SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry

• URL: http://arxiv.org/abs/2603.01168v1
• Date: Sun, 01 Mar 2026 16:11:49 GMT
• 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,
• Abstract summary: We introduce SphUnc, a unified framework combining hyperspherical representation learning with structural causal modeling.<n>A structural causal model on spherical latents enables directed influence identification and interventional reasoning via sample-based simulation.<n> Empirical evaluations on social and affective benchmarks demonstrate improved accuracy, better calibration, and interpretable causal signals.
• Score: 7.816699755198432
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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