[2603.01168] SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry

Computer Science > Machine Learning arXiv:2603.01168 (cs) [Submitted on 1 Mar 2026] Title:SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry Authors:Rong Fu, Chunlei Meng, Jinshuo Liu, Dianyu Zhao, Yongtai Liu, Yibo Meng, Xiaowen Ma, Wangyu Wu, Yangchen Zeng, Kangning Cui, Shuaishuai Cao, Simon Fong View a PDF of the paper titled SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry, by Rong Fu and 11 other authors View PDF HTML (experimental) Abstract:Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty. We introduce SphUnc, a unified framework combining hyperspherical representation learning with structural causal modeling. The model maps features to unit hypersphere latents using von Mises-Fisher distributions, decomposing uncertainty into epistemic and aleatoric components through information-geometric fusion. A structural causal model on spherical latents enables directed influence identification and interventional reasoning via sample-based simulation. Empirical evaluations on social and affective benchmarks demonstrate improved accuracy, better calibration, and interpretable causal signals, establishing a geometric-causal foundation for uncertainty-aware reasoning in multi-agent settings with higher-order interactions. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXi...