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Voronoi tessellations can be incorporated into probabilistic circuits to capture data geometry, but only with careful architectural constraints or approximate inference.
Neural networks can reason better by explicitly incorporating recursive inference mechanisms from classical AI, outperforming existing neural reasoners on challenging benchmarks.
