Move the results-at-a-glance table directly below the opening pitch and above the caveat. Fill the completed native-grouped 10% and 20% full-MMLU values, add ratio-labelled downstream rows, sync the compact summary, and publish the latest report.
Fill the 20% MMLU-STEM 5-shot value in the top-level table, extend the labelled downstream comparison, sync the compact summary, and publish the latest progress report.
Fill the recommended 10% MMLU-STEM 5-shot value in the top-level table, label the downstream MMLU-STEM rows by pruning ratio, sync the compact summary, and publish the latest progress report.
Add random 10% pruning control and audit table labels
Record the completed random native-grouped 10% control, strengthen the importance-ranking insight at the recommended operating point, sync the progress report, and verify that README table rows have distinct labels including all GSM8K pruning ratios.
Record CRUXEval smoke validation and final time-box status
Refresh the progress report and document that CRUXEval-O CoT executable grading was smoke-tested successfully, while the paired quality comparison was stopped due to the remaining time box. Keep the public recommendation centered on the validated 10% structured-pruning 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.
Publish the completed paired GSM8K-CoT 10% pruning point and note the preliminary task-specific threshold pattern relative to the 25% result while the 20% point runs.
Publish the completed same-size random-control loss result and document that HEAPr-ranked structured pruning preserves perplexity materially better than random selection under the same minimum-retention rule.
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.
