition，document retrieval and information extraction. The review of recent progress is divided into sections corresponding to the main processing steps，namely image pre-processing，layout analysis，scene text detection，text recognition，structured symbol and graphics recognition，document retrieval and information extraction. 1）Due to the popularity of camera-captured document images，the current main task in image pre-processing is the rectification of distorted image while the task of binarization is still concerned. Recent methods are mostly end-to-end deep learning based transformation methods. 2）Layout analysis is dichotomized into physical layout analysis（page segmentation）and logical layout analysis（semantic region segmentation and reading order prediction）. Recent page segmentation methods based on fully convolutional network（FCN）or graph neural network（GNN） have shown promises. Logical layout analysis has been addressed by deep neural networks fusing multi-modal information. Table structure analysis is a special task of layout analysis and has been studied intensively in recent years. 3）Scene text detection is a hot topic in document analysis and computer vision fields. Deep learning based methods for text methods can be divided into regression-based methods，segmentation-based methods and hybrid methods. FCN is prevalently used for extracting visual features，based on which models are built to predict text regions. 4）Text recognition is the core task in document analysis. We review recent works for handwritten text recognition and scene text recognition，which share some common strategies but also show different preferences. There are two main streams of methods：segmentation-based and sequence-to-sequence learning methods. The convolutional recurrent neural network（CRNN）model has received high attention in recent years and is being extended in respect of encoding，decoding or learning strategies，while segmentationbased methods combining deep learning are still performing competitively. A noteworthy tendency is the extension of text line recognition to page-level recognition. Following text recognition，we also review the works of end-to-end scene text recognition（also called as text spotting），for which text detection and recognition models are learned jointly. 5）Among symbol and graphics in documents，mathematical expressions and flowcharts have received increasing attention. Recent methods for mathematical expression recognition are mostly image-to-markup generation methods using encoder-decoder models，while graph-based methods promise in generating both recognition and segmentation results. Flowchart recognition is addressed using structured prediction models such as GNN. 6）Document retrieval concerned mainly keyword spotting in pre-deep learning era，while recent works focus on information extraction（spotting semantic entities）by fusing layout and language information. Pre-trained layout and multi-modal language models are showing promises，while visual information is not considered adequately. Overall，the recent progress shows that the objects of recognition are expanded in breadth and depth，the methods are getting closer to deep neural networks and deep learning，the recognition performance is improved constantly，and the technology is applied to extensive scenes. The review also reveals the insufficiencies of the current technology in accuracy and reliability on various tasks，the interpretability，the learning ability and adaptability. Future works are suggested in respect of performance promotion，application extension，and improved learning. Issues of performance promotion include the reliability of recognition，interpretability，omni-element recognition，long-tailed recognition，multilingual documents，complex layout analysis and understanding，recognition of distorted documents. Issues related to applications include new applications（such as robotic process automation（RPA），text scription in natural scenes，archeology），new technical problems involved in applications（such as semantic information extraction，cross-modal fusion，reasoning and decision related to application scenes）. Aiming to improve the automatic system design，learning ability and adaptability，the involved learning problems/methods include small sample learning，transfer learning，multi-task learning，domain adaptation，structured prediction，weakly-supervised learning，self-supervised learning，open set learning， and cross-modal learning.",
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{
"title": "Diagnosis, care pathways, and Complementary and Alternative Medicine (CAM) use among digestive cancer patients in Benin: a qualitative study.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42260244",
"content": "In Benin, patients with digestive cancers face multiple challenges, including diagnostic delays and limited access to care. In this context, the use of complementary and alternative medicine (CAM) is common. This study aimed to describe the diagnostic and care pathways of patients with digestive cancers in Benin, and their experiences with both complementary and alternative medicine (CAM) and conventional treatments.",
"engine": "pubmed",
"category": "science"
},
{
"title": "Risk and liability in the deployment of AI systems for surgery: a SAGES white paper.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42260175",
"content": "Artificial intelligence (AI) is increasingly utilized in surgical care for decision support, operative planning, intraoperative guidance, and autonomous functions. While these systems can enhance efficiency and clinical performance, they also introduce risks related to technology, human factors, legal issues, and ethics. Current regulatory and legal frameworks are not fully equipped to address the challenges of AI-assisted surgery.",
"engine": "pubmed",
"category": "science"
},
{
"title": "Hybrid carbon matrices enable the suppression of polysulfide shuttle effect in Li-S batteries.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42260086",
"content": "Lithium-sulfur (Li-S) batteries offer high theoretical energy density but face critical challenges due to sulfur's poor conductivity and the polysulfide shuttle effect. Here we report a novel cathode design utilizing a hybrid carbon matrix derived from buckwheat biomass and single-walled carbon nanotubes (SWCNT) to overcome these issues. The buckwheat-derived hard carbon (HC), obtained at 1000 °C, provides hierarchical porosity to anchor polysulfides and buffer sulfur expansion, while SWCNT (optimized at 6%) creates a conductive network. As a result, S@SP/HC/SWCNT cathode delivers an initial discharge capacity of ~ 1250 mAh g",
"engine": "pubmed",
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{
"title": "Efficacy and Safety Profile of a Triple Single-Pill Combination of Valsartan/Amlodipine/Chlorthalidone in Patients with Uncontrolled Hypertension.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42259717",
"content": "This randomized, double-blind, multicenter Phase III study evaluated the efficacy and safety profile of a single-pill triple combination of valsartan/amlodipine/chlorthalidone (KDF1901, Valdipine Plus) compared with a dual combination of valsartan/amlodipine (KDF1901-R) in patients with essential hypertension.",
"engine": "pubmed",
"category": "science"
},
{
"title": "Lung Complications after Allogeneic Hematopoietic Cell Transplant and Outcomes: Implications Beyond Bronchiolitis Obliterans Syndrome.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42259420",
"content": "Lung chronic graft-versus-host disease (cGVHD) after allogeneic hematopoietic cell transplantation (HCT) comprises heterogeneous pulmonary phenotypes known as lung complications after transplantation (LCAT). While bronchiolitis obliterans syndrome (BOS) is well recognized and is associated with poor survival, restrictive phenotypes-including HCT-associated organizing pneumonia (HCT-OP) and truncal sclerosis (TS)- remain poorly defined. Prior studies often grouped restrictive phenotypes, potentially obscuring phenotype-specific risk profiles and introducing survival bias by not taking into account the variable timing of LCAT onset. Direct comparison between specific LCAT phenotypes and patients with cGVHD without lung involvement is limited, leaving uncertainty regarding the relative prognostic impact of individual LCAT phenotypes.",
"engine": "pubmed",
"category": "science"
},
{
"title": "Simulated Reasoning and Self-Verification for Psychiatric Diagnosis in Generalist Large Language Models: Comparative Evaluation.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42258613",
"content": "Large language models (LLMs) and, more recently, large reasoning models (LRMs) have rapidly garnered significant interest for application in psychiatry and behavioral health. However, recent studies have identified significant shortcomings and potential risks in the performance of LLM-based systems, complicating their application to psychiatric diagnosis. Two promising approaches to addressing these challenges and improving the efficacy of these models are simulated reasoning (SR) and self-verification (SV), in which additional \"reasoning tokens\" are used to guide model output, either during or after inference.",
"engine": "pubmed",
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},
{
"title": "Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42258213",
"content": "Health care systems are increasingly considering large language model (LLM)-based chatbots for vaccine communication, but evidence that they improve durable, behaviorally relevant outcomes beyond existing health materials is limited.",
"engine": "pubmed",
"category": "science"
},
{
"title": "Divergent modes of episodic organization underlie whether emotional learning enhances memory across event boundaries.",
"url": "https://www.ncbi.nlm.nih.gov/pubmed/42258125",
"content": "Episodic memory organizes continuous experience into discrete events, often limiting integration across temporal gaps. Emotionally salient experiences, such as learning about threats, may nevertheless enhance memory for motivationally relevant information across nominal event boundaries. Yet evidence for such cross-boundary modulation is mixed, suggesting that emotional learning may not exert a uniform effect on episodic memory across individuals. Here, we examined whether this heterogeneity reflects a mixture of divergent tendencies in how individuals structure their experience in memory - by temporal boundaries or by emotional relevance. Young adults (N = 285) incidentally encoded neutral images from two categories (animals, tools) across three temporally separated phases: pre-conditioning, conditioning, and post-conditioning. During conditioning, one category was partially reinforced with mild electric shocks to establish a category-level threat association. Memory was tested 24 h later using a surprise old-new recognition task. Although no reliable cross-phase enhancement was evident at the group level, data-driven individual-level analyses revealed two distinct patterns of episodic memory modulation. One pattern showed declining memory across phases, with emotional effects confined to shock-predictive items encoded during conditioning. The other showed memory structured around the emotional learning episode, with category-selective prioritization extending into the post-conditioning phase. These findings suggest that enhanced memory for subsequent threat-relevant information emerges specifically in individuals for whom the emotional learning episode, rather than nominal temporal structure, becomes the primary anchor of memory organization.",
"engine": "pubmed",
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{
"title": "Drug-associated cytokine release syndrome: a FAERS pharmacovigilance study with complementary transcriptomic analysis.",
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