Tootfinder

Source: Yann LeCun will continue to work at Meta as chief scientist of the AI research group FAIR and will report to Alexandr Wang (Kurt Wagner/Bloomberg)
https://www.bloomberg.com/news/articles/2025-07-25/meta-says-open-ai…

Meta Says Open AI Hire is Superintelligence Group Chief Scientist
Mark Zuckerberg has named Shengjia Zhao, an artificial intelligence researcher who joined Meta Platforms Inc. from OpenAI in June, as the chief scientist for the social media company’s new superintelligence AI group.

Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study
Spyros Rigas, Dhruv Verma, Georgios Alexandridis, Yixuan Wang
https://arxiv.org/abs/2509.03417 https://…

Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study
Kolmogorov-Arnold Networks (KANs) are a recently introduced neural architecture that replace fixed nonlinearities with trainable activation functions, offering enhanced flexibility and interpretability. While KANs have been applied successfully across scientific and machine learning tasks, their initialization strategies remain largely unexplored. In this work, we study initialization schemes for spline-based KANs, proposing two theory-driven approaches inspired by LeCun and Glorot, as well as …

Avoidance of an unexpected obstacle without reinforcement learning: Why not using advanced control-theoretic tools?
C\'edric Join, Michel Fliess
https://arxiv.org/abs/2509.03721

Avoidance of an unexpected obstacle without reinforcement learning: Why not using advanced control-theoretic tools?
This communication on collision avoidance with unexpected obstacles is motivated by some critical appraisals on reinforcement learning (RL) which "requires ridiculously large numbers of trials to learn any new task" (Yann LeCun). We use the classic Dubins' car in order to replace RL with flatness-based control, combined with the HEOL feedback setting, and the latest model-free predictive control approach. The two approaches lead to convincing computer experiments where the results with the mode…

Whole-Body Conditioned Egocentric Video Prediction
Yutong Bai, Danny Tran, Amir Bar, Yann LeCun, Trevor Darrell, Jitendra Malik
https://arxiv.org/abs/2506.21552

Whole-Body Conditioned Egocentric Video Prediction
We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, our model learns to simulate how physical human actions shape the environment from a first-person point of view. We train an auto-regressive conditional diffusion transformer on Nymeria, a large-scale dataset of real-world egocentric video and body pose c…

Back to the Features: DINO as a Foundation for Video World Models
Federico Baldassarre, Marc Szafraniec, Basile Terver, Vasil Khalidov, Francisco Massa, Yann LeCun, Patrick Labatut, Maximilian Seitzer, Piotr Bojanowski
https://arxiv.org/abs/2507.19468

Back to the Features: DINO as a Foundation for Video World Models
We present DINO-world, a powerful generalist video world model trained to predict future frames in the latent space of DINOv2. By leveraging a pre-trained image encoder and training a future predictor on a large-scale uncurated video dataset, DINO-world learns the temporal dynamics of diverse scenes, from driving and indoor scenes to simulated environments. We show that DINO-world outperforms previous models on a variety of video prediction benchmarks, e.g. segmentation and depth forecasting, a…