#模型发布

MAI-Image-2.6 by @MicrosoftAI has landed in the Text-to-Image Arena at #2 (1336 pts)! This release is just 45 pts behind GPT Image 2 (Medium) at #1, and ahead of #3 Grok Imagine Image 2.0 (Low) by 20 pts. MAI-Image-2.6 is a significant improvement from MAI-Image-2.5 overall at #10 with (1256 pts), and across all categories. MAI will also be making it available on Playground with early API access on Foundry next week. See post below for more details. Congrats to the @MicrosoftAI team on this release!

MAI-Image-2.6 by @MicrosoftAI has landed in the Text-to-Image Arena at #2 (1336 pts)! This release is just 45 pts behind GPT Image 2 (Medium) at #1, and ahead of #3 Grok Imagine Image 2.0 (Low) by 20 pts. MAI-Image-2.6 is a significant improvement from MAI-Image-2.5 overall at #10 with (1256 pts), and across all categories. MAI will also be making it available on Playground with early API access on Foundry next week. See post below for more details. Congrats to the @MicrosoftAI team on this release!

Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵

Today, we’re excited to open-source TwiL-LM3, the first formal reasoning model from the webAI Intelligence Lab. At just 3 billion parameters, TwiL-LM3 outperforms OpenAI’s GPT-OSS-120B on 4 of 5 formal reasoning benchmarks while running efficiently on consumer hardware. That’s 40× fewer parameters, 2.6× faster inference, and state-of-the-art performance in the reasoning tasks that power reliable tool calling, code generation, structured outputs, and AI agents. TwiL-LM3 was trained using webAI’s proprietary reasoning pipeline on webAI-owned, verified datasets—not scraped internet data. We believe better reasoning comes from better training pipelines and higher-quality data, not simply larger models. Our approach demonstrates that efficient models can rival—and in many cases surpass—models dozens of times their size. Designed for the edge, TwiL-LM3 runs on hardware people already own—from a Raspberry Pi to an iPhone—bringing advanced reasoning to millions of devices without relying on the cloud. This is our first open-source release from the webAI Intelligence Lab, and it’s only the beginning. Proudly built in Austin, Texas. Article: https://www.webai.com/blog/webai-releases-twil-lm-a-family-of-formal-logic-models-that-outreason-a-120b-model-and-run-on-an-iphone

Today we are releasing GPT-5.6-Cyber. The model is our first large-scale attempt at directly improving capabilities for advanced cybersecurity tasks such as exploit development. We are finding it to be really quite strong for accelerating defensive work. We are using it across our stack for red-teaming, and our security researchers have used it to find and patch a huge host of 0-day vulnerabilities in open-source software. https://openai.com/index/expanding-daybreak-as-the-cyber-defense-window-narrows/

We’re expanding our cybersecurity initiative Daybreak and introducing GPT-5.6-Cyber, a new model for advanced, authorized cybersecurity work. As the threat landscape evolves, we’re putting frontier intelligence in the hands of trusted defenders before attackers can deploy offensive AI at scale.

We’re expanding our cybersecurity initiative Daybreak and introducing GPT-5.6-Cyber, a new model for advanced, authorized cybersecurity work. As the threat landscape evolves, we’re putting frontier intelligence in the hands of trusted defenders before attackers can deploy offensive AI at scale.

We’re expanding our cybersecurity initiative Daybreak and introducing GPT-5.6-Cyber, a new model for advanced, authorized cybersecurity work. As the threat landscape evolves, we’re putting frontier intelligence in the hands of trusted defenders before attackers can deploy offensive AI at scale.

今天我们还将开放 Muse Glimmer 的权重，这是一个出色的 30B 参数稠密模型，可以在本地运行。很快我们还会发布 Muse Spark 1.2 的权重，这是我们最新的基础模型。Meta 是开源的有力支持者，我为这些发布感到自豪。祝贺 @alexandr_wang 和 MSL 团队在这些模型上所做的出色工作。

Mark Zuckerberg just dropped a really long piece on Meta’s vision for the AI future where superintelligence is available to everyone. some strong quotes from the blog. - “Any policy that slows American model releases -- even by a month -- could add significant risk to American leadership while letting foreign models race ahead.” - “For example, if a self-improving AI system focused on optimizing its compute efficiency, it could theoretically invent ways to squeeze 100x or more intelligence out of each gigawatt.” - “There is no such thing as a singular benevolent superintelligence.” - “While there are risks to releasing capable models, the most dangerous scenario from this perspective would be leading AI labs training powerful models and keeping them for themselves.” - “I also propose that frontier AI labs should share intermediate training checkpoints of new models for government use and review rather than waiting until training has completed.”

Meta releases Muse Glimmer, a new 30B open model that runs on 18GB RAM. Muse Glimmer is Apache 2.0 licensed, supports vision and is the strongest agentic model for its size. Run and train the model via Unsloth. GGUF: https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF Guide: https://unsloth.ai/docs/models/muse-glimmer

1/ big announcement today: we will be releasing an open weight version of muse spark 1.2 soon. we also are releasing muse glimmer, a 30B agentic model with open weights under apache 2.0. muse glimmer can run on 24GB of VRAM without losing agentic reliability. 🧵

今天我们还将开放 Muse Glimmer 的权重，这是一个出色的 30B 参数稠密模型，可以在本地运行。很快我们还会发布 Muse Spark 1.2 的权重，这是我们最新的基础模型。Meta 是开源的有力支持者，我为这些发布感到自豪。祝贺 @alexandr_wang 和 MSL 团队在这些模型上所做的出色工作。