CMIB uses a conditional multimodal information bottleneck to create reusable agent skills that separate verbalizable text content from predictive perceptual residuals, improving execution stability.
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KeyStone improves task success rates in diffusion-based physical AI models by up to 13.3% by sampling K trajectories in parallel, clustering them in action space, and returning the medoid of the largest cluster.
DINO-WM builds world models on pre-trained DINOv2 features to enable zero-shot planning from offline data without rewards or demonstrations.
citing papers explorer
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Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck
CMIB uses a conditional multimodal information bottleneck to create reusable agent skills that separate verbalizable text content from predictive perceptual residuals, improving execution stability.
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Geometry Guided Self-Consistency for Physical AI
KeyStone improves task success rates in diffusion-based physical AI models by up to 13.3% by sampling K trajectories in parallel, clustering them in action space, and returning the medoid of the largest cluster.
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DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
DINO-WM builds world models on pre-trained DINOv2 features to enable zero-shot planning from offline data without rewards or demonstrations.