搜索“DeepMind”

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，且在整个实验

匹配字段正文

capability. We believe physical self-play will enable robots to far exceed human capability.” Self-play has previously produced significant advances in software-based AI systems. DeepMind used the approach extensively in its AlphaGo systems, while self-play subsequently contributed to systems capable of playing multiplayer games including StarCraft II and Dota 2. Skild is attempting to extend the same principle into physical AI, where robots must learn not only strategies but also the physical

匹配字段正文

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. Agents were

匹配字段正文

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. ‍

匹配字段正文

Gemini 如何帮助课堂上的学生和教师 2026年10月29日 | x.com Facebook LinkedIn 邮件 来自英国和意大利的新研究显示，Gemini 能够提升学生的学习效果，并帮助教师在课堂上促进更主动的学习。 Kevin McKee Google DeepMind 教育 AI 评估负责人 Amanda Rosenburg Google for Education 专职研究员 分享 x.com Facebook LinkedIn 邮件 收听文章 [[duration]] 分钟 此内容由 Google AI 生成。生成式 AI 仍处于实验阶段 我们正在分享关于 Gemini 如何支持英国和意大利各地课堂中学生与教师的新研究结果。我们的研究表明，AI 辅导能够提供有效的数学支持，而使用 Gemini 共同设计课程的教师则报告称学生参与度更高、自身福祉也有所改善。您可以阅读我们的完整技术报告和实施报告，进一步了解这些工具在不同教育环境中的运作方式。 摘要由 Google AI 生成。生成式 AI 仍处于实验阶段。 我们通过在英国和意大利开展的研究试验，研究了 Gemini

匹配字段正文

做日常动作，后台连着同一个云端大模型，模型在谁那里察觉到了动作分歧，就静默抓取那一小段高质量差分数据回传。具身后训练，终于不需要把所有人都在实验室里，用机械臂完成了。 06 “未来的后训练发展， 比的是速度和效率” 在采访中，启元机器人首席科学家董豪给出了一个非常反共识的判断： “预训练和后训练不是简单的比例关系，它们是一个阶段性更替。”在他看来，就像当年 DeepMind 做 AlphaGo，一开始用棋谱做模仿学习（预训练），但真正让它封神的，是后面自己跟自己博弈、大规模强化学习的后训练阶段。现在的机器人大模型同样如此：预训练解决起步的世界观构建，路线相对清晰；但未来谁能真正走向泛化与极致成功率，胜负手全在“可规模化的后训练”。如果说过去两年具身智能拼的是谁先拿大模型把机器人“点亮”，那么下半场的竞争，拼的就是谁能用最低的边际成本、最快地构建起机器人的纠错网络。在具身模型从“能跑通 Demo”跨向“进驻千万家”的关键转折点上，产业到底在打什么算盘？下一代基模与后训练体系又将如何演进？近期，我们也将带来对北大副教授、启元机器人首席科学家董豪的独家专访，聊聊具身学术和产业的融合和互补，敬

匹配字段正文

to completing any particular narrative. For example, Gemini 3’s training cutoff is January 2025, which predates the release of “AI 2027” by several months (April 3, 2025): https://deepmind.google/models/gemini/pro/. The models could not have been pattern-matching to a story they were never trained on. We additionally tested the model exfiltration scenario with Gemini 3.1 Pro using different company names, including real ones. Results are shown in the table below. We ran 100 rollouts per name in

匹配字段正文

erposition in terms of constrained optimization, and analysis of the role different activation functions play. Replication & Further Results Tom McGrath is a research scientist at DeepMind. The results in this paper are an important contribution – they really further our theoretical understanding of a phenomenon that may be central to interpretability research and understanding network representations more generally. It’s surprising that such simple settings can produce these rich phenomena. We’

匹配字段正文

结论里还藏着一个细节：在美国工作的这批研究者里，本科来自中国的比例又涨了4个百分点。 两国之间的流动也依然高度单向。 每30个在美国工作的中国本科出身研究者，只对应1个在中国工作的美国本科出身者。三年前是46比1。 2022年第一名是谷歌，2025年换成了北京大学，清华第二，韩国KAIST跃升12位到第三，上海交大第四。 2025年4月，《金融时报》援引多位DeepMind现任和前任科学家报道：公司对生成式AI领域的"战略性"论文设了6个月的发表禁令，内部审批也变得繁琐。一位在职研究员说，放到今天，他很难想象公司还会把当年的Transformer论文公开出去。 DeepMind针对生成式AI领域战略性论文设置6个月发布禁令 OpenAI、Anthropic这类前沿实验室，公开发表本来就少。 所以这张榜单测的，严格说是"在顶会上公开发论文的人"。美国最前沿、最涉及商业机密的那批人，有不少在统计里是隐身的。 报告自己也承认， 样本略偏学术界、低估了产业界。 此外，跨国公司员工可能按公司母国计入，微软亚洲研究院在北京的研究员，很可能记在美国账上。 反过来，中国的大模型公司恰恰最愿意开源、发论

匹配字段正文

. 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://data

匹配字段正文

raining 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 hackin

匹配字段正文

port. 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 editable do

匹配字段正文