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FT just reported Nvidia is in early talks to acquire, or invest further in, Reflection AI, a $25B open-weight model start-up. One option is an acqui-hire, in which Nvidia hires Reflection’s staff and licenses its technology without the antitrust review a full takeover would trigger. Nvidia used that structure for its $20B Groq deal last December, and a deal could come within weeks, though the talks could still fall apart. Nvidia is already one of Reflection’s largest shareholders after investing

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n and angle 120 times a second with about 10ms delay or less, and software turns that into joint angles the robot hand copies. A 526-point pressure grid on the glove's palm also records how hard the person grips, which makes it useful for collecting training data for robots that learn by copying humans. WUJI released the code, motion clips, trained models and printable pen files under the open Apache 2.0 license, so a lab with a WUJI Hand 2, a camera and an NVIDIA GPU can repeat the whole setup.

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raction of compute, with the majority being employed for large-scale RL on synthetic and human-created environments. Inkling was our first major training effort and was trained on NVIDIA GB300 NVL72 systems. Future models will further push the scale of compute across pre-training, post-training and RL. RL at scale We relied on large-scale asynchronous RL to shape model behavior and improve its reasoning and overall performance. The chart below shows the model’s score on a held-out aggregate of r

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odel that achieves comparable performance to Inkling at a quarter of its size. Inkling-Small is a Mixture-of-Experts transformer with 276B total parameters, 12B active, trained on NVIDIA GB300 NVL72 systems. Like Inkling, it features native reasoning over audio and images, variable thinking effort, a context window of up to 1M tokens, and well-rounded performance across a range of benchmarks. Inkling-Small Inkling Compared to Inkling, Inkling-Small achieves comparable performance with much less

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updated weights, and those weights return to serving for the next round. Slow checkpoint loading slows the whole cycle. Our new RL rollouts feature in Dedicated Inference uses the NVIDIA Dynamo foundation to hot-load updated checkpoints with minimal downtime. We partnered with NVIDIA and you.com’s engineering team to post-train NVIDIA Nemotron 3.5 Lightning using RL Rollouts in NeMo gym with you.com’s web search application programming interface (API). Our launch post reports a 15-fold improveme

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ccording to Beard, developers can use Standard Bots’ APIs to build robotics applications with whichever parts of its stack they need, including bringing their own models. He cited NVIDIA Cosmos, an open family of omnimodal world models for physical AI, as an example (version 3 was released late-May). Today, this requires developers to write their own integration code. But Standard Bots plans to make collecting data, training models, and deploying them onto its robots easier. Focus on targeted da

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ccording to Beard, developers can use Standard Bots’ APIs to build robotics applications with whichever parts of its stack they need, including bringing their own models. He cited NVIDIA Cosmos, an open family of omnimodal world models for physical AI, as an example (version 3 was released late-May). Today, this requires developers to write their own integration code. But Standard Bots plans to make collecting data, training models, and deploying them onto its robots easier. Focus on targeted da

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complex reasoning AFM 3 Core, AFM 3 Core Advanced, and AFM 3 Cloud, along with ADM 3 Cloud, are all purpose-built for Apple silicon. For AFM 3 Cloud Pro, we worked with Google and NVIDIA to extend Private Cloud Compute to NVIDIA GPUs in Google Cloud, while maintaining the same guarantees to protect our users’ privacy. More details are available on our Security Research Website. Our third generation of Apple Foundation Models delivers significant advancements across capabilities and quality. In t

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密的前提下促进高价值数据流通；推动公共数据底座建设。 产业侧的动作已经开始。解耦层面：鹿明机器人的 FastUMI 与本体解耦，适配市面 90% 以上机械臂；光轮智能的 RoboFinals 支持异构本体接入。标准层面：光轮智能是 Newton 仿真技术指导委员会唯一中国企业，参与 EgoVerse 人类数据委员会，主导或参编 20 项国家及行业标准，并与 NVIDIA 共同定义 SimReady 标准。流通层面：觅蜂数据商城、库帕思、百度数据平台以及北京／上海／深圳数据交易所，构成了交易平台的第一批供给；北京人形机器人创新中心推动公共数据底座与标准制定。 结语 “造治用通”四个字，本质上回答的是同一个问题： 数据如何从一次性消耗品变成可积累、可复售、可增值的资产。 造数据解决供给，治数据解决质量，转飞轮解决增值，通数据解决流动。四者缺一环，行业就会回到“采得少、训不好、用不了”的低水平循环。 中国具身数据产业链生态体系正在快速发展与完善中，商业机会巨大，更多板块详细分析 ， 请关注易 观分析 近期发布 的 《中国具身智能数据产业分析报告2026》。 易观分析将及时更新《中国 具身数据

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t does not eliminate the effect; Agent research:Agent plasticity (Meta): Measures held-out gain per learning dollar. The best performers are not the most efficient learners (@omarsar0); MIMESIS: A 9B user simulator that beats Opus 5 on behavioral fidelity by 13.4 points (@dair ai); Base-model selection (NVIDIA): Ranks checkpoints by whether the base model can reproduce the “decisive edit,” a signal that tracks post-trained SWE-bench Verified scores (@dair ai) Keep reading with a 7-day free trial

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时报》Ezra Klein 访谈时，他表达了自己一贯的观点，即限制美国企业进入中国市场也可能损害美国芯片产业、开放模型生态，并尖锐提问，“我们究竟是在阻止他们（中国）的产业获得芯片，还是在剥夺我们美国自己在市场中参与竞争的机会？”Meta首席执行官扎克伯格同样反对美国政府禁止中国人工智能模型，认为这并不是提升美国竞争力的有效办法。7月24日，微软与Meta、NVIDIA、AMD、Amazon、Google、IBM、Hugging Face、Mistral等大量企业和机构共同签署了题为《开放权重和美国AI领导力》“的公开信，指出“美国的AI领导力不取决于某一个前沿AI模型，而是取决于美国能否构建一个强大、开放且能够扩散至每一个领域的AI生态系统”。 “选择开放型”则在美国AI政策工具层面集中体现。2025年10月，美国商务部正式启动的“美国人工智能出口计划”，组织企业联合提供覆盖算力基础设施、数据、模型、网络安全和行业应用的“全栈式”人工智能出口方案，并通过政府外交推介、出口许可、联邦融资和跨部门协调支持美国人工智能技术进入海外市场。这类“开放”逻辑并非单纯追求技术自由流动，而具有明显的

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d. For example, using the representative workloads and assumptions in this analysis: These break-evens are based on representative consumption-based pricing and a single dedicated NVIDIA A10 GPU instance billed hourly, assuming no negotiated discounts and traffic sustained 24×7. Workloads that run only part of the day require a higher request rate during active hours to reach the same economic crossover. A different instance type changes both cost and capacity, potentially shifting the break-eve

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utton we saw earlier, the solid green top gives the system a distinct two-tone color scheme. And perhaps a smidge of irony for the Ryzen-powered system, since it is much closer to NVIDIA’s shade of green than AMD’s. In any case, the top features another airflow vent as well. So, like virtually every other side of the system, it needs to be kept clear for cooling purposes. This also serves as the one significant non-metallic portion of the system for cleanly routing the wireless radio antennas. L

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eten Platform, including launching Base Labs to conduct open safety research, partnering with Hugging Face and Goodfire AI to launch our safety infrastructure, and partnering with NVIDIA on the Open Agent Safety Platform. Today, we’re announcing Project Beacon in partnership with Goodfire AI to bring proactive, inline safety controls to model inference. The risks we’re building for Project Beacon will enable us to build toward a world where safety isn't a static feature, or something that happen

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eten Platform, including launching Base Labs to conduct open safety research, partnering with Hugging Face and Goodfire AI to launch our safety infrastructure, and partnering with NVIDIA on the Open Agent Safety Platform. Today, we’re announcing Project Beacon in partnership with Goodfire AI to bring proactive, inline safety controls to model inference. The risks we’re building for Project Beacon will enable us to build toward a world where safety isn't a static feature, or something that happen

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this year shatters 2021 records but slows in latest quarter. Singapore startup Manus raises after its forced split from Meta. Google’s AI push in the classroom proves a nightmare. Nvidia CEO Jensen Huang’s son-in-law takes on a bigger role inside the company. USV among the VC firms raising new funds. A new lawsuit over Groq’s deal with Nvidia could throw a wrench in the reverse acquihire strategy. Tech moguls get the Hollywood treatment. The Main Item OpenAI Wanted Its Revenue Run-Rate to Match

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f hectares of forest had been cleared without required environmental reviews. Microsoft opened preorders for its Surface Laptop Ultra, starting at $2,599 and arriving Oct.16, with Nvidia hardware designed to run powerful AI models locally. Anduril landed a Navy contract worth up to $2.9B for submarine production and plans a $3.7B shipyard, days after Palmer Luckey joined a Pentagon weapons advisory group. Rivian R2 owners can watch pets left inside their parked car via live video in the app or u

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类示范采集，SimFoundry负责仿真生成， 其定位不是单一路线的数据商，而是物理 AI 的数据与评测基础设施 。 图 3：中国具身数据市场发展的三线协同 2. 物理引擎 ×生成式AI 重定义 仿真数据 上限 引擎的物理正确性是仿真数据能否迁移到真机的底线，生成式 AI的自动化生成解 决了 人工建模的规模瓶颈。光轮智能自研高精度 GPU物理求解器， 参与 NVIDIA Newton 仿真技术 指导 委员会， 并与 NVIDIA共同定义SimReady 资产 规范 ，支持 PhysX／MuJoCo／Newton／GENESIS多种引擎；无问智科把SimReady资产构建从3天压缩到5分钟（300倍），柔性物体相关性达90%、接触误差小于1%；群核科技则用十余年沉淀的5亿+结构化3D场景、4.8亿带物理参数的3D模型，把渲染空间批量转成物理正确的三维表示。三家的共同点是：先把 “ 物理正确 ” 这件事做硬，再谈生成规模。 3. 无本体采集承接人类操作数据入口 以 “人持 / 穿戴设备采操作”绕开真机遥操作的高成本与本体硬件依赖，在真实物理环境中获取带时序对齐、体感细节的人类行为数据。定位

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rithmetic is matrix multiplication, which runs on the GPU's tensor cores. The benefit of quantization for compute-bound workloads is higher arithmetic throughput: on hardware like NVIDIA Blackwell GPUs with native FP4 support, FP4 tensor core operations can deliver up to 4× the peak FLOPS of FP16. What gets quantized and in what form? Weights are parameters learned during training. They stay fixed at inference. Each connection between two neurons has a weight, and each layer has its own tensor o

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perform quantized aware distillation on traces from the higher precision model in order to quantize to MXFP4. At inference time, Context-1 is served via vLLM. The model runs on an Nvidia B200 with MXFP4 quantization for the MoE layers, enabling fast inference despite the 20B total parameter count. The serving layer exposes a streaming API that executes the full observe-reason-act loop, and returns tool calls, observations, and the final retrieved document, allowing downstream applications to ren

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