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This paper presents an agentic AI scientific community of virtual labs that autonomously discover neural operator architectures for PDE problems. Using LLM planners, numerical workers, and reviewers under a citation-based economy, the system produces high-accuracy hybrid architectures, with results suggesting no universal winner among operator families.
The paper introduces GAMBLe, a framework that decomposes AI-Driven Research Systems into generator, assessor, discovery mechanism, and budget, revealing how component interactions shape optimization landscapes. Experiments on NP-hard problems show no universally best configuration, emphasizing the need for careful component selection.
This paper presents AI CFD Scientist, an open-source AI agent for computational fluid dynamics that autonomously discovers physics corrections using vision-language verification and code modification, outperforming general AI scientists on CFD tasks.