Archived
2026-09-20JEPA-Anything: Learning Predictive Models across Different Worlds
Taoyong Cui, Zhongyao Wang, Xinyue Xu, Weiyang Liu, Zhaochen Yu, Yuying Zhang, et al.
arXiv:2609.20800v1Today’s standout paper is JEPA-Anything, a new attempt to build one predictive model that works across very different kinds of worlds. The problem is that most world models are narrow: a system that predicts video frames usually does not transfer cleanly to biology, weather, control, or molecular dynamics. JEPA-Anything tackles that by extending joint-embedding predictive architectures with orthogonal predictive factorization, which splits latent targets into complementary pieces and learns them through separate pathways before recombining them. The result is a shared predictive framework that can adapt to many domains without being rewritten from scratch. The authors test it across seven areas, from vision and clinical trajectories to molecular simulation and physical fields, and report gains on every matched dynamics task they study. Why this matters is simple: better world models can improve forecasting, planning, scientific discovery, and intervention design. If a single predictive principle really generalizes this widely, it could become a foundation for AI systems that learn from the structure of the world rather than from one narrow dataset.
Also spotted that day
Coding Agents with an Obstacle-Aware Harness for Safe Robot ManipulationEmbedding Models Measure in Peculiar WaysWorkspace Models: Lightweight Robotic Memory via Saliency-Driven SupervisionFAMOS: Feed-Forward 3D Articulation Modeling from Sparse ObservationsPaint-Anything: Unified Any-Color Control for Image Generation and EditingERCPMP-Gx: Endoscopic Image and Video Dataset for Morphological, Histopathological, and Genomic Characterization of Colorectal PolyposisHow Does Distribution Shift Shape Pretraining Gains in Neural PDE Surrogates?Quantifying Overclaiming Propensity in Frontier LLM AgentsUnifying Models of Intergroup Hostility in Online DiscourseScore Centering Stabilizes Off-policy Reinforcement LearningAn Empirical Study of Harness Design for Coding AgentsPosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse SolversRetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement LearningHarm Laundering in GPT Models: Evidence That Gender Discrimination Is Transformed Rather Than Reduced Across Safety-Trained Generations
Previous daily papers
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