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Hierarchical MoE routing with a novel arrival-weighted mean estimation prevents expert collapse and enables localized co-activation, outperforming flat routing on large embedding models.
Just two incoming connections per agent completely break the finite-depth impossibility of single-parent aggregation, recovering the globally optimal predictor across arbitrary distributions in $O(d \log d)$ depth.
