AI RESEARCH
Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
arXiv CS.LG
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ArXi:2606.01595v1 Announce Type: new Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networks by treating predictions as distributions rather than deterministic values. Meanwhile, diffusion-based models for 3D molecular graph generation operate on fragile geometric structures governed by strict chemical constraints, making inference highly sensitive to uncertainty miscalibration.