Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates

ArXi:2510.21523v2 Announce Type: replace Sequential generative models conditioned on uncertain rewards are central to AI-driven scientific discovery, yet the epistemic uncertainty they inherit from imperfect reward estimates remains unquantified. We propagate this uncertainty through generative flow networks (GFlowNets) by fitting polynomial chaos expansions (PCEs) to small ensembles of trained models.