COREY: Entropy-Guided Runtime Chunk Scheduling for Selective Scan Kernels

Mamba selective state space models (SSMs) provide linear-time sequence modeling but remain sensitive to selective-scan chunk scheduling. We present COREY, a \emph{concept-and-feasibility} runtime scheduler that maps fixed-bin activation entropy to chunk size. We evaluate COREY in three tiers: a prototype cost model, real-checkpoint kernel timing, and routed end-to-end ablations on modern GPUs. At the kernel level, a calibrated rule, $H_{\mathrm{ref}}=\log K$, recovers the locally optimal chunk and matches a one-time static oracle, yielding $4.41\times$ lower latency than an unoptimized baseline on a consumer GPU and $3.90\times$--$4.04\times$ lower latency on a data-center accelerator. Routing this choice into a patched live scan kernel closes the engineering loop without improving end-to-end speed: in unified routed ablations, the best static chunk outperforms all entropy-guided and proxy schedulers. Sampled-histogram COREY adds $+4.6\%$ overhead; a guarded fallback to Static-512 reduces this to $+1.3\%$; and a lightweight sequence-length-keyed table further reduces it to $+0.7\%$. However, both remain slower than the static oracle because they retain scheduling cost. On an 80-prompt LongBench subset, passive and routed inference are exactly output-equivalent, with $100\%$ greedy-token agreement and zero metric deltas. A mixed-regime study shows that a single sequence-length rule matches the per-regime chunk oracle for balanced serving. COREY is therefore validated as a quality-preserving scheduling prototype, but current entropy statistics are not a robust throughput win over static chunk tuning on measured SSM checkpoint workloads. SourceCode: https://github.com/mabo1215/COREY_Transformer/.