An AI model just rediscovered Kepler's law from latent space, with zero knowledge of orbital mechanics. And the same core architecture spans molecular dynamics, cell prediction, and even generating cancer treatment hypotheses. Title: JEPA-Anything: Learning Predictive Models across Different Worlds URL: In one sentence: it decomposes a target's representation into K orthogonal subspaces, each with its own dedicated predictor — "Orthogonal Predictive Factorization" (OPF) — and applies this single mechanism across radically different domains, from vision and biology to clinical data, control, and molecular dynamics. 🔭 Highlight 1: Rediscovering a physical law from latent space Trained only on orbital motion data, its latent frequency modes recovered Kepler's law f=(2π)⁻¹a⁻³/². The fitted slope was -1.4991 against a theoretical -1.5, with R²=0.9999999. 🧬 Highlight 2: Generating and validating a cancer treatment hypothesis Factor analysis on liver cancer data proposed combining IL-18 and CD73 blockade, which then showed the strongest tumor cell killing in actual patient-derived organoids. ⚛️ Highlight 3: Consistently strong across molecular dynamics and cell prediction It achieved the lowest error across 100-step molecular simulations of water, quartz, paracetamol, and benzene, and also beat prior methods on single-cell perturbation prediction. It's striking that one core architecture spans such wildly different scientific domains this well. #WorldModels# #AIforScience#

what if google is merging the world model + diffusion + omni stuff into Gemini 4 i feels like they're moving away from the pure LLM + CoT/coding path and toward something more native to reasoning about the world not yann leCun's v-JEPA exactly, but directionally similar vs. standard LLMs

Job alert 🚨 My team @AIatMeta is looking for a PhD intern to join us in 2025 in Paris. We are working on self-supervised learning from video, world modelling and JEPA ! Apply here or reach out directly:

NEW WORLD MODEL: @ylecun's team is back with an efficient model! This project involves @ylecun, @lukaskuhn77, @lucasmaes_, @quentinlldc, and @randall_balestr. A couple definitions first: - DINO: self-DIstillation with NO labels. A self-supervised image model (Meta, 2021) where a student network learns to match a teacher (an EMA copy of itself) across two crops of the same image, with no labels and no negatives. - SIGReg: a regularizer that prevents embedding collapse by forcing the embeddings to match an isotropic Gaussian, tested with a normality test (Epps–Pulley) on many random 1-D projections instead of in full dimension. LeVJEPA is a self-supervised video pretraining method, released with open code, weights, and checkpoints. It learns a video representation by pushing the embeddings of global and local crops of the same clip together (an invariance loss), while a regularizer called SIGReg forces the embeddings toward an isotropic Gaussian to provably prevent representation collapse. Unlike V-JEPA and V-JEPA 2 it uses a single shared encoder with a projector and no target network, no predictor and no stop-gradient. It drops 95% of tokens per view, uses block-causal attention (each frame attends only to past frames), and has a single loss weight. It is evaluated purely as a representation learner via frozen probing on ImageNet-1K, Something-Something-v2 and Kinetics-400, not on any robot. What I find interesting, is that V-JEPA and V-JEPA 2 need an EMA target encoder, stop-gradients and a capacity-limited predictor to avoid collapse; LeVJEPA drops all of it for one shared encoder plus projector, preventing collapse instead with the SIGReg regularizer under a provable guarantee and a single hyperparameter. The "P" (predictor) in JEPA is effectively gone. LeVJEPA is also less compute intensive: - 5.6x to 20.8x lower total pretraining compute than V-JEPA 2 - 7.6 points higher on ImageNet-1K at matched FLOPs - trains at batch size 128 within 8GB where V-JEPA 2 saturates at batch size 28 Also worth mentioning: ImageNet-1K accuracy rises monotonically with the token-drop rate, from 33.9% at rho = 0 to 47.6% at rho = 0.95. The aggressive dropping is actually doing regularization work. On the JEPA-versus-DINO debate: - it loses to DINOv2 by 3.1 points on ImageNet-1K (appearance, static) - but wins on Something-Something-v2 by nearly 2x (motion, temporal) - and beats V-JEPA 2 by 1.9 points on ViT-L at 5.6x lower cost. -> optimized for temporal and motion understanding per compute dollar.