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Decentralized models can learn to route queries to specialized neighbors at inference time, unlocking significant performance gains without a central orchestrator.
Tabular foundation models often fail subgroup calibration despite strong marginal coverage, and their reliability rankings drastically shift when moving beyond simple accuracy.