36氪获悉，康养具身智能公司「如身机器人」（RobotGym）近日获千万元级人民币天使++轮融资，由力合金融独家投资，资金主要用于核心技术的持续迭 代、产品工程化落地推进、养老场景规模化试点及市场前期布局。当前，如身机器人已启动Pre-A轮融资。 格物系列，主要面向居家康复场景，覆盖手部、上肢与下肢等全身康复训练；能够支持个性化康复训练计划，实时调节训练参数，查看训练报告等。该系列 产品相对轻量，已实现千台量产并出口至北美、欧洲、东南亚等市场。除了为公司带来持续现金流外，格物系列产品也是如身机器人深入康复场景、积累真 实世界数据和用户的触角。 数据积累，于具身智能的价值毋庸置疑。在师云雷看来，数据价值高度依赖于AI模型的架构，未来能够满足高级照护需求的AI模型，必然需要更多模态的 数据，比如触觉、力觉等现在尚未大规模采集的数据。因此如身机器人选择商业化先行，尽可能多地卖出产品，建立起能够快速、大规模采集多模态数据的 硬件网络，为未来技术迭代积累先机。 齐家系列，则直接切入当下养老刚需，尝试让智能机器人进入独居、半失能及失能老人的日常照护场景。基于深入调研，齐家Q1系列养老机器人的核心功 能被规划为三个层级模块 ...

Core Insights - The article discusses the launch of Ling-1T, a trillion-parameter open-source language model by Ant Group, highlighting its efficiency and performance in various benchmarks [2][5][52]. Group 1: Model Performance - Ling-1T has achieved impressive results in multiple benchmark tests, outperforming several leading models in key areas such as knowledge understanding and reasoning [6][9][10]. - In coding and math reasoning tasks, Ling-1T consistently ranks among the top performers, demonstrating strong logical consistency and cross-domain reasoning capabilities [8][11]. - The model's performance in specific benchmarks includes a score of 92.19 in C-Eval and 87.45 in FinanceReasoning, indicating its high knowledge density and reasoning ability [9][10]. Group 2: Efficiency and Architecture - Ling-1T utilizes a Mixture of Experts (MoE) architecture, allowing it to maintain high reasoning capabilities while significantly reducing computational costs [5][52]. - The model operates on a paradigm of "large parameter reserves + small parameter activation," enabling it to handle complex problems efficiently with a lower energy footprint [53][54]. - It supports a context length of 128K, enhancing its ability to process long documents without losing context, which is crucial for industries like finance and law [62]. Group 3: Open Source Philosophy - The article emphasizes the importance of open-source models in the AI landscape, suggesting that they enable faster iteration and lower costs for technology development [72][73]. - Ant Group's approach to open-sourcing Ling-1T allows for broader accessibility and collaboration, fostering an ecosystem where developers and small businesses can participate [74][75]. - The open-source model not only democratizes access to advanced AI capabilities but also enhances transparency and trust in AI applications across various sectors [72][74].

当机器人能像人类一样理解自然语言指令，还能预判环境变化、自主规避物理风险时，通用人工智能的落地似 乎不再遥远。 近日，清华大学计算机科学与技术系，北京信息科学与技术国家研究中心，复旦大学可信具身 智能研究所联合发布《 Embodied AI: From LLMs to World Models》。 系统 性 梳理了具身智能的技术 脉络，尤其聚焦大语言模型与世界模型的协同 。 ▍ 先搞懂什么是具身智能？它和普通 AI 有啥不一样？ 单模态与多模态具身智能 纯视觉的机器人，在昏暗环境或动态场景里很容易迷路；纯语言控制的机器人，可能会因为没考虑物理规律提 出离谱指令。 | | | Embodied AI: From LLMs to World Models | | | --- | --- | --- | --- | | EAI § II | EAI with LLMs/MLLMs § III | EAI with WMs § IV | EAI with MLLMs and WMs § V | | The Historical View § II-A | LLMs Boost EAI § III-A | WMs Bo ...

"知人者智，自知者明。"——《道德经》 古人早已洞见：真正的人类智慧，不仅仅在于公式推演、掌握技艺，更是能理解他人、洞察人心。今天的大语言模型已能在代码、数学与工具使用上 出色 地完 成 任务 ，然而距离成为真正的 用户伙伴 ，它们依旧缺少那份 "知人" 的能力。这主要源于现实交互远比解题更加复杂： 这正是智能体面临的下一个时代课题： 从 "会解题" 迈向 "懂用户" 。而要真正回答这一课题，我们需要全新的动态评测框架与训练机制：不仅能测量模型在交互 中的表现，还能驱动其学会在用户不确定与多目标的世界里，问之有道，断之有衡，答之有据。为此，来自 UIUC 与 Salesforce 的研究团队提出了一套系统化方 案： 二者相辅相成，把 "以用户为中心" 从理念落地为 可复现的流程、接口与评测指标 。 UserBench 论文链接：https://arxiv.org/pdf/2507.22034 UserBench 代码仓库：https://github.com/SalesforceAIResearch/UserBench 现实交互中， 用户目标常常未在最初完全成形 （underspecification）、而是 ...

Core Viewpoint - The article discusses the importance of reinforcement learning (RL) in enhancing the intelligence of large models, emphasizing the need for effective interaction between models and their environments to obtain high-quality feedback [1][2]. Summary by Sections Section 1: Importance of Reinforcement Learning - The article highlights that RL is crucial for the advancement of large model intelligence, with a focus on how to enable models to interact with broader environments to achieve capability generalization [1][8]. - It mentions various RL techniques such as RLHF (Reinforcement Learning from Human Feedback), RLAIF (AI Feedback Reinforcement Learning), and RLVR (Verifiable Reward Reinforcement Learning) as key areas of exploration [1][8]. Section 2: RuscaRL Framework - The RuscaRL framework is introduced as a solution to the exploration bottleneck in RL, utilizing educational psychology's scaffolding theory to enhance the reasoning capabilities of large language models (LLMs) [12][13]. - The framework employs explicit scaffolding and verifiable rewards to guide model training and improve response quality [13][15]. Section 3: Mechanisms of RuscaRL - **Explicit Scaffolding**: This mechanism provides structured guidance through rubrics, helping models generate diverse and high-quality responses while gradually reducing external support as the model's capabilities improve [14]. - **Verifiable Rewards**: RuscaRL designs rewards based on rubrics, allowing for stable and reliable feedback during training, which enhances exploration diversity and ensures knowledge consistency across tasks [15][16]. Section 4: Future Implications - The article suggests that both MindGPT and MindVLA, which target digital and physical worlds respectively, could benefit from the advancements made through RuscaRL, indicating a promising future for self-evolving models [9][10]. - It emphasizes that the current challenges in RL are not just algorithmic but also involve systemic integration of algorithms and infrastructure, highlighting the need for innovative approaches in building capabilities [9].

中新网10月3日电 大语言模型只是人工智能技术中的一种，二者并不等同。人工智能是一个非常广泛的研究领 域，各种机器学习算法、图像识别、语音识别、机器人的行动策略优化以及自然语言处理等，都属于人工智能的 研究范畴。大语言模型最初是人工智能在自然语言处理领域取得的突破性进展，如今在多模态技术的帮助下，大 语言模型还能处理更多类型的信息，比如声音、图片甚至是视频等。它只是人工智能领域的一个分支，并不能直 接和人工智能划等号。(来源：@科学辟谣 中国新闻网微博) 人工智能就是大语言模型？丨中新真探 来源：中国新闻网 编辑：陈俊明 广告等商务合作，请点击这里 本文为转载内容，授权事宜请联系原著作权人 中新经纬版权所有，未经书面授权，任何单位及个人不得转载、摘编或以其它方式使用。 关注中新经纬微信公众号(微信搜索"中新经纬"或"jwview")，看更多精彩财经资讯。 ...

来源：环球网 【环球网科技综合报道】据海外科技媒体MacRumors透露，苹果正加速研发一款智能眼镜，旨在与Meta 的雷朋系列展开竞争。面对Meta已推出带显示屏的智能眼镜，苹果希望加快其首代产品的开发进程， 甚至已暂停下一代Vision Pro的部分工作，以优先推进眼镜产品上市。 | MacRumors | | | | | Got a tip for us? Let us knov | | | --- | --- | --- | --- | --- | --- | --- | | Front Page | Roundups | Guides | How Tos | Reviews | Buyer's Guide | Forums | | IOS 26 | macOS Tahoe 26 | iPadOS 26 | iPhone 17 | iPhone Air | iPhone 17 Pro iPhor | | 1. 时尚配饰定位 与初代Apple Watch类似，苹果眼镜将首先是一款时尚配饰，而非款式有限或造型笨重的设备。苹果计 划提供多种镜框与镜腿材质选项，以满足用户的个性化审美。尽管眼镜内部需容纳电池、 ...

西方石油涨0.6%，伯克希尔将以97亿美元收购其石化业务。 格隆汇10月2日｜美股开盘，道指开涨0.04%，标普500指数开涨0.26%，纳指涨0.59%。 Rivian跌超3%，公司下调本财年交付量。 Nebius涨6.6%，微软将使用Nebius数据中心进行大语言模型开发。 特斯拉涨2.2%，第三季度交付量超出预期； 美股半导体板块走强，阿斯麦涨3.45%，英伟达涨1.50%。 AMD涨近3%，报道称英特尔正与台积电展开早期洽谈，拟将AMD纳入代工客户名单。 ...

美股开盘，道指开涨0.04%，标普500指数开涨0.26%，纳指涨0.59%。 美股半导体板块走强，阿斯麦涨3.45%，英伟达涨1.50%。 西方石油涨0.6%，伯克希尔将以97亿美元收购其石化业务。 Nebius涨6.6%，微软将使用Nebius数据中心进行大语言模型开发。 特斯拉涨2.2%，第三季度交付量超出预期； AMD涨近3%，报道称英特尔正与台积电展开早期洽谈，拟将AMD纳入代工客户名单。 Rivian跌超3%，公司下调本财年交付量。 ...

格隆汇10月2日｜英伟达持仓概念股Nebius美股盘前涨超6%，微软据悉将使用Nebius数据中心进行大语 言模型开发。 ...