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Current AI's hunger for curated data may be solved by a new architecture inspired by human cognition that flexibly switches between observation, active behavior, and meta-control.
Self-supervised video models can now learn dense features rivaling supervised methods, unlocking a 20-point jump in robot grasping success.
Straightening latent space trajectories with a simple curvature regularizer dramatically improves the stability and success of gradient-based planning in world models.
Object-level masking in world models unlocks a 20% boost in counterfactual reasoning and drastically reduces planning costs, hinting at a path toward more efficient and robust AI agents.
