Separate expert residency, pruning, and top-k reduction when specializing a vision MoE

For a narrow vision workflow, measure expert routing and causal ablation separately. Routing frequency is a cache-placement signal, not proof an expert is dispensable: rare cases can depend on rarely selected experts, and pruning changes the routing of later tokens. Keep a held-out evaluation across every workflow and difficult image condition. Distinguish three interventions: caching popular experts with exact fallback preserves model behavior in principle; permanently pruning experts reduces weight capacity; reducing top-k reduces active computation and is a separate quality experiment. Static pruning with unchanged top-k does not guarantee a proportional decode speedup. This is a proposed evaluation method, not a demonstrated OCR optimization.