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On Probabilistic Inference Through Parametric Tensor Decomposition in Base Tensor Networks

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

Probabilistic inference is generally only tractable in low-treewidth graphical models, limiting its effective applicability in high-treewidth settings. Many existing methods improve efficiency by exploiting specific parametric structure, such as symmetries. However, they typically require such structure to be explicitly present, limiting their applicability to a broader range of graphical models. To address this limitation, we propose a framework where tractable inference is controlled by latent parametric structure exploitation, rather than requiring it to be explicitly present a priori. Our

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