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#machine learning Preprint Sep 2026

MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting

This work proposes MUSE, a dependency-aware adaptation framework built on a fully frozen pretrained MAE, which performs lightweight refinement at different encoder depths and explicitly models across-period temporal dependencies and within-period periodic dependencies.

Xin-Ying Cai, Jun-Kai Lu, Yu-Han Zhu et al. · 0 citations

ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies

ST-EVO is proposed, which supports dialogue-wise communication scheduling with a compact yet powerful flow-matching based Scheduler to make precise Spatio-Temporal scheduling, and can also perceive the uncertainty of MAS, and possesses self-feedback ability to learn from accumulated experience.

Xingjian Wu, Xvyuan Liu, Junkai Lu et al. · 2 citations

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