Overlay the measured random 10% and 25% controls on the pruning-quality figure and update the model-card insight to foreground the advantage of HEAPr-style importance ranking.
Make the layer-wise score chart explicit that its y-axis is log10 importance. Explain in the card that negative tick labels denote small positive scores below one, rather than negative importance values.
Plot atomic importance distributions across sparse layers
Replace the pruning-count chart with layer-wise atomic-score box plots. Reframe the insight directly: later sparse layers contain lower-importance atomic experts under the calibration set and HEAPr-style score.
Pivot recommended deployment target to 10% pruning
Make 10% structured MoE pruning the recommended operating point based on the completed perplexity and GSM8K curves. Publish the 10% mask, projected 3.14B removable parameters / 9.39% full-model reduction, refreshed figures, and retain 25% as an exploratory stress point.
Add the paired GSM8K-CoT 20% structured-pruning point, revise the downstream insight to reflect increasingly steep reasoning-quality degradation, and add a reproducible seaborn curve plot. Record the newly started CRUXEval-O CoT evaluation.
Add the HEAPr-style grouped atomic-expert pruning submission: model card, curated runnable source snapshot, compact results summary, selected reports, and 25% native-group reproducibility artifacts. This intentionally excludes model weights and does not claim kernel speedup yet.
