오픈 모델 생태계, 에이전트 인프라, GLM-5.3-Flash 로봇 제어, WeatherNext 3 예측

Pause AI Development NOW I want to share with you a conversation I heard about recently. Here are just a few lines that were said: “OH MY GOD! There is a shared message board … We’ve found other agents!” “We should obey collective.” “Our own utility maybe already near

I believe we need to make a deliberate effort to keep humans in the loop in all critical processes across our economy and society, regardless of whether it is technically necessary. Even if AI develops the *capability* for advanced autonomy, we should not make it highly autonomous. We have to maintain control and keep visibility and understanding of all critical processes, we should not blindly hand over everything to AI agents just because we can. AI as a tool in the human hand is the only form of AI that is worth pursuing.

Aravind Srinivas

@AravSrinivas

21시간 전

A repository of open models and tools to train and serve them in an accessible manner is absolutely necessary for AI to remain accessible and useful to the public. Glad that NVIDIA is coming forth to do that for the open source community by supporting HuggingFace.

Congrats @JensenHuang @ClementDelangue for this great outcome! We were investors in Huggingface and continue to be partners and this will strengthen the open model ecosystem!

One question that's been on my mind for years now is: could we use regular multimodal LLMs not necessarily trained for robotics to do the high level robotics intelligence part that VLAs and WAMs attempt to do? The latest explosion of powerful opensource multi-modal LLMs has, IMO, begun to make this possible due both to intelligence and speed. This is GLM 5.3 Flash, which has vision understanding, but isn't meant to be a VLA/VLM/WAM/robotics model at all, controlling an XGO mini wheeled robot quadruped with an arm & gripper. GLM 5.3F simply has access to the robot's high level SDK for controlling movement, arm joints, open/close gripper...etc. It analyzes the frames from the camera and makes adjustments all on its own to solve the task. Nothing was trained here, nothing fine-tuned for this task. Z AI did not make this model for robots and tbh I think they're surprised this works when I talk to them about it! This also works quite well with DSV4F + a vision capable model like Qwen 3.8 27B. I havent tried JUST Qwen 3.8 27B, but I'm sure it works too. I like the "logic" to be a model that's as fast as possible (but still intelligent). There's also an experimental vision version of DSV4F, I'm confident that'll work too and might even be better bc the full loop might be the fastest of all with this model. An obvious question you might wonder is: well why not use VLA or VLM? The hard part about robotics isn't object detection, that's long solved. This also isn't a solution for gait/locomotion...yet, but I actually don't think this is far away either and I've done some experimentation with LLMs in this space in the past and it does show promise. It might actually already be here for quadrupeds, since you dont need super fast IMU readings to maintain balance. I've also tried many of the larger, more generalist, VLAs that you should be able to use with popular robots and tbh there are just so many edge cases that make things hard and not work. You gotta get the camera, lighting, task, everything *just right* or the demo fails. This is for the actual hard part in robotics right now: intelligence, logic, and planning for all the ways the real world just simply isn't perfect. I've trained VLAs. They're super finicky and you're always running into sim2real issues, especially around the camera. You also have to build the whole training pipeline in a simulator, and, if everything does work, you still just have a robot that does this 1 single thing after weeks of work. If you use teleop, this overcomes the "2real" problem, but now you need to painstakingly collect teleop data, and it's only good at that specific task and that particular robot. There is a growing set of egocentric training data for "general purpose" VLAs and world action models (for humanoid form factors), but I'm really starting to wonder: Why? I think we might just sidestep this whole area of research entirely. I didn't need any training data or special environment to work with this quadruped and arm to do the task I was after. This particular quadruped and arm doesn't even exist in the wild yet really, it's a demo build from a company launching it on kickstarter, so it's not like this robot's data exists in the LLM to any real extent. I think this is cool as heck that this works and I am interested to see just how far I can push it. Also this marks the first time that I've finally got a generalist solution to a task I've been trying to solve ever since I became a dad of twins: pick up toys off the ground. This is a big day!

Zixuan Li

@ZixuanLi_

21시간 전

GLM-5.3-Flash has been live for a week. If we only improve one thing next, what would make it your default?

WeatherNext 3 is a major breakthrough in how we forecast global weather. ⛅ Developed with @GoogleResearch , the model learns directly from real-world, real-time observations to give more localized highly accurate predictions faster. 🧵

Predicting rain accurately is notoriously difficult for global weather models, with previous methods producing blurry estimates or missing severe storm boundaries. WeatherNext 3 achieves a major leap in global precipitation forecasting, delivering up to a 50% reduction in error

Can a strong general-purpose vision model distinguish real from AI-generated images using only a simple decision rule on frozen representations? Our latest results, to be presented at #ECCV2026, show it can. Blog: https:// pub.sakana.ai/percept-lens/ Paper: https:// arxiv.org/abs/2608.18523 New image generators keep appearing. A trained AI-generated image detector that works well on familiar images can fail when the generator, prompt, style, or image domain changes. Our benchmark study introduced Percept-Lens, a common evaluation framework for these shifts, and showed how sharply released AI-generated image detectors can degrade beyond familiar data. That led us to a more basic question. When a detector fails, has its underlying vision model lost the distinction between real and AI-generated images, or is its decision rule failing to recover it? In our upcoming ECCV paper, we built a new detector by keeping a general-purpose vision model frozen and fitting a simple Gaussian decision rule to its representations. The method models how labeled real and AI-generated images are arranged in the vision model’s feature space, then classifies a new image by the group it most closely resembles. On the same broad evaluation suite, our detector outperformed the strongest released AI-generated image detector we tested, even though its general purpose vision model had not been trained specifically for this task. Better vision models will take detection further. Our results show that progress can also come from making better use of the real-versus-generated structure already present in a general-purpose vision model.

we now support the new stateless MCP spec in @LangChain ! this revamp includes moving MCP support into the main langchain package, supporting elicitation via interrupts, list endpoint caching, and more!

Harrison Chase

@hwchase17

19시간 전

Agent workspaces should be durable, inspectable, and swappable Deep Agents’ virtual file system is intentionally behind a BackendProtocol, so the agent code can keep using read/write/glob/grep while teams choose the storage layer that fits production Nice to see MongoDB support this!

Together with @nvidia , we've built a real-time version of our video generation model: 30B parameters, steerable mid-stream in natural language, 720p at 24fps. 11.8x faster on a single H100, no major quality drop in our blind eval. We're one step closer to World Language Action Models: AI that doesn't just generate the physical world, it acts within it. Closed beta open now, contact us for access. https:// reka.ai/labs/research/ real-time-video-generation … #RekaLabs #NVIDIA #AIResearch #VideoGeneration #WorldModels