University of Hawaiʻi at Mānoa

Autism resting-state fMRI classifiers rarely generalize across acquisition sites, and many reported accuracies are inflated by data leakage. ASD Neuro Agentic (ANA) has language-model agents propose features from the literature and researcher input while fixed tools perform every calculation; across 1,028 participants from 17 ABIDE I centers it searched pipelines and selected a ComBat-harmonized ensemble of tangent-space connectivity models. In leave-one-center-out testing the ensemble reached an AUC of 0.814 (95% CI, 0.773–0.841), above ASD-DiagNet and ASD-SAENet.

Autism resting-state fMRI classifiers rarely generalize across acquisition sites, and many reported accuracies are inflated by data leakage. ASD Neuro Agentic (ANA) has language-model agents propose features from the literature and researcher input while fixed tools perform every calculation; across 1,028 participants from 17 ABIDE I centers it searched pipelines and selected a ComBat-harmonized ensemble of tangent-space connectivity models. In leave-one-center-out testing the ensemble reached an AUC of 0.814 (95% CI, 0.773–0.841), above ASD-DiagNet and ASD-SAENet.

Subsurface fracture networks were previously sampled fracture by fracture from prescribed statistics, which made sparse well observations hard to enforce. Here 2D and 3D networks are represented as binary occupancy fields, compressed by a variational autoencoder into a compact latent space, and learned by a diffusion model, with well observations injected through a learned conditioning encoder, classifier-free guidance, and diffusion posterior sampling. The 2D conditional model satisfies 99.6% of prescribed well observations, while the native 3D volumetric field satisfies 84.2% of observed well voxels while keeping fracture volume fraction close to the reference ensemble.

Subsurface fracture networks were previously sampled fracture by fracture from prescribed statistics, which made sparse well observations hard to enforce. Here 2D and 3D networks are represented as binary occupancy fields, compressed by a variational autoencoder into a compact latent space, and learned by a diffusion model, with well observations injected through a learned conditioning encoder, classifier-free guidance, and diffusion posterior sampling. The 2D conditional model satisfies 99.6% of prescribed well observations, while the native 3D volumetric field satisfies 84.2% of observed well voxels while keeping fracture volume fraction close to the reference ensemble.