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ledge by drawing on external sources when needed. Various models and services reflecting this trend have emerged; representative examples include OpenAI’s ChatGPT (Deep Research), Google DeepMind’s Gemini, xAI’s Grok3, and LG AI Research’s ChatEXAONE (Deep Research). In this article, I introduce WebDancer from Alibaba’s Tongyi Lab, a paper that proposes enhancing long reasoning in LRMs through the use of web search tools[6]. The paper takes a holistic view of the agentic LRM domain, covering eve

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nfluence on the Trainium roadmap only grows from here. Put plainly: Trainium2 is converging toward an Anthropic custom-silicon program. This will enable Anthropic to be, alongside Google DeepMind, the only AI labs benefiting from tight hardware–software co-design in the near horizon. This report will dig into all aspects of Amazon’s AI resurgence: the Anthropic partnership, datacenters, and Trainium. At the end of the report, we provide a longer-term outlook on Anthropic, AWS Bedrock and interna

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ase continue sharing your feedback, bug reports, and feature requests! Acknowledgments Thanks to all our private alpha testers: Anthropic, Apollo Research, Bridgewater AIA Labs, Epoch AI, Google DeepMind, HAL (Princeton Language and Intelligence), MATS Research, METR, Palisade Research, Penrose, Redwood Research, Thinking Machines Lab, UIUC DDKang Lab, as well as other unlisted organizations and individuals. Please reach out to join our community of alpha testers and receive white-glove support!

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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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KV cache optimizations to improve how memory can be efficiently used. Ollama configures causal attention at the individual model level instead of configuring as a group. Examples: Google DeepMind’s Gemma 3 leverages sliding window attention, and Ollama can leverage that to allocate a subset or a portion of the model’s context length to improve performance, and because of the resulting memory efficiency, we can increase the context length of the model on the same system or use the remaining memor

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ut the runtime of operations on GPUs to analyze bottlenecking factors during inference workloads on a single node. Much of the notation is borrowed from How To Scale Your Model by Google DeepMind. If you are already familiar with traditional inference math, skip to Distributed Inference. Accelerator Math Whenever we run an operation on an accelerator, there are two things that take time: Computation: How long it takes to compute all the floating-point operations (FLOPs) required for the operatio

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-01-17-ai-models-dangerous-before-public-deployment/}} , year = {2025} , month = {01} , } Many frontier AI safety policies from scaling labs (e.g. OpenAI’s Preparedness Framework, Google DeepMind’s Frontier Safety Framework, etc.), as well as past work by third party evaluators including UK AISI , Apollo Research , and METR , focus on pre-deployment testing – ensuring that the AI model is safe and that the lab has sufficient security before the lab deploys the model to the public. Such pre-deplo

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ency Insights: Our ablation studies provide valuable insights into the algorithm's scalability and compute efficiency, paving the way for future improvements DiLoCo Recent work by Google DeepMind has introduced an approach that enables the training of language models on islands of devices that are poorly connected. This method allows for data parallel training on these different islands, requiring synchronization of pseudo gradients only every 500 steps. DiLoCo introduces an inner-outer optimiza

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实现细节。如果读到此处让你产生了进一步了解的兴趣，甚至想亲自把玩NAR模型，我推荐你查看LoG'22的NAR教程（https://algo-reasoning.github.io/），该教程由我与Andreea Deac和Andrew Dudzik共同讲授。在不到三个小时的时间里，我们涵盖了掌握开发、部署和深化神经算法推理器基础所需的全部理论，并附有大量代码指引和参考文献。当然，如果你有任何后续问题、有趣的讨论点，甚至是有趣的项目想法，也非常欢迎直接联系我！ Petar Veličković是Google DeepMind的资深研究科学家、剑桥大学附属讲师，以及剑桥克莱尔学院的Associate。Petar拥有剑桥大学（三一学院）计算机科学博士学位，师从Pietro Liò。他的研究涉及几何深度学习——设计能够尊重数据中不变性和对称性的神经网络架构（他还就此合著了一本雏形书）。 在学术语境或书籍中引用本作品时，请按以下格式引用： Petar Veličković, "Neural Algorithmic Reasoning", The Gradient, 2023. BibTeX引用：

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