NVIDIA Cosmos
NVIDIA Cosmos is a developer-first platform of state-of-the-art generative World Foundation Models (WFMs), advanced video tokenizers, guardrails, and an accelerated data processing and curation pipeline designed to supercharge physical AI development. It enables developers working on autonomous vehicles, robotics, and video analytics AI agents to generate photorealistic, physics-aware synthetic video data, trained on an immense dataset including 20 million hours of real-world and simulated video, to rapidly simulate future scenarios, train world models, and fine‑tune custom behaviors. It includes three core WFM types; Cosmos Predict, capable of generating up to 30 seconds of continuous video from multimodal inputs; Cosmos Transfer, which adapts simulations across environments and lighting for versatile domain augmentation; and Cosmos Reason, a vision-language model that applies structured reasoning to interpret spatial-temporal data for planning and decision-making.
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GWM-1
GWM-1 is Runway’s state-of-the-art General World Model designed to simulate the real world in real time. It is an interactive, controllable, and general-purpose model built on top of Runway’s Gen-4.5 architecture. GWM-1 generates high-fidelity video frame by frame while maintaining long-term spatial and behavioral consistency. The model supports action-conditioning through inputs such as camera movement, robot actions, events, and speech. GWM-1 enables realistic visual simulation paired with synchronized video and audio outputs. It is designed to help AI systems experience environments rather than just describe them. GWM-1 represents a major step toward general-purpose simulation beyond language-only models.
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Starchild-1
Starchild-1 is the first real-time multimodal world model, built to simulate both the visuals and sounds of the world in real time. Unlike language models, which learn from text, world models learn directly from the world itself through pixels, motion, and actions encoded in large-scale video, becoming capable of understanding and simulating an approximation of the world as it evolves. Starchild-1 goes beyond traditional world models, which have mostly focused on visual generation alone, by autoregressively generating synchronized audio and video while continuously responding to streaming user input. Instead of producing a fixed offline clip, it predicts the next audio and video state of a world based on past observations and live inputs, enabling environments, conversations, ambient sound, and world dynamics to change interactively. Users can stream text, speech, and action inputs into the model during rollout, dynamically altering what is seen and heard in real time.
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MiMo-V2.6-Flash
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MiMo-V2.6-Flash is an open-source, natively omnimodal AI model from Xiaomi MiMo designed to balance intelligence, efficiency, and cost. The model supports coding, general agent workflows, visual reasoning, computer use, automation, and multimodal creative tasks. Its capabilities extend beyond software engineering into frontend design, presentation creation, 3D modeling, interactive world generation, video production, and embodied simulation. Xiaomi trained MiMo-V2.6-Flash with large-scale reinforcement learning across coding, general agent, visual, and cybersecurity tasks, completing roughly 750,000 training trajectories. The model is positioned as the more cost-efficient member of the MiMo-V2.6 family while retaining strong performance across software engineering, tool use, automation, and visual coding benchmarks. MiMo-V2.6-Flash is available through MiMo Desktop, AI Studio, MiMo Code, the Xiaomi MiMo API Platform, OpenRouter, and the open-source MiMo-V2.6 release.
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