ReactHuman is a benchmark for evaluating how well embodied multimodal large language models (MLLMs) can make quick, human-like decisions in response to sudden physical dangers. It includes over 1,000 reproducible scenes with precise physics-based simulations and measures reactions based on reasonableness, safety, and physical accuracy.
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This benchmark helps assess and improve the safety and physical understanding of AI systems in real-world environments.
The paper introduces Vidu S2, a system with two components: Vidu S2-Avatar for real-time interactive digital characters and Vidu S2-Editing for real-time video editing. It also explores real-time spatial video generation for both components. Compared to its predecessor, Vidu S2-Avatar offers improved real-time 720p video generation, dynamic reference updates, and enhanced instruction following. Vidu S2-Editing enables real-time video stream editing with features like style rendering and background replacement.
5 days agoDaily PapersPhysBrain 1.5 is a unified model for understanding physical environments, generating actions, and predicting future states. It uses a vision-language foundation, encodes language responses and motion as sequences, and is pre-trained on human interaction videos. The model achieves strong performance on embodied understanding benchmarks.
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4 days agoDaily PapersAtria Dawn Preview is a foundation agentic language model designed for scientific research and engineering workflows, trained via a Verifiable Experience Pipeline that connects tool-mediated interactions to executable environments and externally verified outcomes. It achieves competitive performance across 16 benchmarks and shows potential for human-AI collaboration in research and development.
yesterdayDaily PapersAlayaVista is a streaming video world model that separates panoramic world evolution from perspective observation synthesis. It uses a pretrained panorama expansion model to create a 360-degree scene prior and evolves it as a camera-conditioned panoramic latent state, then maps this to the requested perspective video latents.
2 days agoDaily PapersRSIAgent is a training-free framework that enables digital agents to autonomously explore and improve themselves in new environments by building and reusing memory. It uses a combination of broad and deep exploration strategies to discover diverse structures and uncover hidden constraints, leading to improved performance on tasks without updating model parameters.
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