5 papers
A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residual
Jiaming Ma, Binwu Wang, Pengkun Wang +3
Prevailing spatiotemporal prediction models typically operate under a forward (unidirectional) learning paradigm, in which models extract spatiotemporal features from historical ob…
Planning-Augmented Sampling with Early Guidance for High-Reward Discovery
Rui Zhu, Yudong Zhang, Xuan Yu +3
Generative Flow Networks (GFlowNets) enable structured generation with inherent diversity, but existing sampling strategies often rely on weak guided exploration, slowing early dis…
MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Kuo Yang, Xingjie Yang, Linhui Yu +5
Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks. However, existing Mas construction m…
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Jiayue Liu, Zhongchao Yi, Zhengyang Zhou +4
Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually train an independent model from a s…
A Powder Diffraction-AI Solution for Crystalline Structure
Di Wu, Pengkun Wang, Shiming Zhou +8
Determining the atomic-level structure of crystalline solids is critically important across a wide array of scientific disciplines. The challenges associated with obtaining samples…