6 papers
Chain of Operators: An Inference-Time Harness for In-Context Operator Learning
Minghui Yang, Ling Guo, Chenghan Wu +1
While scientific foundation models show immense promise in accelerating physical simulations and numerical forecasting, they remain notoriously brittle when encountering out-of-dis…
VICX: Generalizable Robot Manipulation via Video Generation and In-Context Operator Network
Song Chen, Linyan Xiang, Ying Zhou +1
Generalizable robot manipulation requires not only task-level reasoning over unseen scenes, but also reliable grounding of visual plans into embodiment-specific execution. To bridg…
ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement
Yutong Shen, Hangxu Liu, Lei Zhang +4
Long-Horizon (LH) tasks in Human-Scene Interaction (HSI) are complex multi-step tasks that require continuous planning, sequential decision-making, and extended execution across do…
Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
Chenghan Wu, Zongmin Yu, Boai Sun +1
In-context operator learning enables neural networks to infer solution operators from contextual examples without weight updates. While prior work has demonstrated the effectivenes…
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
Yadi Cao, Yuxuan Liu, Liu Yang +3
In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, ex…
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions
Elisa Negrini, Yuxuan Liu, Liu Yang +2
Neural networks are one tool for approximating non-linear differential equations used in scientific computing tasks such as surrogate modeling, real-time predictions, and optimal c…