Yann LeCun

Achieving a 40% performance boost in robot control without the heavy computational costs of traditional vision-language models could revolutionize how we leverage visual representations in robotics.

Adaptive latent models can recalibrate in real-time, boosting planning success rates even in shifting environments.

Achieving robust zero-shot sim-to-real transfer for quadrotors, this work redefines the boundaries of long-horizon prediction in robotic control.

S-JEPA sets a new standard in speech representation learning by achieving top performance with fewer parameters and without the cumbersome offline re-clustering process.

Relying on causal relationships rather than strong inductive biases, TDV achieves state-of-the-art performance in visual representation learning, challenging the status quo of self-supervised methods.

WorldDP achieves superior performance in multi-stage robotic tasks by seamlessly integrating high-level planning with low-level execution, outperforming traditional methods.

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.