Core Viewpoint - The article discusses the development of OpenNovelty, an automated novelty analysis system designed to enhance the academic review process by providing verifiable evidence for claims of novelty in research papers [4][25]. Group 1: System Overview - OpenNovelty is a collaboration between Fudan University's NLP research team and the academic search platform WisPaper, aimed at addressing the challenges of assessing novelty in academic submissions [4]. - The system emphasizes the need for verifiable evidence when judging the novelty of a paper, requiring that any claim of insufficient novelty be supported by traceable evidence from published literature [7][25]. Group 2: Analysis Process - The analysis process consists of four steps: 1. Core information extraction from the paper's title, abstract, and introduction [9]. 2. Literature retrieval and filtering to generate a candidate set of relevant papers [11]. 3. Hierarchical analysis and evidence comparison to assess the novelty claims [14]. 4. Generation of a novelty investigation report that consolidates findings and provides traceable evidence [20][21]. Group 3: System Functionality - The system utilizes a query expansion mechanism to generate multiple semantically equivalent variations of extracted information, ensuring comprehensive literature retrieval [7]. - It categorizes the comparison results into three outcomes: can refute, cannot refute, and unclear, based on the evidence found [15][17][19]. Group 4: Impact and Utility - OpenNovelty serves as an auxiliary tool for reviewers, helping them navigate the literature landscape and focus on critical aspects of the review process [26]. - For authors, it acts as a self-check tool to verify the novelty of their research and identify any overlooked relevant literature [27]. - The system aims to provide a verifiable path for novelty assessment, enhancing accountability in academic publishing [27]. Group 5: Limitations and Future Directions - The team acknowledges the system's limitations, emphasizing that it is a supportive tool rather than a decision-making entity, with final judgments still resting with human reviewers [29][30]. - OpenNovelty is positioned as a third-party auditing system, intended to clarify evidence during the final decision-making phase of the review process [31].

当你在电商平台搜索"苹果"，系统会推荐"水果"还是"手机"？或者直接跳到某个品牌旗舰店？短短一个词，背后承载了完全不同的购买意图。而推荐是否精 准，直接影响用户的搜索体验，也影响平台的转化效率。 查询推荐（Query Suggestion）是现代电商搜索系统中的关键功能，通过在用户输入过程中实时推荐相关查询，帮助用户快速明确意图，提升搜索体验与转化效 率。传统方法通常采用多阶段级联架构（MCA），虽然在效率与效果之间取得了一定平衡，但由于各阶段目标不一致、长尾查询召回困难等问题，限制了系统性 能的进一步突破。 基于上述问题，快手在业界首次提出端到端的生成式统一查询推荐框架 ——OneSug，成功将召回、粗排、精排等多个阶段统一在一个生成模型中，显著提升了推 荐效果与系统效率，在快手电商场景中实现了业务指标与用户体验的双重提升。 本工作相关成果《OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion》已被人工智能顶级会议 AAAI 2026 接收。 论文链接：https://arxiv.org/abs ...

以下文章来源于瑞芯微电子 ，作者瑞芯微电子 瑞芯微电子 . 传递瑞芯微电子的最新产品信息及市场动态 机器之心发布 一年一度的"科技春晚"CES2026于上周落下帷幕，从今年的主题"定义AI的物理边界（Physical AI）"，可以看出全球科技新趋势正在推动AI从虚拟走向现 实应用，通过多元化的消费电子、机器人、智能汽车等实体形态让生活智能化变得具象。 瑞芯微作为国内AIoT芯片 领域的领军企 业，正在这一科技浪潮中扮演着重要角色。全球首颗3D架构协处理器RK182X系列芯片的技术突破，不仅为全 球"Physical AI"的发展提供强大的硬件和算力支撑，更是推进千行百业用AI重做一遍的AIoT2.0时代的落地进程。同时，瑞芯微将举办AI软件生态大会， 依 托在AIoT千行百业、超过5000家全球客户的广大生态， 搭建起AI软件与市场的桥梁。 瑞芯微RK1 82X：AIoT 2.0的算力引擎 从"功能机"到"智能体"的本质进化 。 基于实测数据显示， RK182X运行Qwen2.5-3B模型输出速度突破百Token，是市场对标产品的3倍； 同时 在多模态视觉语言模型任务上，瑞芯微已率先支 持Qwen3-VL- ...

Group 1 - The core theme of the events is the exploration of human agency in the context of AI, focusing on how to preserve meaningful human decision-making rights amidst the evolving landscape of artificial intelligence [2][4] - The first seminar titled "The Right to Work, Learn, Own & Choose" aims to integrate the technical AI community with AI governance to promote respect for human agency and protect rights related to work, learning, ownership, and choice [2][4] - The event features prominent speakers from various institutions, including Ashok Goel from Georgia Tech and Jungpil Hahn from the National University of Singapore [4] Group 2 - The second seminar, "Agentic AI meets Autonomous Agents and Multiagent Systems," focuses on advancements in intelligent agents based on large language models (LLMs) and the lessons learned in building and deploying these systems [11][13] - This seminar emphasizes the transition of modern "Agentic AI" systems from demonstrations to practical deployment, requiring capabilities in long-term planning, reliable tool usage, and robust interaction with humans and environments [13][14] - Notable speakers include Leslie Kaelbling from MIT and Bo Li from the University of Illinois at Urbana-Champaign, contributing to discussions on the long-term challenges in robotics and multi-agent systems [17]

Core Viewpoint - The article discusses the challenges faced by generative AI voice assistants, particularly in executing simple commands reliably, highlighting a gap between user expectations and actual performance [14][18]. Group 1: User Experience with AI Assistants - Users have reported frustrations with AI voice assistants like Alexa, which fail to execute basic commands such as brewing coffee or turning on lights, despite their advanced capabilities [4][8]. - The transition to generative AI has led to a situation where users experience inconsistent responses, with the AI providing creative but unhelpful reasons for not executing commands [7][16]. Group 2: Technical Limitations of Generative AI - Generative AI introduces a level of randomness that can lead to misunderstandings in command execution, making it unsuitable for tasks requiring precision and reliability [18][22]. - Traditional voice assistants operated on a template-matching basis, ensuring predictable outcomes, while generative models struggle to maintain consistency in system calls [19][23]. Group 3: Potential and Future Directions - Despite current limitations, there is recognition of the potential of generative AI to understand complex tasks and improve user interactions, suggesting a paradigm shift in capabilities [30][34]. - The article suggests that the chaos observed may not be a failure of generative AI but rather a misalignment of its application in contexts where deterministic execution is critical [44].

我们常问：AGI 什么时候到来？你有没有想过，可能它已经来了。 最近，红杉资本合伙人 Pat Grady、Sonya Huang 联合发表了一篇博客，指出 AGI 已经到来，就在此刻。 机器之心编辑部 在他们看来，AGI 不需要一个玄乎的技术定义 —— 它的本质就是「能把事情搞清楚的能力」。而以 Claude Code 为代表的长周期智能体，正是这种能力的第一批 例证。 文中举了一个例子：一位创始人让智能体帮他找一个开发者关系负责人。智能体先在 LinkedIn 上搜索，发现职位头衔说明不了问题；于是转向 YouTube 找技术演 讲，筛选出互动数据亮眼的演讲者；再与 Twitter 交叉比对，找出真正有品味、有粉丝的人；然后检查谁最近发帖变少了 —— 这往往意味着对现职的倦怠；最后锁 定一位刚经历公司裁员、专业方向完全匹配的候选人，起草了一封精准的挖角邮件。 全程 31 分钟。 没有人告诉它该怎么做，它自己形成假设、验证、碰壁、转向，直到找到答案。这就是「把事情搞清楚」。而长周期智能体已经具备了这种能力。 更令人振奋的是，他们给出了一条清晰的指数曲线：长周期智能体的能力每 7 个月翻一番。按此推算，2028 ...

Core Insights - The software industry is at a pivotal moment as AI evolves from code completion to autonomous development agents [1] - Both junior and senior developers face unique challenges due to AI's impact on job roles and responsibilities [2][3] Junior Developer Challenges - Junior developers are experiencing a contraction in growth opportunities as companies are less willing to invest in training, leading to a reduction in entry-level positions [8] - A Harvard study covering 62 million workers found that after the adoption of generative AI, the employment of junior developers decreased by approximately 9%-10% within six quarters, while senior developer employment remained stable [8] - The traditional career path of learning to code and gradually advancing to senior roles is being disrupted, with many companies opting not to hire junior developers [8] Senior Developer Challenges - Senior developers are facing increased pressure as they must manage both architectural decisions and the risks associated with AI and automation systems [2] - The responsibilities of senior engineers are expanding, requiring them to ensure code quality, performance, security, and compliance, while the proportion of time spent writing code is decreasing [2] Future Scenarios - There are two potential futures for junior developers: one where entry-level hiring collapses due to AI automation, and another where demand for developers rebounds as software permeates various industries [8] - The U.S. Bureau of Labor Statistics projects a 15% growth in software-related jobs from 2024 to 2034, indicating a potential resurgence in demand for developers [9] Skills Transition - As AI takes over routine coding tasks, the fundamental coding skills of developers may either degrade or become more critical as developers shift to oversight roles [14] - A significant 84% of developers regularly use AI tools in their work, changing the nature of problem-solving from coding from scratch to assembling AI-generated code snippets [14] Developer Roles Evolution - Developers may evolve into roles focused on overseeing AI-generated outputs or become orchestrators responsible for designing and governing AI-driven systems [19][20] - The industry is witnessing a split in developer discussions, with some advocating for a shift in assessment methods to reflect the new reality of AI-assisted coding [16] Educational Shifts - The traditional four-year computer science degree is being challenged by faster learning paths such as coding bootcamps and online platforms, which are becoming more relevant in a rapidly changing industry [31][32] - By 2024, nearly 45% of companies plan to eliminate the bachelor's degree requirement for certain positions, reflecting a shift towards skills-based hiring [33] Adaptation Strategies - Junior developers should focus on building a broad skill set and actively seek opportunities beyond coding, such as testing and application monitoring [21] - Senior developers need to embrace leadership and architectural responsibilities, ensuring quality standards and mentoring junior staff [23] T-Shaped Engineers - The industry is favoring T-shaped engineers who possess both broad adaptability and deep expertise in one or two areas, as opposed to narrow specialists [25][26] - Nearly 45% of engineering roles now expect candidates to have multi-domain capabilities, highlighting the demand for versatile skill sets [27]

视频世界模型领域又迎来了新的突破！ 复旦大学与腾讯 PCG ARC Lab 等机构的研究者们提出了 VerseCrafter， 这是一个通过显式 4D 几何控制（4D Geometric Control）实现的动态逼真视频世界模型。 它不仅能像「导演」一样精准控制运镜，还能同时指挥场景中多个物体的 3D 运动轨迹，为视频生成引入了物理世界维度。 自 Sora 问世以来，视频世界模型（Video World Models）成为了 AI 领域最热门的研究方向之一。我们希望 AI 不仅能生成视频，更能理解和模拟真实的物理世界。 然而，现有的视频模型往往面临一个核心困境： 视频是在 2D 平面上播放的，但真实世界是 4D（3D 空间 + 时间）的。 VerseCrafter 的核心理念在于： 用一个统一的 4D 几何世界状态（4D Geometric World State）以此驱动视频生成。 它利用静态背景点云和每个物体的 3D 高斯轨 迹，实现了对相机和物体运动的解耦与协同控制。 论文地址： https://arxiv.org/pdf/2601.05138 项目主页： https://sixiaozheng.gi ...

Group 1 - The core discussion revolves around the limitations of the current AI interaction model, primarily chat interfaces, and the need for more personalized and adaptable user interfaces in AI applications [2][4]. - The dominance of chat interfaces is attributed to several factors: the naturalness of text-based commands for models, the anchoring effect of ChatGPT on product design, high fault tolerance in operations, and the simplicity of designing chat interfaces [5][6][7]. - There is a belief that the era of chat-based interactions will be short-lived, as more mature interaction paradigms are expected to emerge, similar to the evolution from early computer models to modern interfaces [4][7]. Group 2 - The pain points of single chat interfaces have prompted industry players to explore various interaction designs that better align with user preferences in specific work scenarios [9]. - Users have reported that chat interfaces lead to unnecessary back-and-forth interactions, which can waste time, and many LLM products are now incorporating specialized functions and interfaces to address these issues [7][9]. - There are significant concerns regarding the high learning curve and context management difficulties associated with chat interfaces, which can alienate nearly half of potential users [7][9].

编辑｜泽南、杨文 真实到有一点点可怕。 总有人说直播网红是「换头怪」，全靠滤镜整容，现在 AI 给你直接换个人，你受得了吗？ 最近，社交媒体上疯传的一些视频让无数人感到震惊。 有网友做出任意表情、动作，然后无缝替换到《怪奇物语》中的米莉・博比・布朗、芬恩・伍夫哈德等多位演员身上，实现零成本的「无限角色互换」。 现在 AI 可以精准地捕捉像眨眼、张嘴、侧脸等微表情，效果和画面背景之间也没有任何的割裂感，几乎看不出破绽来。 有的人已经把这些技术整合成了 APP。比如这个叫 levelsio 的人就展示了一系列 AI 直播的效果，并表示，虚拟网红的时代已经来临。 风险投资机构 a16z 合伙人 Justine Moore 直言：「我们对 AI 如何迅速改变生产流程完全没有准备好。一些最新的视频模型已经对好莱坞产生了直接而重大的影 响，角色可以无限替换，成本却几乎可以忽略不计。」 在 X 上，这类视频动辄就能获得超百万播放量，评论区也两极分化严重。有人惊讶技术进步的飞速，有人则担心深伪用于诈骗与破坏信任，「连人类身份都难以 证明」，有人甚至提到以后或许需要「眼球扫描」来验证真实性。 这已经不是普通的 3D 皮套了，很 ...