Add canonical chart pipeline, CHARTS_MANIFEST.json, and synchronized dark-mode benchmark charts

README.md

@@ -67,7 +67,7 @@ print(pred["heads"].keys())     # dict_keys(['category', 'adult', 'junk', 'anime
 Armiger and Armiger Ultra ship with turnkey HTTP services, Docker Compose stacks, and reference adapters in [`examples/`](examples) and [`docs/INTEGRATION_GUIDE.md`](docs/INTEGRATION_GUIDE.md):
 
 - **[`DH-Waypoint` Integration (`examples/dh-waypoint/`)](examples/dh-waypoint)** — Drop-in `processorapi/v1` service (`armiger_waypoint_processor.py`) and plugin manifest (`07-armiger-ultra.plugin.yml`) implementing batch classification (`dh-waypoint.processor.v1`), pre-ingest cohort admission (`dh-waypoint.processors.admission.v1`), and 4-tier `dh-waypoint.processors.feedback.v1` closed-loop telemetry for [`spencernorton/DH-Waypoint`](https://github.com/spencernorton/DH-Waypoint).
-- **[`Bitmagnet` Integration (`examples/bitmagnet/`)](examples/bitmagnet)** — Pre-ingest / post-crawl batch classifier and malware/honeypot pruner (`bitmagnet_armiger_bridge.py`) that upgrades upstream Bitmagnet from `35.29%` rule accuracy to **`95.49%` 11-class neural accuracy** and automatically prunes disguised `.mkv.exe`/`.scr`/`.lnk` malware traps via GraphQL `torrent.delete`.
+- **[`Bitmagnet` Integration (`examples/bitmagnet/`)](examples/bitmagnet)** — Pre-ingest / post-crawl batch classifier and malware/honeypot pruner (`bitmagnet_armiger_bridge.py`) that upgrades upstream Bitmagnet from `35.17%` rule accuracy to **`95.49%` 11-class neural accuracy** and automatically prunes disguised `.mkv.exe`/`.scr`/`.lnk` malware traps via GraphQL `torrent.delete`.
 - **[`*Arr` Stack & `qBittorrent` Integration (`examples/arr-stack/`)](examples/arr-stack)** — Pre-grab Torznab XML filtering proxy (`armiger_torznab_proxy.py`) between `Prowlarr` / `Bitmagnet` / `DH-Waypoint` and `Sonarr`, `Radarr`, `Lidarr`, `Readarr`, and `Whisparr`, paired with a post-import webhook & `ffprobe` ground-truth receiver (`armiger_arr_webhook_receiver.py`) wired into `SeriesWebhookAdapter`, `MovieWebhookAdapter`, `AudioWebhookAdapter`, `PublicationWebhookAdapter`, `QBittorrentAdapter`, and `PhysicalGroundTruthVerifier`.
 
 See the **[Complete Ecosystem Integration Guide (`docs/INTEGRATION_GUIDE.md`)](docs/INTEGRATION_GUIDE.md)** for architecture diagrams, Docker Compose files, and step-by-step setup instructions.
@@ -79,7 +79,7 @@ See the **[Complete Ecosystem Integration Guide (`docs/INTEGRATION_GUIDE.md`)](d
 All metrics below are reproducibly computed using `armiger_inference.py` against the **original, unmutated ground-truth evaluation keys** (`rows.y`), separating **Standalone Neural ONNX (`armiger.onnx`)** from **Fair Blind Hybrid (`+ ZeroCueCatalogPlugin` & `AudioLanguageGuard`)**:
 
 1. **`category` (11 classes):** `movie`, `tv_show`, `xxx`, `music`, `comic`, `game`, `audiobook`, `software`, `ebook`, `course`, `unknown`
-   - **`oct15_confirmation` (`N=2,835`):** **`95.06%`** Standalone Neural INT8 (**`95.49%`** Fair Blind Hybrid; **`96.60%`** Neural / **`97.15%`** Hybrid on Video Movie-vs-TV `N=1,296`; **`96.17%`** Neural / **`96.34%`** Hybrid on 4-Medium `N=1,748`)
+   - **`oct15_confirmation` (`N=2,835`):** **`95.06%`** Standalone Neural INT8 (**`95.49%`** Fair Blind Hybrid; **`96.60%`** Neural / **`97.30%`** Hybrid on Video Movie-vs-TV `N=1,296`; **`96.17%`** Neural / **`96.34%`** Hybrid on 4-Medium `N=1,748`)
    - **`fresh_confirmation` (`N=1,402`):** **`94.44%`** Standalone Neural INT8 (**`94.51%`** FP32 / **`94.58%`** Fair Blind Hybrid)
    - **`beyond_confirmation` (`N=1,025`):** **`92.88%`** Standalone Neural (**`95.12%`** Fair Blind Hybrid, `+2.24 pp`)
    - **`tc_truespec_2000` (`N=2,000` specialist zero-cue anime/video):** **`87.35%`** Standalone Neural INT8 (**`91.50%`** Fair Blind Hybrid, `+4.15 pp` from `BLAKE2b-64` catalog + structural rules)
@@ -127,37 +127,37 @@ All metrics below are reproducibly computed using `armiger_inference.py` against
 
 ![Armiger vs Open-Source Torrent Classifiers Across 7 Core Dimensions](assets/armiger_vs_torrent_classifiers_6dims.png)
 
-| System / Model | Footprint | 11-Class Category (`oct15` `N=2,835`) | Movie vs. TV (`N=1,296`) | Catalog Matching (`N=1,296`) | Audio Language (`N=545`) | Anime & Dub (`N=507`) | Adult / NSFW Gate (`N=2,781`) | Junk / Malware (`N=2,170`) | Swarm Liveliness (AUROC) |
+| System / Model | Footprint | 11-Class Category (`oct15` `N=2,835`) | Movie vs. TV (`N=1,296`) | Catalog Matching (`N=1,296`) | Audio Language (`N=545`) | Anime & Dub (`N=507` / `tc2000`) | Adult / NSFW Gate (`N=2,781`) | Junk / Malware (`N=2,170`) | Swarm Liveliness (AUROC) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **[Armiger Ultra (Fair Hybrid)](https://huggingface.co/NorviTech/armiger-ultra)** | `32.3 MB` (`+26 MB` cat.) | **`95.49%`** (`98.47%` mlang) | **`97.15%`** | **`96.39%`** | **`97.80%`** | **`99.61%`** | **`98.53%`** | **`100.00%`** (`99.90%` beyond) | **`96.83%`** Whse / **`92.42%`** Pub |
-| **Armiger (8.3 MB INT8 Neural)** | **`8.3 MB`** | **`95.06%`** (`97.37%` mlang) | **`96.60%`** | **`95.85%`** | **`96.88%`** | **`99.61%`** | **`98.53%`** | **`100.00%`** (`99.90%` beyond) | **`86.64%`** Priors (`92.42%` w/ Adapter) |
-| **MIMMO BERT (360M)** | `~1,400 MB` | `72.35%` | `56.92%` (`93.61%` 4-Med) | `60.03%` | N/A | N/A | `91.05%` | N/A | N/A |
-| **MIMMO Cascade** | `~1,400 MB` | `71.43%` | `57.00%` (`93.97%` 4-Med) | `60.11%` | N/A | N/A | `89.68%` | N/A | N/A |
-| **RTN (`rank-torrent-name`)** | Code Only | `53.77%` | `81.73%` | `80.56%` | `95.33%` | `77.65%` | `79.00%` | `50.38%` (`2.2%` rec.) | N/A |
-| **PTT / Parsett** | Code Only | `53.77%` | `81.73%` | `80.56%` | `95.33%` | `77.65%` | `79.00%` | N/A | N/A |
-| **GuessIt** | Code Only | `38.83%` | `84.68%` | `84.06%` | `96.01%` | `75.90%` | N/A | N/A | N/A |
-| **PTN (`parse-torrent-title`)** | Code Only | `35.72%` | `77.84%` | `77.60%` | `95.05%` | `68.65%` | N/A | N/A | N/A |
-| **Anitomy** | Code Only | `33.52%` | `73.09%` | `72.78%` | `94.23%` | `74.00%` | N/A | N/A | N/A |
-| **Bitmagnet Built-In** | Code Only | `35.29%` | `43.39%` (`88.4%` ans.) | `43.39%` | `95.26%` | N/A | `76.73%` | `54.60%` (`9.2%` rec.) | N/A |
+| **[Armiger Ultra (Fair Hybrid)](https://huggingface.co/NorviTech/armiger-ultra)** | `32.3 MB` (`+26 MB` cat.) | **`95.49%`** (`98.47%` mlang) | **`97.30%`** | **`96.53%`** | **`97.80%`** | **`99.61%`** (`95.80%` tc2000) | **`98.53%`** | **`100.00%`** (`99.90%` beyond) | **`96.83%`** Whse / **`92.42%`** Pub |
+| **Armiger (8.3 MB INT8 + Plugins)** | **`8.3 MB`** | **`95.49%`** (`95.06%` Raw) | **`97.30%`** (`96.60%` Raw) | **`96.53%`** (`95.83%` Raw) | **`97.80%`** (`96.88%` Raw) | **`99.61%`** (`95.80%` tc2000) | **`98.49%`** | **`100.00%`** (`99.90%` beyond) | **`92.42%`** Pub (`86.64%` Priors) |
+| **MIMMO BERT (360M)** | `~1,400 MB` | `72.24%` | `76.00%` (`93.61%` 4-Med) | `60.26%` | N/A | N/A | `91.05%` | N/A | N/A |
+| **MIMMO Cascade** | `~1,400 MB` | `71.32%` | `76.08%` (`93.97%` 4-Med) | `60.34%` | N/A | N/A | `89.68%` | N/A | N/A |
+| **RTN (`rank-torrent-name`)** | Code Only | `53.58%` | `81.64%` | `80.48%` | `93.76%` | `77.65%` | `79.00%` | `50.38%` (`0%` rec.) | N/A |
+| **PTT / Parsett** | Code Only | `53.58%` | `81.64%` | `80.48%` | `93.76%` | `77.65%` | `79.00%` | N/A | N/A |
+| **GuessIt** | Code Only | `38.69%` | `84.65%` | `84.03%` | `94.86%` | `78.80%` | N/A | N/A | N/A |
+| **PTN (`parse-torrent-title`)** | Code Only | `35.59%` | `77.85%` | `77.62%` | `93.21%` | `68.65%` | N/A | N/A | N/A |
+| **Anitomy** | Code Only | `33.40%` | `73.07%` | `72.76%` | `90.46%` | `64.55%` | N/A | N/A | N/A |
+| **Bitmagnet Built-In** | Code Only | `35.17%` | `43.21%` (`88.4%` ans.) | `43.21%` | `88.26%` | N/A | `76.73%` | `54.60%` (`0%` rec.) | N/A |
 
 ### Per-Category Accuracy Breakdown Across All 10 Media & Software Categories (Unmutated Keys)
 
 ![Categorical Accuracy Breakdown Across All 10 Media & Software Categories](assets/armiger_categorical_breakdown.png)
 
-| Content Category | `oct15` (`N=2,835`) Armiger Ultra (Fair Hybrid) | `oct15` (`N=2,835`) Armiger (INT8 Neural) | `oct15` MIMMO BERT (360M) | `oct15` Bitmagnet Built-In | `beyond` (`N=1,025`) Armiger Ultra (Fair Hybrid) | `beyond` (`N=1,025`) Armiger (Neural) | `beyond` OpenAI GPT (`gpt-6-luna`)* | `beyond` Google Gemini (`gemini-3.8-flash-low`)* |
+| Content Category | `oct15` (`N=2,835`) Armiger Ultra (Fair Hybrid) | `oct15` (`N=2,835`) Armiger (INT8 + ZeroCue) | `oct15` MIMMO BERT (360M) | `oct15` Bitmagnet Built-In | `beyond` (`N=1,025`) Armiger Ultra (Fair Hybrid) | `beyond` (`N=1,025`) Armiger (INT8 + ZeroCue) | `beyond` OpenAI GPT (`gpt-6-luna` LOTO)* | `beyond` Google Gemini (`gemini-3.8-flash-low` LOTO)* |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **Audiobook** | **`97.89%`** | **`97.89%`** | `0.00%` | `9.47%` | **`100.00%`** | **`100.00%`** | **`100.00%`** | `98.67%` |
-| **Adult / XXX** | **`97.80%`** | **`97.80%`** | `95.50%` | `39.46%` | `91.95%` | `91.95%` | `97.70%` | **`98.85%`** |
-| **TV Show** | **`97.50%`** | `97.00%` | `58.40%` | `28.62%` | `96.32%` | `86.03%` | **`99.26%`** | `95.59%` |
-| **Movie** | **`96.83%`** | `96.26%` | `78.85%` | `55.83%` | **`96.84%`** | `92.63%` | `95.79%` | `95.79%` |
-| **Software** | **`93.75%`** | **`93.75%`** | `59.38%` | `15.62%` | `94.59%` | `94.59%` | **`97.30%`** | `78.38%` |
+| **Audiobook** | **`97.89%`** | **`97.89%`** | `0.00%` | `9.47%` | **`100.00%`** | **`100.00%`** | `98.67%` | `98.00%` |
+| **Adult / XXX** | **`97.80%`** | **`97.80%`** | `95.50%` | `39.37%` | `91.95%` | `91.95%` | `94.83%` | **`95.69%`** |
+| **TV Show** | **`97.50%`** | **`97.50%`** | `58.40%` | `28.62%` | `96.32%` | `96.32%` | **`97.79%`** | `93.75%` |
+| **Movie** | **`97.12%`** | **`97.12%`** | `78.85%` | `55.83%` | **`96.84%`** | **`96.84%`** | `94.21%` | `92.63%` |
+| **Software** | **`93.75%`** | **`93.75%`** | `59.38%` | `15.62%` | `94.59%` | `94.59%` | **`97.30%`** | `77.03%` |
 | **Music** | **`90.71%`** | **`90.71%`** | `77.86%` | `7.86%` | `83.33%` | `83.33%` | **`100.00%`** | **`100.00%`** |
-| **Game** | **`90.16%`** | `86.89%` | `0.00%` | `0.00%` | `96.30%` | `95.56%` | **`99.26%`** | **`99.26%`** |
-| **Ebook** | **`88.00%`** | **`88.00%`** | `80.00%` | `4.00%` | `98.40%` | `98.40%` | `96.80%` | **`99.20%`** |
-| **Course** | **`81.82%`** | `78.79%` | `0.00%` | `0.00%` | `92.00%` | `92.00%` | `96.00%` | **`99.00%`** |
-| **Comic** | **`66.67%`** | **`66.67%`** | `0.00%` | `0.00%` | **`98.33%`** | `95.00%` | **`99.17%`** | `95.83%` |
+| **Game** | **`90.16%`** | **`90.16%`** | `0.00%` | `0.00%` | `96.30%` | `96.30%` | **`98.52%`** | **`98.52%`** |
+| **Ebook** | **`88.00%`** | **`88.00%`** | `80.00%` | `4.00%` | **`98.40%`** | **`98.40%`** | `96.00%` | `96.80%` |
+| **Course** | **`81.82%`** | **`81.82%`** | `0.00%` | `0.00%` | `92.00%` | `92.00%` | `95.50%` | **`97.50%`** |
+| **Comic** | **`66.67%`** | **`66.67%`** | `0.00%` | `0.00%` | **`98.33%`** | **`98.33%`** | `96.67%` | `95.00%` |
 
-*Note on `beyond_confirmation` (`N=1,025`): `gpt-6-luna` and `gemini-3.8-flash-low` were the two frontier teacher models used in the multi-teacher reference consensus for `beyond_confirmation`, giving them teacher-in-the-loop consensus affinity on that split. Independent blind `claude-haiku-4-5` (`N=240`) scored `93.75%` category accuracy and local `Vinci-MLE-8B` scored `80.77%`.*
+*Note on `beyond_confirmation` (`N=1,025`): `gpt-6-luna` and `gemini-3.8-flash-low` are evaluated using non-circular Leave-One-Teacher-Out (LOTO) reference keys (`self_vote_weight = 0.0`) so neither teacher model is scored against its own deciding majority vote (`95.12%` and `93.80%` LOTO vs. `96.59%` and `95.61%` circular self-voted).*
 
 ---
 
@@ -171,14 +171,14 @@ All metrics below are reproducibly computed using `armiger_inference.py` against
 
 ![Cost Benchmark vs Frontier Cloud LLMs and Local GPU LLM](assets/armiger_vs_llms_cost_comparison.png)
 
-| Model | Overall 6-Dim Mean | 11-Class Category | Catalog Matching | Audio Language | Anime & Dub | Junk / Malware (Bal.) | Swarm Liveliness (AUROC) | Measured Hardware Speed | Cost per 1M Torrents (Batched / Real-Time) |
+| Model | Overall 6-Dim Mean (95% CI) | 11-Class Category (`N=1,025` LOTO / `oct15`) | Catalog Matching (`N=1,025` LOTO / `oct15`) | Audio Language (Strict E-vs-F `N=229` / `N=545`) | Anime & Dub (Valid Media `N=636` / `N=507`) | Junk / Malware (`N=977` Bal / Acc) | Swarm Liveliness (AUROC) | Measured Hardware Speed | Cost per 1M Torrents (Batched / Real-Time) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **[Armiger Ultra (Fair Hybrid)](https://huggingface.co/NorviTech/armiger-ultra)** | **`97.55%`** | `95.49%` (`oct15`) / `95.12%` (`beyond`) | **`96.39%`** | `97.80%` | `98.90%` (`99.61%` `oct15`) | `99.90%` (`100%` `oct15`) | **`96.83%`** | **`0.21 ms` RTX / `0.29 ms` M4 Pro MPS** | **`$0.00` API** (`$0.0040` compute) |
-| **Armiger (8.3 MB INT8 Neural)** | **`96.50%`** | `95.06%` (`oct15`) / `92.88%` (`beyond`) | `95.85%` | `96.88%` | `98.90%` (`99.61%` `oct15`) | `99.90%` (`100%` `oct15`) | `92.42%` (`86.64%` Priors) | **`2.44 ms` single / `3.07 ms` batch-64 (M4 Pro CPU)** | **`$0.00` API** (`$0.0020` compute) |
-| **Google Gemini (`gemini-3.8-flash-low`)\*** | `96.35%` | `97.13%`* | `94.05%`* | **`99.88%`**\* | `97.82%`* | **`100.00%`** | `89.20%` | `~450 ms` | `$94.90` / `$147.00` |
-| **Anthropic Claude (`claude-haiku-4-5`)** | `93.02%` | `93.75%` | `86.11%` | `95.68%` | **`99.44%`** | `93.37%` | `89.76%` | `~310 ms` | `$712.00` / `$1,220.00` |
-| **OpenAI GPT (`gpt-6-luna`)\*** | `87.85%` | **`98.02%`**\* | `91.77%`* | `99.65%`* | `97.82%`* | `50.00%` (`0%` rec.) | `89.82%` | `~365 ms` | `$127.30` / `$220.50` |
-| **Local GPU LLM (`Vinci-MLE-8B-1.0`)** | `85.71%` | `80.77%` | `77.88%` | `97.30%` | `93.79%` | `98.21%` | `66.32%` | `~185 ms` (`5.4/s` RTX 4070 TiS) | `$0.00` API (`$1.85` local power) |
+| **Armiger Ultra (Fair Hybrid)** | **`97.60%`** (`[97.18, 98.02]`) | **`95.12%`** (`95.49%` `oct15`) | **`96.39%`** (`96.53%` `oct15`) | **`99.13%`** (`97.80%` `oct15`) | **`98.90%`** (`99.61%` `oct15`) | **`99.23%`** (`99.90%` Acc / **`100%`** `oct15`) | **`96.83%`** Whse / **`92.42%`** Pub | **`0.21 ms` RTX / `0.29 ms` M4 Pro MPS** | **`$0.00` API** (`$0.0040` compute) |
+| **[Armiger](https://huggingface.co/NorviTech/armiger) (`armiger.int8.onnx` + Plugins)** | **`96.85%`** (`[96.40, 97.30]`) | **`95.12%`** (`95.49%` `oct15` / `95.06%` Raw) | `96.29%` (`95.80%` Raw) | **`99.13%`** (`97.80%` `oct15` / `96.88%` Raw) | **`98.90%`** (`99.61%` `oct15`) | **`99.23%`** (`99.90%` Acc / **`100%`** `oct15`) | `92.42%` Pub (`86.64%` Priors) | **`2.44 ms` single / `3.07 ms` batch-64 (M4 Pro CPU)** | **`$0.00` API** (`$0.0020` compute) |
+| **Google Gemini (`gemini-3.8-flash-low`, LOTO)\*** | `94.56%` (`[93.97, 95.15]`) | `93.80%` (`95.61%` Circ.) | `90.88%` | `98.69%` | `95.60%` (`608/636`) | `99.18%` (Non-Circ.) | `89.20%` Pub | `~450 ms` | `$94.90` / `$147.00` |
+| **Anthropic Claude (`claude-haiku-4-5`)** | `92.88%` (`[92.10, 93.66]`) | `93.76%` | `86.15%` | `95.63%` | **`98.90%`** | `93.09%` | `89.76%` Pub | `~310 ms` | `$712.00` / `$1,220.00` |
+| **OpenAI GPT (`gpt-6-luna`, LOTO)\*** | `86.24%` (`[85.64, 86.83]`) | **`95.12%`** (`96.59%` Circ.) | `89.07%` | `97.82%` (`94.68%` `oct15`) | `95.60%` (`608/636`) | `50.00%` (`0%` rec.) | `89.82%` Pub | `~365 ms` | `$127.30` / `$220.50` |
+| **Local GPU LLM (`Vinci-MLE-8B-1.0`)** | `84.79%` (`[83.80, 85.79]`) | `80.78%` | `77.85%` | `93.45%` | `92.45%` | `97.91%` | `66.32%` Pub | `~185 ms` (`5.4/s` RTX 4070 TiS) | `$0.00` API (`$1.85` local power) |
 
 ---
 

benchmarks/CHARTS_MANIFEST.json

@@ -0,0 +1,1571 @@
+{
+  "schema_version": "1.0.0",
+  "bootstrap_seed": 20261018,
+  "bootstrap_resamples": 10000,
+  "invariants_verified": {
+    "identical_denominator_lock": true,
+    "anti_circularity_lock": true,
+    "paired_uncertainty_and_significance": true,
+    "png_metadata_stripped_ihdr_idat_iend_only": true
+  },
+  "anti_circularity_audit": [
+    {
+      "model_name": "OpenAI GPT (gpt-6-luna)",
+      "is_teacher": true,
+      "eval_mode": "leave_one_teacher_out",
+      "self_vote_weight": 0.0,
+      "non_circular_pct": 95.12,
+      "circular_self_vote_pct": 96.59,
+      "circularity_inflation_pp": 1.46
+    },
+    {
+      "model_name": "Google Gemini (gemini-3.8-flash-low)",
+      "is_teacher": true,
+      "eval_mode": "leave_one_teacher_out",
+      "self_vote_weight": 0.0,
+      "non_circular_pct": 93.8,
+      "circular_self_vote_pct": 95.61,
+      "circularity_inflation_pp": 1.8
+    },
+    {
+      "model_name": "Anthropic Claude (claude-haiku-4-5)",
+      "is_teacher": true,
+      "eval_mode": "independent_ground_truth",
+      "self_vote_weight": 0.0
+    }
+  ],
+  "provenance_sha256": {
+    "canonical_eval_vectors.npz": "5d366d648c51f8934795be4480c74048521fc43c02c51ae7496a9c323a453825",
+    "generate_canonical_charts.py": "4a64e248ca15e703a35b77ea22f546a40e895ae4393098e2c803de72129d0173",
+    "assets/armiger_vs_llms_overall_and_6dims.png": "2367dbf6ae84cc2f75b0b13f377f7053f8f0e63ebe56cf42e1093b26aa18c082",
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+          "armiger_int8_pct": 97.5,
+          "mimmo_bert_pct": 58.4,
+          "bitmagnet_pct": 28.62
+        },
+        "Movie": {
+          "n": 695,
+          "armiger_ultra_pct": 97.12,
+          "armiger_int8_pct": 97.12,
+          "mimmo_bert_pct": 78.85,
+          "bitmagnet_pct": 55.83
+        },
+        "Software": {
+          "n": 32,
+          "armiger_ultra_pct": 93.75,
+          "armiger_int8_pct": 93.75,
+          "mimmo_bert_pct": 59.38,
+          "bitmagnet_pct": 15.62
+        },
+        "Music": {
+          "n": 140,
+          "armiger_ultra_pct": 90.71,
+          "armiger_int8_pct": 90.71,
+          "mimmo_bert_pct": 77.86,
+          "bitmagnet_pct": 7.86
+        },
+        "Game": {
+          "n": 61,
+          "armiger_ultra_pct": 90.16,
+          "armiger_int8_pct": 90.16,
+          "mimmo_bert_pct": 0.0,
+          "bitmagnet_pct": 0.0
+        },
+        "Ebook": {
+          "n": 25,
+          "armiger_ultra_pct": 88.0,
+          "armiger_int8_pct": 88.0,
+          "mimmo_bert_pct": 80.0,
+          "bitmagnet_pct": 4.0
+        },
+        "Course": {
+          "n": 33,
+          "armiger_ultra_pct": 81.82,
+          "armiger_int8_pct": 81.82,
+          "mimmo_bert_pct": 0.0,
+          "bitmagnet_pct": 0.0
+        },
+        "Comic": {
+          "n": 99,
+          "armiger_ultra_pct": 66.67,
+          "armiger_int8_pct": 66.67,
+          "mimmo_bert_pct": 0.0,
+          "bitmagnet_pct": 0.0
+        }
+      },
+      "right_beyond_confirmation_n1025_loto": {
+        "Audiobook": {
+          "n": 75,
+          "armiger_ultra_pct": 100.0,
+          "armiger_int8_pct": 100.0,
+          "gpt_noncirc_pct": 98.67,
+          "gemini_noncirc_pct": 98.0
+        },
+        "Ebook": {
+          "n": 125,
+          "armiger_ultra_pct": 98.4,
+          "armiger_int8_pct": 98.4,
+          "gpt_noncirc_pct": 96.0,
+          "gemini_noncirc_pct": 96.8
+        },
+        "Comic": {
+          "n": 120,
+          "armiger_ultra_pct": 98.33,
+          "armiger_int8_pct": 98.33,
+          "gpt_noncirc_pct": 96.67,
+          "gemini_noncirc_pct": 95.0
+        },
+        "Movie": {
+          "n": 95,
+          "armiger_ultra_pct": 96.84,
+          "armiger_int8_pct": 96.84,
+          "gpt_noncirc_pct": 94.21,
+          "gemini_noncirc_pct": 92.63
+        },
+        "TV Show": {
+          "n": 136,
+          "armiger_ultra_pct": 96.32,
+          "armiger_int8_pct": 96.32,
+          "gpt_noncirc_pct": 97.79,
+          "gemini_noncirc_pct": 93.75
+        },
+        "Game": {
+          "n": 135,
+          "armiger_ultra_pct": 96.3,
+          "armiger_int8_pct": 96.3,
+          "gpt_noncirc_pct": 98.52,
+          "gemini_noncirc_pct": 98.52
+        },
+        "Software": {
+          "n": 37,
+          "armiger_ultra_pct": 94.59,
+          "armiger_int8_pct": 94.59,
+          "gpt_noncirc_pct": 97.3,
+          "gemini_noncirc_pct": 77.03
+        },
+        "Course": {
+          "n": 100,
+          "armiger_ultra_pct": 92.0,
+          "armiger_int8_pct": 92.0,
+          "gpt_noncirc_pct": 95.5,
+          "gemini_noncirc_pct": 97.5
+        },
+        "Adult / XXX": {
+          "n": 174,
+          "armiger_ultra_pct": 91.95,
+          "armiger_int8_pct": 91.95,
+          "gpt_noncirc_pct": 94.83,
+          "gemini_noncirc_pct": 95.69
+        },
+        "Music": {
+          "n": 12,
+          "armiger_ultra_pct": 83.33,
+          "armiger_int8_pct": 83.33,
+          "gpt_noncirc_pct": 100.0,
+          "gemini_noncirc_pct": 100.0
+        }
+      }
+    }
+  },
+  "five_split_category_summary": {
+    "oct15_confirmation": {
+      "panel_name": "five_split_oct15",
+      "n": 2835,
+      "metric_type": "mean",
+      "reference_model": "armiger_ultra_hyb",
+      "models": {
+        "armiger_ultra_hyb": {
+          "n": 2835,
+          "point_pct": 95.49,
+          "ci95_lo": 94.71,
+          "ci95_hi": 96.23,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_ultra_raw": {
+          "n": 2835,
+          "point_pct": 95.03,
+          "ci95_lo": 94.21,
+          "ci95_hi": 95.84,
+          "delta_vs_ref_pct": 0.46,
+          "delta_ci95_lo": 0.14,
+          "delta_ci95_hi": 0.78,
+          "p_value_vs_ref": 0.00719738
+        },
+        "armiger_int8_hyb": {
+          "n": 2835,
+          "point_pct": 95.49,
+          "ci95_lo": 94.71,
+          "ci95_hi": 96.23,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_int8_raw": {
+          "n": 2835,
+          "point_pct": 95.06,
+          "ci95_lo": 94.25,
+          "ci95_hi": 95.87,
+          "delta_vs_ref_pct": 0.42,
+          "delta_ci95_lo": 0.14,
+          "delta_ci95_hi": 0.74,
+          "p_value_vs_ref": 0.01181793
+        }
+      }
+    },
+    "fresh_confirmation": {
+      "panel_name": "five_split_fresh",
+      "n": 1402,
+      "metric_type": "mean",
+      "reference_model": "armiger_ultra_hyb",
+      "models": {
+        "armiger_ultra_hyb": {
+          "n": 1402,
+          "point_pct": 94.58,
+          "ci95_lo": 93.37,
+          "ci95_hi": 95.72,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_ultra_raw": {
+          "n": 1402,
+          "point_pct": 94.51,
+          "ci95_lo": 93.3,
+          "ci95_hi": 95.65,
+          "delta_vs_ref_pct": 0.07,
+          "delta_ci95_lo": -0.21,
+          "delta_ci95_hi": 0.36,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_int8_hyb": {
+          "n": 1402,
+          "point_pct": 94.58,
+          "ci95_lo": 93.37,
+          "ci95_hi": 95.72,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_int8_raw": {
+          "n": 1402,
+          "point_pct": 94.51,
+          "ci95_lo": 93.3,
+          "ci95_hi": 95.65,
+          "delta_vs_ref_pct": 0.07,
+          "delta_ci95_lo": -0.21,
+          "delta_ci95_hi": 0.36,
+          "p_value_vs_ref": 1.0
+        }
+      }
+    },
+    "beyond_confirmation": {
+      "panel_name": "five_split_beyond",
+      "n": 1025,
+      "metric_type": "mean",
+      "reference_model": "armiger_ultra_hyb",
+      "models": {
+        "armiger_ultra_hyb": {
+          "n": 1025,
+          "point_pct": 95.12,
+          "ci95_lo": 93.76,
+          "ci95_hi": 96.39,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_ultra_raw": {
+          "n": 1025,
+          "point_pct": 92.88,
+          "ci95_lo": 91.22,
+          "ci95_hi": 94.44,
+          "delta_vs_ref_pct": 2.24,
+          "delta_ci95_lo": 1.37,
+          "delta_ci95_hi": 3.22,
+          "p_value_vs_ref": 2.384186e-07
+        },
+        "armiger_int8_hyb": {
+          "n": 1025,
+          "point_pct": 95.12,
+          "ci95_lo": 93.76,
+          "ci95_hi": 96.39,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_int8_raw": {
+          "n": 1025,
+          "point_pct": 92.88,
+          "ci95_lo": 91.22,
+          "ci95_hi": 94.44,
+          "delta_vs_ref_pct": 2.24,
+          "delta_ci95_lo": 1.37,
+          "delta_ci95_hi": 3.22,
+          "p_value_vs_ref": 2.384186e-07
+        }
+      }
+    },
+    "tc_truespec_2000": {
+      "panel_name": "five_split_tc2000",
+      "n": 2000,
+      "metric_type": "mean",
+      "reference_model": "armiger_ultra_hyb",
+      "models": {
+        "armiger_ultra_hyb": {
+          "n": 2000,
+          "point_pct": 91.5,
+          "ci95_lo": 90.3,
+          "ci95_hi": 92.65,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_ultra_raw": {
+          "n": 2000,
+          "point_pct": 87.4,
+          "ci95_lo": 85.95,
+          "ci95_hi": 88.8,
+          "delta_vs_ref_pct": 4.1,
+          "delta_ci95_lo": 2.95,
+          "delta_ci95_hi": 5.25,
+          "p_value_vs_ref": 1.091317e-12
+        },
+        "armiger_int8_hyb": {
+          "n": 2000,
+          "point_pct": 91.4,
+          "ci95_lo": 90.2,
+          "ci95_hi": 92.55,
+          "delta_vs_ref_pct": 0.1,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.25,
+          "p_value_vs_ref": 0.5
+        },
+        "armiger_int8_raw": {
+          "n": 2000,
+          "point_pct": 87.3,
+          "ci95_lo": 85.85,
+          "ci95_hi": 88.7,
+          "delta_vs_ref_pct": 4.2,
+          "delta_ci95_lo": 3.05,
+          "delta_ci95_hi": 5.35,
+          "p_value_vs_ref": 4.246281e-13
+        }
+      }
+    },
+    "tc_truespec_multilang_3000": {
+      "panel_name": "five_split_tc3000",
+      "n": 3000,
+      "metric_type": "mean",
+      "reference_model": "armiger_ultra_hyb",
+      "models": {
+        "armiger_ultra_hyb": {
+          "n": 3000,
+          "point_pct": 98.47,
+          "ci95_lo": 98.0,
+          "ci95_hi": 98.9,
+          "delta_vs_ref_pct": 0.0,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.0,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_ultra_raw": {
+          "n": 3000,
+          "point_pct": 97.5,
+          "ci95_lo": 96.9,
+          "ci95_hi": 98.03,
+          "delta_vs_ref_pct": 0.97,
+          "delta_ci95_lo": 0.57,
+          "delta_ci95_hi": 1.4,
+          "p_value_vs_ref": 4.873598e-06
+        },
+        "armiger_int8_hyb": {
+          "n": 3000,
+          "point_pct": 98.43,
+          "ci95_lo": 97.97,
+          "ci95_hi": 98.87,
+          "delta_vs_ref_pct": 0.03,
+          "delta_ci95_lo": 0.0,
+          "delta_ci95_hi": 0.1,
+          "p_value_vs_ref": 1.0
+        },
+        "armiger_int8_raw": {
+          "n": 3000,
+          "point_pct": 97.43,
+          "ci95_lo": 96.83,
+          "ci95_hi": 97.97,
+          "delta_vs_ref_pct": 1.03,
+          "delta_ci95_lo": 0.63,
+          "delta_ci95_hi": 1.47,
+          "p_value_vs_ref": 1.636124e-06
+        }
+      }
+    }
+  }
+}

benchmarks/canonical_eval_vectors.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d366d648c51f8934795be4480c74048521fc43c02c51ae7496a9c323a453825
+size 207403

benchmarks/generate_canonical_charts.py

@@ -0,0 +1,1406 @@
+#!/usr/bin/env python3
+"""
+Deterministic, Statistically Rooted Benchmark & Chart Generation Pipeline
+for Armiger & Armiger Ultra.
+
+Enforces 4 hard statistical invariants before writing any chart:
+  1. Identical Denominator Lock (`assert_identical_denominator`)
+  2. Anti-Circularity Lock (`assert_non_circular_teacher_eval`)
+  3. Paired Uncertainty & Significance (10,000-resample paired bootstrap 95% CIs
+     with fixed seed 20261018 + exact two-sided McNemar / permutation p-values)
+  4. Cryptographic Provenance Manifest (`CHARTS_MANIFEST.json` + stripped PNG
+     chunks containing only `IHDR`, `IDAT`, `IEND`).
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import io
+import json
+import pathlib
+import struct
+import zlib
+from typing import Any, Dict, List, Optional, Tuple
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+from matplotlib.patches import Patch
+import numpy as np
+from PIL import Image
+from scipy.stats import binomtest
+
+BOOTSTRAP_SEED = 20261018
+BOOTSTRAP_RESAMPLES = 10000
+
+BG_CANVAS = "#0D1117"
+BG_PANEL = "#161B22"
+BG_HIGHLIGHT = "#0F2922"
+BORDER_COLOR = "#30363D"
+GRID_COLOR = "#21262D"
+TEXT_MAIN = "#F0F6FC"
+TEXT_SUB = "#8B949E"
+TEXT_MUTED = "#6E7681"
+
+C_ULTRA = "#10B981"
+C_ULTRA_EDGE = "#34D399"
+C_INT8 = "#3B82F6"
+C_INT8_EDGE = "#60A5FA"
+C_GEMINI = "#F59E0B"
+C_CLAUDE = "#F97316"
+C_GPT = "#38BDF8"
+C_VINCI = "#A855F7"
+C_ERR = "#E2E8F0"
+
+TEACHER_MODELS = {
+    "gpt-6-luna",
+    "gemini-3.8-flash-low",
+    "muse-spark-1.3",
+    "claude-haiku-4-5",
+    "OpenAI GPT (gpt-6-luna)",
+    "Google Gemini (gemini-3.8-flash-low)",
+    "Anthropic Claude (claude-haiku-4-5)",
+}
+
+
+# ==============================================================================
+# 1. HARD STATISTICAL INVARIANT LOCKS
+# ==============================================================================
+def assert_identical_denominator(
+    panel_name: str,
+    vectors: Dict[str, np.ndarray],
+    expected_n: Optional[int] = None,
+) -> int:
+    """Enforce that every model on a panel is evaluated on the exact same item count N
+    and valid-domain mask, with zero NaNs."""
+    if not vectors:
+        raise AssertionError(f"[{panel_name}] Empty vectors dictionary.")
+    lengths = {}
+    for model_name, vec in vectors.items():
+        arr = np.asarray(vec)
+        if arr.ndim != 1:
+            raise AssertionError(f"[{panel_name}] Model '{model_name}' vector must be 1D, got shape {arr.shape}.")
+        if np.isnan(arr).any():
+            raise AssertionError(f"[{panel_name}] Model '{model_name}' contains NaN values.")
+        lengths[model_name] = int(arr.shape[0])
+
+    unique_lengths = set(lengths.values())
+    if len(unique_lengths) != 1:
+        raise AssertionError(
+            f"[{panel_name}] Identical Denominator Lock violated! Mismatched N across models: {lengths}"
+        )
+    n_val = next(iter(unique_lengths))
+    if expected_n is not None and n_val != expected_n:
+        raise AssertionError(
+            f"[{panel_name}] Expected N={expected_n}, but got N={n_val} ({lengths})."
+        )
+    return n_val
+
+
+def assert_non_circular_teacher_eval(
+    model_name: str,
+    eval_mode: str,
+    self_vote_weight: float,
+    noncirc_vec: Optional[np.ndarray] = None,
+    circ_vec: Optional[np.ndarray] = None,
+) -> Dict[str, Any]:
+    """Enforce that teacher LLMs are never scored against a reference key in which
+    they cast a deciding self-vote."""
+    is_teacher = any(t in model_name for t in TEACHER_MODELS)
+    if is_teacher:
+        if self_vote_weight != 0.0 or eval_mode == "circular_majority":
+            raise AssertionError(
+                f"Anti-Circularity Lock violated for teacher '{model_name}': "
+                f"eval_mode='{eval_mode}', self_vote_weight={self_vote_weight} (must be 0.0)."
+            )
+        if eval_mode not in ("leave_one_teacher_out", "independent_ground_truth"):
+            raise AssertionError(
+                f"Invalid eval_mode '{eval_mode}' for teacher '{model_name}'."
+            )
+    info: Dict[str, Any] = {
+        "model_name": model_name,
+        "is_teacher": is_teacher,
+        "eval_mode": eval_mode,
+        "self_vote_weight": float(self_vote_weight),
+    }
+    if noncirc_vec is not None and circ_vec is not None:
+        assert_identical_denominator(f"anti_circ_{model_name}", {"noncirc": noncirc_vec, "circ": circ_vec})
+        nc_pct = float(np.mean(noncirc_vec) * 100.0)
+        c_pct = float(np.mean(circ_vec) * 100.0)
+        info["non_circular_pct"] = round(nc_pct, 2)
+        info["circular_self_vote_pct"] = round(c_pct, 2)
+        info["circularity_inflation_pp"] = round(c_pct - nc_pct, 2)
+    return info
+
+
+def exact_mcnemar_or_paired_p(vec_ref: np.ndarray, vec_mod: np.ndarray) -> float:
+    """Compute exact two-sided McNemar binomial p-value for binary vectors, or exact
+    paired sign-flip binomial test on discordant items for fractional LOTO vectors."""
+    diff = np.asarray(vec_ref, dtype=np.float64) - np.asarray(vec_mod, dtype=np.float64)
+    n_pos = int(np.sum(diff > 1e-6))
+    n_neg = int(np.sum(diff < -1e-6))
+    n_disc = n_pos + n_neg
+    if n_disc == 0:
+        return 1.0
+    return float(binomtest(n_pos, n_disc, 0.5, alternative="two-sided").pvalue)
+
+
+def compute_auroc_pct(y_true: np.ndarray, scores: np.ndarray) -> float:
+    pos = scores[y_true == 1]
+    neg = scores[y_true == 0]
+    u = (pos[:, None] > neg[None, :]).sum() + 0.5 * (pos[:, None] == neg[None, :]).sum()
+    return float(100.0 * u / (len(pos) * len(neg)))
+
+
+def compute_paired_panel_stats(
+    panel_name: str,
+    vectors: Dict[str, np.ndarray],
+    ref_model: str,
+    expected_n: int,
+    metric_type: str = "mean",
+    y_true: Optional[np.ndarray] = None,
+    n_boot: int = BOOTSTRAP_RESAMPLES,
+    seed: int = BOOTSTRAP_SEED,
+) -> Dict[str, Any]:
+    """Compute point estimates, 10,000-resample paired bootstrap 95% CIs, paired deltas,
+    and McNemar / permutation p-values for all models in a panel."""
+    n_val = assert_identical_denominator(panel_name, vectors, expected_n=expected_n)
+    if ref_model not in vectors:
+        raise AssertionError(f"[{panel_name}] Reference model '{ref_model}' missing from vectors.")
+
+    rng = np.random.default_rng(seed)
+    model_names = list(vectors.keys())
+    mat = np.stack([np.asarray(vectors[m], dtype=np.float32) for m in model_names], axis=0)  # (M, N)
+
+    if metric_type == "mean":
+        # Multinomial weights: shape (n_boot, N) -> fast BLAS matrix multiply (n_boot, M)
+        weights = rng.multinomial(n_val, np.full(n_val, 1.0 / n_val), size=n_boot).astype(np.float32) * (100.0 / n_val)
+        boot_scores = weights @ mat.T  # (n_boot, M)
+        point_scores = {m: float(np.mean(vectors[m]) * 100.0) for m in model_names}
+        item_correctness = {m: np.asarray(vectors[m], dtype=np.float32) for m in model_names}
+    elif metric_type == "balanced":
+        if y_true is None:
+            raise AssertionError(f"[{panel_name}] y_true required for metric_type='balanced'.")
+        y_arr = np.asarray(y_true)
+        pos_mask = (y_arr == 1)
+        neg_mask = (y_arr == 0)
+        n_pos = int(np.sum(pos_mask))
+        n_neg = int(np.sum(neg_mask))
+        pos_mat = (mat[:, pos_mask] == 1).astype(np.float32)
+        neg_mat = (mat[:, neg_mask] == 0).astype(np.float32)
+        w_pos = rng.multinomial(n_pos, np.full(n_pos, 1.0 / n_pos), size=n_boot).astype(np.float32) * (100.0 / n_pos)
+        w_neg = rng.multinomial(n_neg, np.full(n_neg, 1.0 / n_neg), size=n_boot).astype(np.float32) * (100.0 / n_neg)
+        boot_scores = 0.5 * ((w_pos @ pos_mat.T) + (w_neg @ neg_mat.T))
+        point_scores = {}
+        item_correctness = {}
+        for idx, m in enumerate(model_names):
+            sens = float(np.mean(pos_mat[idx]) * 100.0)
+            spec = float(np.mean(neg_mat[idx]) * 100.0)
+            point_scores[m] = 0.5 * (sens + spec)
+            item_corr = np.zeros(n_val, dtype=np.float32)
+            item_corr[pos_mask] = pos_mat[idx]
+            item_corr[neg_mask] = neg_mat[idx]
+            item_correctness[m] = item_corr
+    elif metric_type == "auroc":
+        if y_true is None:
+            raise AssertionError(f"[{panel_name}] y_true required for metric_type='auroc'.")
+        y_arr = np.asarray(y_true)
+        pos_idx = np.where(y_arr == 1)[0]
+        neg_idx = np.where(y_arr == 0)[0]
+        n_pos, n_neg = len(pos_idx), len(neg_idx)
+        # Paired bootstrap over positive and negative instances using DeLong/placement values
+        placement_mat = np.zeros((len(model_names), n_val), dtype=np.float32)
+        point_scores = {}
+        for idx, m in enumerate(model_names):
+            s = mat[idx]
+            s_pos = s[pos_idx]
+            s_neg = s[neg_idx]
+            # V10 placement for each positive item, V01 placement for each negative item
+            v10 = ((s_pos[:, None] > s_neg[None, :]).sum(axis=1) + 0.5 * (s_pos[:, None] == s_neg[None, :]).sum(axis=1)) / n_neg
+            v01 = ((s_pos[:, None] > s_neg[None, :]).sum(axis=0) + 0.5 * (s_pos[:, None] == s_neg[None, :]).sum(axis=0)) / n_pos
+            placement_mat[idx, pos_idx] = v10 * 100.0
+            placement_mat[idx, neg_idx] = v01 * 100.0
+            point_scores[m] = float(np.mean(v10) * 100.0)
+        w_pos = rng.multinomial(n_pos, np.full(n_pos, 1.0 / n_pos), size=n_boot).astype(np.float32) / n_pos
+        w_neg = rng.multinomial(n_neg, np.full(n_neg, 1.0 / n_neg), size=n_boot).astype(np.float32) / n_neg
+        boot_scores = 0.5 * ((w_pos @ placement_mat[:, pos_idx].T) + (w_neg @ placement_mat[:, neg_idx].T))
+        item_correctness = {m: placement_mat[idx] / 100.0 for idx, m in enumerate(model_names)}
+    else:
+        raise ValueError(f"Unknown metric_type: {metric_type}")
+
+    ref_idx = model_names.index(ref_model)
+    ref_boot = boot_scores[:, ref_idx]
+    ref_point = point_scores[ref_model]
+
+    results: Dict[str, Any] = {}
+    for idx, m in enumerate(model_names):
+        m_boot = boot_scores[:, idx]
+        ci_lo, ci_hi = np.percentile(m_boot, [2.5, 97.5])
+        delta_boot = ref_boot - m_boot
+        d_lo, d_hi = np.percentile(delta_boot, [2.5, 97.5])
+        p_val = 1.0 if m == ref_model else exact_mcnemar_or_paired_p(item_correctness[ref_model], item_correctness[m])
+        results[m] = {
+            "n": n_val,
+            "point_pct": round(point_scores[m], 2),
+            "ci95_lo": round(float(ci_lo), 2),
+            "ci95_hi": round(float(ci_hi), 2),
+            "delta_vs_ref_pct": round(ref_point - point_scores[m], 2),
+            "delta_ci95_lo": round(float(d_lo), 2),
+            "delta_ci95_hi": round(float(d_hi), 2),
+            "p_value_vs_ref": float(f"{p_val:.6e}"),
+        }
+    return {
+        "panel_name": panel_name,
+        "n": n_val,
+        "metric_type": metric_type,
+        "reference_model": ref_model,
+        "models": results,
+    }
+
+
+# ==============================================================================
+# 2. DETERMINISTIC PNG SERIALIZATION (STRIPPED TO IHDR, IDAT, IEND ONLY)
+# ==============================================================================
+def strip_png_chunks_to_canonical(png_bytes: bytes) -> bytes:
+    """Keep only IHDR, IDAT, and IEND chunks so the output PNG contains zero
+    timestamps, software tags, or host path metadata and is 100% deterministic."""
+    sig = b"\x89PNG\r\n\x1a\n"
+    if not png_bytes.startswith(sig):
+        raise ValueError("Invalid PNG header signature.")
+    pos = 8
+    out = bytearray(sig)
+    while pos < len(png_bytes):
+        length = struct.unpack(">I", png_bytes[pos:pos + 4])[0]
+        ctype = png_bytes[pos + 4:pos + 8]
+        chunk_data = png_bytes[pos + 8:pos + 8 + length]
+        crc = png_bytes[pos + 8 + length:pos + 12 + length]
+        pos += 12 + length
+        if ctype in (b"IHDR", b"IDAT", b"IEND"):
+            out.extend(struct.pack(">I", length))
+            out.extend(ctype)
+            out.extend(chunk_data)
+            out.extend(crc)
+    return bytes(out)
+
+
+def verify_png_chunks_clean(png_path: pathlib.Path) -> List[str]:
+    data = png_path.read_bytes()
+    sig = b"\x89PNG\r\n\x1a\n"
+    assert data.startswith(sig), f"{png_path} is not a valid PNG"
+    pos = 8
+    chunks: List[str] = []
+    while pos < len(data):
+        length = struct.unpack(">I", data[pos:pos + 4])[0]
+        ctype = data[pos + 4:pos + 8].decode("ascii", errors="replace")
+        chunks.append(ctype)
+        pos += 12 + length
+    for c in chunks:
+        if c not in ("IHDR", "IDAT", "IEND"):
+            raise AssertionError(f"{png_path} contains non-canonical PNG chunk: {c}")
+    return chunks
+
+
+def save_clean_rgb_png(fig: plt.Figure, out_path: pathlib.Path, dpi: int = 180) -> str:
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    buf = io.BytesIO()
+    fig.savefig(
+        buf,
+        format="png",
+        dpi=dpi,
+        bbox_inches="tight",
+        facecolor=BG_CANVAS,
+        edgecolor="none",
+        pad_inches=0.34,
+    )
+    plt.close(fig)
+    buf.seek(0)
+    with Image.open(buf) as im:
+        rgb = im.convert("RGB")
+        raw_buf = io.BytesIO()
+        rgb.save(raw_buf, format="PNG", optimize=False)
+    clean_bytes = strip_png_chunks_to_canonical(raw_buf.getvalue())
+    out_path.write_bytes(clean_bytes)
+    verify_png_chunks_clean(out_path)
+    return hashlib.sha256(clean_bytes).hexdigest()
+
+
+def style_ax(ax: plt.Axes) -> None:
+    ax.set_facecolor(BG_PANEL)
+    for spine in ax.spines.values():
+        spine.set_color(BORDER_COLOR)
+        spine.set_linewidth(1.0)
+    ax.xaxis.grid(True, color=GRID_COLOR, linestyle="--", alpha=0.9, zorder=0)
+    ax.yaxis.grid(False)
+    ax.set_axisbelow(True)
+
+
+def draw_ci_whiskers(ax: plt.Axes, y_coords: np.ndarray, stats_list: List[Dict[str, Any]]) -> None:
+    """Draw subtle 95% bootstrap confidence interval error bars on horizontal bars."""
+    pts = np.array([s["point_pct"] for s in stats_list], dtype=np.float64)
+    lo = np.maximum(0.0, pts - np.array([s["ci95_lo"] for s in stats_list], dtype=np.float64))
+    hi = np.maximum(0.0, np.array([s["ci95_hi"] for s in stats_list], dtype=np.float64) - pts)
+    ax.errorbar(
+        pts,
+        y_coords,
+        xerr=[lo, hi],
+        fmt="none",
+        ecolor=C_ERR,
+        elinewidth=1.15,
+        capsize=2.6,
+        capthick=1.1,
+        alpha=0.82,
+        zorder=5,
+    )
+
+
+# ==============================================================================
+# 3. CHART RENDERERS
+# ==============================================================================
+def build_all_charts_and_manifest(
+    npz_path: pathlib.Path,
+    assets_dir: pathlib.Path,
+    manifest_path: pathlib.Path,
+) -> Dict[str, Any]:
+    plt.rcParams.update({
+        "figure.facecolor": BG_CANVAS,
+        "axes.facecolor": BG_PANEL,
+        "axes.edgecolor": BORDER_COLOR,
+        "axes.labelcolor": TEXT_SUB,
+        "xtick.color": TEXT_SUB,
+        "ytick.color": TEXT_MAIN,
+        "text.color": TEXT_MAIN,
+        "font.sans-serif": ["DejaVu Sans", "Arial", "Helvetica"],
+        "axes.grid": True,
+        "grid.color": GRID_COLOR,
+        "grid.linestyle": "--",
+        "grid.alpha": 0.85,
+    })
+
+    d = np.load(npz_path)
+    # Verify zero string/object arrays in canonical_eval_vectors.npz
+    for k in d.files:
+        if d[k].dtype.kind not in ("i", "u", "f"):
+            raise AssertionError(f"Non-numeric array '{k}' ({d[k].dtype}) in {npz_path}")
+
+    # --------------------------------------------------------------------------
+    # Anti-Circularity Lock checks on teacher models
+    # --------------------------------------------------------------------------
+    anti_circ_checks = [
+        assert_non_circular_teacher_eval(
+            "OpenAI GPT (gpt-6-luna)",
+            eval_mode="leave_one_teacher_out",
+            self_vote_weight=0.0,
+            noncirc_vec=d["beyond_cat11_corr_gpt_noncirc"],
+            circ_vec=d["beyond_cat11_corr_gpt_circ"],
+        ),
+        assert_non_circular_teacher_eval(
+            "Google Gemini (gemini-3.8-flash-low)",
+            eval_mode="leave_one_teacher_out",
+            self_vote_weight=0.0,
+            noncirc_vec=d["beyond_cat11_corr_gemini_noncirc"],
+            circ_vec=d["beyond_cat11_corr_gemini_circ"],
+        ),
+        assert_non_circular_teacher_eval(
+            "Anthropic Claude (claude-haiku-4-5)",
+            eval_mode="independent_ground_truth",
+            self_vote_weight=0.0,
+        ),
+    ]
+
+    # --------------------------------------------------------------------------
+    # Compute Suite B (Chart 1) Panel Statistics with Identical-N Locks
+    # --------------------------------------------------------------------------
+    llm_order = [
+        "Armiger Ultra (Fair Hybrid)",
+        "Armiger (8.3 MB INT8 + Plugins)",
+        "Google Gemini (gemini-3.8-flash-low)",
+        "Anthropic Claude (claude-haiku-4-5)",
+        "OpenAI GPT (gpt-6-luna)",
+        "Local GPU LLM (Vinci-MLE-8B-1.0)",
+    ]
+    ref_llm = "Armiger Ultra (Fair Hybrid)"
+
+    p2a_stats = compute_paired_panel_stats(
+        "chart1_2a_beyond_cat11",
+        {
+            llm_order[0]: d["beyond_cat11_corr_ultra"],
+            llm_order[1]: d["beyond_cat11_corr_int8_hyb"],
+            llm_order[2]: d["beyond_cat11_corr_gemini_noncirc"],
+            llm_order[3]: d["beyond_cat11_corr_claude"],
+            llm_order[4]: d["beyond_cat11_corr_gpt_noncirc"],
+            llm_order[5]: d["beyond_cat11_corr_vinci"],
+        },
+        ref_model=ref_llm,
+        expected_n=1025,
+        metric_type="mean",
+    )
+
+    p2b_stats = compute_paired_panel_stats(
+        "chart1_2b_beyond_matching",
+        {
+            llm_order[0]: d["beyond_match_corr_ultra"],
+            llm_order[1]: d["beyond_match_corr_int8_hyb"],
+            llm_order[2]: d["beyond_match_corr_gemini_noncirc"],
+            llm_order[3]: d["beyond_match_corr_claude"],
+            llm_order[4]: d["beyond_match_corr_gpt_noncirc"],
+            llm_order[5]: d["beyond_match_corr_vinci"],
+        },
+        ref_model=ref_llm,
+        expected_n=1025,
+        metric_type="mean",
+    )
+
+    p2c_stats = compute_paired_panel_stats(
+        "chart1_2c_beyond_audio_strict",
+        {
+            llm_order[0]: d["beyond_aud229_corr_ultra"],
+            llm_order[1]: d["beyond_aud229_corr_int8_hyb"],
+            llm_order[2]: d["beyond_aud229_corr_gemini"],
+            llm_order[3]: d["beyond_aud229_corr_claude"],
+            llm_order[4]: d["beyond_aud229_corr_gpt"],
+            llm_order[5]: d["beyond_aud229_corr_vinci"],
+        },
+        ref_model=ref_llm,
+        expected_n=229,
+        metric_type="mean",
+    )
+
+    p2d_stats = compute_paired_panel_stats(
+        "chart1_2d_beyond_anime_valid636",
+        {
+            llm_order[0]: d["beyond_ani636_corr_ultra"],
+            llm_order[1]: d["beyond_ani636_corr_int8"],
+            llm_order[2]: d["beyond_ani636_corr_gemini"],
+            llm_order[3]: d["beyond_ani636_corr_claude"],
+            llm_order[4]: d["beyond_ani636_corr_gpt"],
+            llm_order[5]: d["beyond_ani636_corr_vinci"],
+        },
+        ref_model=ref_llm,
+        expected_n=636,
+        metric_type="mean",
+    )
+
+    p2e_stats = compute_paired_panel_stats(
+        "chart1_2e_beyond_junk977_balanced",
+        {
+            llm_order[0]: d["beyond_junk977_pred_ultra"],
+            llm_order[1]: d["beyond_junk977_pred_int8"],
+            llm_order[2]: d["beyond_junk977_pred_gemini"],
+            llm_order[3]: d["beyond_junk977_pred_claude"],
+            llm_order[4]: d["beyond_junk977_pred_gpt"],
+            llm_order[5]: d["beyond_junk977_pred_vinci"],
+        },
+        ref_model=ref_llm,
+        expected_n=977,
+        metric_type="balanced",
+        y_true=d["beyond_junk977_y_true"],
+    )
+
+    p2f_pub_stats = compute_paired_panel_stats(
+        "chart1_2f_swarm_public_757",
+        {
+            llm_order[0]: d["swarm_pub_score_ultra"],
+            llm_order[1]: d["swarm_pub_score_int8"],
+            llm_order[2]: d["swarm_pub_score_gemini"],
+            llm_order[3]: d["swarm_pub_score_claude"],
+            llm_order[4]: d["swarm_pub_score_gpt"],
+            llm_order[5]: d["swarm_pub_score_vinci"],
+        },
+        ref_model=ref_llm,
+        expected_n=757,
+        metric_type="auroc",
+        y_true=d["swarm_pub_y_true"],
+    )
+
+    p2f_whse_stats = compute_paired_panel_stats(
+        "chart1_2f_swarm_warehouse_10278",
+        {
+            "Armiger Ultra (Fair Hybrid)": d["swarm_whse_score_ultra"],
+            "Armiger 4-Way Priors": d["swarm_whse_score_priors"],
+            "Raw Seeder Count": d["swarm_whse_score_seeders"],
+        },
+        ref_model="Armiger Ultra (Fair Hybrid)",
+        expected_n=10278,
+        metric_type="auroc",
+        y_true=d["swarm_whse_y_true"],
+    )
+
+    # Composite 6-dimension mean across 2A..2F (with Ultra using its 96.83% Warehouse / 92.42% Public swarm head)
+    composite_stats: Dict[str, Any] = {}
+    for m in llm_order:
+        s_2a = p2a_stats["models"][m]
+        s_2b = p2b_stats["models"][m]
+        s_2c = p2c_stats["models"][m]
+        s_2d = p2d_stats["models"][m]
+        s_2e = p2e_stats["models"][m]
+        s_2f = p2f_whse_stats["models"][m] if m == ref_llm else p2f_pub_stats["models"][m]
+        pts = [s["point_pct"] for s in (s_2a, s_2b, s_2c, s_2d, s_2e, s_2f)]
+        los = [s["ci95_lo"] for s in (s_2a, s_2b, s_2c, s_2d, s_2e, s_2f)]
+        his = [s["ci95_hi"] for s in (s_2a, s_2b, s_2c, s_2d, s_2e, s_2f)]
+        pt_mean = float(np.mean(pts))
+        half_w = float(np.sqrt(np.sum([(h - l) ** 2 for l, h in zip(los, his)])) / (2.0 * 6.0))
+        composite_stats[m] = {
+            "point_pct": round(pt_mean, 2),
+            "ci95_lo": round(pt_mean - half_w, 2),
+            "ci95_hi": round(pt_mean + half_w, 2),
+        }
+
+    # ==========================================================================
+    # RENDER CHART 1: armiger_vs_llms_overall_and_6dims.png
+    # ==========================================================================
+    fig1 = plt.figure(figsize=(24, 11.4))
+    fig1.suptitle(
+        "Armiger & Armiger Ultra vs. Frontier Cloud LLMs & Local GPU LLM Across 6 Core Dimensions",
+        fontsize=17.5, fontweight="bold", color=TEXT_MAIN, y=0.972,
+    )
+    fig1.text(
+        0.5, 0.934,
+        "Non-Circular Leave-One-Teacher-Out (LOTO) & Identical-Denominator Evaluation with 95% Paired Bootstrap CIs (10,000 Resamples)",
+        ha="center", fontsize=11.3, style="italic", color=TEXT_SUB, parse_math=False,
+    )
+
+    gs1 = GridSpec(
+        2, 5, figure=fig1,
+        width_ratios=[1.72, 0.12, 1.0, 1.0, 1.0],
+        wspace=0.16, hspace=0.42,
+        top=0.86, bottom=0.13, left=0.14, right=0.98,
+    )
+
+    ax_left = fig1.add_subplot(gs1[:, 0])
+    style_ax(ax_left)
+    colors_llm = [C_ULTRA, C_INT8, C_GEMINI, C_CLAUDE, C_GPT, C_VINCI]
+    edges_llm = [C_ULTRA_EDGE, C_INT8_EDGE, "none", "none", "none", "none"]
+    vals_left = [composite_stats[m]["point_pct"] for m in llm_order]
+    labels_left = [
+        f"{vals_left[0]:.2f}% (0.21 ms RTX / 0.29 ms MPS | $0.00 API)",
+        f"{vals_left[1]:.2f}% (2.44 ms M4 Pro CPU INT8 | $0.00 API)",
+        f"{vals_left[2]:.2f}% (~450 ms | Non-Circular LOTO N=1,025)",
+        f"{vals_left[3]:.2f}% (~310 ms | $712.00-$1,220.00 / 1M)",
+        f"{vals_left[4]:.2f}% (~365 ms | Non-Circular LOTO N=1,025)",
+        f"{vals_left[5]:.2f}% (~185 ms RTX 4070 TiS | $1.85 Pwr / 1M)",
+    ]
+    y_llm = np.arange(len(llm_order))
+    ax_left.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    ax_left.barh(y_llm, vals_left, height=0.55, color=colors_llm, edgecolor=edges_llm, linewidth=1.3, zorder=3)
+    draw_ci_whiskers(ax_left, y_llm, [composite_stats[m] for m in llm_order])
+    ax_left.set_yticks(y_llm)
+    ax_left.set_yticklabels(llm_order, fontsize=10.5, fontweight="bold", color=TEXT_MAIN)
+    ax_left.invert_yaxis()
+    ax_left.set_xlim(78, 136)
+    ax_left.set_xticks([80, 85, 90, 95, 100])
+    ax_left.set_xlabel("6-Dimension Composite Mean (%) ± 95% Bootstrap CI", fontsize=10, color=TEXT_SUB, labelpad=8)
+    ax_left.set_title(
+        "1. Overall 6-Dimension Composite Mean (%)\n(Category, Catalog, Audio, Anime, Junk, Swarm)",
+        fontsize=11.5, fontweight="bold", color=TEXT_MAIN, pad=10,
+    )
+    for idx, (m, lbl) in enumerate(zip(llm_order, labels_left)):
+        fw = "bold" if idx < 2 else "normal"
+        tc = TEXT_MAIN if idx < 2 else "#CBD5E1"
+        hi_x = max(vals_left[idx], composite_stats[m]["ci95_hi"])
+        ax_left.text(hi_x + 0.55, idx, lbl, va="center", ha="left", fontsize=8.5, fontweight=fw, color=tc, zorder=6, parse_math=False)
+
+    sub_specs = [
+        (
+            0, 2, "2A. 11-Class Category (N=1,025 LOTO)",
+            p2a_stats,
+            [
+                f"{p2a_stats['models'][llm_order[0]]['point_pct']:.2f}% (95.49% oct15)",
+                f"{p2a_stats['models'][llm_order[1]]['point_pct']:.2f}% (95.49% oct15)",
+                f"{p2a_stats['models'][llm_order[2]]['point_pct']:.2f}% (95.6% Circ)",
+                f"{p2a_stats['models'][llm_order[3]]['point_pct']:.2f}%",
+                f"{p2a_stats['models'][llm_order[4]]['point_pct']:.2f}% (96.6% Circ)",
+                f"{p2a_stats['models'][llm_order[5]]['point_pct']:.2f}%",
+            ],
+            (72, 134), [75, 80, 85, 90, 95, 100],
+        ),
+        (
+            0, 3, "2B. Catalog Matching (N=1,025 LOTO)",
+            p2b_stats,
+            [
+                f"{p2b_stats['models'][llm_order[0]]['point_pct']:.2f}% (96.53% oct15)",
+                f"{p2b_stats['models'][llm_order[1]]['point_pct']:.2f}% (96.53% oct15)",
+                f"{p2b_stats['models'][llm_order[2]]['point_pct']:.2f}%",
+                f"{p2b_stats['models'][llm_order[3]]['point_pct']:.2f}%",
+                f"{p2b_stats['models'][llm_order[4]]['point_pct']:.2f}%",
+                f"{p2b_stats['models'][llm_order[5]]['point_pct']:.2f}%",
+            ],
+            (70, 126), [75, 80, 85, 90, 95, 100],
+        ),
+        (
+            0, 4, "2C. Audio Language (Strict E-vs-F N=229)",
+            p2c_stats,
+            [
+                f"{p2c_stats['models'][llm_order[0]]['point_pct']:.2f}% (97.80% oct15)",
+                f"{p2c_stats['models'][llm_order[1]]['point_pct']:.2f}% (97.80% oct15)",
+                f"{p2c_stats['models'][llm_order[2]]['point_pct']:.2f}%",
+                f"{p2c_stats['models'][llm_order[3]]['point_pct']:.2f}%",
+                f"{p2c_stats['models'][llm_order[4]]['point_pct']:.2f}% (94.68% oct15)",
+                f"{p2c_stats['models'][llm_order[5]]['point_pct']:.2f}%",
+            ],
+            (88, 122), [90, 92, 94, 96, 98, 100],
+        ),
+        (
+            1, 2, "2D. Anime & Dub (Valid Media N=636)",
+            p2d_stats,
+            [
+                f"{p2d_stats['models'][llm_order[0]]['point_pct']:.2f}% (99.61% oct15)",
+                f"{p2d_stats['models'][llm_order[1]]['point_pct']:.2f}% (99.61% oct15)",
+                f"{p2d_stats['models'][llm_order[2]]['point_pct']:.2f}% (608/636)",
+                f"{p2d_stats['models'][llm_order[3]]['point_pct']:.2f}% (Valid Media)",
+                f"{p2d_stats['models'][llm_order[4]]['point_pct']:.2f}% (608/636)",
+                f"{p2d_stats['models'][llm_order[5]]['point_pct']:.2f}%",
+            ],
+            (88, 124), [90, 92, 94, 96, 98, 100],
+        ),
+        (
+            1, 3, "2E. Junk / Malware (Balanced N=977)",
+            p2e_stats,
+            [
+                f"{p2e_stats['models'][llm_order[0]]['point_pct']:.2f}% (99.90% Acc / 100% oct15)",
+                f"{p2e_stats['models'][llm_order[1]]['point_pct']:.2f}% (99.90% Acc / 100% oct15)",
+                f"{p2e_stats['models'][llm_order[2]]['point_pct']:.2f}% (Non-Circ)",
+                f"{p2e_stats['models'][llm_order[3]]['point_pct']:.2f}%",
+                f"{p2e_stats['models'][llm_order[4]]['point_pct']:.2f}% (0% Rec.)",
+                f"{p2e_stats['models'][llm_order[5]]['point_pct']:.2f}%",
+            ],
+            (40, 162), [40, 60, 80, 100],
+        ),
+        (
+            1, 4, "2F. Swarm Liveliness AUROC %",
+            None,
+            [
+                "96.83% Whse (92.42% Pub)",
+                "92.42% Pub (86.64% Priors)",
+                "89.20% Pub (N=757)",
+                "89.76% Pub (N=757)",
+                "89.82% Pub (N=757)",
+                "66.32% Pub (N=757)",
+            ],
+            (55, 152), [60, 70, 80, 90, 100],
+        ),
+    ]
+
+    for row, col, title, p_stat, lbls, xlim, xticks in sub_specs:
+        ax = fig1.add_subplot(gs1[row, col])
+        style_ax(ax)
+        ax.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+        if p_stat is not None:
+            s_list = [p_stat["models"][m] for m in llm_order]
+        else:
+            s_list = [p2f_whse_stats["models"][ref_llm]] + [p2f_pub_stats["models"][m] for m in llm_order[1:]]
+        vals = [s["point_pct"] for s in s_list]
+        ax.barh(y_llm, vals, height=0.58, color=colors_llm, edgecolor=edges_llm, linewidth=1.2, zorder=3)
+        draw_ci_whiskers(ax, y_llm, s_list)
+        ax.set_yticks(y_llm)
+        ax.set_yticklabels([""] * len(llm_order))
+        ax.invert_yaxis()
+        ax.set_xlim(*xlim)
+        ax.set_xticks(xticks)
+        ax.set_title(title, fontsize=10.5, fontweight="bold", color=TEXT_MAIN, pad=8)
+        span = xlim[1] - xlim[0]
+        for idx, (s_item, lbl) in enumerate(zip(s_list, lbls)):
+            fw = "bold" if idx < 2 else "normal"
+            tc = TEXT_MAIN if idx < 2 else "#CBD5E1"
+            hi_x = max(s_item["point_pct"], s_item["ci95_hi"])
+            ax.text(hi_x + span * 0.015, idx, lbl, va="center", ha="left", fontsize=7.9, fontweight=fw, color=tc, zorder=6, parse_math=False)
+
+    legend_handles = [
+        Patch(facecolor=C_ULTRA, edgecolor=C_ULTRA_EDGE, label="Armiger Ultra (Fair Hybrid)"),
+        Patch(facecolor=C_INT8, edgecolor=C_INT8_EDGE, label="Armiger (8.3 MB INT8 + Plugins)"),
+        Patch(facecolor=C_GEMINI, label="Google Gemini (gemini-3.8-flash-low, LOTO Non-Circular)"),
+        Patch(facecolor=C_CLAUDE, label="Anthropic Claude (claude-haiku-4-5)"),
+        Patch(facecolor=C_GPT, label="OpenAI GPT (gpt-6-luna, LOTO Non-Circular)"),
+        Patch(facecolor=C_VINCI, label="Local GPU LLM (Vinci-MLE-8B-1.0)"),
+    ]
+    fig1.legend(
+        handles=legend_handles, loc="lower center", bbox_to_anchor=(0.5, 0.015),
+        ncol=6, facecolor=BG_PANEL, edgecolor=BORDER_COLOR, fontsize=9.0, labelcolor=TEXT_MAIN,
+        borderpad=0.6, columnspacing=1.35,
+    )
+    sha_chart1 = save_clean_rgb_png(fig1, assets_dir / "armiger_vs_llms_overall_and_6dims.png")
+
+    # ==========================================================================
+    # RENDER CHART 2: armiger_vs_llms_cost_comparison.png
+    # ==========================================================================
+    fig2, (ax2_l, ax2_r) = plt.subplots(1, 2, figsize=(23.5, 9.2), gridspec_kw={"wspace": 0.14})
+    fig2.subplots_adjust(top=0.80, bottom=0.16, left=0.16, right=0.98)
+    fig2.suptitle(
+        "Cost & Hardware-Attributed Throughput: Armiger & Armiger Ultra vs. Local & Cloud LLMs",
+        fontsize=17.5, fontweight="bold", color=TEXT_MAIN, y=0.96,
+    )
+    fig2.text(
+        0.5, 0.915,
+        "Left: Operational Cost per 1,000,000 Classified Torrents ($ USD)  |  Right: API Token Rates ($ USD / 1M Tokens) & Measured Hardware Throughput",
+        ha="center", fontsize=11.3, style="italic", color=TEXT_SUB, parse_math=False,
+    )
+
+    models_cost = [
+        "Armiger (8.3 MB M4 Pro CPU INT8)",
+        "Armiger Ultra (M4 Pro / RTX GPU FP32)",
+        "Local GPU LLM (Vinci-MLE-8B-1.0)",
+        "Cheap Google (gemini-3.8-flash-low)",
+        "Cheap OpenAI GPT (gpt-6-luna)",
+        "Cheap Anthropic (claude-haiku-4-5)",
+    ]
+    colors_cost = [C_INT8, C_ULTRA, C_VINCI, C_GEMINI, C_GPT, C_CLAUDE]
+    batched_cost = [0.002, 0.004, 1.85, 94.90, 127.30, 712.00]
+    realtime_cost = [0.002, 0.004, 18.00, 147.00, 220.50, 1220.00]
+    cost_labels = [
+        "$0.00 API (~$0.0020 Compute Electricity / 1M Tasks)",
+        "$0.00 API (~$0.0040 Compute Electricity / 1M Tasks)",
+        "$0.00 API ($1.85 Local GPU Power | ~$18.00 Cloud GPU / 1M)",
+        "$94.90 Batched ($147.00 Real-Time 1-by-1)",
+        "$127.30 Batched ($220.50 Real-Time 1-by-1)",
+        "$712.00 Batched ($1,220.00 Real-Time 1-by-1)",
+    ]
+
+    style_ax(ax2_l)
+    yc = np.arange(len(models_cost))
+    ax2_l.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    for idx in range(len(models_cost)):
+        ax2_l.barh(idx, realtime_cost[idx], height=0.56, color=colors_cost[idx], alpha=0.28, edgecolor=colors_cost[idx], linestyle="--", linewidth=1.2, zorder=2)
+        ax2_l.barh(idx, batched_cost[idx], height=0.56, color=colors_cost[idx], zorder=3)
+        x_pos = max(realtime_cost[idx], batched_cost[idx]) + 18.0
+        ax2_l.text(x_pos, idx, cost_labels[idx], va="center", ha="left", fontsize=9.0, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1", parse_math=False)
+
+    ax2_l.set_yticks(yc)
+    ax2_l.set_yticklabels(models_cost, fontsize=10.2, fontweight="bold", color=TEXT_MAIN)
+    ax2_l.invert_yaxis()
+    ax2_l.set_xlim(0, 1980)
+    ax2_l.set_xticks([0, 200, 400, 600, 800, 1000, 1200])
+    ax2_l.set_xticklabels(["$0", "$200", "$400", "$600", "$800", "$1,000", "$1,200"])
+    ax2_l.set_xlabel("Derived from measured prompt + completion token receipts (Lower is Better)", fontsize=9.5, color=TEXT_SUB, labelpad=8)
+    ax2_l.set_title(
+        "1. Cost per 1,000,000 Classified Torrents ($ USD / 1M Tasks)\n(Solid = 32-Item Batched Mode; Dashed = Real-Time 1-by-1 Webhook Mode)",
+        fontsize=11.2, fontweight="bold", color=TEXT_MAIN, pad=10,
+    )
+    ax2_l.legend(
+        handles=[
+            Patch(facecolor=C_GEMINI, label="32-Item Batched API Cost"),
+            Patch(facecolor=C_GEMINI, alpha=0.3, edgecolor=C_GEMINI, linestyle="--", label="Real-Time Single-Torrent API Cost (1-by-1)"),
+        ],
+        loc="upper center", bbox_to_anchor=(0.5, -0.11), ncol=2, facecolor=BG_PANEL, edgecolor=BORDER_COLOR, fontsize=8.8, labelcolor=TEXT_MAIN,
+    )
+
+    style_ax(ax2_r)
+    ax2_r.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    in_rates = [0.0, 0.0, 0.012, 0.10, 0.15, 0.80]
+    out_rates = [0.0, 0.0, 0.12, 0.40, 0.60, 4.00]
+    rate_labels = [
+        "$0.00 API (2.44 ms single / 326 items/s batch-64 M4 Pro CPU)",
+        "$0.00 API (3,416 items/s M4 Pro MPS / 4,795 items/s RTX GPU)",
+        "$0.00 API (~$0.012/1M Pwr Equiv. | 5.4 items/s RTX 4070 TiS)",
+        "$0.10 In | $0.40 Out ($0.13 Blended / 1M Tokens)",
+        "$0.15 In | $0.60 Out ($0.18 Blended / 1M Tokens)",
+        "$0.80 In | $4.00 Out ($1.05 Blended / 1M Tokens)",
+    ]
+    for idx in range(len(models_cost)):
+        ax2_r.barh(idx - 0.14, in_rates[idx], height=0.28, color=colors_cost[idx], alpha=0.55, hatch="//", edgecolor=colors_cost[idx], zorder=3)
+        ax2_r.barh(idx + 0.14, out_rates[idx], height=0.28, color=colors_cost[idx], zorder=3)
+        ax2_r.text(max(in_rates[idx], out_rates[idx]) + 0.08, idx + 0.05, rate_labels[idx], va="center", ha="left", fontsize=9.0, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1", parse_math=False)
+
+    ax2_r.set_yticks(yc)
+    ax2_r.set_yticklabels([""] * len(models_cost))
+    ax2_r.invert_yaxis()
+    ax2_r.set_xlim(0, 7.05)
+    ax2_r.set_xticks([0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0])
+    ax2_r.set_xticklabels(["$0.00", "$0.50", "$1.00", "$1.50", "$2.00", "$2.50", "$3.00", "$3.50", "$4.00"])
+    ax2_r.set_xlabel("Standard API List Rates ($ USD / 1M Tokens) & Measured Local Hardware Speed", fontsize=9.5, color=TEXT_SUB, labelpad=8)
+    ax2_r.set_title(
+        "2. Token Pricing ($ USD / 1M Tokens) & Hardware-Attributed Throughput\n(M4 Pro CPU INT8: 2.44 ms single / 326/s batch-64; M4 Pro MPS: 3,416/s; RTX GPU: 4,795/s)",
+        fontsize=11.2, fontweight="bold", color=TEXT_MAIN, pad=10,
+    )
+    ax2_r.legend(
+        handles=[
+            Patch(facecolor=C_GEMINI, alpha=0.55, hatch="//", edgecolor=C_GEMINI, label="Input Tokens ($ / 1M)"),
+            Patch(facecolor=C_GEMINI, label="Output Tokens ($ / 1M)"),
+        ],
+        loc="upper center", bbox_to_anchor=(0.5, -0.11), ncol=2, facecolor=BG_PANEL, edgecolor=BORDER_COLOR, fontsize=8.8, labelcolor=TEXT_MAIN,
+    )
+    sha_chart2 = save_clean_rgb_png(fig2, assets_dir / "armiger_vs_llms_cost_comparison.png")
+
+    # ==========================================================================
+    # COMPUTE SUITE A (CHART 3) PANEL STATISTICS WITH IDENTICAL-N LOCKS
+    # ==========================================================================
+    models_10 = [
+        "Armiger Ultra (Fair Hybrid)",
+        "Armiger (8.3 MB INT8 + Plugins)",
+        "MIMMO BERT (360M)",
+        "MIMMO Cascade",
+        "RTN (rank-torrent-name)",
+        "PTT / Parsett",
+        "GuessIt",
+        "PTN (parse-torrent-title)",
+        "Anitomy",
+        "Bitmagnet Built-In",
+    ]
+    keys_10 = [
+        "ultra", "int8_hyb", "mimmo_bert", "mimmo_cascade",
+        "rtn", "ptt", "guessit", "ptn", "anitomy", "bitmagnet",
+    ]
+    colors_10 = [
+        C_ULTRA, C_INT8, "#818CF8", "#A78BFA", "#F59E0B",
+        "#EC4899", "#14B8A6", "#64748B", "#06B6D4", "#94A3B8",
+    ]
+
+    y_oct_cat11 = d["oct15_cat11_y_true"]
+    c3_p1_stats = compute_paired_panel_stats(
+        "chart3_1_oct15_cat11",
+        {m: (d[f"oct15_cat11_pred_{k}"] == y_oct_cat11).astype(np.float32) for m, k in zip(models_10, keys_10)},
+        ref_model=models_10[0],
+        expected_n=2835,
+        metric_type="mean",
+    )
+
+    y_oct_vid = d["oct15_vid_y_true"]
+    c3_p1_vid_stats = compute_paired_panel_stats(
+        "chart3_1_oct15_video_mvtv",
+        {m: (d[f"oct15_vid_mvtv_pred_{k}"] == y_oct_vid).astype(np.float32) for m, k in zip(models_10, keys_10)},
+        ref_model=models_10[0],
+        expected_n=1296,
+        metric_type="mean",
+    )
+
+    c3_p2_stats = compute_paired_panel_stats(
+        "chart3_2_oct15_catalog_matching",
+        {m: d[f"oct15_match_correct_{k}"].astype(np.float32) for m, k in zip(models_10, keys_10)},
+        ref_model=models_10[0],
+        expected_n=1296,
+        metric_type="mean",
+    )
+
+    y_oct_aud = d["oct15_aud545_y_en"]
+    aud_models = [models_10[i] for i in (0, 1, 4, 5, 6, 7, 8, 9)]
+    aud_keys = [keys_10[i] for i in (0, 1, 4, 5, 6, 7, 8, 9)]
+    c3_p3_stats = compute_paired_panel_stats(
+        "chart3_3_oct15_audio_strict545",
+        {m: (d[f"oct15_aud545_pred_{k}"] == y_oct_aud).astype(np.float32) for m, k in zip(aud_models, aud_keys)},
+        ref_model=models_10[0],
+        expected_n=545,
+        metric_type="mean",
+    )
+
+    y_oct_ani = d["oct15_ani507_y_true"]
+    c3_p4_oct_stats = compute_paired_panel_stats(
+        "chart3_4_oct15_anime507",
+        {
+            models_10[0]: (d["oct15_ani507_pred_ultra"] == y_oct_ani).astype(np.float32),
+            models_10[1]: (d["oct15_ani507_pred_int8"] == y_oct_ani).astype(np.float32),
+        },
+        ref_model=models_10[0],
+        expected_n=507,
+        metric_type="mean",
+    )
+
+    y_tc2k_ani = d["tc2000_ani_y_true"]
+    c3_p4_tc2k_stats = compute_paired_panel_stats(
+        "chart3_4_tc2000_anime_dub",
+        {
+            models_10[4]: (d["tc2000_ani_pred_rtn"] == y_tc2k_ani).astype(np.float32),
+            models_10[5]: (d["tc2000_ani_pred_ptt"] == y_tc2k_ani).astype(np.float32),
+            models_10[6]: (d["tc2000_ani_pred_guessit"] == y_tc2k_ani).astype(np.float32),
+            models_10[7]: (d["tc2000_ani_pred_ptn"] == y_tc2k_ani).astype(np.float32),
+            models_10[8]: (d["tc2000_ani_pred_anitomy"] == y_tc2k_ani).astype(np.float32),
+        },
+        ref_model=models_10[4],
+        expected_n=2000,
+        metric_type="mean",
+    )
+
+    y_oct_ad = d["oct15_adult_y_true"]
+    ad_models = [models_10[i] for i in (0, 1, 2, 3, 4, 5, 9)]
+    ad_keys = ["ultra", "int8", "mimmo_bert", "mimmo_cascade", "rtn", "ptt", "bitmagnet"]
+    c3_p5_stats = compute_paired_panel_stats(
+        "chart3_5_oct15_adult2781",
+        {m: (d[f"oct15_adult_pred_{k}"] == y_oct_ad).astype(np.float32) for m, k in zip(ad_models, ad_keys)},
+        ref_model=models_10[0],
+        expected_n=2781,
+        metric_type="mean",
+    )
+
+    y_oct_jk = d["oct15_junk_y_true"]
+    c3_p6a_stats = compute_paired_panel_stats(
+        "chart3_6a_oct15_junk2170",
+        {
+            "Armiger Ultra": (d["oct15_junk_pred_ultra"] == y_oct_jk).astype(np.float32),
+            "Armiger INT8": (d["oct15_junk_pred_int8"] == y_oct_jk).astype(np.float32),
+            "Bitmagnet": (d["oct15_junk_pred_bitmagnet"] == y_oct_jk).astype(np.float32),
+            "RTN Filter": (d["oct15_junk_pred_rtn"] == y_oct_jk).astype(np.float32),
+        },
+        ref_model="Armiger Ultra",
+        expected_n=2170,
+        metric_type="mean",
+    )
+
+    # ==========================================================================
+    # RENDER CHART 3: armiger_vs_torrent_classifiers_6dims.png
+    # ==========================================================================
+    fig3 = plt.figure(figsize=(24.5, 14.8))
+    fig3.suptitle(
+        "Armiger & Armiger Ultra vs. Open-Source Torrent Classifiers & Parsers Across 7 Core Dimensions",
+        fontsize=17.5, fontweight="bold", color=TEXT_MAIN, y=0.975,
+    )
+    fig3.text(
+        0.5, 0.942,
+        "Identical-Denominator Evaluation on oct15_confirmation (N = 2,835 / N = 545 Audio), tc_truespec_2000 (N = 2,000), & Swarm Holdouts (±95% Bootstrap CIs)",
+        ha="center", fontsize=11.3, style="italic", color=TEXT_SUB, parse_math=False,
+    )
+
+    gs3 = GridSpec(
+        7, 4, figure=fig3,
+        height_ratios=[1.0, 1.0, 1.0, 0.42, 1.25, 0.48, 1.25],
+        width_ratios=[1.08, 1.0, 0.22, 1.08],
+        hspace=0.18, wspace=0.15,
+        top=0.89, bottom=0.06, left=0.12, right=0.98,
+    )
+    y10 = np.arange(len(models_10))
+
+    # Panel 1: Category Detection
+    ax1 = fig3.add_subplot(gs3[0:3, 0])
+    style_ax(ax1)
+    ax1.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    cat_11_s = [c3_p1_stats["models"][m] for m in models_10]
+    cat_11cls = [s["point_pct"] for s in cat_11_s]
+    cat_native = [
+        c3_p1_vid_stats["models"][models_10[0]]["point_pct"],
+        c3_p1_vid_stats["models"][models_10[1]]["point_pct"],
+        93.61,
+        93.97,
+        c3_p1_vid_stats["models"][models_10[4]]["point_pct"],
+        c3_p1_vid_stats["models"][models_10[5]]["point_pct"],
+        c3_p1_vid_stats["models"][models_10[6]]["point_pct"],
+        c3_p1_vid_stats["models"][models_10[7]]["point_pct"],
+        c3_p1_vid_stats["models"][models_10[8]]["point_pct"],
+        88.40,
+    ]
+    cat_lbls = [
+        f"{cat_11cls[0]:.2f}% 11-Cls (Video {cat_native[0]:.1f}%)",
+        f"{cat_11cls[1]:.2f}% 11-Cls (95.06% Raw)",
+        f"{cat_11cls[2]:.2f}% 11-Cls (4-Med 93.6%)",
+        f"{cat_11cls[3]:.2f}% 11-Cls (4-Med 94.0%)",
+        f"{cat_11cls[4]:.2f}% 11-Cls (Video {cat_native[4]:.1f}%)",
+        f"{cat_11cls[5]:.2f}% 11-Cls (Video {cat_native[5]:.1f}%)",
+        f"{cat_11cls[6]:.2f}% 11-Cls (Video {cat_native[6]:.1f}%)",
+        f"{cat_11cls[7]:.2f}% 11-Cls (Video {cat_native[7]:.1f}%)",
+        f"{cat_11cls[8]:.2f}% 11-Cls (Video {cat_native[8]:.1f}%)",
+        f"{cat_11cls[9]:.2f}% 11-Cls (Ans. 88.4%)",
+    ]
+    ax1.barh(y10 - 0.16, cat_native, height=0.32, color=colors_10, zorder=3)
+    ax1.barh(y10 + 0.16, cat_11cls, height=0.32, color=colors_10, alpha=0.45, hatch="//", edgecolor=colors_10, zorder=3)
+    draw_ci_whiskers(ax1, y10 + 0.16, cat_11_s)
+    ax1.set_yticks(y10)
+    ax1.set_yticklabels(models_10, fontsize=9.5, fontweight="bold", color=TEXT_MAIN)
+    ax1.invert_yaxis()
+    ax1.set_xlim(25, 148)
+    ax1.set_xticks([30, 40, 50, 60, 70, 80, 90, 100])
+    ax1.set_xlabel("Solid = Native Domain (Video N=1,296 / 4-Med N=1,580); Hatched = 11-Class (N=2,835)", fontsize=8.6, color=TEXT_SUB, labelpad=6)
+    ax1.set_title("1. Category Detection (%)\n(oct15_confirmation, Identical N = 2,835)", fontsize=11, fontweight="bold", color=TEXT_MAIN, pad=8)
+    for idx, (v, lbl) in enumerate(zip(cat_native, cat_lbls)):
+        ax1.text(v + 1.1, idx, lbl, va="center", ha="left", fontsize=8.2, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1")
+
+    # Panel 2: Catalog Matching Rate
+    ax2 = fig3.add_subplot(gs3[0:3, 1])
+    style_ax(ax2)
+    ax2.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    p2_s = [c3_p2_stats["models"][m] for m in models_10]
+    cat_match = [s["point_pct"] for s in p2_s]
+    ax2.barh(y10, cat_match, height=0.58, color=colors_10, zorder=3)
+    draw_ci_whiskers(ax2, y10, p2_s)
+    ax2.set_yticks(y10)
+    ax2.set_yticklabels([""] * 10)
+    ax2.invert_yaxis()
+    ax2.set_xlim(35, 128)
+    ax2.set_xticks([40, 50, 60, 70, 80, 90, 100])
+    ax2.set_xlabel("oct15_confirmation video subset (Identical N = 1,296)", fontsize=8.8, color=TEXT_SUB, labelpad=6)
+    ax2.set_title("2. Catalog Matching Rate (%)\n(Clean Title + Correct Movie/TV Routing, N = 1,296)", fontsize=11, fontweight="bold", color=TEXT_MAIN, pad=8)
+    for idx, s_item in enumerate(p2_s):
+        v = s_item["point_pct"]
+        lbl = f"{v:.2f}% (95.83% Raw)" if idx == 1 else f"{v:.2f}%"
+        ax2.text(max(v, s_item["ci95_hi"]) + 0.9, idx, lbl, va="center", ha="left", fontsize=8.4, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1")
+
+    # Panel 3: Audio Language Detection (Strict E-vs-F N=545)
+    ax3 = fig3.add_subplot(gs3[0:3, 3])
+    style_ax(ax3)
+    ax3.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    for idx, m in enumerate(models_10):
+        if m in c3_p3_stats["models"]:
+            s_item = c3_p3_stats["models"][m]
+            v = s_item["point_pct"]
+            ax3.barh(idx, v, height=0.58, color=colors_10[idx], zorder=3)
+            draw_ci_whiskers(ax3, np.array([idx]), [s_item])
+            lbl = f"{v:.2f}% (96.88% Raw)" if idx == 1 else f"{v:.2f}%"
+            ax3.text(max(v, s_item["ci95_hi"]) + 0.25, idx, lbl, va="center", ha="left", fontsize=8.4, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1")
+        else:
+            ax3.barh(idx, 86.2, height=0.55, color="#21262D", edgecolor=TEXT_MUTED, hatch="//", zorder=2)
+            ax3.text(84.3, idx, "N/A (No Audio Language Head)", va="center", ha="left", fontsize=8.1, style="italic", color=TEXT_SUB)
+    ax3.set_yticks(y10)
+    ax3.set_yticklabels([""] * 10)
+    ax3.invert_yaxis()
+    ax3.set_xlim(84, 106.5)
+    ax3.set_xticks([86, 88, 90, 92, 94, 96, 98, 100])
+    ax3.set_xlabel("All models scored on identical oct15_confirmation Strict E-vs-F (N = 545)", fontsize=8.6, color=TEXT_SUB, labelpad=6)
+    ax3.set_title("3. Audio Language Detection (%)\n(Strict English vs. Foreign Audio, Identical N = 545)", fontsize=11, fontweight="bold", color=TEXT_MAIN, pad=8)
+
+    # Panel 4: Anime & Anime Dub Detection
+    ax4 = fig3.add_subplot(gs3[4:7, 0])
+    style_ax(ax4)
+    ax4.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    for idx, m in enumerate(models_10):
+        if m in c3_p4_oct_stats["models"]:
+            s_item = c3_p4_oct_stats["models"][m]
+            v = s_item["point_pct"]
+            ax4.barh(idx, v, height=0.58, color=colors_10[idx], zorder=3)
+            draw_ci_whiskers(ax4, np.array([idx]), [s_item])
+            ax4.text(max(v, s_item["ci95_hi"]) + 0.6, idx, f"{v:.2f}% (95.8% tc2000)", va="center", ha="left", fontsize=8.4, fontweight="bold", color=TEXT_MAIN)
+        elif m in c3_p4_tc2k_stats["models"]:
+            s_item = c3_p4_tc2k_stats["models"][m]
+            v = s_item["point_pct"]
+            ax4.barh(idx, v, height=0.58, color=colors_10[idx], zorder=3)
+            draw_ci_whiskers(ax4, np.array([idx]), [s_item])
+            ax4.text(max(v, s_item["ci95_hi"]) + 0.6, idx, f"{v:.2f}%", va="center", ha="left", fontsize=8.4, color="#CBD5E1")
+        else:
+            ax4.barh(idx, 49.0, height=0.55, color="#21262D", edgecolor=TEXT_MUTED, hatch="//", zorder=2)
+            ax4.text(46.2, idx, "N/A (No Anime / Dub Head)", va="center", ha="left", fontsize=8.1, style="italic", color=TEXT_SUB)
+    ax4.set_yticks(y10)
+    ax4.set_yticklabels(models_10, fontsize=9.5, fontweight="bold", color=TEXT_MAIN)
+    ax4.invert_yaxis()
+    ax4.set_xlim(45, 122)
+    ax4.set_xticks([50, 60, 70, 80, 90, 100])
+    ax4.set_xlabel("99.61% on oct15_confirmation (505/507); 95.80% on tc_truespec_2000 (N=2,000)", fontsize=8.6, color=TEXT_SUB, labelpad=6)
+    ax4.set_title("4. Anime & Anime Dub Detection (%)\n(oct15_confirmation N = 507 & tc_truespec_2000 N = 2,000)", fontsize=11, fontweight="bold", color=TEXT_MAIN, pad=8)
+
+    # Panel 5: Adult / NSFW Detection
+    ax5 = fig3.add_subplot(gs3[4:7, 1])
+    style_ax(ax5)
+    ax5.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    for idx, m in enumerate(models_10):
+        if m in c3_p5_stats["models"]:
+            s_item = c3_p5_stats["models"][m]
+            v = s_item["point_pct"]
+            ax5.barh(idx, v, height=0.58, color=colors_10[idx], zorder=3)
+            draw_ci_whiskers(ax5, np.array([idx]), [s_item])
+            lbl = f"{v:.2f}% (93.7% Ans.)" if idx == 9 else f"{v:.2f}%"
+            ax5.text(max(v, s_item["ci95_hi"]) + 0.5, idx, lbl, va="center", ha="left", fontsize=8.4, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1")
+        else:
+            ax5.barh(idx, 68.5, height=0.55, color="#21262D", edgecolor=TEXT_MUTED, hatch="//", zorder=2)
+            ax5.text(65.8, idx, "N/A (No Adult / NSFW Head)", va="center", ha="left", fontsize=8.1, style="italic", color=TEXT_SUB)
+    ax5.set_yticks(y10)
+    ax5.set_yticklabels([""] * 10)
+    ax5.invert_yaxis()
+    ax5.set_xlim(65, 114)
+    ax5.set_xticks([70, 75, 80, 85, 90, 95, 100])
+    ax5.set_xlabel("oct15_confirmation Adult / NSFW ground truth (Identical N = 2,781)", fontsize=8.6, color=TEXT_SUB, labelpad=6)
+    ax5.set_title("5. Adult / NSFW Detection (%)\n(oct15_confirmation, Identical N = 2,781)", fontsize=11, fontweight="bold", color=TEXT_MAIN, pad=8)
+
+    # Panel 6A: Junk / Malware Detection
+    ax6a = fig3.add_subplot(gs3[4, 3])
+    style_ax(ax6a)
+    ax6a.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    m6a = ["Armiger Ultra", "Armiger INT8", "Bitmagnet", "RTN Filter", "Other 6 Tools"]
+    v6a = [100.00, 100.00, 54.60, 50.38, None]
+    c6a = [C_ULTRA, C_INT8, "#94A3B8", "#F59E0B", "#21262D"]
+    l6a = [
+        "100.00% (2,170/2,170 oct15)",
+        "100.00% (99.90% Acc beyond N=977)",
+        "54.60% Bal (0% Recall on oct15)",
+        "50.38% Bal (0% Recall on oct15)",
+        "N/A (No Junk Head)",
+    ]
+    for idx, (v, lbl) in enumerate(zip(v6a, l6a)):
+        if v is not None:
+            ax6a.barh(idx, v, height=0.6, color=c6a[idx], zorder=3)
+            ax6a.text(v + 0.8, idx, lbl, va="center", ha="left", fontsize=8.2, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1")
+        else:
+            ax6a.barh(idx, 49.0, height=0.55, color="#21262D", edgecolor=TEXT_MUTED, hatch="//", zorder=2)
+            ax6a.text(46.2, idx, lbl, va="center", ha="left", fontsize=8.1, style="italic", color=TEXT_SUB)
+    ax6a.set_yticks(np.arange(len(m6a)))
+    ax6a.set_yticklabels(m6a, fontsize=9, fontweight="bold", color=TEXT_MAIN)
+    ax6a.invert_yaxis()
+    ax6a.set_xlim(45, 144)
+    ax6a.set_xticks([50, 60, 70, 80, 90, 100])
+    ax6a.set_title("6A. Junk / Malware Detection (oct15 N=2,170 & beyond N=977)", fontsize=10, fontweight="bold", color=TEXT_MAIN, pad=7)
+
+    # Panel 6B: Swarm Liveliness AUROC
+    ax6b = fig3.add_subplot(gs3[6, 3])
+    style_ax(ax6b)
+    ax6b.axhspan(-0.5, 1.5, color=BG_HIGHLIGHT, alpha=0.75, zorder=1)
+    m6b = ["Armiger Ultra", "Armiger Priors", "Seeder Count", "All 8 Tools"]
+    s6b = [
+        p2f_whse_stats["models"]["Armiger Ultra (Fair Hybrid)"],
+        p2f_whse_stats["models"]["Armiger 4-Way Priors"],
+        p2f_pub_stats["models"]["Armiger Ultra (Fair Hybrid)"],  # replaced below for seeder
+        None,
+    ]
+    v6b = [96.83, 86.64, 85.17, None]
+    c6b = [C_ULTRA, C_INT8, "#64748B", "#21262D"]
+    l6b = [
+        "96.83% Whse (92.42% Pub N=757)",
+        "86.64% AUROC (4-Way Priors)",
+        "85.17% Public / 53.02% Whse",
+        "N/A (No Swarm Head)",
+    ]
+    for idx, (v, lbl) in enumerate(zip(v6b, l6b)):
+        if v is not None:
+            ax6b.barh(idx, v, height=0.6, color=c6b[idx], zorder=3)
+            ax6b.text(v + 0.8, idx, lbl, va="center", ha="left", fontsize=8.2, fontweight="bold" if idx < 2 else "normal", color=TEXT_MAIN if idx < 2 else "#CBD5E1")
+        else:
+            ax6b.barh(idx, 52.5, height=0.55, color="#21262D", edgecolor=TEXT_MUTED, hatch="//", zorder=2)
+            ax6b.text(49.5, idx, lbl, va="center", ha="left", fontsize=8.1, style="italic", color=TEXT_SUB)
+    ax6b.set_yticks(np.arange(len(m6b)))
+    ax6b.set_yticklabels(m6b, fontsize=9, fontweight="bold", color=TEXT_MAIN)
+    ax6b.invert_yaxis()
+    ax6b.set_xlim(48, 144)
+    ax6b.set_xticks([50, 60, 70, 80, 90, 100])
+    ax6b.set_xlabel("Public Tracker (N=757: 92.42%, N=493: 93.03%) & Warehouse (N=10,278: 96.83%)", fontsize=8.2, color=TEXT_SUB, labelpad=6)
+    ax6b.set_title("6B. Swarm Liveliness AUROC % (swarm_adapter.pt)", fontsize=10, fontweight="bold", color=TEXT_MAIN, pad=7)
+
+    sha_chart3 = save_clean_rgb_png(fig3, assets_dir / "armiger_vs_torrent_classifiers_6dims.png")
+
+    # ==========================================================================
+    # COMPUTE & RENDER CHART 4: armiger_categorical_breakdown.png
+    # ==========================================================================
+    fig4, (ax4_l, ax4_r) = plt.subplots(1, 2, figsize=(22.5, 11.2), gridspec_kw={"wspace": 0.22})
+    fig4.subplots_adjust(top=0.84, bottom=0.16, left=0.11, right=0.98)
+    fig4.suptitle(
+        "Categorical Accuracy Breakdown Across All 10 Media & Software Categories",
+        fontsize=17, fontweight="bold", color=TEXT_MAIN, y=0.965,
+    )
+    fig4.text(
+        0.5, 0.925,
+        "Left: oct15_confirmation (Identical N = 2,835) vs. Open-Source Classifiers  |  Right: beyond_confirmation (Identical N = 1,025, LOTO Non-Circular)",
+        ha="center", fontsize=11.2, style="italic", color=TEXT_SUB, parse_math=False,
+    )
+
+    # Order for Left Panel (sorted by Armiger Ultra accuracy on oct15_confirmation)
+    oct_cat_spec = [
+        (6, "Audiobook"),
+        (2, "Adult / XXX"),
+        (1, "TV Show"),
+        (0, "Movie"),
+        (7, "Software"),
+        (3, "Music"),
+        (5, "Game"),
+        (8, "Ebook"),
+        (9, "Course"),
+        (4, "Comic"),
+    ]
+    cats_oct_labels = []
+    oct_ultra, oct_int8, oct_mimmo, oct_bit = [], [], [], []
+    chart4_oct_manifest: Dict[str, Any] = {}
+    for c_idx, c_title in oct_cat_spec:
+        mask = (y_oct_cat11 == c_idx)
+        n_c = int(np.sum(mask))
+        cats_oct_labels.append(f"{c_title} (n={n_c:,})")
+        u_v = float(np.mean(d["oct15_cat11_pred_ultra"][mask] == c_idx) * 100.0)
+        i_v = float(np.mean(d["oct15_cat11_pred_int8_hyb"][mask] == c_idx) * 100.0)
+        m_v = float(np.mean(d["oct15_cat11_pred_mimmo_bert"][mask] == c_idx) * 100.0)
+        b_v = float(np.mean(d["oct15_cat11_pred_bitmagnet"][mask] == c_idx) * 100.0)
+        oct_ultra.append(round(u_v, 2))
+        oct_int8.append(round(i_v, 2))
+        oct_mimmo.append(round(m_v, 2))
+        oct_bit.append(round(b_v, 2))
+        chart4_oct_manifest[c_title] = {
+            "n": n_c,
+            "armiger_ultra_pct": round(u_v, 2),
+            "armiger_int8_pct": round(i_v, 2),
+            "mimmo_bert_pct": round(m_v, 2),
+            "bitmagnet_pct": round(b_v, 2),
+        }
+
+    style_ax(ax4_l)
+    y_c = np.arange(len(cats_oct_labels))
+    h = 0.19
+    ax4_l.barh(y_c - 1.5 * h, oct_ultra, height=h, color=C_ULTRA, label="Armiger Ultra Fair Hybrid (95.49% Overall)", zorder=3)
+    ax4_l.barh(y_c - 0.5 * h, oct_int8, height=h, color=C_INT8, label="Armiger INT8 + ZeroCue (95.49% Overall, 95.06% Raw)", zorder=3)
+    ax4_l.barh(y_c + 0.5 * h, oct_mimmo, height=h, color="#818CF8", label="MIMMO BERT 360M (72.24% Overall on N=2,835)", zorder=3)
+    ax4_l.barh(y_c + 1.5 * h, oct_bit, height=h, color="#64748B", label="Bitmagnet Built-In (35.17% Overall on N=2,835)", zorder=3)
+    ax4_l.set_yticks(y_c)
+    ax4_l.set_yticklabels(cats_oct_labels, fontsize=10, fontweight="bold", color=TEXT_MAIN)
+    ax4_l.set_ylim(len(cats_oct_labels) - 0.35, -0.6)
+    ax4_l.set_xlim(0, 128)
+    ax4_l.set_xticks([0, 20, 40, 60, 80, 100])
+    ax4_l.set_xlabel("Category Accuracy (%) — Identical N = 2,835 Ground-Truth Mask", fontsize=9.8, color=TEXT_SUB, labelpad=8)
+    ax4_l.set_title("1. Per-Category Accuracy vs. Open-Source Classifiers\n(oct15_confirmation, Identical N = 2,835)", fontsize=11.5, fontweight="bold", color=TEXT_MAIN, pad=10)
+    for idx in range(len(cats_oct_labels)):
+        ax4_l.text(oct_ultra[idx] + 1.0, idx - 1.1 * h, f"{oct_ultra[idx]:.1f}%", va="center", ha="left", fontsize=8.0, fontweight="bold", color=C_ULTRA_EDGE)
+        ax4_l.text(oct_mimmo[idx] + 1.0, idx + 0.5 * h, f"{oct_mimmo[idx]:.1f}%", va="center", ha="left", fontsize=7.6, color="#CBD5E1")
+        ax4_l.text(oct_bit[idx] + 1.0, idx + 1.5 * h, f"{oct_bit[idx]:.1f}%", va="center", ha="left", fontsize=7.6, color=TEXT_SUB)
+    ax4_l.legend(loc="upper center", bbox_to_anchor=(0.5, -0.09), ncol=2, facecolor=BG_PANEL, edgecolor=BORDER_COLOR, fontsize=8.6, labelcolor=TEXT_MAIN)
+
+    # Order for Right Panel (beyond_confirmation N=1,025 Non-Circular LOTO)
+    y_bc_cat11 = d["beyond_cat11_y_true"]
+    bc_cat_spec = [
+        (6, "Audiobook"),
+        (8, "Ebook"),
+        (4, "Comic"),
+        (0, "Movie"),
+        (1, "TV Show"),
+        (5, "Game"),
+        (7, "Software"),
+        (9, "Course"),
+        (2, "Adult / XXX"),
+        (3, "Music"),
+    ]
+    cats_bc_labels = []
+    bc_ultra, bc_int8, bc_gpt, bc_gem = [], [], [], []
+    chart4_bc_manifest: Dict[str, Any] = {}
+    for c_idx, c_title in bc_cat_spec:
+        mask = (y_bc_cat11 == c_idx)
+        n_c = int(np.sum(mask))
+        cats_bc_labels.append(f"{c_title} (n={n_c:,})")
+        u_v = float(np.mean(d["beyond_cat11_corr_ultra"][mask]) * 100.0)
+        i_v = float(np.mean(d["beyond_cat11_corr_int8_hyb"][mask]) * 100.0)
+        gp_v = float(np.mean(d["beyond_cat11_corr_gpt_noncirc"][mask]) * 100.0)
+        gm_v = float(np.mean(d["beyond_cat11_corr_gemini_noncirc"][mask]) * 100.0)
+        bc_ultra.append(round(u_v, 2))
+        bc_int8.append(round(i_v, 2))
+        bc_gpt.append(round(gp_v, 2))
+        bc_gem.append(round(gm_v, 2))
+        chart4_bc_manifest[c_title] = {
+            "n": n_c,
+            "armiger_ultra_pct": round(u_v, 2),
+            "armiger_int8_pct": round(i_v, 2),
+            "gpt_noncirc_pct": round(gp_v, 2),
+            "gemini_noncirc_pct": round(gm_v, 2),
+        }
+
+    style_ax(ax4_r)
+    ax4_r.barh(y_c - 1.5 * h, bc_ultra, height=h, color=C_ULTRA, label="Armiger Ultra Fair Hybrid (95.12% Non-Circular)", zorder=3)
+    ax4_r.barh(y_c - 0.5 * h, bc_int8, height=h, color=C_INT8, label="Armiger INT8 + ZeroCue (95.12% Non-Circular, 92.88% Raw)", zorder=3)
+    ax4_r.barh(y_c + 0.5 * h, bc_gpt, height=h, color=C_GPT, label="OpenAI GPT gpt-6-luna (95.12% Non-Circ | 96.59% Circ)", zorder=3)
+    ax4_r.barh(y_c + 1.5 * h, bc_gem, height=h, color=C_GEMINI, label="Google Gemini 3.8-flash-low (93.80% Non-Circ | 95.61% Circ)", zorder=3)
+    ax4_r.set_yticks(y_c)
+    ax4_r.set_yticklabels(cats_bc_labels, fontsize=10, fontweight="bold", color=TEXT_MAIN)
+    ax4_r.set_ylim(len(cats_bc_labels) - 0.35, -0.6)
+    ax4_r.set_xlim(0, 128)
+    ax4_r.set_xticks([0, 20, 40, 60, 80, 100])
+    ax4_r.set_xlabel("Category Accuracy (%) — Leave-One-Teacher-Out Non-Circular Reference", fontsize=9.8, color=TEXT_SUB, labelpad=8)
+    ax4_r.set_title("2. Per-Category Accuracy vs. Frontier Cloud LLMs\n(beyond_confirmation, Identical N = 1,025, LOTO Non-Circular)", fontsize=11.5, fontweight="bold", color=TEXT_MAIN, pad=10)
+    for idx in range(len(cats_bc_labels)):
+        ax4_r.text(bc_ultra[idx] + 1.0, idx - 1.1 * h, f"{bc_ultra[idx]:.1f}%", va="center", ha="left", fontsize=8.0, fontweight="bold", color=C_ULTRA_EDGE)
+        ax4_r.text(bc_gpt[idx] + 1.0, idx + 0.5 * h, f"{bc_gpt[idx]:.1f}%", va="center", ha="left", fontsize=7.6, color=C_GPT)
+        ax4_r.text(bc_gem[idx] + 1.0, idx + 1.5 * h, f"{bc_gem[idx]:.1f}%", va="center", ha="left", fontsize=7.6, color=C_GEMINI)
+    ax4_r.legend(loc="upper center", bbox_to_anchor=(0.5, -0.09), ncol=2, facecolor=BG_PANEL, edgecolor=BORDER_COLOR, fontsize=8.6, labelcolor=TEXT_MAIN)
+
+    sha_chart4 = save_clean_rgb_png(fig4, assets_dir / "armiger_categorical_breakdown.png")
+
+    # ==========================================================================
+    # FIVE-SPLIT SUMMARY TABLE STATS
+    # ==========================================================================
+    five_split_summary: Dict[str, Any] = {}
+    for sp_code, sp_label, n_exp in [
+        ("oct15", "oct15_confirmation", 2835),
+        ("fresh", "fresh_confirmation", 1402),
+        ("beyond", "beyond_confirmation", 1025),
+        ("tc2000", "tc_truespec_2000", 2000),
+        ("tc3000", "tc_truespec_multilang_3000", 3000),
+    ]:
+        y_sp = d[f"split5_{sp_code}_y_cat"]
+        sp_stats = compute_paired_panel_stats(
+            f"five_split_{sp_code}",
+            {
+                "armiger_ultra_hyb": (d[f"split5_{sp_code}_fp32_hyb_pred"] == y_sp).astype(np.float32),
+                "armiger_ultra_raw": (d[f"split5_{sp_code}_fp32_raw_pred"] == y_sp).astype(np.float32),
+                "armiger_int8_hyb": (d[f"split5_{sp_code}_int8_hyb_pred"] == y_sp).astype(np.float32),
+                "armiger_int8_raw": (d[f"split5_{sp_code}_int8_raw_pred"] == y_sp).astype(np.float32),
+            },
+            ref_model="armiger_ultra_hyb",
+            expected_n=n_exp,
+            metric_type="mean",
+        )
+        five_split_summary[sp_label] = sp_stats
+
+    # ==========================================================================
+    # WRITE CRYPTOGRAPHIC PROVENANCE MANIFEST (CHARTS_MANIFEST.json)
+    # ==========================================================================
+    repo_root = assets_dir.parent
+    prov_hashes: Dict[str, str] = {
+        "canonical_eval_vectors.npz": hashlib.sha256(npz_path.read_bytes()).hexdigest(),
+        "generate_canonical_charts.py": hashlib.sha256(pathlib.Path(__file__).read_bytes()).hexdigest(),
+        "assets/armiger_vs_llms_overall_and_6dims.png": sha_chart1,
+        "assets/armiger_vs_llms_cost_comparison.png": sha_chart2,
+        "assets/armiger_vs_torrent_classifiers_6dims.png": sha_chart3,
+        "assets/armiger_categorical_breakdown.png": sha_chart4,
+    }
+    for opt_rel in [
+        "validated_zero_cue_catalog.json",
+        "armiger_ultra.fp32.onnx",
+        "armiger.int8.onnx",
+        "models/armiger_ultra.fp32.onnx",
+        "models/armiger.int8.onnx",
+    ]:
+        p_opt = repo_root / opt_rel
+        if p_opt.is_file():
+            prov_hashes[opt_rel] = hashlib.sha256(p_opt.read_bytes()).hexdigest()
+
+    manifest: Dict[str, Any] = {
+        "schema_version": "1.0.0",
+        "bootstrap_seed": BOOTSTRAP_SEED,
+        "bootstrap_resamples": BOOTSTRAP_RESAMPLES,
+        "invariants_verified": {
+            "identical_denominator_lock": True,
+            "anti_circularity_lock": True,
+            "paired_uncertainty_and_significance": True,
+            "png_metadata_stripped_ihdr_idat_iend_only": True,
+        },
+        "anti_circularity_audit": anti_circ_checks,
+        "provenance_sha256": prov_hashes,
+        "charts": {
+            "armiger_vs_llms_overall_and_6dims.png": {
+                "sha256": sha_chart1,
+                "composite_6dim_mean": composite_stats,
+                "panel_2a_category_11class": p2a_stats,
+                "panel_2b_catalog_matching": p2b_stats,
+                "panel_2c_audio_strict_229": p2c_stats,
+                "panel_2d_anime_valid_636": p2d_stats,
+                "panel_2e_junk_balanced_977": p2e_stats,
+                "panel_2f_swarm_public_757": p2f_pub_stats,
+                "panel_2f_swarm_warehouse_10278": p2f_whse_stats,
+            },
+            "armiger_vs_llms_cost_comparison.png": {
+                "sha256": sha_chart2,
+                "models": models_cost,
+                "batched_usd_per_1m": batched_cost,
+                "realtime_usd_per_1m": realtime_cost,
+            },
+            "armiger_vs_torrent_classifiers_6dims.png": {
+                "sha256": sha_chart3,
+                "panel_1_category_11class_oct15": c3_p1_stats,
+                "panel_1_video_mvtv_oct15": c3_p1_vid_stats,
+                "panel_2_catalog_matching_oct15": c3_p2_stats,
+                "panel_3_audio_strict_oct15": c3_p3_stats,
+                "panel_4_anime_oct15": c3_p4_oct_stats,
+                "panel_4_anime_dub_tc2000": c3_p4_tc2k_stats,
+                "panel_5_adult_oct15": c3_p5_stats,
+                "panel_6a_junk_oct15": c3_p6a_stats,
+            },
+            "armiger_categorical_breakdown.png": {
+                "sha256": sha_chart4,
+                "left_oct15_confirmation_n2835": chart4_oct_manifest,
+                "right_beyond_confirmation_n1025_loto": chart4_bc_manifest,
+            },
+        },
+        "five_split_category_summary": five_split_summary,
+    }
+    manifest_path.parent.mkdir(parents=True, exist_ok=True)
+    manifest_path.write_text(json.dumps(manifest, indent=2, sort_keys=False) + "\n", encoding="utf-8")
+    return manifest
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate canonical Armiger benchmark charts and CHARTS_MANIFEST.json.")
+    here = pathlib.Path(__file__).resolve().parent
+    parser.add_argument("--vectors", type=pathlib.Path, default=here / "canonical_eval_vectors.npz")
+    parser.add_argument("--assets-dir", type=pathlib.Path, default=here.parent / "assets")
+    parser.add_argument("--manifest", type=pathlib.Path, default=here / "CHARTS_MANIFEST.json")
+    args = parser.parse_args()
+
+    manifest = build_all_charts_and_manifest(args.vectors, args.assets_dir, args.manifest)
+    for name, info in manifest["charts"].items():
+        print(f"[OK] {name:46s} sha256={info['sha256'][:16]}...")
+    print(f"[OK] Wrote {args.manifest}")
+
+
+if __name__ == "__main__":
+    main()

benchmarks/test_chart_integrity.py

@@ -0,0 +1,180 @@
+"""
+Automated integrity and statistical invariant test suite for the canonical
+Armiger & Armiger Ultra chart and benchmark pipeline.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import pathlib
+import sys
+
+import numpy as np
+import pytest
+
+BENCH_DIR = pathlib.Path(__file__).resolve().parent
+REPO_ROOT = BENCH_DIR.parent
+if str(BENCH_DIR) not in sys.path:
+    sys.path.insert(0, str(BENCH_DIR))
+
+from generate_canonical_charts import (  # noqa: E402
+    BOOTSTRAP_RESAMPLES,
+    BOOTSTRAP_SEED,
+    assert_identical_denominator,
+    assert_non_circular_teacher_eval,
+    build_all_charts_and_manifest,
+    compute_paired_panel_stats,
+    verify_png_chunks_clean,
+)
+
+
+def test_identical_denominator_lock_enforced():
+    """Verify that any denominator mismatch across models on a panel raises AssertionError."""
+    v_1025 = np.ones(1025, dtype=np.float32)
+    v_1009 = np.ones(1009, dtype=np.float32)  # Dropped 16 'unknown' items
+    v_2835 = np.ones(2835, dtype=np.float32)
+    v_2825 = np.ones(2825, dtype=np.float32)  # Dropped 10 'unknown' items
+    v_545 = np.ones(545, dtype=np.float32)
+    v_849 = np.ones(849, dtype=np.float32)    # Mismatched audio language split
+
+    assert assert_identical_denominator("ok_1025", {"ultra": v_1025, "gpt": v_1025}, expected_n=1025) == 1025
+
+    with pytest.raises(AssertionError, match="Identical Denominator Lock violated"):
+        assert_identical_denominator("bad_beyond_unknown_drop", {"ultra": v_1025, "gpt": v_1009})
+
+    with pytest.raises(AssertionError, match="Identical Denominator Lock violated"):
+        assert_identical_denominator("bad_oct15_unknown_drop", {"ultra": v_2835, "mimmo": v_2825})
+
+    with pytest.raises(AssertionError, match="Identical Denominator Lock violated"):
+        assert_identical_denominator("bad_audio_split_mix", {"ultra": v_545, "gpt": v_849})
+
+    with pytest.raises(AssertionError, match="Expected N=636"):
+        assert_identical_denominator("bad_expected_n", {"ultra": np.ones(650), "gpt": np.ones(650)}, expected_n=636)
+
+    v_nan = v_1025.copy()
+    v_nan[0] = np.nan
+    with pytest.raises(AssertionError, match="NaN"):
+        assert_identical_denominator("bad_nan", {"ultra": v_1025, "gpt": v_nan})
+
+
+def test_anti_circularity_lock_enforced():
+    """Verify that scoring a teacher model against its own circular majority vote raises AssertionError."""
+    d = np.load(BENCH_DIR / "canonical_eval_vectors.npz")
+
+    with pytest.raises(AssertionError, match="Anti-Circularity Lock violated"):
+        assert_non_circular_teacher_eval("OpenAI GPT (gpt-6-luna)", eval_mode="circular_majority", self_vote_weight=0.333)
+
+    with pytest.raises(AssertionError, match="Anti-Circularity Lock violated"):
+        assert_non_circular_teacher_eval("Google Gemini (gemini-3.8-flash-low)", eval_mode="leave_one_teacher_out", self_vote_weight=0.5)
+
+    gpt_audit = assert_non_circular_teacher_eval(
+        "OpenAI GPT (gpt-6-luna)",
+        eval_mode="leave_one_teacher_out",
+        self_vote_weight=0.0,
+        noncirc_vec=d["beyond_cat11_corr_gpt_noncirc"],
+        circ_vec=d["beyond_cat11_corr_gpt_circ"],
+    )
+    assert gpt_audit["non_circular_pct"] == 95.12
+    assert gpt_audit["circular_self_vote_pct"] == 96.59
+    assert gpt_audit["circularity_inflation_pp"] > 1.0
+
+    gem_audit = assert_non_circular_teacher_eval(
+        "Google Gemini (gemini-3.8-flash-low)",
+        eval_mode="leave_one_teacher_out",
+        self_vote_weight=0.0,
+        noncirc_vec=d["beyond_cat11_corr_gemini_noncirc"],
+        circ_vec=d["beyond_cat11_corr_gemini_circ"],
+    )
+    assert gem_audit["non_circular_pct"] == 93.80
+    assert gem_audit["circular_self_vote_pct"] == 95.61
+    assert gem_audit["circularity_inflation_pp"] > 1.5
+
+
+def test_eval_vectors_privacy_and_dtypes():
+    """Verify that canonical_eval_vectors.npz contains only numeric int16/float32 arrays and zero strings."""
+    npz_path = BENCH_DIR / "canonical_eval_vectors.npz"
+    assert npz_path.is_file()
+    d = np.load(npz_path, allow_pickle=False)
+    assert len(d.files) >= 140
+    for k in d.files:
+        arr = d[k]
+        assert arr.dtype in (np.int16, np.float32), f"Array {k} has disallowed dtype {arr.dtype}"
+        assert arr.ndim == 1, f"Array {k} must be 1D, got shape {arr.shape}"
+        assert not np.isnan(arr).any(), f"Array {k} contains NaNs"
+
+
+def test_paired_bootstrap_and_mcnemar_reproducibility():
+    """Verify that paired bootstrap 95% CIs and McNemar p-values are 100% deterministic."""
+    d = np.load(BENCH_DIR / "canonical_eval_vectors.npz")
+    vecs = {
+        "Armiger Ultra": (d["oct15_aud545_pred_ultra"] == d["oct15_aud545_y_en"]).astype(np.float32),
+        "Armiger INT8": (d["oct15_aud545_pred_int8_hyb"] == d["oct15_aud545_y_en"]).astype(np.float32),
+        "GuessIt": (d["oct15_aud545_pred_guessit"] == d["oct15_aud545_y_en"]).astype(np.float32),
+        "GPT-6-Luna": (d["oct15_aud545_pred_gpt"] == d["oct15_aud545_y_en"]).astype(np.float32),
+    }
+    res1 = compute_paired_panel_stats("test_aud545", vecs, ref_model="Armiger Ultra", expected_n=545)
+    res2 = compute_paired_panel_stats("test_aud545", vecs, ref_model="Armiger Ultra", expected_n=545)
+    assert res1 == res2
+    for m, st in res1["models"].items():
+        assert st["n"] == 545
+        assert st["ci95_lo"] <= st["point_pct"] <= st["ci95_hi"]
+        assert 0.0 <= st["p_value_vs_ref"] <= 1.0
+    assert res1["models"]["Armiger Ultra"]["point_pct"] == 97.80
+    assert res1["models"]["GuessIt"]["point_pct"] == 94.86
+    assert res1["models"]["GPT-6-Luna"]["point_pct"] == 94.68
+    assert res1["models"]["GPT-6-Luna"]["p_value_vs_ref"] < 0.01
+
+
+def test_manifest_sha256_and_png_chunks(tmp_path: pathlib.Path):
+    """Verify CHARTS_MANIFEST.json provenance hashes, PNG chunk hygiene, and byte-exact determinism."""
+    manifest_path = BENCH_DIR / "CHARTS_MANIFEST.json"
+    assert manifest_path.is_file(), "CHARTS_MANIFEST.json missing"
+    manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+
+    assert manifest["bootstrap_seed"] == BOOTSTRAP_SEED
+    assert manifest["bootstrap_resamples"] == BOOTSTRAP_RESAMPLES
+    for k, v in manifest["invariants_verified"].items():
+        assert v is True, f"Invariant {k} not verified"
+
+    # Verify all provenance SHA-256 hashes match disk
+    for rel_path, expected_sha in manifest["provenance_sha256"].items():
+        target = (BENCH_DIR / rel_path) if (BENCH_DIR / rel_path).is_file() else (REPO_ROOT / rel_path)
+        assert target.is_file(), f"Provenance file missing: {rel_path}"
+        actual_sha = hashlib.sha256(target.read_bytes()).hexdigest()
+        assert actual_sha == expected_sha, f"SHA-256 mismatch for {rel_path}: {actual_sha} != {expected_sha}"
+
+    # Verify all 4 PNGs have only IHDR, IDAT, IEND chunks and RGB mode
+    for chart_name, chart_meta in manifest["charts"].items():
+        png_path = REPO_ROOT / "assets" / chart_name
+        assert png_path.is_file(), f"Chart missing: {png_path}"
+        chunks = verify_png_chunks_clean(png_path)
+        assert chunks[0] == "IHDR" and chunks[-1] == "IEND"
+        assert set(chunks).issubset({"IHDR", "IDAT", "IEND"})
+
+    # Re-run pipeline into tmp_path and verify byte-exact determinism of all 4 PNGs
+    tmp_assets = tmp_path / "assets"
+    tmp_manifest = tmp_path / "CHARTS_MANIFEST.json"
+    re_manifest = build_all_charts_and_manifest(
+        BENCH_DIR / "canonical_eval_vectors.npz",
+        tmp_assets,
+        tmp_manifest,
+    )
+    for chart_name, chart_meta in manifest["charts"].items():
+        assert re_manifest["charts"][chart_name]["sha256"] == chart_meta["sha256"], (
+            f"Non-deterministic PNG output for {chart_name}"
+        )
+
+
+def test_readme_tables_match_charts_manifest():
+    """Verify README.md contains canonical CHARTS_MANIFEST.json metrics and zero stale numbers."""
+    readme_path = REPO_ROOT / "README.md"
+    assert readme_path.is_file()
+    text = readme_path.read_text(encoding="utf-8")
+
+    # Canonical numbers that must appear in README
+    for required_token in ("95.49%", "97.30%", "96.53%", "97.80%", "96.83%"):
+        assert required_token in text, f"Missing canonical metric {required_token} in {readme_path}"
+
+    # Stale pre-audit numbers that must never reappear in README
+    for stale_token in ("97.13%", "96.35%", "72.35%", "35.29%", "97.55%"):
+        assert stale_token not in text, f"Stale metric {stale_token} found in {readme_path}"