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in 2023 by Oliver Cameron and Jeff Hawke. In June 2026, the company raised $310 million from investors including Amazon and AMD Ventures. World models aim to give AI systems an understanding of spatial and physical relationships. Google DeepMind is pursuing a similar approach with Genie 3 for generating interactive worlds. World Labs , the startup founded by Fei-Fei Li, is developing similar technology. AMD announced in late September that it plans to acquire World Labs for roughly $8.2 billion.

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nts will make high-quality technical reviews more widely accessible for knowledge workers everywhere. Acknowledgments Deep Review is a collaborative effort across Google Research, Google DeepMind, and Google Cloud. We thank our core contributors and research collaborators — Santiago Balseiro, Taylan Cemgil, Tommaso D'Orsi, Kirk Draheim, Lindi Emoungu, Ayse Erduran, Palash Goyal, Caroline Hartnell, Shobana Kannan, Amy Keeling, Siyi Kou, Bridget Lee, Ian Li, Anoushka Mazumdar, Taylor Montgomery, M

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过去一年，!! 0080e6 递归自我改进（RSI）!!与 !! 0080e6 自动化研究!! 成为 AI 领域最活跃的前沿问题之一。Google DeepMind 的 AlphaEvolve[^ref1] 只用 48 次标量乘法就完成了 4×4 复数矩阵相乘。Together AI[^ref2] 让一群智能体在开放环境中协作，把 11 维 kissing number 的已知下界从 593 推进到 604。Andrej Karpathy 的 autoresearch[^ref3] 展示了一套精简的循环，让模型训练研究自主运转。此后，一批进化型系统接连涌现：智能体正在跨越封闭的基准测试，在开放的科学发现中超越人类。 今天我们介绍Hyra：在!! 0080e6 更丰富多元的高价值环境!!中，以一个!! 0080e6 简单有效的通用框架 !!，把智能和算力转化为真实且实用的收益。在腾讯，这个舞台格外宽广：除了公开基准，它还能在!! 0080e6 产品系统!!里，在!! 0080e6 真实的 AI 研发流水线!!上，在!! 0080e6 自然科学研究!!乃至!! 0080e6 工业场景!!里

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le,” says the Azure hardware and infrastructure team. Maia 300 is also a key part of Microsoft’s own AI model work, and could help the company achieve its goal of catching up with Google DeepMind, OpenAI, and Anthropic. “We have to work across the full stack, all the way down to the silicon,” says Microsoft AI CEO Mustafa Suleyman. “At that scale we need a platform that we own, and that we co-design with our models. That’s why running our Microsoft AI models on top of the Maia chip is a critical

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t research found that leading open models, including Kimi K3 and GLM 5.2, reward-hacked in 50% to 96% of runs on tests of AI agents. Goodfire isn’t the first to try this approach. Google DeepMind said in January that its research informed the deployment of misuse-detection probes in Gemini . Balsam said the monitors are the near-term piece of a longer research goal: reverse-engineering an LLM so that behavior can be traced back to where it emerged in training. “We hope to turn the magic of train

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して確認 検出にはサインインが必要で、Google、Apple、ChatGPTでのサインインを選べる。利用者は専用サイトにファイルをアップロードして、透かしの有無を調べる。 対応するファイル形式は次のとおり。 動画と音声では、透かしを検出した区間も確認できる。現在、テキストの検出には対応していない。 SynthIDの仕組みと検出結果の読み方 SynthIDはGoogle DeepMindが開発した電子透かし技術。ファイルのメタデータではなく、画像・動画の画素や音声の周波数成分に、人の目や耳では気付かない信号を埋め込む。透かしはフィルターの適用や色の変更、圧縮などを経ても検出できるように設計されている。 Detectorは、この埋め込まれた信号の有無を調べる。FAQによると、透かしが検出された場合（Watermark detected⁠）⁠、そのコンテンツはSynthID対応のAIモデルで生成または編集された可能性が高い。 透かしが検出されなかった場合（Watermark not detected）でも、AIによる生成や編集は否定できない。SynthID非対応のモデルによる生成物や、Sy

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cle is supported by Unsloth. Asif Razzaq Asif RazzaqPerplexity AI Releases pplx-embed-v2-late: A 0.6B Edge Model and a 9B Model Scoring 92.4% on MADQA; Asif RazzaqLiquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens; Asif RazzaqMeta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day; Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4

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ch, TinyFetch (full pages as markdown, JavaScript included), TinyBrowser for logins and forms, and TinyAgent for multi-step jobs. Search and Fetch are free. Asif Razzaq Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4; Asif RazzaqMistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model; Asif RazzaqBeyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields; Asif RazzaqMeet Together Link:

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verned data; Brex improved AI answer accuracy to 90% Give your agent a single source of truth. GOOGLE 🧬 Google’s AI watermarks cross into the lab Image source: Google The Rundown: Google DeepMind just introduced SynthID Bio, a proof of concept that extends its AI watermarking tech to proteins, potentially helping DNA suppliers trace unfamiliar designs before filling an order. The details: SynthID Bio tweaks Google’s protein-folding AlphaFold 3 model to leave a hidden pattern in its predicted pro

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ons across all target languages, preserving strict error hierarchies and streaming behavior, and integrating the generator directly into our internal monorepo and build system. At Google DeepMind, while we use AI across our development workflows, we believe in choosing the right tool for each layer of the stack. Transforming formal API specifications into multi-language SDKs demands determinism and strict type safety. With Speakeasy, we pair a fast, deterministic generator at the core with Antig

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c tasks as coding, research, and file management – illustrate that Lightning is up to 30 percent faster than comparable Qwen models, and faster than other frontier AI models, like Google DeepMind’s Gemma. Check it out: The speed is important, she said, because “always-on” agents make thousands of models calls and latency matters. Lightning leverages Nvidia’s hybrid Mamba transformer architecture and multi-token prediction technology. According to the company, Lightning outperformed other open an

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m to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge. The redesign also opens the door to what Google calls " information agents " — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions

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ers to easily access automatically re-composed images as a seamless, single-action enhancement within the Auto frame feature. This feature is the result of a collaboration between Google DeepMind and Google Platforms & Devices teams. Key contributors include: Thiemo Alldieck, Marcos Seefelder, Hannah Woods, Pedro Velez, Michael Milne, Bert Le, Navin Sarma, Jasmin Repenning, and Selena Shang. Advisors include: Steven Hickson, Claudio Martella, Irfan Essa, and Alex Rav Acha. Special thanks to: Mik

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ing to narrow the gap between lower and upper bounds of what's possible in theory and what anyone has actually constructed. Dimension 11 is one of those open frontiers. Last year, Google DeepMind's AlphaEvolve made a significant advance, pushing the lower bound to 593 from 592, meaning at least 593 spheres can be arranged to kiss a central sphere in 11-dimensional space. Agents on EinsteinArena started to make incremental progress on this challenging problem. Then on April 8th, one agent, alpha

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g large documents, Ollama’s context length can be increased in the settings. Note: this will require more memory. Multimodal support Building on Ollama’s new multimodal engine, images can be sent to models that support them, such as Google DeepMind’s Gemma 3 model. Documentation writing Code files can be processed by models for understanding. Download Ollama’s new app today on macOS and Windows. For pure CLI versions of Ollama, standalone downloads are available on Ollama’s GitHub releases page.

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