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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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nd 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 project

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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 that advantage the rest of the industry could actually get. Could you take an open-weight model, serve it through a familiar inference engine, and beat NVIDIA on the economics that matter to your business? Today,

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bs and robots. The details: Generalist raised nearly $200M in fresh funding led by 8VC, extending June’s $400M Series B and bringing the round to a hefty $600M. Founded in 2024 by DeepMind and Boston Dynamics veterans, the startup is building intelligence designed to work across different robot form factors. Its GEN-1.5 model learns unfamiliar physical tasks from a single demo, with Generalist showing examples learned from 12s of data, without fine-tuning. Skild AI, valued above $14B, and Physic

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nthropic 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 Preview’s a

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could one day augment care by engaging with the sensory complexity of clinical practice. The research described here is joint work across many teams at Google Research and Google Deepmind. We are grateful to all our co-authors - Mahvish Nagda, Jihyeon Lee, Matthew Thompson, CJ Park, Tim Strother, Valentin Liévin, Roma Ruparel, Akshay Goel, Teya Bergamaschi, Suhana Bedi, Meet Shah, Pavel Dubov, Liviu Panait, Toshiyuki Fukuzawa, Sam Schmidgall, Craig Schiff, Joseph Xu, Aliya Rysbek, Yana Lunts, J

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tudent 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 researchers’

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tomAI’s end-to-end real-world performance through DDx accuracy on a population sample, and show how SymptomAI diagnoses can enable analysis of population-scale signals like wearable biosignals for identifying associations in physiological signals with reported illness. This work is the result of equal contributions from Joe Breda, Jake Sunshine and Daniel McDuff. We would like to thank our co-authors and collaborators from Google Research and Google DeepMind for their contributions to this work.

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y, and at scale. By learning from over a trillion minutes of unlabeled sensor data, it generalizes across cardiovascular, metabolic, sleep, and mental health; supports label-efficient adaptation and robust daily-metric estimation; can be specialized automatically by an agentic classroom; and can ground a Personal Health Agent in a person’s own signals. We would like to thank our co-authors and collaborators from Google Research, Google DeepMind, and academia for their contributions to this work.

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e LLM efficiency, with Filippo Galgani, Omri Homburger, Pooja Consul, Matthew Markwell, and Vivek Kumar. Certain elements were built on developments from the Gemini team in Google DeepMind: Tal Schuster, Ziwei ji, Ivan Korotkov, and Ganesh Jawahar. We’d also like to extend a big thank you for reviews and valuable feedback and support to Nadav Bar, Utku Evci, Nir Shabat, Joe Zou, and teams in Google Research, Google Deepmind, and Platforms & Devices. Compared to Pixel 9 and Pixel 10 phones prior

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lled comparisons . This project was an extensive collaboration between Beth Israel Deaconess Medical Center , Beth Israel Lahey Health , and many teams at Google Research , Google DeepMind and Google for Health . We thank our many collaborators, sponsors and reviewers of this work. We are grateful to the many contributions from our co-authors throughout this research: Peter Brodeur, Jacob M. Koshy, Anil Palepu, Khaled Saab, Ava Homiar, Roma Ruparel, Charles Wu, Ryutaro Tanno, Joseph Xu, Amy Wang

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