Midway Network is a new self-supervised learning architecture that is the first to learn visual representations for both object recognition and motion prediction solely from natural videos by leveraging latent dynamics.

LaMo is a structured latent dynamics model that enables long-horizon prediction by autoregressively predicting latent motion tokens used to advance a visual scene's latent state over time.

We introduce Discrete-JEPA, which learns discrete semantic tokens for improved symbolic reasoning and long-horizon planning.

We propose a self-supervised learning framework that combines pooled and dense objectives to learn representations with spatial and semantic understanding from naturalistic videos.

We leverage successor features to formulate a graph-based planning framework and goal-conditioned policy, enabling long-horizon goal-reaching in visual environments.

We use an agent-based model and empirical game-theoretic analysis to study price manipulation in financial markets and propose mitigation mechanisms.

We design learning-based trading strategies that have improved robustness to market manipulation and evaluate them with agent-based simulation.