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设计送进实验室，验证它们能否真正发挥作用。 实验使用的检测分子，其酯键被切断后，生成的产物会在紫外光下发出荧光。按照谷歌披露的结果，五个设计均表现出较强的催化活性。 模型交出的方案，已经走到了实验验证这一步。 谷歌还宣布，将向符合条件并获选的1万名科学家，免费提供六个月的Antigravity最高使用额度，分批开放。 One more thing 前面虽然调侃谷歌怕出事，但这次安全能力确实值得单独说两句。 在评估漏洞修复能力的CWE-bench v1中，Argon取得68%的成绩，与GPT-6 Astra同处官方对比表的最高分。 前Anthropic，现DeepMind Mechanistic Interpretability负责人表示，Argon的安全报告是Google目前最详尽的。 217页可不详尽吗？ 在Gray Swan间接提示词注入评测中，经过15次自适应攻击尝试，Argon的攻击成功率为0.7%。 本文来自微信公众号“量子位”，作者：henry，36氪经授权发布。 该文观点仅代表作者本人，36氪平台仅提供信息存储空间服务。 聚焦全球优秀创业者，项目融资率接近97%，领跑行业

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rs领投，WndrCo、Amplify Partners、Sky9 Capital、FPV Ventures及其他投资者参投的5900万美元种子轮+A轮融资，近日它又获得NVIDIA与Sapphire Ventures的A轮追加轮融资，总融资额达到7400万美元。 世界/视频模型走向实时交互，对基础设施提出新的要求 技术的变革首先出现在模型端，Google DeepMind的Genie 3能以24 FPS的帧率生成720p交互式世界；StreamDiT在单张H100上实现了16 FPS的文生视频，阿里、腾讯、包括NVIDIA也都发布了交互式的视频模型或世界模型。 在生成式多模态模型发展的早期，它生成的是一次性、被动的内容，大家都有用提示词批量化“抽卡”的经历。 但交互式视频模型，尤其是可交互的世界模型出现后，模型的形态变了，这些模型本质上是经过学习的物理引擎。通过观察数十亿个样本，它们内化了底层的物理规律，从而“懂得”火会蔓延、水会溅起、金属受击会凹陷。用户置身于环境中进行操作，模型则以逐帧的形式实时给出反馈。 新形态的模型，也催生了新形态的应用，实时视频、游戏以及数字人领域涌现出了大

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evidence of agent-driven pressure on developer infrastructure; merge volume alone does not measure delivered value (engineering announcement); Harness learning and explicit state: DeepMind’s Evo2 co-evolves harness edits and model weights through proposer/reflector roles, SFT and interleaved GRPO. Reported results reach 48.3% SWE-bench Verified and 82.1% Polyglot, versus 41.0% and 62.6% for the strongest baselines (paper summary).Continual memory: Meta/MIT’s ReHarness rewrites retrieved reflecti

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Fuel Ventures, alongside Antler, and includes a £497,787 grant from Innovate UK, part of UK Research and Innovation (UKRI). The funding also attracted angel investors from Google DeepMind, Meta and Playfair VC, as well as content creator Marcello Ascani and Devran Amaratunga Karaca, CEO of creator economy company Kyra. Founded by Edoardo Ricci (CEO), Ahmed Eltom (COO) and Sunny Panchal (CTO), Affinity Labs develops software products in partnership with content creators. The company combines cre

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derjähriger respektive deren Verlassenschaften anhängig. Etwaige Vergleiche oder Urteile drohen, sich zu erheblichen Beträgen zu addieren. Google-Geld für Selbstaufgabe 2024 hat Google laut Medienberichten 2,7 Milliarden US-Dollar an Character Technologies überwiesen, offiziell um dessen KI zu lizenzieren. Wichtiger dürfte Google gewesen sein, Noam Shazeer wieder zurückzuholen. Auch de Freitas und 30 weitere KI-Experten sind als Teil der Vereinbarung zu Google DeepMind gewechselt. Lesen Sie auch

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our company lives in Google’s ecosystem, and a strong option for frontier hoppers as a secondary model for certain tasks. I Here’s our vibe check: My new favorite writing partner. DeepMind hired professional writers with red pens to stamp out AI slop, and it worked. I use it to untangle structure and sharpen metaphors; it also taught me something new tracing in a few of my favorite books. It's got a natural feel for structure and detailed explanations that Astra and Opus lack. Shaky at refactors

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Agent Arena update: we’ve evolved the Bash Recovery signal, with Bash Reliability. Here’s how it impacts the leaderboard. At the top for Agent Arena: - 1 Claude Opus 5.5 (Max), @AnthropicAI holds the top - 2 GPT-6.1 Sol (Max) by @OpenAI - 3 Claude Fable 5.1 (Max) by @AnthropicAI - 4 GPT-6 Astra (Max) by @OpenAI - 5 Gemini 4 Argon (High) by @GoogleDeepMind The first 14 spots are all proprietary models with the best open weights models being GLM 5.2 at 15, followed by MiMo V2.6 Pro at 18.

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startups. We also cover world-action models versus vision-language-action models, GPT-6 and coding agents in robotics, and Jason’s journey from AlphaGo and Penn to NVIDIA, Google DeepMind, and founding Dyna. We discuss: Why Dyna-2 was trained on over one million hours of human video; World-action models: predicting future video to guide physical robot actions; Diffusion transformers, flow matching, and real-time robotic control; How robots learn new tasks with as little as 45 minutes of demonst

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startups. We also cover world-action models versus vision-language-action models, GPT-6 and coding agents in robotics, and Jason’s journey from AlphaGo and Penn to NVIDIA, Google DeepMind, and founding Dyna. We discuss: Why Dyna-2 was trained on over one million hours of human video World-action models: predicting future video to guide physical robot actions Diffusion transformers, flow matching, and real-time robotic control How robots learn new tasks with as little as 45 minutes of demonstrat

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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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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

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s, for example by enabling the production of chemical, biological, radiological and nuclear (CBRN) weapons, as detailed recent safety frameworks from, among others, OpenAI, Google DeepMind and Anthropic. Developers therefore need to ensure that the risks from deploying their model is acceptably low. Anthropic’s recent Constitutional Classifiers paper (Sharma et al, 2025) demonstrated that using a separately trained model to identify dangerous inputs provides increased robustness. However, this c

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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’

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ower than the ‘true’ activation leading to less accurate reconstruction for a given level of sparsity. These papers include: Gated Sparse Autoencoders (Rajamanoharan et al., GoogleDeepmind); Using a TopK penalty instead of an L1 (Gao et al, OpenAI); Finetuning a subset of the parameters after the main SAE training phase (Wright and Sharkey, MATS/Apollo) There was also a post in our February Update – Using a Tanh penalty. (Jermyn et al) – which found that replacing the L1 penalty with a Tanh func

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the steering damper network to control complex, next-generation video models. Acknowledgements We would like to thank our co-authors and collaborators from Google Research, Google DeepMind, and academia for their contributions to this work. Labels:; Algorithms & Theory; Machine Intelligence Automating coherent long-form video generation Generative AI ·; Machine Intelligence; September 18, 2026 MilleMiglia: A realistic instance generator for middle-mile logistics Algorithms & Theory; September 17

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lities have not yet translated into visible improvements in high-salience domains like energy, healthcare, housing, public services, or broadly available robotics/autonomy; Google DeepMind’s AI weather model picked up Hurricane Isaias 11 days before landfall, pinned where it would hit 4 days out and nailed its peak strength 3 days out—potentially giving communities days more time to evacuate and prepare (Activity: 1279): The image is a screenshot of an X post claiming Google DeepMind’s FNV3 weat

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oving more accurate than traditional forecasts, although it is still a jagged frontier, with both types of forecasts required to enable useful understanding of the weather. Google DeepMind’s latest weather model offers important new capabilities, and its cyclone forecasting tools are becoming increasingly central to how the U.S. government responds to hurricane season. In August WindBorne became the first organization to send an unpowered flight system — one with no engine — to collect data in t

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dev work. Anthropic released new research demonstrating that small AI models are getting closer to being able to copy themselves between everyday gadgets like smart ovens or TVs. DeepMind published its AlphaProof Nexus math agent, showing it cracked 9 of 353 unsolved Erdős problems by pairing Gemini proofs with software checking every step. Coinbase released a new set of blogs detailing how the crypto exchange trained Alibaba’s small, open Qwen model on its own data to beat top AI models at cat

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quickly he believed there is a greater than 10% chance it "could kill all humans" within the next decade. The bosses of the US-based AI giants such as Anthropic, OpenAI and Google DeepMind have all backed the idea of an AI development slowdown. However, there are currently no global safety rules in place to regulate the companies and their technology. President Trump has called for the sector to regulate itself so that US firms do not lose out to Chinese rivals. Speaking to the BBC following the

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prets their results. Results are checked against a human baseline “drawn from 24 experts (at least two per task) who have recently worked at organizations including OpenAI, Google DeepMind, NVIDIA and Microsoft Research”. Results: “The experimental research taste of frontier models has doubled every 3.0 months since December 2025 (95% CI 1.7-5.0 months). The best model, Opus 5.5, significantly exceeds our human baseline with a compute multiplier of 2.30x (95% CI 1.15-4.37x), at roughly 1/30 of o

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