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rom extraction, we retrieve the full text of referenced prior art patents. For domestic patents after 2001, we pull directly from USPTO. For older or international patents, we use Google Patents via SearchAPI. We extract the abstract, description, and claims from each referenced patent. For distractors, we collect patents returned as “similar” in the Google Patents search results, which share topical overlap but were not actually cited as prior art. Task generation Eask asks the model to find th

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ty's report. AWS has made AgentCore available to all enterprises , and Amazon says its users include Sony and Ericsson. Research on agent memory has documented similar weaknesses. Google DeepMind lists long-term memory manipulation as a separate attack class in its taxonomy of "AI Agent Traps," finding that just a few poisoned documents in a knowledge base can steer responses. In the red-teaming study "Agents of Chaos," researchers remotely controlled an OpenClaw agent through an externally edit

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JEPA-Anything提出并排序实验，然后将结果反馈给模型。代码和模型已公开。 LeCun于2022年提出JEPA，作为生成模型的替代方案。2025年6月，Meta发布了V-JEPA 2，这是一个拥有12亿参数的JEPA视频模型，无需额外训练即可在陌生环境中控制机械臂。2025年11月，LeCun与Randall Balestriero随后推出了LeJEPA，这是一个旨在无需常规变通手段即可保持训练稳定的理论基础。LeCun目前正通过其初创公司AMI Labs推进这一方法，该公司于2026年3月融资超过十亿美元，用于构建世界模型。 Google Deepmind已于2025年10月在实验室中测试了一个由AI生成的癌症假说。其基于Gemma的模型C2S-Scale 27B提出使用药物silmitasertib使肿瘤细胞更容易被免疫系统识别，而利用人类细胞模型进行的实验证实了这一预测。Google Deepmind的多智能体系统Co-Scientist如今能够规划实验并操作实验室设备，覆盖了JEPA-Anything团队想要构建的循环中的部分环节。不过，将样本装入机器仍然需要人工完成。

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ects it was trained on large amounts of 3D data like Blender scenes . 3D software makers are already gearing up for these kinds of agents. Unity has released official plugins for Claude Code and Codex . Other approaches skip code entirely and reconstruct scenes directly inside the model, like the Atlas world model from World Labs . Google Deepmind takes yet another route with GenCeption, using a video model for depth estimation and segmentation that matches the performance of specialized models.

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iedel, William C., Jr., Thomas S. Bettencourt, and Daniel L. Freeman, "Tomato Harvesting Employing Electronic Sorting," U.S. Patent 4,033,099, issued July 5, 1977. https://patents.google.com/patent/US4033099A/en Friedel, William C., Jr. and Charles F. Dietz, "Tomato Harvesting Sorting System," U.S. Patent 4,118,311, issued October 3, 1978. https://patents.google.com/patent/US4118311A/en Goldman, Sharon, "A Year Ago, Nvidia's Jensen Huang Said the 'ChatGPT Moment' for Robotics Was Around the Corn

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hemical and thermal processes each gained 8.00 points. Fine-tuning uses LoRA on attention projections, with no architecture or objective change. Physis-Lang on Cosmos3-Nano versus Google's Veo 3.1: Gains hold across backbones: +7.05 on Wan2.1-14B, +3.24 on Cosmos3-Edge-4B, +6.22 on Cosmos3-Nano-16B and +5.02 on Cosmos3-Super-64B. General quality held steady on VBench-I2V, where Cosmos3-Nano moved from 88.32 to 88.69. Prompting alone also helps. Physics reasoning plus negative prompts lifted a fr

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cent fund raise of $315 million in February . Its first release, GWM Worlds, was launched last December. Alongside Runway, there are several other notable projects in this domain: Google DeepMind’s Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro , and World Labs’ RTFM (Real-Time Frame Model). We’ve summarized their differences in the following table: Given the complexity and massive latency demands of real-time video and audio generation (which we’ll get into below), all of the

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et: One-press capture in a 0.4 oz wearable built for all-day use One connected memory across conversations, instant summaries, and action items Seamless sync with ChatGPT, Claude, Google Calendar, and Notion AI agents that can access context from your conversations via MCP The promo code is valid till the end of Sept. and valid for one use per customer. The Rundown: Each week, Rundown AI educator Nate Grahek breaks down what he's seeing while teaching real teams to use AI. Nate: It's iPhone seas

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rial, and the system automatically extracts the core workflows, then spins up a tested, runnable toolkit that you can use on your own datasets. The concept may sound a little like Google’s NotebookLM (now called Gemini Notebook ), which lets you upload documents and chat with an AI about what’s in them. But Paper2Agent aims to go a step further: Rather than simply answering questions about a paper, its agents can actually run the methods described in it—and potentially combine those methods with

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performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-s

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I labs to decide which chips are most efficient in what scenarios. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Thank you to Jensen Huang, Ian Buck, Nick Comly, Kedar Potda

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For more than a decade, the industry has watched Google build an empire on its own silicon. Search, Ads, YouTube, and every generation of Gemini run on TPUs. Few accelerators have attracted as much architectural scrutiny or as much debate about what their performance and economics would look like outside the company that designed them. Anthropic being the biggest user of TPUs, surpassing Deepmind’s own use by 2029. Google’s internal success was never the question. The question was how much of th

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arge, we find that AI-stack CVE growth gets big-O dominated. This is remarkable because Anthropic is an AI safety and research company acting for the global good. Jokes aside, the Google Chrome team may have given a preview of the future of software engineering in an insightful recent blog post that describes, with the help of agents, how their bug fixes have been skyrocketing. They also highlighted a sandbox escape bug that existed in their code for 13 years. Imagine someone exploiting this sec

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tremely fast ASIC development cycle. In general first generation chips are not competitive, but OpenAI bucks the trend by being industry leading and beating every Nvidia, AMD, and Google chip we have been able to test on multiple top open source models. OpenAI does this with extreme hardware software codesign. Surprisingly, OpenAI is not over specialization on any specific part of model inference, but instead by focusing on being a general chip that delivers high performance in all scenarios. In

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be aimed at the mutations inside one patient’s tumor, and melanoma is just the start. In today’s tech rundown: An mRNA cancer vaccine clears Phase 3 trials Flight attendants stall Google’s data deal Daniel Ek’s body scanner reaches NYC Colossal’s startup factory LATEST DEVELOPMENTS Image source: Julio C. Valencia at the NCI Center for Cancer Research (melanoma) The Rundown: Moderna and Merck’s made-to-order mRNA cancer vaccine has cleared its biggest test yet, beating Keytruda alone in a Phase 3

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Cowork returns a finished, self-contained HTML file.” See Matthew’s workflow Visit The Rundown University . How do you use AI? Tell us for a chance to be featured. 📚 Student Hub - Google’s Gemini-dedicated area for students 🚀 Replit Free Mode - Replit’s new Luna-powered credit-free usage mode 🤖 GLM-5.3 - Z AI’s open model with strong coding, agentic, and cyber skills Sponsored Listing Stripe officially announced its acquisition of OpenRouter, while reportedly telling investors that Jan. 1, 2026,

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r, an Anthropic staff member (and non-mathematician), prompted Claude to “take a real stab” at the hypothesis itself, leaving the mathematical choices from there up to the model.” Google DeepMind’s AlphaEvolve paper says: “in contrast to [traditional computational or theoretical methods performed by human experts], we have found that AlphaEvolve can be readily scaled up to study large classes of problems at a time, without requiring extensive expert supervision for each new problem.” Mythos Prev

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T PhD student in electrical engineering and computer science (EECS); Kaiming He, a principal investigator in the lab, associate professor of EECS, and a distinguished scientist at Google DeepMind; and senior author Wojciech Matusik, the Joan and Irwin M. (1957) Jacobs Professor of EECS and a CSAIL principal investigator. Tsinghua University Associate Professor Mingsheng Long was also a co-author. The team presented the paper at the International Conference on Machine Learning in July. The resear

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as models become more capable. Conclusions This is not the first time that language models have performed research-level mathematics. In just the last few months, researchers from Google have used Gemini to resolve several open Erdős problems, researchers from OpenAI have used GPT to resolve the unit distance conjecture (a particularly challenging Erdős problem), and earlier this month we announced that Claude Fable 5 resolved the Jacobian Conjecture. Our result here—that Claude is able to perfo

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自消费者（Free/Pro/Max）账户在以下两个平台上的聊天对话和 Cowork 会话： Claude.ai 以及 Claude 桌面应用。“第一方 API”或 1P API 指的是直接通过 Anthropic 自有编程接口路由的开发者流量，这既不同于 Anthropic 面向消费者的 Claude.ai 应用，也不同于 Amazon Bedrock 或 Google Cloud Vertex 等第三方平台。我们继续按照隐私和保留政策管理数据，我们的分析与我们的条款、政策和合同协议保持一致。 02 在整个过程中，所有分析均基于隐私保护分类器：转录文本仅由另一个 Claude 实例读取。随后，我们过滤掉任何观测数据不足的单元格，以确保隐私保护分析。 03 这包括在 Claude.ai 和 Claude 桌面应用这两个平台上进行的全部聊天对话和 Cowork 会话。 Claude.ai 以及通过 Claude 桌面应用进行的对话。Claude Code 与 API 流量则单独呈现。 04“第一方 API”或 1P API 指的是直接通过 Anthropic 自有编程接口路由的开发者流量，

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