# Matplotlib benchmark figures

Figures 01–04 are deterministically rendered from the frozen v2 task-agnostic
transfer bundle. Figures 05–07 are recomputed from frozen propaganda, UNO Q,
and MALINT aggregate records.
PNG files are intended for the Hugging Face model card; PDF files are vector
exports for reports. Each plot has a compact source CSV beside it.

1. Overall Macro/Micro-F1: three-seed means with sample-SD whiskers.
2. Per-label precision/recall/F1 for the task-agnostic base.
3. Macro-F1 against article inference time and peak CUDA allocation.
4. Parameters and BF16 size across the 24L→8L→6L→4L task-free ladder.
5. Controlled UNO Q depth resources plus descriptive propaganda 24L/8L/4L
   quality and resources.
6. MALINT repeated-OOF weighted/macro/micro F1, subset exact match, and
   label-wise accuracy.
7. MALINT internal weighted-F1 gain beside measured CUDA-forward latency and
   peak-allocation changes.

The 55-article test split was previously opened. Error bars are not confidence
intervals. The specialized checkpoint is a separate reference lineage.

`manifest.json` and `source_summary.json` cover figures 01–04.
`cross_task/manifest.json` and `cross_task/source_summary.json` cover figures
05–07 and bind every frozen input by SHA-256. The latter bundle is aggregate
only and contains no raw text, IDs, row labels, logits, probabilities, or
predictions.

## 한국어

01–04는 기존 task-free base/propaganda transfer 결과이고, 05–07은 4L 선택 근거와
MALINT 내부 반복평가를 추가한다. MALINT의 Qwen student는 모두 4L이므로 06은
4L-vs-8L/24L depth 비교가 아니다. 07은 Mean+KD가 더 빠르지 않으며 제한된 latency와
peak CUDA allocation 비용이 있음을 함께 표시한다.