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arXiv AI By Chia-Han Kuo, Chengshuai Zhao, Huan Liu, Lo Pang-Yun Ting

Self-Supervised Latent Action-Conditioned World Modeling for Time Series

Read the original on arXiv AI →

The paper introduces ACTS-WM, a self‑supervised framework that models latent actions underlying time‑series transitions. It combines a Joint‑Embedding Predictive Architecture encoder with an action‑conditioned world model to predict future representations conditioned on inferred latent actions and task information. Experiments on 22 datasets across four tasks show that ACTS‑WM improves downstream performance compared to pretrained JEPA‑based encoders.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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