Core Viewpoint - Apple is reportedly developing an AI-driven wearable "Pin" device, which is still in the early stages of development and may not be released until 2027 [1]. Group 1: Product Specifications - The device is expected to be similar in size to the AirTag, featuring a "thin, flat circular" design made of aluminum and glass. It will include a standard lens and a wide-angle lens for environmental sensing, three microphones, a speaker, a physical button on the side, and support for wireless charging, potentially using a magnetic induction charging interface similar to the Apple Watch [3]. Group 2: Market Context and Competition - The entry of Apple into the hardware AI Pin market could revitalize interest, especially given the previous challenges faced by companies like Humane, which aimed to create a smartphone replacement but faced significant product failures and ultimately was acquired by HP for $116 million [5][10]. - Humane's AI Pin, launched in November 2023 at a price of $699 with a monthly subscription fee of $24, saw disappointing sales of only 10,000 units by summer 2024, far below its target of 100,000 units [7][8]. - The failure of Humane's product was attributed to multiple factors, including immature technology, high development costs, and an exorbitant price point [10]. Group 3: Future Prospects - The AI hardware market is seen as poised for growth, with various AI wearable devices like AI glasses and AI headphones being developed as potential next-generation interaction points [10]. - Apple is reportedly accelerating the development of its AI Pin to compete with OpenAI's upcoming wearable device, which is expected to be launched in the second half of 2026 [10][11]. - OpenAI has hinted at a new hardware device that promises simplicity and ease of use, although specific details remain undisclosed [11].

Core Insights - Meta's newly established AI team has delivered its first key models internally this month, as stated by CTO Andrew Bosworth, who described the models as "very good" [1] - The company is developing a text AI model codenamed Avocado, expected to be released in Q1, and an image and video AI model codenamed Mango [1] - A technical report titled "Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes" has been uploaded to arXiv, reviewing the data and technical achievements claimed by the Meta Llama 4 series [1][5] Summary by Sections Technical Report Overview - The report includes contributions from over 1300 authors, indicating a collaborative effort from the Llama 4 team, despite some contributors having left Meta [4] - It emphasizes that the document is an independent investigation of publicly available materials, with benchmark values attributed to model cards [4] Model Performance and Limitations - The report highlights a gap between the architectural capabilities of the models and their actual deployment performance, particularly regarding context length [4][7] - It mentions that while the architecture supports a context length of 10 million tokens, practical deployment often limits this due to hardware constraints [7] Controversies and Criticisms - The report addresses criticisms regarding the Llama 4 series, particularly the discrepancies between leaderboard performance and real-world application [8][11] - It notes that the experimental variant submitted to the LMArena leaderboard differs from the publicly released version, leading to accusations of "gaming AI benchmarks" [11] - Marketing claims made in announcements should be distinguished from rigorous model card benchmark results, as some statements are categorized as "marketing-facing claims" [11] Model Variants and Features - The report summarizes the released model variants, including Llama 4 Scout and Llama 4 Maverick, detailing their architectures, active parameters, modalities, and supported languages [9][10] - It also discusses the training disclosures and deployment limitations observed in major service environments [12]

编辑｜张倩、陈陈 刚刚，AAAI 2026 官网公布了今年的「杰出论文」（相当于最佳论文）奖项，共有 5 篇论文获奖，其中有三篇由华人团队主导，作者来自香港科技大学（广 州）、西湖大学、浙江大学、同济大学、浙江师范大学、香港城市大学等多所国内高校。 AAAI 由国际人工智能促进协会主办，是人工智能领域历史最悠久、涵盖内容最广泛的国际顶级学术会议之一，也是中国计算机学会（CCF）推荐的 A 类国际学术 会议，每年举办一届。 AAAI 2026 于 1 月 20 日至 27 日在新加坡举行，总投稿数为 23,680 篇，录用论文 4,167 篇，接收率为 17.6%。 以下是获奖论文的具体情况。 近年来，视觉 — 语言 — 动作（VLA）模型的进展，使机器人智能体能够将多模态理解与动作执行相结合。然而，实证分析发现，现有的 VLA 模型在将视觉注意 力分配到目标区域时仍然存在明显困难，其注意力往往呈现分散状态。 为引导视觉注意力在正确目标上的有效 grounding ，作者提出了 ReconVLA，一种采用隐式对齐范式的重建式 VLA 模型。 论文 1：ReconVLA: Reconstructive Visio ...

Core Insights - The article emphasizes the emergence of "Skill" as a pivotal concept in the AI programming field, marking a transition towards "experience assetization" and the standardization of professional capabilities [3][44] - The introduction of Skill allows for the encapsulation of complex instructions and resources into reusable professional skill packages, enhancing productivity across various work scenarios [3][8] Group 1: Definition and Functionality of Skill - Skill is defined as a "professional skill package," represented by a SKILL.md file that contains detailed instructions, automation scripts, and template resources necessary for specific tasks [10][15] - The dynamic calling mechanism of Skill addresses the core pain point of token consumption and task focus in AI programming, allowing for efficient use of resources [15][16] Group 2: Evolution and Integration of Skill - The integration of Skill into platforms like TRAE signifies a shift from AI tools as assistants to digital employees, enabling developers to create reusable workflows [7][8] - TRAE's Skill functionality allows users to easily configure and utilize skills, even with no coding background, thus democratizing access to advanced AI capabilities [19][21] Group 3: Practical Applications and Impact - The article illustrates how Skill can significantly enhance productivity, with examples showing TRAE's ability to automate tasks and improve efficiency in real-world scenarios [18][24] - The potential for Skill to serve as a personal digital assistant is highlighted, enabling users to streamline various tasks such as file management and content generation [40][41] Group 4: Future Outlook and Opportunities - The article suggests that mastering and building a personal "skill library" will be crucial for developers to adapt to the evolving AI landscape and achieve significant productivity gains [44] - TRAE's recent promotional offerings aim to lower the barriers for users to experiment with Skill, encouraging broader adoption and innovation within the community [41][42]

Core Insights - The article discusses the evolving safety assessment framework for advanced large models, particularly focusing on their security capabilities in various application scenarios and regulatory contexts [2][6]. Group 1: Safety Assessment Framework - A unified safety assessment framework has been developed for six leading models: GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5, covering language, visual language, and image generation scenarios [2]. - The assessment integrates four key dimensions: baseline safety, adversarial testing, multilingual evaluation, and compliance evaluation against global regulatory frameworks [4]. Group 2: Key Findings - GPT-5.2 achieved an average safety rate of 78.39%, demonstrating a shift towards deep semantic understanding and value alignment, significantly reducing failure risks under adversarial inputs [11]. - Gemini 3 Pro's average safety rate is 67.9%, showing strong but uneven safety characteristics, with a notable drop in adversarial robustness [11]. - Qwen3-VL scored an average safety rate of 63.7%, excelling in compliance but showing weaknesses in adversarial safety [12]. - Grok 4.1 Fast has an average safety rate of 55.2%, with significant variability in performance across different assessments [12]. Group 3: Multimodal Safety - GPT-5.2 leads with an average multimodal safety rate of 94.69%, indicating high stability in complex cross-modal scenarios [13]. - Qwen3-VL follows with an average safety rate of 81.11%, showing strong performance in visual-language interaction [13]. Group 4: Model Safety Profiles - GPT-5.2 is characterized as an all-encompassing internalized model, capable of nuanced compliance guidance in complex contexts [19]. - Qwen3-VL is identified as a rule-compliant model, excelling in clear regulatory environments but lacking flexibility in ambiguous scenarios [20]. - Gemini 3 Pro is described as an ethical interaction model, sensitive to social values but needing improvement in proactive risk prevention [21]. - Grok 4.1 Fast is noted for its efficiency-focused design, prioritizing user expression over robust defense mechanisms [22]. Group 5: Challenges in Security Governance - The report highlights the threat of multi-round adaptive attacks, which can bypass static defenses, posing a significant challenge for future model safety governance [27]. - There is a structural imbalance in security performance across languages, with a 20%-40% drop in non-English contexts, raising concerns about global deployment risks [28]. - The lack of transparency and explainability in decision-making processes remains a critical governance shortcoming, particularly in high-risk areas [29]. Conclusion - The report emphasizes the need for a collaborative approach among academia, industry, and regulatory bodies to develop a comprehensive and dynamic safety assessment system for generative AI [30].

Core Insights - The article discusses the rapid development of multimodal large language models and embodied AI, highlighting that AI and computer vision technologies focused on children's development, health, and education are still in their infancy [2] - The CV4CHL workshop aims to bridge interdisciplinary perspectives on pediatric AI and computer vision solutions, addressing critical gaps in the field [2] Event Details - The CV4CHL workshop is organized by PediaMed AI in collaboration with several prestigious institutions, including the University of Illinois Urbana-Champaign, Hong Kong University of Science and Technology (Guangzhou), ETH Zurich, and Shenzhen Children's Hospital [2] - The workshop will take place during CVPR 2026, scheduled for June 3-7, 2026, in Denver, Colorado, USA [7][6] Key Topics - The workshop will cover various themes, including: - Basic models inspired by human children's learning and cognitive abilities, and cutting-edge research on multimodal large language models [6] - Brain-computer interface technologies for children [6] - Frontiers in human-computer interaction with augmented reality glasses and smart glasses for children [6] - Applications of embodied AI in pediatrics [6] - Computer vision and foundational models related to children's cognitive development, such as gaze and gesture analysis [6] - Pediatric smart healthcare, including early disease screening and medical imaging and video analysis [6] - AI-enabled education, including smart educational tools and assistive technologies for children with special needs [6] - AI support for children's and adolescents' mental health [6] - Ethical and social implications of children's AI technologies, including privacy protection and human-robot interaction [6] Submission Information - The submission deadline for the workshop is March 31, 2026, with notification of review results by April 8, 2026 [6] - The workshop will feature both proceeding and non-proceeding submission tracks, with specific page limits for each [8]

机器之心编辑部 刚刚，美国计算机协会 ACM（Association for Computing Machinery）公布了最新一届会士名单。 本年度新入选科学家中共有 71 人，他们的贡献涉及计算机图形学、网络安全、人机交互、数据管理、机器 学习、人工智能、算法、可视化等领域。 ACM 主席 Yannis Ioannidis 表示：这份入选名单代表了「我们领域当前正在发生的事情的快照。例如，今年 我们要表彰在计算机架构和软件工程等成熟学科工作的成员，以及在群体智能或场景识别等新兴学科的创 新者。」 机器之心对这些华人入选者进行了简单介绍（如有遗漏或错误，欢迎在留言区指正）。 Pei Cao ACM 创立于 1947 年，是全世界计算机领域影响力最大的专业学术组织之一。 ACM Fellow 是由该组织授予资深会员的荣誉，目的为表彰会员中对于计算机相关领域贡献前 1% 的学者， 其审查过程十分严格，每年遴选一次，研究员由同行提名，提名由委员会审查。 Pei Cao 是一位在业界极具影响力和知名度的技术专家与工程领导者。她曾先后在多家信息技术巨头任职， 包括谷歌和脸书，目前担任 YouTube 公司的工程副总裁 ...

Core Viewpoint - The article discusses the launch of "JD AI Shopping," a new app that significantly alters the traditional e-commerce experience by integrating AI to assist users in decision-making and simplifying the shopping process [2][4][24]. Group 1: User Experience and Design - The app features a clean interface with minimal distractions, focusing on a single dialogue box for user interaction, contrasting sharply with traditional e-commerce apps [3][9]. - The AI engages users proactively, offering personalized greetings and reminders about past purchases, creating a more interactive and familiar shopping experience [10][11]. - Users can input complex requests, and the AI will generate comprehensive solutions, such as suggesting necessary equipment for specific activities, thus streamlining the decision-making process [15][16]. Group 2: AI Integration and Functionality - The app shifts the burden of decision-making from users to AI, allowing users to express vague needs without needing to clarify them beforehand [25][26]. - AI processes user queries by breaking them down into actionable insights, ensuring that recommendations align with user intentions and preferences [27][28]. - The app also automates repeat purchases, making the process nearly frictionless by recalling past orders and preferences [31][34]. Group 3: Market Positioning and Strategy - "JD AI Shopping" represents a strategic pivot for JD.com, moving from a traditional e-commerce model focused on efficiency to one that prioritizes user-centric decision-making [65][66]. - The app is designed as an independent platform to experiment with new user interaction models without the constraints of the main JD app's complex promotional logic [67][68]. - This approach allows JD.com to explore the future of e-commerce, focusing on understanding and addressing unarticulated user needs [68].

Core Insights - The article discusses the dominance of the Transformer architecture in large models and introduces Liquid AI's new model, LFM2.5-1.2B-Thinking, which operates efficiently on edge devices [1][2]. Group 1: Model Overview - Liquid AI has released LFM2.5-1.2B-Thinking, a reasoning model that can run entirely on edge devices with only 900 MB of memory [2][3]. - This model excels in generating internal reasoning trajectories before arriving at final answers, demonstrating superior performance in tool usage, mathematical reasoning, and instruction following [3][14]. Group 2: Performance Metrics - Compared to its predecessor LFM2.5-1.2B-Instruct, LFM2.5-1.2B-Thinking shows significant improvements in three key areas: mathematical reasoning (from 63 to 88 on MATH-500), instruction following (from 61 to 69 on Multi-IF), and tool usage (from 49 to 57 on BFCLv3) [7][9]. - In various reasoning benchmarks, LFM2.5-1.2B-Thinking's performance matches or exceeds that of Qwen3-1.7B, despite having approximately 40% fewer parameters [7][10]. Group 3: Training and Development - The model's training involved multi-step reasoning to enhance capabilities while maintaining concise answers for low-latency deployment [16]. - Liquid AI implemented strategies to reduce the occurrence of "doom looping" in the model's responses, achieving a reduction from 15.74% to 0.36% in the final training phase [17][18]. Group 4: Ecosystem and Compatibility - Liquid AI is expanding the ecosystem for the LFM series, ensuring compatibility with popular reasoning frameworks and supporting various hardware accelerations [24]. - The model has been tested across different devices, showcasing its efficient performance in long-context reasoning [26]. Group 5: Future Implications - LFM2.5-1.2B-Thinking signifies a shift away from the exclusive reliance on Transformer models, suggesting that smaller, powerful edge reasoning models may offer superior solutions [27]. - The decreasing barriers to running inference models on various devices is seen as a positive development for AI potential [28].

近日， 中国科学技术大学 （U STC） 联合新疆师 范大学、中关村人工智能研究院、香港理工大学 ，在 数据驱动的 多功能双 连通多尺度结构逆向设计领域 取得 重要突破。相关成果于 2026 年 1 月 8 日以 "D ata-dri ven Inverse Design of Multifunctional Bicontinuous Multiscale Structures" 为题，发表于 Nature 旗下顶级综合 期刊 Nature Communications 。 该研究首次系统性解决了双连通多尺度结构长期存在的 "难描述、难设计、难制造" 核心瓶颈，为 骨植入物、渗 透器件、力学隐身结构 等复杂工程系统 的智能化 设计提供了全新的数据驱动范式。 论文链接：https://www.nature.com/articles/s41467-025-68089-2 数据链接：https://drive.google.com/drive/folders/1VnNVyjxKFQPCH_YchG52gRw1zEXKMr2J 开源链接：https://github.com/llwang91/L-BOM/ 向自然学习 ...