Sparse tensor compilers struggle to vectorize the data-dependent $\texttt{scf.while}$ co-iteration loops inherent to compressed format intersections. Because standard heuristics assume masked gather operations will negate SIMD throughput gains without high data reuse, existing vectorization efforts largely avoid pure sparse-sparse co-iteration. We challenge this assumption by proposing a novel MLIR optimization pass featuring a dual-path execution model. By natively operating on compressed hierarchies, our pass prepends a predicated, vectorized $\textit{fast lane}$ governed by dynamic runtime bounds checking, retaining the original scalar loop as a $\textit{slow lane}$ epilogue. Contrary to the assumption that such techniques require high-arithmetic-intensity workloads$-$high-order tensor contractions$-$to be profitable, our empirical evaluation demonstrates massive speedups on foundational, low-intensity kernels (SpGEMM, SpMV, MTTKRP). This proves that modern SIMD throughput can effectively overcome indirect memory latencies, unlocking generalized vectorization for dynamic sparse computations.