From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts
Test-time scaling produces cohorts of reasoning rollouts, yet there is no standard label-free account of how their internal computation reorganizes as inference unfolds. We introduce routing effective rank deff, the entropy-effective dimensionality of a cross-rollout graph built from MoE expert-routing similarity. Across ten MoE configurations and five math/science benchmarks, deff exhibits a reproducible low-high-low trajectory, with a prominent interior maximum in 98.5% of 3,105 model-question cohorts: routing similarity is concentrated early, maximally differentiated at intermediate budgets, and reconcentrated later, and the timing of this maximum varies systematically with architectur...
| Authors | Kang Chen, Sihan Zhao, Yixin Cao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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