arXiv:2606. 05264v1 Announce Type: new Abstract: Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences.
By Moulik Gupta (Birla AI Labs), Dhruv Kumar (Birla AI Labs, Birla Institute of Technology and Science, Pilani), Murari Mandal (Birla AI Labs, Kalinga Institute of Industrial Technology), Saurabh Deshpande (Birla AI Labs)
arXiv:2608. 13741v1 Announce Type: cross Abstract: Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation.
By Haochen Zhang, Gengwei Zhang, Laura Yao, Nicholas Knoz, Tianlong Chen
arXiv:2606. 09287v1 Announce Type: new Abstract: Understanding how transformer representations evolve across layers, not merely what they encode, remains an open problem in mechanistic interpretability.
By Vishal Pandey, Gopal Singh
arXiv:2607. 27581v1 Announce Type: new Abstract: Grounding human motion in language, and language in motion, is a central step toward physical AI systems that can understand, generate, and communicate human behavior.
By Zhankai Ye, Yukai Jin, Bingyang Wei, Bofan Li, Yusen Wu, Fangyi Li, Shangqian Gao, Xin Liu
arXiv:2511. 09173v3 Announce Type: replace-cross Abstract: External trajectories can improve offline decision-sequence learning, but dynamics shift may make some source subsequences inconsistent with the target environment.
By Guojian Wang, Quinson Hon, Xuyang Chen, Lin Zhao
arXiv:2410. 07299v3 Announce Type: replace-cross Abstract: We introduce OTIS, an open time series encoder that yields high-quality time series features for downstream deployment on any system, including resource-constrained wearables and industrial sensors.
By \"Ozg\"un Turgut, Philip M\"uller, Martin J. Menten, Daniel Rueckert