We introduce UNified In-Context Operator Networks (UNICON), a foundation model of numerical intelligence. Trained once on graph-based numerical systems spanning hydrology, traffic, power systems, weather, land, ocean, soil, solar resources and human mobility, the frozen model infers the predictive relation shared across contextual examples and applies it to queries from the same system, approaching specialist performance even in disciplines absent from training. Combining UNICON with language-model agents to perform contextual ensemble learning (CEL) yields further gains, enabling it to surpass state-of-the-art specialists in a discipline unseen during training.

Chenghan Wu, Zongmin Yu, Liu Yang^†

VICX is a decoupled closed-loop manipulation framework for generalizable robot manipulation. A frozen video generation model produces vision-language-conditioned high-level visual plans, while a Video-to-Trajectory In-Context Operator Network grounds these plans into executable robot-state trajectories.

Song Chen^*, Linyan Xiang^*, Ying Zhou, Liu Yang^†

We introduce the Evolving Ensemble of Agents (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving system for algorithmic discovery. Rather than reinventing the wheel, EvE fixes the base agent substrate and focuses entirely on evolving the cumulative guidance and skills that dictate agent behaviors. When applied to a research bottleneck in In-Context Operator Networks (ICON), EvE autonomously discovered a robust rescale-then-interpolate mechanism that enables reliable example-count generalization.
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Zongmin Yu, Liu Yang^†

We introduce In-Context Modeling (ICM), a retrain-free paradigm that infers physical relationships directly from observational fields. Rather than encoding system-specific behavior in fixed parameters, ICM assimilates measurements as physical context and performs inference through a single forward pass. Trained in a physics-informed, label-free manner using governing equations, a single model generalizes across unseen materials, geometries, and loading conditions.

Lingfeng Li^*, Zhuoyuan Li^*, Shun Li^*, Kaixin Zhan, Huajian Gao^†, Changqing Chen^†, Liu Yang^†

Can we build a single large model for a wide range of PDE-related scientific learning tasks? We propose in-context operator learning framework with corresponding model In-Context Operator Network (ICON). A singe ICON model can act as an operator learner for a diversified type of differential equation problems, including forward and inverse problems of ODEs, PDEs, and mean-field control problems, without any fine-tuning.

Liu Yang, Siting Liu, Tingwei Meng, Stanley J. Osher^†