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Trillion-token scale looped MoE models achieve equivalent reasoning performance to 2x larger non-looped MoE models with matched training compute, unlocking test-time scaling via recurrence.
Long-horizon agents plateau under naive inference scaling unless compute is explicitly redirected from task workers to a dedicated meta-reasoning controller that manages execution flow.
