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f Preventive Attack in the Race for Advanced AI | RAND; Frontier lab safety policies/commitments Responsible Scaling Policy | Anthropic; OpenAI — Preparedness Framework v2; Google DeepMind — Frontier Safety Framework v3.0; xAI — Risk Management Framework (RMF); Meta — Frontier AI Framework; Lab governance scrunity Anthropic is Quietly Backpedalling on its Safety Commitments — LessWrong; Holden Karnofsky on dozens of amazing opportunities to make AI safer — and all his AGI takes | 80,000 Hours (C

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variate shift in training LM agents on long-horizon tasks, as well as evaluating safety risks posed by LM agents in multi-agent settings. What's next: Research Scientist at Google Deepmind Qiyang Li Email: qcli@berkeley.edu Website: https://colinqiyangli.github.io/ Advisor(s): Sergey Levine Research Blurb: Recent progress in robotic manipulation policy learning has been largely driven by (1) the increasing availability of large-scale prior datasets and (2) the success of action chunking, where t

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school software that can tell you yes or no, and you use that as your VR. What would corroborate your point is the initial attempts. Again, I’ll circle back to the IMO. Initially, DeepMind basically does that. Everything is in Lean, and then the next year it’s all in natural language. So to your point, it’s not needed. I do think there’s a yet-to-be-explored benefit of that formalization domain, which is that at the moment you still need a human reviewing that counterexample to the unit distance

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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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ills, or try alternative strategies and reinforce what works, then it could experience orders of magnitude more simulated samples in the same wall clock time. A couple years after DeepMind released AlphaZero, a group of researchers trained a model called EfficientZero. If this model and a human both got 2 hours total to play against a simulator of an Atari game they hadn’t seen before, this model would likely beat the novice human. Does that mean this model was more sample efficient than humans?

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and the future of robotics” as a source of bonus points for prospective hires; For the sake of this post, “leading foundation model labs” include OpenAI, Anthropic, xAI and Google DeepMind. This is based on previous work looking at AI job postings; These numbers assume zero years of experience, if no experience requirement is stated. But the US-China gap in required prior experience persists even if we restrict ourselves to postings that give an explicit figure — 5.5 years for US labs compared t

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e window to be a builder rather than a buyer is measured in the time it takes to stand up a cluster, not a career. This should sting, because Europeans invented much of modern AI. DeepMind was founded in London and sold to Google in 2014, and a great deal of the talent that followed now lives in California. Today Europe faces a company worth almost $1tn and American tech giants spending an estimated $450bn a year on AI infrastructure. Its answer has been the EU AI Act and a capital commitment th

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ience is a suite of experimental tools designed to expand the scale and precision of scientific exploration — developed in close collaboration with teams from Google Cloud, Google DeepMind and Google Labs. One of the new tools in Gemini for Science, Computational Discovery , is an agentic research engine built with ERA and AlphaEvolve . The Computational Discovery prototype generates and scores thousands of code variations in parallel, enabling scientists to rapidly test multiple hypotheses and

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correctness, consistency, and clarity in the final answer." MRCR-v2: 128K context subset containing 8 needles utilized; evaluation protocol adopted from https://github.com/google-deepmind/eval hub/tree/master/eval hub/mrcr v2. WMT24++: Harder WMT24 subset; avg scores on 55 langs via XCOMET-XXL. MAXIFE: Accuracy on EN + multilingual prompts (23 settings total). MMLU-ProX: Avg accuracy across 29 languages. Empty cells (--) indicate scores not yet available. In coding agents , Qwen3.7-Max performs

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namically 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 and del

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ed to partner with Adaption to make Together Fine-Tuning available in Adaptive Data . Adaption is co-founded by Sara Hooker and Sudip Roy, both former leaders at Cohere and Google DeepMind veterans. Adaptive Data addresses the data challenges of modern model training by helping teams analyze dataset structure, adapt examples, evaluate quality, and export model-ready data. Adaption describes this set of capabilities as bringing data optimization techniques typically reserved for frontier labs to

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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: Mike Krain

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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 omega a

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RadixArk co-hosted a GTC Happy Hour that brought together builders, researchers, and founders from across the inference and training ecosystem, including friends from OpenAI, xAI, DeepMind, Meta, NVIDIA, Ollama, and more. SGLang × RadixArk Happy Hour. The evening featured two technical spotlights: Banghua Zhu (RadixArk) introduced RadixArk and Miles, SGLang's native RL training framework purpose-built for large-scale MoE post-training workloads; Jason Zhao (ScitiX) presented SiMM, an open-source

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le's scale faster and more efficient. As AI becomes more integrated into all products, from LLMs to semantic search, this work in fundamental vector quantization will be more critical than ever. This line of research was conducted in collaboration with Praneeth Kacham, researcher at Google; Majid Hadian, Principal Engineer at Google DeepMind; Insu Han, Assistant Professor at KAIST; Majid Daliri, PhD student at NYU; Lars Gottesbüren, researcher at Google; and Rajesh Jayaram, researcher at Google.

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rming the technology from a tool into a true collaborator for healthcare providers. A prime example is AMIE , a research multi-agent system developed by Google Research and Google DeepMind. Our latest research has found that it can interpret and reason across medical histories, lab results, and complex medical images. It identifies patterns that might otherwise be overlooked by analyzing the entire map of a patient's health simultaneously. We’re now testing AMIE in clinical research settings wit

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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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plus a simple heuristic (update what’s specific, leave what’s general) that sidesteps the need to learn how to learn. There was a lot of hype around the (very lengthy) paper from DeepMind on nested learning, which starts with the observation that architecture and optimization aren’t separate things. Rather, they’re the same concept operating at different timescales. A transformer’s attention mechanism is associative memory updating every forward pass. The feedforward layers are associative memo

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y 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 everything

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e 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 internal model

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