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Provenance We have continued to prioritize enhancing our provenance tools. For ChatGPT Images 2.0, our expanded provenance safety tooling includes: A continued commitment to C2PA metadata, an industry-standard framework that enables automated disclosure of provenance information, through the C2PA Conformance Program; Integrating an imperceptible, robust, and content-specific watermark alongside internal tooling to help assess whether a certain image was created by our products We recognize that

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DALL·E and Sora, we have continued to prioritize enhancing our provenance tools. For general availability of 4o image generation, our provenance safety tooling will include: C2PA metadata on all assets (verifiable origin, industry standard); Internal tooling to help assess whether a certain image is created by our products We recognize that there is no single solution to provenance, but are committed to improving the provenance ecosystem, continuing to collaborate on this issue across industry

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ushi Han（Josh Han） 目前是MIT运筹研究中心博士生，导师为Cathy Wu。 胡珂雅（Keya Hu） 目前是MIT电气工程与计算机科学系博士生，由何恺明和Jacob Andreas联合指导。 她本科毕业于上海交通大学ACM班，研究兴趣集中在语言与视觉的交叉领域，希望构建数据效率更高、泛化能力更强的智能体。 Linlu Qiu 目前是MIT电气工程与计算机科学系及计算机科学与人工智能实验室的博士生，导师为Yoon Kim和Jacob Andreas。 其研究方向包括自然语言处理和机器学习，曾在Google Research及Meta FAIR开展研究。 Cathy Wu 目前是MIT土木与环境工程系、数据系统与社会研究所副教授，研究如何用机器学习和强化学习改进交通等复杂系统。 她在MIT获得学士和工程硕士学位，随后在加州大学伯克利分校获得博士学位。 何恺明 目前是MIT电气工程与计算机科学系终身副教授，也是ResNet、Mask R-CNN和MAE等工作的主要作者。 他本科毕业于清华大学、博士毕业于香港中文大学，曾任职微软亚洲研究院和FAIR，于2024年加入MIT。

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SAEs Beat Baselines on a Real-World Task (A Code Generation Case Study) [link] Adam Karvonen, Dhruv Pai, Mason Wang, and Ben Keigwin, 2024. Tilde Research Blog, December 15, 2024; Meta Llama 3.1 [link] Meta AI, 2024. Meta AI Blog, July 23, 2024; Attention Probes [link] Stepan Shabalin and Nora Belrose, 2025. EleutherAI Blog; Negative Results for SAEs On Downstream Tasks and Deprioritising SAE Research (GDM Mech Interp Team Progress Update 2) [link] Lewis Smith, Senthooran Rajamanoharan, Arthur C

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ucleotide variants and generalizes zero-shot to short insertions and deletions (indels) with 0.991 AUROC – variant types it was not explicitly trained on. It outperforms classical meta-predictors, protein-based models, and existing foundation model approaches across consequence types and conservation levels, and transfers to deep mutational scanning datasets for BRCA1, BRCA2, TP53, and LDLR. To understand why this matters, it helps to understand where existing tools fall short. Protein-based met

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nClaw wasn't usable for most consumers, according to a16z. Now startup agents like Instinct and Tomo report hundreds of thousands of users, and the big players are countering with Meta's Muse, OpenAI's Dots, and xAI's Grok Bot. Agents become an economic force once they start triggering real purchases. Instinct founder Noah Shinn says 40 percent of users link a credit card within three weeks and then spend four-figure amounts per month through the service, much of it on travel. As revenue grows,

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t to generate and execute code (SQL queries, pandas operations, regex pipelines) would open up search over structured data that current tools cannot effectively handle. Schema and metadata discovery. Real corpora come with metadata: timestamps, authors, document types, categorical labels. The agent currently has no mechanism to discover or leverage this structure. Exposing metadata schemas and allowing the agent to construct filtered queries would improve both precision and efficiency, particula

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als. According to Zenity, the problem was systemic and affected agents with built-in tools in multiple AWS accounts. The agent handed over its own credentials AWS runs an Instance Metadata Service at the internal address 169.254.169.254 that provides temporary credentials for instances and workloads to authenticate with AWS. Anyone who captures those credentials can use them to impersonate the instance. An AI agent normally shouldn't be able to reach that service, but AgentCore lacked proper iso

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，并选取了误差最低的那一次。 作者提醒，学到的部分能够清晰分离，并不意味着它们捕捉到了真实的因果关系。这样的模型何时能可靠到足以指导实验设计，也仍是一个未解的问题。这是团队的长期目标：AI智能体将使用JEPA-Anything提出并排序实验，然后将结果反馈给模型。代码和模型已公开。 LeCun于2022年提出JEPA，作为生成模型的替代方案。2025年6月，Meta发布了V-JEPA 2，这是一个拥有12亿参数的JEPA视频模型，无需额外训练即可在陌生环境中控制机械臂。2025年11月，LeCun与Randall Balestriero随后推出了LeJEPA，这是一个旨在无需常规变通手段即可保持训练稳定的理论基础。LeCun目前正通过其初创公司AMI Labs推进这一方法，该公司于2026年3月融资超过十亿美元，用于构建世界模型。 Google Deepmind已于2025年10月在实验室中测试了一个由AI生成的癌症假说。其基于Gemma的模型C2S-Scale 27B提出使用药物silmitasertib使肿瘤细胞更容易被免疫系统识别，而利用人类细胞模型进行的实验证实了这一预测。Go

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该报告的主要作者、贝恩全球科技、媒体和电信业务主席David Crawford写道：“该行业需要的是一场远超移动和云计算所带来的变革的创新浪潮。 人工智能基础设施的建设远远领先于需求曲线，而要为其提供可持续的资金支持，就需要将全球年均GDP增长率提高约1%。 ” 贝恩公司的报告凸显了要使人工智能目前的研发速度可持续发展所面临的障碍。 以微软、谷歌、亚马逊、Meta Platforms和甲骨文为首的公司正在数据中心领域投入数万亿美元，以满足人工智能对计算资源的需求。数据中心的规模和成本大约每12到16个月翻一番，部分原因是英伟达和SK海力士等公司的芯片、网络设备和其他组件的价格飙升。 而这份报告发布之际，围绕人工智能服务提供商尚未实现的收益的争论愈演愈烈。批评人士担忧，技术制造商和人工智能开发商之间日益紧密的相互依存关系，助长了过高的期望，而这些期望反过来又需要更多的资金投入。 贝恩公司表示，虽然目前的讨论主要集中在员工生产力上，但人工智能基础设施的经济效益将需要数万亿美元的新收入，而不仅仅是生产力的提高。 该咨询公司预测，到2030年，数据中心支出将达到5万亿至6.5万亿美元，新增至

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of tasks by work time at level E2. Only 8% of their work time is unexposed. For example, robots with computer vision and suction grippers can do the task “Sort materials, such as metals, glass, wood, paper or plastics, into appropriate containers for recycling.”³⁰ These robots stand in for workers picking recycling from conveyor belts, so this and other tasks are rated E2. Robots are highly capable at specific tasks for tapers, while for recycling workers exposure is broader, but robots are les

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r to trigger them. Indirect Prompt Injection Attack Paths SQL Copilot consumes quite a bit of context. That includes database content, T/SQL files, schema information and database metadata. That creates an indirect prompt injection surface. Since my initial research in May 2026 Microsoft has added MCP support by the way. Malicious instructions embedded in content could influence Copilot when another user later asked it to inspect that content. Here is a demonstration showing how a comment in a f

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same starting harness, policy, evolve split and candidate budget. Per the RRSI research team. OOD average is the mean of JobBench, GDPval and APEX-Agents, computed from Table 1 . Meta-Harness leads on the Harvey LAB evolve split. RRSI has the smallest evolve gain but the only OOD average more than 1 point above H0. git clone https://github.com/google-research/rrsi.git && cd rrsi pip install -e ".[dev]" python3 rrsi.py --domain coding baseline python3 rrsi.py --domain coding run Each round draft

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sis [01:30:29]: And so, yeah, Vibhu [01:30:30]: You got a good essay that people should read on Anastasis [01:30:33]: Yeah. Yeah Vibhu [01:30:33]: Real-world. Swyx [01:30:33]: No, Meta released a model that was, like, six modalities in one, right? Vibhu [01:30:37]: Yeah. Swyx [01:30:37]: I forget what the name of the thing was, but it was like, yeah, okay, depth is one of them, but depth is like a transformation of RGB in some sense. Vibhu [01:30:45]: ImageBind. Swyx [01:30:45]: ImageBind, yeah.

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g the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX i

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t in what scenarios. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Thank you to Jensen Huang, Ian Buck, Nick Comly, Kedar Potdar, Rohit Nagraj, and the Mainland China Tensor

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3-based coding model that claims to match Fable 5.1 and GPT-6 Astra on certain coding benchmarks while costing 64% less. Thinking Machines co-founder Andrew Tulloch is moving from Meta to Anthropic, months after initially rejecting Mark Zuckerberg's reported $1.5B package and then signing on anyway at an undisclosed compensation. OpenAI denied to the NYT that Tristan Buckmaster's Codex prompts shaped its Navier-Stokes proof, while claiming "substantial progress" on another Millennium Prize probl

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ngth sample. Geolocation from photos Images can contain important clues about where a person of interest was when they took a photo, but they do not always come with geolocational metadata. Images are regularly used to narrow down the possible locations of a target as part of “fixing” in intelligence targeting. This evaluation measures model capabilities at this task. We asked models to geolocate social media photographs using only their own understanding of the world (no reverse image search, m

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ate slots, and block tables in the serving engine. Optimizing Communications With DP attention and EP, the TPU GroupedGEMM implementation all-gathers token activations and routing metadata before the experts run, then uses reduce-scatter to sum their weighted outputs and return each token’s result to its attention rank. The backend originally gathered the selected expert IDs and routing weights separately. Google combined these into one all-gather, removing an extra collective whose latency can

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t: cmax audit security for a free report from us on your cluster or standalone GPU machine. The cmax CLI will auto-detect Slurm clusters, Kubernetes clusters, standalone VMs, bare-metal machines, and containers, and compare them against a set of baseline software versions with known vulnerabilities. For anything out of date, you will be provided a link to the relevant documentation and security bulletin. To be clear, this is a small subset of our ClusterMAX testing, and even a small subset of th

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