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We’ve developed a new approach to training models, Harmonic Reasoning, which creates a "harmonic" interplay between asynchronous, continuous-time streams of sensing and acting tokens. ⚙️🎵 Watch GEN-0 pack a camera. 🤖📸

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50,111 görüntüleme • 8 ay önce •via X (Twitter)

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Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

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OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

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TimeFusion is built on a new multimodal encoding and decoding framework based on Universal Tokens. This approach can easily extend to a wide range of modalities beyond time-series data and language. Here’s a preview of our ongoing work using Universal Tokens to unify images, sensor data, and language in a single embedding space. This allows dynamic cross-modal mappings—such as converting a video stream directly into physical measurements, all controlled through natural language. #PhysicalAI #multimodal

Ivan Poupyrev

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Simulating and training robots requires a physics-first approach. 🤖 With Newton, the open-source physics simulation engine co-developed by Disney Research, Walt Disney Imagineering, Google DeepMind and NVIDIA, Disney’s BDX Droid can move through shifting sand and gravel with exceptional precision. Watch to see how Newton is playing a key role in teaching robots how to move, behave, and interact with different objects and materials in the real world. #CES2026

Simulating and training robots requires a physics-first approach. 🤖 With Newton, the open-source physics simulation engine co-developed by Disney Research, Walt Disney Imagineering, Google DeepMind and NVIDIA, Disney’s BDX Droid can move through shifting sand and gravel with exceptional precision. Watch to see how Newton is playing a key role in teaching robots how to move, behave, and interact with different objects and materials in the real world. #CES2026

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Like LLMs, autoregressive transformers for action tokens need a reasoning layer to reduce hallucinations and boost reliability. Grounding this layer in the physics of the action space using DVBFs makes for scalable, task-agnostic training—far simpler than creating RL reward functions for each task. Learn more about our novel approach in the video below 👇🏼

Like LLMs, autoregressive transformers for action tokens need a reasoning layer to reduce hallucinations and boost reliability. Grounding this layer in the physics of the action space using DVBFs makes for scalable, task-agnostic training—far simpler than creating RL reward functions for each task. Learn more about our novel approach in the video below 👇🏼

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We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.

We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.

Physical Intelligence

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Emerging as the global frontier of model-reasoning has been a core vision of Affine since its inception. In pursuit of this vision, we've been working relentlessly to forge a repeatable training loop: a mechanism that uses benchmark driven post-training methodology to facilitate the improvement of reasoning itself. Through applying our training methodology onto Qwen3-32B as our base model, Affine’s miners have been able to push the Pareto frontier outward. We’ve now reached saturation; our models have become so good at reasoning that any further improvements are producing diminishing returns. As a result we now move our base model to Qwen3.6-35B-A3B. It provides a stronger basis, offering an extended context window, as well as an improved foundation for agentic work. The same training methodology will be applied going forward. Our objective is no longer simply to improve a single model, but to demonstrate that the process in itself is repeatable, scalable, and model agnostic. We view the commoditization of reasoning as vital to ensure our technology is not only transparent, but also co-owned by humanity.

Emerging as the global frontier of model-reasoning has been a core vision of Affine since its inception. In pursuit of this vision, we've been working relentlessly to forge a repeatable training loop: a mechanism that uses benchmark driven post-training methodology to facilitate the improvement of reasoning itself. Through applying our training methodology onto Qwen3-32B as our base model, Affine’s miners have been able to push the Pareto frontier outward. We’ve now reached saturation; our models have become so good at reasoning that any further improvements are producing diminishing returns. As a result we now move our base model to Qwen3.6-35B-A3B. It provides a stronger basis, offering an extended context window, as well as an improved foundation for agentic work. The same training methodology will be applied going forward. Our objective is no longer simply to improve a single model, but to demonstrate that the process in itself is repeatable, scalable, and model agnostic. We view the commoditization of reasoning as vital to ensure our technology is not only transparent, but also co-owned by humanity.

Affine

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A breakthrough in real-time video generation. As a research preview developed with NVIDIA and shared at NVIDIAGTC this week, we trained a new real-time video model running on Vera Rubin. HD videos generate instantly, with time-to-first-frame under 100ms. Unlocking an entirely new creative paradigm and bolstering the foundations of our General World Model, GWM-1. Real-time generation opens a fundamentally different design space for video models and world simulation. We're investing in co-designing our models alongside advances in hardware to keep pushing this frontier.

Runway

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