搜索“Google DeepMind”

访问网络搜索的情况下接受测试，在关于变化事实的 103 个问题中仅正确回答了 23.3%。 一个未指明的 AI 模型被用来将上述数据集中 FDA 标签条款改写为可单独测试的事实陈述，每一条随后都经过人工审核 。 所测试的开放权重模型配置包括来自 Z.ai 的 GLM-5.3-Flash；OpenAI 的 gpt-oss-120b 和 gpt-oss-20b；Google DeepMind 的 gemma-4-31b（分别在有推理和无推理情况下测试）；以及 Qwen 的 qwen3-8b。 开放权重模型和 GPT-5.6 Luna 获得了相同的网络搜索结果，以便公平比较它们的回答。Luna 还使用其自带的网络搜索单独进行了测试。 在225条事实中，有223条事先使用Brave Search收集了十条搜索结果。每个模型都收到相同的结果，并可使用相同的工具打开页面或在其中搜索。 包含“正确”答案的页面始终可用——如果它自然出现在前十条结果中，其位置保持不变；否则，它会被插入到事先确定的位置，使研究人员能够在可比条件下考察模型如何利用现有证据。 为使测试更加一致，对支持该设置的模型将温度设为零

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w that AI models can run experiments but lack scientific self-criticism and genuine creative thinking. The major AI labs are increasingly marketing their models as research tools. Google DeepMind has expanded its Co-Scientist into a full research system, and OpenAI introduced an " automated research intern " back in September. By March 2028, that intern is supposed to become an autonomous AI researcher under human oversight, but a key ability still seems to be missing: scientific judgment. Agent

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eless reinforce the importance of continued monitoring for sabotage-related tendencies. Our full report has many more details, including more analysis of results from the unprompted sabotage evaluation and detailed transcripts. Understanding how these risks manifest in frontier AI models is central to AISI's mission, and something we pursue in close collaboration with leading AI companies. For more information, see details of our ongoing partnerships with Anthropic, Google DeepMind and OpenAI. ‍

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(2025). OLMo 3. arXiv. https://doi.org/10.48550/arXiv.2512.13961; GPT-5. OpenAI. (2025). OpenAI GPT-5 system card. arXiv. https://doi.org/10.48550/arXiv.2601.03267; Gemini 3 Pro. Google DeepMind. (2025). Gemini 3 Pro model card. https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Pro-Model-Card.pdf; Gemini 3.1 Pro. Google DeepMind. (2026). Gemini 3.1 Pro model card. https://deepmind.google/models/model-cards/gemini-3-1-pro/; Grok 4 Fast. xAI. (2025). Grok 4 Fast model card. https

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ntier training and inferenceWhile many assume that frontier inference stacks are too heavily optimized to easily deploy probes, frontier labs (in particular OpenAI, Anthropic, and Google DeepMind) actually already use activation probes for monitoring! So this sort of monitoring is likely to be feasible from an infrastructure perspective. can support these interventions and mitigate reward hacking in practice. In addition to methods like ours, we believe that other approaches to mitigating reward

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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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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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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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ms that 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 L

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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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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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n-device 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

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controlled 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, A

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