aiqbench analysis
SphUnc presents a novel mathematical framework integrating hyperspherical representation learning with structural causal modeling to decompose uncertainty in multi-agent systems. By mapping features onto unit hyperspheres using von Mises-Fisher distributions, the approach enables interpretable identification of directed influences and interventional reasoning within complex social networks. This work establishes a rigorous geometric-causal foundation for improving calibration and accuracy in high-order interaction scenarios without relying on standard Euclidean assumptions.
SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry
arXiv:2603.01168v3 Announce Type: replace 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2603.01168 [cs.LG]
(or arXiv:2603.01168v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.01168 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 189 words
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