Add Matplotlib benchmark visualizations and source data

.gitattributes

@@ -1,4 +1,3 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
-models/semeval-propaganda/seeds/seed42/tokenizer.json filter=lfs diff=lfs merge=lfs -text
-models/semeval-propaganda/seeds/seed43/tokenizer.json filter=lfs diff=lfs merge=lfs -text
-models/semeval-propaganda/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+benchmark/figures/02_per_label_transfer_metrics.png filter=lfs diff=lfs merge=lfs -text
+benchmark/figures/04_compression_ladder.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -101,6 +101,12 @@ parameter counts, BF16 file sizes, hashes, and stage values are in
 [`benchmark/compression_ladder.csv`](benchmark/compression_ladder.csv) and
 [`provenance/task_agnostic_stages/`](provenance/task_agnostic_stages/).
 
+![Task-free 24L to 4L compression ladder](benchmark/figures/04_compression_ladder.png)
+
+The chart reports the actual unsharded safetensors parameter counts and BF16
+file sizes. It does not imply that downstream quality scales monotonically with
+layer count.
+
 ## Downstream benchmark contract
 
 Because the root is headless, it cannot be scored as a classifier without
@@ -158,6 +164,12 @@ The task-agnostic KD initialization improved same-seed mean Macro-F1 by
 `+0.06939` over the structural-copy control. This is an observed comparison
 under this protocol, not a significance or universal-superiority claim.
 
+![Three-seed Macro-F1 and Micro-F1 comparison](benchmark/figures/01_overall_transfer_quality.png)
+
+Bars are three-seed means and whiskers are sample SD. The specialized 4L point
+is a separate historical lineage rather than a fine-tuned child of the root
+base.
+
 ### Task-agnostic base: per-label test results
 
 Precision, recall, and F1 are three-seed means. Support is the fixed number of
@@ -184,6 +196,11 @@ Per-seed values and sample SDs for all labels and all arms are available in
 [`benchmark/per_label_metrics.csv`](benchmark/per_label_metrics.csv) and
 [`benchmark/per_label_summary.csv`](benchmark/per_label_summary.csv).
 
+![Per-label precision recall and F1](benchmark/figures/02_per_label_transfer_metrics.png)
+
+Labels are ordered by their three-seed mean F1. Support remains visible because
+several classes have only 7–14 positive test articles.
+
 ### Efficiency and resource measurements
 
 These numbers belong to the fresh-head transfer checkpoints, not a standalone
@@ -203,6 +220,12 @@ about CPU, mobile, UNO Q, or other deployment performance. Full seed-level
 resource fields are in
 [`benchmark/resource_metrics.csv`](benchmark/resource_metrics.csv).
 
+![Quality efficiency comparison](benchmark/figures/03_quality_efficiency.png)
+
+These plots compare the recorded fresh-head transfer checkpoints only.
+Apparent differences must not be generalized to the headless root on untested
+hardware.
+
 ### Machine-readable benchmark bundle
 
 - [`benchmark/BENCHMARK_CARD.md`](benchmark/BENCHMARK_CARD.md): generated
@@ -220,6 +243,8 @@ resource fields are in
   parameters, CUDA memory, wall time, and test timing
 - [`benchmark/reports/`](benchmark/reports/): normalized full frozen reports,
   including article-level probability vectors
+- [`benchmark/figures/`](benchmark/figures/): Matplotlib PNG/PDF figures,
+  per-figure source CSVs, and a hash manifest
 
 ## Multilingual scope
 

SHA256SUMS

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benchmark/figures/01_overall_transfer_quality.csv

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+arm,metric,mean,sample_sd,n_seeds
+task_agnostic_base,macro_f1,0.5874762842563245,0.01579462678670608,3
+task_agnostic_base,micro_f1,0.6529504772586388,0.010884055708931178,3
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benchmark/figures/02_per_label_transfer_metrics.csv

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+label,support,precision_mean,recall_mean,f1_mean
+Thought-terminating_Cliches,7,0.3492063492063492,0.3333333333333333,0.319014319014319
+"Whataboutism,Straw_Men,Red_Herring",10,0.2761904761904762,0.5,0.330448343079922
+Black-and-White_Fallacy,12,0.3667929292929293,0.47222222222222227,0.4053892966936445
+Appeal_to_Authority,11,0.3958614484930274,0.5757575757575757,0.4657088122605364
+"Exaggeration,Minimisation",22,0.4620811287477954,0.7727272727272727,0.5727040816326531
+Slogans,14,0.5161361626878869,0.7380952380952381,0.5834824090638044
+"Bandwagon,Reductio_ad_hitlerum",7,0.4851851851851852,0.8095238095238095,0.6006778309409889
+Repetition,23,0.5196620583717357,0.7536231884057971,0.6147520781519571
+Appeal_to_fear-prejudice,24,0.5540244550770866,0.7361111111111112,0.6277521761392729
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+Loaded_Language,45,0.8649032907793016,0.9851851851851852,0.9208741623431562

benchmark/figures/03_quality_efficiency.csv

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benchmark/figures/04_compression_ladder.csv

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+name,layers,parameters_millions,weight_mib,kd_stage,mean_total_loss
+24L upstream text teacher,24,752.393024,1435.1135559082031,,
+8L task-free KD,8,420.318144,801.7042922973633,24to8,0.17622653172702485
+6L task-free KD,6,377.207424,719.4742431640625,8to6,0.03382104352249371
+4L task-free KD root,4,334.096704,637.2442016601562,6to4,0.047020394468859195

benchmark/figures/README.md

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+# Matplotlib benchmark figures
+
+All figures are deterministically rendered from the frozen v2 benchmark bundle.
+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.
+
+The 55-article test split was previously opened. Error bars are not confidence
+intervals. The specialized checkpoint is a separate reference lineage.

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+  ]
+}

benchmark/figures/source_summary.json

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+{
+  "schema": "standalone4l-complete-transfer-benchmark-v2",
+  "status": "complete_from_frozen_reports",
+  "claim_scope": "Exploratory fresh-head transfer evidence on one English task and a previously opened 55-article SemEval-derived test split.",
+  "headless_root_requires_adaptation": true,
+  "test_previously_opened": true,
+  "test_articles": 55,
+  "test_windows": 434,
+  "seeds": [
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+    42,
+    43
+  ],
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+  "multilingual_downstream_evaluation_performed": false,
+  "upstream_multilingual_metadata": {
+    "source": "Qwen/Qwen3.5-0.8B official model card",
+    "languages_and_dialects_claimed_upstream": 201,
+    "scope": "inherited tokenizer and architecture coverage only"
+  },
+  "protocol": {
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