与「blog」相关的搜索结果

Claim: we've solved the AI slop problem (!) 💩🧹✨ Blog post: 🧵1/5 Key idea: take *expert* human writing and learn rubrics that find the gap between experts and models. Train with those rubrics. We train with RL-XAR (RL with eXpert Aligned Rubrics) & see large performance gains on writing scientific paper sections, Pulitzer prize novel continuations and high quality Wikipedia pages.

I’ve seen a couple of posts about this so wanted to demystify. Today, every Muse user gets a free computer in the cloud. It's a real computer, and we’ve designed the security architecture of the Muse Secure VM carefully so you and your Muse can do almost anything you could with a computer sitting under your desk while keeping you and the system safe from threats like prompt injection. We wrote about this at length in our security blog post – Activity in the “runtime cell”, which you share with your Muse is unfettered, but sensitive actions are all overseen by the Sentinel, which runs outside of that cell. Similarly, all sensitive secrets - like the passwords you enter into Muse’s secure credential storage - are also stored outside the runtime cell. The runtime cell gets its own root filesystem (including a full Ubuntu linux image) separate from the host filesystem where your other more sensitive data lives. Because it is isolated from the sensitive stuff that runs on the same box, this means that we can, and do, offer users full visibility and control over the files in the runtime cell. Just as you can when you install Linux on your home computer, you can poke around and see all the files that make the system work - both debian system files and the binaries and data files that implement the parts of Muse which run in the runtime cell. This was a very deliberate choice - your Muse Secure VM truly is your own computer in the cloud. You can install software in it, write and compile code, use the browser to surf the web: it is your own Linux box that you can operate as you choose with your Muse. Poking around in this computer doesn't give you any privileged access to Meta infrastructure, or to other people's data If I may geek out a little here for a second… As a kid I loved to take things apart to see how they worked. As a teenager I got into computers and soon found myself drawn to C:\WINDOWS\SYSTEM and the system registry, later Slackware’s /dev/, /proc/ etc – I could see how the system was laid out and as I explored what DLL files and .so files actually did, I gradually became able to meld the computer to my own will. We’re really proud to be able to put a real computer in millions of people’s hands with a similar level of transparency. We built a file explorer right into the Library tab of the UI. We want you to be able to see the markdown files Muse writes while it thinks about how to serve you better, and explore the internals of the system if you’d like to. So, when you ask your Muse to show you its entire filesystem, and receive gigabytes of files you’re seeing the full contents of the runtime cell. It’s yours to explore and enjoy! If you’re not a geek like me, or simply want to download the data that you personally have created directly with your Muse, we added a feature for that too in Settings > Data controls > Download your agent data.

What will be the “RLHF” moment for robotics? What will it take to get robotics to where LLMs are today and beyond? New blog post with @chelseabfinn sharing some thoughts on the state of RL for frontier robotics models and what's missing 👇 Blog:

Introducing Xiaomi MiMo-V2.6 — Pro & Flash. Frontier intelligence, all the modalities, built in public. 🔹 Two omnimodal models, advancing through scaled reinforcement learning 🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks 🔹 Pro scores 46 on the Artificial Analysis Intelligence Index — the highest among open-source models 🔹 Stronger coding, computer use, 3D reasoning and creative capabilities 🔹 Open model weights, technical report, RL environments and training code Blog：

Thanks for your interest in Agent OS. There are many instances of users in the community without programming experience who have discovered the benefits of using Agent OS. This actually is the point of Agent OS, to empower users who don't necessarily have programming experience with the same tools that pro developers have. You can read more about such examples from this blog post: Risk Warning: Digital asset prices can be volatile and you may not get back the amount you invest. Read our Risk Warning. AI Outputs may contain errors. Not financial advice. DYOR. See our AI Policy.

Feature availability varies across Google AI Plus, Pro, and Ultra tiers. See blog for details. Learn more about the latest updates and features available in your Google AI subscription plan:

Today, we are announcing the second generation of Agentic Document Extraction (ADE). It is faster, more accurate, and more affordable than ever before. ADE Gen2 delivers higher performance while optimizing cost for every document. Pricing has been completely overhauled. Customers running mixed workloads should see 25% to 80% cost reductions. Groundings and citations now reach the word level following a page > block> line > word hierarchy. This allows every extracted value to point to the exact location on the page it came from. The outputs from the v2 Parse and Extract APIs are fully agent-ready and agent-friendly. Your agents get a clear hierarchy, stable IDs, and clean, standardized Markdown designed just for them. ADE Gen2 is a step change in document intelligence not an incremental update. Read the full announcement on the blog (link in comments). Create an account and get started for free at

Introducing OUI-1: the first open-weights model for Generative UI 71.7% on Generative UI Bench at 4B params. Beats Gemma 4 31B with 8× fewer active params, and scores 5.5× the base DiffusionGemma it was fine-tuned from. Methodology, weights, and full benchmark results in the blog 👇

Great blog post by @Dimillian about how he used Astra to build a house in Blender, then to Unreal Engine 5. He reviewed the floor plan before expanding the house, then refined the furniture, materials and lighting. The details are actually insane. The kitchen drawers open. Light switches work. The espresso machine brews coffee.

Grateful to @cicada_mm for featuring us in their research blog. Dive into the full piece for our thinking on scaling data volume without sacrificing quality, how we prioritize robot embodiments, and what foundation-model scale actually means.