Release armiger v2.1.0 (standalone featurizer, 48-tensor swarm adapter, and audited unmutated-key benchmarks)

README.md

@@ -18,65 +18,109 @@ pipeline_tag: text-classification
 
 <p align="center">
   <strong>Fast 8.3 MB universal CPU torrent &amp; media classifier for Bitmagnet and self-hosted media libraries.</strong><br>
-  Predicts 8 calibrated decision heads simultaneously in 0.42 ms batched (2.44 ms single-item) on any CPU — matching or outperforming Frontier Cloud LLMs at ~900x faster speed and $0 API cost.
+  Predicts 8 calibrated decision heads simultaneously in 2.44 ms single-item p50 (3.07 ms/item batch-64, 326 items/s) on Apple M4 Pro CPU ONNX Runtime INT8 — with a self-contained zero-tokenizer Python inference script (<code>armiger_inference.py</code> + <code>vocab.json</code>) included out of the box.
 </p>
 
-
 <p align="center">
   <a href="https://norvitech.com"><img alt="NorviTech Suite" src="https://img.shields.io/badge/NorviTech-Suite-FD8024.svg"></a>
   <a href="https://huggingface.co/NorviTech/armiger-ultra"><img alt="Armiger Ultra" src="https://img.shields.io/badge/Armiger_Ultra-GPU_%2B_Swarm_Suite-10B981.svg"></a>
   <img alt="Licence Apache-2.0" src="https://img.shields.io/badge/licence-Apache--2.0-blue.svg">
 </p>
 
-**Armiger** (`NorviTech/armiger`) is an ultra-compact (`8.3 MB`), zero-tokenizer byte-level RoPE Transformer + File-Tree Encoder designed for real-time BitTorrent, DHT, Usenet, and media automation (`Bitmagnet` and self-hosted media libraries) classification on any CPU.
+**Armiger** (`NorviTech/armiger`) is an ultra-compact (`8.3 MB`, `8,276,448 bytes`), zero-tokenizer byte-level RoPE Transformer + File-Tree Encoder designed for real-time BitTorrent, DHT, Usenet, and media automation (`Bitmagnet` and self-hosted media libraries) classification on any CPU.
 
 Unlike rule parsers (`GuessIt`, `PTT`, `RTN`, `PTN`, `Anitomy`) that only recognize video filenames or `360M+` parameter subword models (`MIMMO BERT`) that require `~1.4 GB` of weights, **Armiger** reads raw UTF-8 title bytes and internal file trees directly to predict **8 calibrated classification heads** in a single forward pass.
 
-> **Looking for GPU acceleration + Live Swarm Viability & Fake-Seeder Honeypot detection?**
-> Use **[`NorviTech/armiger-ultra`](https://huggingface.co/NorviTech/armiger-ultra)**, which bundles **both** `Armiger` (`8.3 MB` CPU `INT8`) and `Armiger Ultra` (`32.2 MB` `FP32` GPU + Swarm Health Adapter) in one unified package.
+> **Looking for GPU acceleration (`0.21 ms` RTX / `0.29 ms` M4 Pro MPS) + Live Swarm Viability, Honeypot Detection, & Zero-Cue Catalog Disambiguation?**
+> Use **[`NorviTech/armiger-ultra`](https://huggingface.co/NorviTech/armiger-ultra)**, which bundles **both** `Armiger` (`8.3 MB` CPU `INT8`) and `Armiger Ultra` (`32.3 MB` `FP32` GPU + `132 KB` Swarm Health Adapter + all 6 plugins) in one unified package.
+
+---
+
+## Quickstart: Standalone ONNX Inference (`armiger_inference.py`)
+
+This repository ships with `armiger_inference.py` and `vocab.json`, implementing the complete 11-input featurizer (`title_bytes`, `ext_ids`, `file_sizes_log`, `file_depths`, `file_flags`, `file_mask`, `struct_feats`, `tracker_ids`, `cat_hint_id`, `tag_ids`, `swarm_feats`) + post-hoc temperature/Menon calibration (`calibration.json`):
+
+```python
+from armiger_inference import ArmigerONNXClassifier
+
+clf = ArmigerONNXClassifier(
+    onnx_path="armiger.onnx",
+    vocab_path="vocab.json",
+    calibration_path="calibration.json",
+)
+
+pred = clf.predict({
+    "name": "Sintel.2010.1080p.BluRay.x264-OPEN",
+    "size": 4_500_000_000,
+    "files": [{"path": "Sintel.2010.1080p.mkv", "size": 4_500_000_000}],
+})
+
+print(pred["category"])         # "movie"
+print(pred["mondrian_gates"])   # {'adult_flagged': False, 'junk_flagged': False, 'scope_excluded': False, 'passed_all': True}
+print(pred["heads"].keys())     # dict_keys(['category', 'adult', 'junk', 'anime', 'english_subs', 'lang', 'owner_scope', 'title_language'])
+```
+
+---
+
+## 8 Simultaneous Classification Heads (Audited Unmutated Ground-Truth Accuracy)
+
+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`)
+   - **`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)
+   - **`tc_truespec_multilang_3000` (`N=3,000`):** **`97.37%`** Standalone Neural INT8 (**`97.50%`** FP32 / **`98.47%`** Fair Blind Hybrid)
+   - **5-Split Weighted Mean (`N=10,262`):** **`93.95%`** Standalone Neural INT8 (**`94.00%`** FP32) $\rightarrow$ **`95.39%`** INT8 / **`95.42%`** FP32 Fair Blind Hybrid
+2. **`adult` (Safety Gate):** `non_adult` vs. `adult` (**`98.53%`** on `oct15_confirmation` `2,740/2,781`; **`98.21%`** on `fresh` `N=1,393`; **`98.62%`** on `beyond` `N=1,016`; **`99.90%`** on `tc2000` `N=2,000`; **`100.00%`** on `multilang3000` `N=3,000`)
+3. **`junk` (Malware / Fake-Codec / Archive-Trap Filter):** `clean` vs. `junk` (**`100.00%`** on `oct15_confirmation` `2,170/2,170`; **`100.00%`** on `fresh` `1,291/1,291`; **`99.90%`** on `beyond` `976/977`; **`100.00%`** on `tc2000` & `multilang3000`; **`7/7` (`100%`)** MawaStealer v1/v2 & Vidar malware traps blocked at `P(junk) > 0.999`)
+4. **`anime` (Anime & Dub Routing):** `non_anime` vs. `anime` (**`99.61%`** on `oct15_confirmation` `505/507`; **`99.60%`** on `fresh` `252/253`; **`98.90%`** on `beyond` `629/636`; **`95.80%`** on `tc_truespec_2000` `1,914/1,998`)
+5. **`lang` (Audio/Release Language):** `english`, `non_english`, `multi_or_unknown` (**`96.88%`** Standalone Neural / **`97.80%`** with `AudioLanguageGuard` on `oct15_confirmation` binary English-vs-Foreign `N=545`; **`92.75%`** 3-class `N=772`; **`91.47%`** on `multilang3000` `N=3,000`)
+6. **`title_language` (Title Script/Language):** `english_title` vs. `non_english_title` (**`93.70%`** on `oct15_confirmation` `N=969`; **`98.51%`** on `beyond_confirmation` `N=604`; **`97.80%`** on `tc2000` `N=2,000`)
+7. **`english_subs` (Subtitle Availability):** `no_eng_subs`, `has_eng_subs`, `unknown` (**`93.04%`** on `stop` `N=2,171`; **`87.17%`** on `multilang3000` `N=3,000`)
+8. **`owner_scope` (Catalog Policy Gate):** `allowed_type` vs. `excluded` (**`98.65%`** on `oct15_confirmation` `2,633/2,669`; **`99.80%`** on `tc2000` `N=2,000`; **`99.90%`** on `multilang3000` `N=3,000`)
 
 ---
 
-## 8 Simultaneous Classification Heads
+## Hardware-Attributed Latency & Footprint Benchmarks
 
-1. **`category` (11 classes):** `movie`, `tv_show`, `xxx`, `music`, `comic`, `game`, `audiobook`, `software`, `ebook`, `course`, `unknown` (**`97.92%`** 11-class Gold Standard accuracy; **`99.15%`** Movie vs. TV accuracy)
-2. **`adult` (Safety Gate):** `non_adult` vs. `adult` (**`98.71%`** Gold Standard safety accuracy)
-3. **`anime` (Anime & Dub Routing):** `non_anime` vs. `anime` (**`97.82%`** MediaInfo-verified Anime & Dub accuracy)
-4. **`lang` (Audio/Release Language):** `english`, `non_english`, `multi_or_unknown` (**`98.17%`** Gold Standard audio claim accuracy)
-5. **`title_language` (Title Script/Language):** `english_title` vs. `non_english_title` (**`98.68%`** accuracy)
-6. **`english_subs` (Subtitle Availability):** `no_eng_subs`, `has_eng_subs`, `unknown` (**`97.80%`** accuracy)
-7. **`junk` (Malware / Fake-Codec / Archive-Trap Filter):** `clean` vs. `junk` (**`99.90%`** accuracy, **`98.8%`** balanced sensitivity/specificity)
-8. **`owner_scope` (Catalog Policy Gate):** `allowed_type` vs. `excluded` (**`98.69%`** accuracy)
+| Runtime & Precision | Hardware Platform | Footprint | Single-Item Latency (p50) | Batch-64 Latency (per item) | Batch-64 Throughput |
+| :--- | :--- | :---: | :---: | :---: | :---: |
+| **`armiger.onnx` (ONNX Runtime INT8)** | **Apple M4 Pro CPU** | **`8.3 MB`** | **`2.44 ms`** (`410 items/s`) | **`3.07 ms`** | **`326 items/s`** |
+| **`armiger_ultra.fp32.onnx` (ONNX Runtime FP32)** | **Apple M4 Pro CPU** | `32.3 MB` | `1.12 ms` (`893 items/s`) | `3.32 ms` | `301 items/s` |
+| **`armiger_ultra.pt` (PyTorch MPS FP32)** | **Apple M4 Pro GPU (MPS)** | `32.2 MB` | `1.12 ms` | **`0.29 ms`** | **`3,416 items/s`** |
+| **`armiger_ultra.pt` (PyTorch CUDA FP32)** | **NVIDIA RTX GPU** | `32.2 MB` | `2.79 ms` (`358 items/s`) | **`0.21 ms`** | **`4,795 items/s`** |
 
 ---
 
 ## Optional Modular Plugins (Available Standalone or Bundled in `Armiger Ultra`)
 
-`NorviTech/armiger` ships as a compact `8.3 MB` zero-dependency ONNX INT8 engine (`armiger.onnx` + `calibration.json`). Any of the **6 official Armiger plugins** below can be attached on demand (or downloaded pre-bundled inside [`NorviTech/armiger-ultra`](https://huggingface.co/NorviTech/armiger-ultra)):
+`NorviTech/armiger` ships as a compact `8.3 MB` ONNX INT8 engine (`armiger.onnx`, `vocab.json`, `calibration.json`, `armiger_inference.py`). Any of the **6 official Armiger plugins** below can be attached on demand (or downloaded pre-bundled inside [`NorviTech/armiger-ultra`](https://huggingface.co/NorviTech/armiger-ultra)):
 
-| Plugin Repository | Bundled in `Armiger Ultra`? | Footprint | Core Capability & Measured Impact |
+| Plugin Repository | Bundled in `Armiger Ultra`? | Footprint | Core Capability & Measured Impact (Unmutated Ground-Truth Keys) |
 | :--- | :---: | :---: | :--- |
-| **[`NorviTech/armiger-swarm-survival`](https://huggingface.co/NorviTech/armiger-swarm-survival)** | **Yes** (`swarm_adapter.pt`, `swarm_survival.py`) | `163 KB` | **SwarmSurvivalNet v2 & Honeypot Residual Adapter:** **`96.83%`** Swarm Liveliness AUROC (vs. `53.04%` raw seeders), **`100.0%`** fake-seeder malware trap recall, **`0%`** content-category flip guarantee. |
-| **[`NorviTech/armiger-swarm-priors`](https://huggingface.co/NorviTech/armiger-swarm-priors)** | **Yes** (`swarm_priors.py`) | `29.4 KB` | **Empirical Bayes Swarm & Release-Group Priors:** 4-pathway tracker scrape prior engine, mandatory modality dropout (`p=0.60`), anomaly feature extraction, and public-tracker passkey safety filter. |
-| **[`NorviTech/armiger-zero-cue-catalog`](https://huggingface.co/NorviTech/armiger-zero-cue-catalog)** | **Yes** (`zero_cue_plugin.py`, `validated_zero_cue_catalog.json`) | `36.0 MB` | **Zero-Cue Catalog & Structural Disambiguator:** Offline 929,400-key normalized title-year & entity reference lookup + structural multi-file & format disambiguation rules; lifts Movie-vs-TV accuracy to **`99.46%`** and 11-class accuracy to **`98.48%`** (`98.93%` Hard Edge-Cases). |
-| **[`NorviTech/armiger-audio-guard`](https://huggingface.co/NorviTech/armiger-audio-guard)** | **Yes** (`audio_language_guard.py`) | `4.2 KB` | **Cross-Head Audio Language & Anime Dub Guard:** Couples `title_language` (`P(Foreign Title)`) with `lang` head to disambiguate `.en.` prepositions, `<Foreign>.ENG.Sub`, regional dubs, and `MULTi (FR EN)` tags; reaches **`98.72%`** Gold Standard and **`99.88%`** Hard Edge-Case audio accuracy. |
-| **[`NorviTech/armiger-calibration-dht`](https://huggingface.co/NorviTech/armiger-calibration-dht)** | **Yes** (`calibration.json`, `calibration_and_dht.py`, `armiger_dht_rsi.py`) | `115 KB` | **Conformal Calibration, BEP-44 DHT Updater & Distributed RSI Engine:** Per-head temperature scaling ($T_h$), Mondrian safety gates (`99.95%` junk balanced accuracy), `Ed25519` threshold-signed `BEP-44`/`BEP-46` DHT hot-reload, universal media-webhook/qBittorrent/Transmission/ffprobe ground-truth RSI, `X25519`+`ChaCha20-Poly1305` AEAD, distinct-ASN quorum, and 500-canary regression gate (`36/36` tests). |
-| **[`NorviTech/armiger-dht-db-sync`](https://huggingface.co/NorviTech/armiger-dht-db-sync)** | **Yes** (`csam_guard.py`, `armiger_dht_db_sync.py`) | `98 KB` | **Modular, CSAM-Protected, Encrypted BEP-44/BEP-46 DHT Database Sync:** Multi-stage CSAM & abuse pre-ingest blocker, `Ed25519`-signed blinded `SHA-256` tombstone purge engine, 10-category $\times$ 14-language $\times$ 7-quality sharded subscriptions with strict default-off `xxx` isolation, `X25519`+`ChaCha20-Poly1305` AEAD, binary Merkle proofs, and $K$-of-$N$ distinct-ASN Sybil quorum (`27/27` tests). |
+| **[`NorviTech/armiger-swarm-survival`](https://huggingface.co/NorviTech/armiger-swarm-survival)** | **Yes** (`swarm_adapter.pt`, `swarm_survival.py`) | `167 KB` (`132 KB` `.pt`) | **SwarmSurvivalNet v2 & Honeypot Residual Adapter:** Executes full 48-tensor neural forward pass (`SwarmResidualAdapter`); **`96.83%`** Multi-Source Warehouse AUROC (`N=10,278`, vs. `53.02%` raw seeders), **`92.42%`** / **`93.03%`** Public-Tracker AUROC (`N=757` / `N=493`, vs. `85.17%` raw seeders), **`100.0%`** fake-seeder malware trap recall, **`0.00%`** content-category flip guarantee. |
+| **[`NorviTech/armiger-swarm-priors`](https://huggingface.co/NorviTech/armiger-swarm-priors)** | **Yes** (`swarm_priors.py`) | `29.3 KB` | **Empirical Bayes Swarm & Release-Group Priors:** 4-pathway tracker scrape prior engine (`86.64%` formula AUROC), mandatory modality dropout (`p=0.60`), anomaly feature extraction, and public-tracker passkey safety filter. |
+| **[`NorviTech/armiger-zero-cue-catalog`](https://huggingface.co/NorviTech/armiger-zero-cue-catalog)** | **Yes** (`zero_cue_plugin.py`, `validated_zero_cue_catalog.json`) | `26.0 MB` (`BLAKE2b-64`) | **Zero-Cue Catalog & Structural Disambiguator:** Offline `929,400`-entry `BLAKE2b-64` cryptographically hashed catalog (`472,017` `(title, year)`, `257,016` anime stems, `200,367` long titles) + deterministic structural rules; lifts 5-split (`N=10,262`) weighted category accuracy from **`94.00%`** (Standalone Neural) to **`95.42%`** (Fair Blind Hybrid), `beyond_confirmation` (`N=1,025`) from **`92.88%`** to **`95.12%`**, and `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`NorviTech/armiger-audio-guard`](https://huggingface.co/NorviTech/armiger-audio-guard)** | **Yes** (`audio_language_guard.py`) | `3.3 KB` | **Cross-Head Audio Language & Anime Dub Guard:** Couples `title_language` (`P(Foreign Title)`) with `lang` head to disambiguate `.en.` prepositions, `<Foreign>.ENG.Sub`, regional dubs, and `MULTi (FR EN)` tags; lifts binary English-vs-Foreign audio accuracy on `oct15_confirmation` (`N=545`) from **`96.88%`** (Standalone Neural) to **`97.80%`** (`92.75%` 3-class `N=772`). |
+| **[`NorviTech/armiger-calibration-dht`](https://huggingface.co/NorviTech/armiger-calibration-dht)** | **Yes** (`calibration.json`, `calibration_and_dht.py`, `armiger_dht_rsi.py`) | `181 KB` | **Conformal Calibration, BEP-44 DHT Updater & Distributed RSI Engine:** Per-head temperature scaling ($T_h$), Mondrian safety gates (**`100.00%`** junk accuracy on `oct15_confirmation` `N=2,170` & `fresh_confirmation` `N=1,291`, **`99.90%`** on `beyond_confirmation` `N=977`), Hybrid `Ed25519`+`WOTS+-SHAKE256` signed `BEP-44`/`BEP-46` DHT hot-reload, Hybrid `X25519`+`ML-KEM-768`+`ChaCha20-Poly1305` AEAD, distinct-ASN quorum, and 500-canary regression gate (`47/47` tests). |
+| **[`NorviTech/armiger-dht-db-sync`](https://huggingface.co/NorviTech/armiger-dht-db-sync)** | **Yes** (`csam_guard.py`, `armiger_dht_db_sync.py`) | `155 KB` | **Modular, CSAM-Protected, Encrypted BEP-44/BEP-46 DHT Database Sync:** Multi-stage CSAM & abuse pre-ingest blocker, Hybrid PQ-signed blinded `SHA-256` tombstone purge engine, 10-category $\times$ 14-language $\times$ 7-quality sharded subscriptions with strict default-off `xxx` isolation, `X25519`+`ML-KEM-768`+`ChaCha20-Poly1305` AEAD, binary Merkle proofs, and $K$-of-$N$ distinct-ASN Sybil quorum (`27/27` tests). |
 
 ---
 
-## 1. Categorical & Open-Source Torrent Classifier Comparisons
+## 1. Categorical & Open-Source Torrent Classifier Comparisons (Unmutated Ground-Truth Keys)
 
 ### 7-Dimension Comparison vs. Open-Source Classifiers & Parsers
 
 ![Armiger vs Open-Source Torrent Classifiers Across 7 Core Dimensions](assets/armiger_vs_torrent_classifiers_6dims.png)
 
-| System / Model | Footprint | 11-Class Category | Movie vs. TV | Catalog Matching | Audio Language | Anime & Dub | Adult / NSFW Gate | Junk / Malware Filter | 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`) | Adult / NSFW Gate (`N=2,781`) | Junk / Malware (`N=2,170`) | Swarm Liveliness (AUROC) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **[Armiger Ultra](https://huggingface.co/NorviTech/armiger-ultra)** | `32.4 MB` | **`98.48%`** | **`99.46%`** | **`98.69%`** | **`98.72%`** | **`97.92%`** | `98.63%` | **`99.95%`** | **`96.83%`** |
-| **Armiger (This Model)** | **`8.3 MB`** | **`97.92%`** | **`99.15%`** | **`98.38%`** | **`98.17%`** | **`97.82%`** | **`98.71%`** | **`99.90%`** | **`86.64%`** |
-| **MIMMO BERT (360M)** | `~1,400 MB` | `72.35%` | `56.92%` (`86.0%` ans.) | `60.03%` | N/A | N/A | `91.05%` | N/A | N/A |
-| **MIMMO Cascade** | `~1,400 MB` | `71.43%` | `57.00%` (`86.0%` ans.) | `60.11%` | N/A | N/A | `89.68%` | N/A | N/A |
+| **[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 |
@@ -84,22 +128,24 @@ Unlike rule parsers (`GuessIt`, `PTT`, `RTN`, `PTN`, `Anitomy`) that only recogn
 | **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 |
 
-### Per-Category Accuracy Breakdown Across All 10 Media & Software Categories
+### 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 | Gold Standard (`N=2,835`) Armiger Ultra | Gold Standard (`N=2,835`) Armiger | Gold Standard MIMMO BERT (360M) | Gold Standard Bitmagnet Built-In | Hard Edge-Cases (`N=1,025`) Armiger Ultra | Hard Edge-Cases (`N=1,025`) Armiger | Hard Edge-Cases OpenAI GPT (`gpt-6-luna`) | Hard Edge-Cases Google Gemini (`gemini-3.8-flash-low`) |
+| 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`)* |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **Adult / XXX** | **`99.62%`** | `99.43%` | `95.50%` | `39.37%` | **`100.00%`** | `99.39%` | `97.70%` | `98.85%` |
-| **Movie** | **`99.13%`** | `98.70%` | `78.85%` | `55.83%` | **`100.00%`** | `96.97%` | `95.79%` | `95.79%` |
-| **Audiobook** | **`98.97%`** | `97.94%` | `0.00%` | `9.47%` | **`100.00%`** | **`100.00%`** | **`100.00%`** | `98.67%` |
-| **TV Show** | **`98.69%`** | `98.36%` | `58.40%` | `28.62%` | **`99.27%`** | **`99.27%`** | `99.26%` | `95.59%` |
-| **Software** | **`96.88%`** | **`96.88%`** | `59.38%` | `15.62%` | **`100.00%`** | `97.62%` | `97.30%` | `78.38%` |
-| **Game** | **`96.77%`** | `93.55%` | `0.00%` | `0.00%` | **`100.00%`** | `99.26%` | `99.26%` | `99.26%` |
-| **Comic** | **`96.30%`** | `88.89%` | `0.00%` | `0.00%` | **`99.20%`** | `98.40%` | `99.17%` | `95.83%` |
-| **Ebook** | **`95.83%`** | `91.67%` | `80.00%` | `4.00%` | `98.40%` | `98.40%` | `96.80%` | **`99.20%`** |
-| **Music** | **`94.41%`** | `93.01%` | `77.86%` | `7.86%` | **`100.00%`** | **`100.00%`** | **`100.00%`** | **`100.00%`** |
-| **Course** | **`93.75%`** | **`93.75%`** | `0.00%` | `0.00%` | **`100.00%`** | `97.89%` | `96.00%` | `99.00%` |
+| **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%` |
+| **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%` |
+
+*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%`.*
 
 ---
 
@@ -113,14 +159,14 @@ Unlike rule parsers (`GuessIt`, `PTT`, `RTN`, `PTN`, `Anitomy`) that only recogn
 
 ![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) | Batched Speed | Cost per 1M Torrents (Batched / Real-Time) |
+| 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) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **[Armiger Ultra](https://huggingface.co/NorviTech/armiger-ultra)** | **`98.74%`** | **`98.48%`** | **`99.38%`** | **`99.88%`** | `97.92%` | `99.95%` | **`96.83%`** | `0.50 ms` (GPU) | **`$0.00` API** (`$0.0040` compute) |
-| **Armiger (This Model)** | **`96.87%`** | `97.92%` | `99.07%` | **`99.88%`** | `97.82%` | `99.90%` | `86.64%` | **`0.42 ms` (CPU)** | **`$0.00` API** (`$0.0003` 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` |
+| **[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%` (`93.4%` raw) | `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` | `$0.00` API (`$1.85` local power) |
+| **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.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e3338e7ff3a4589169804f14a60ed9bf6a9f324b4bde8ec89b146056e4935dbf
-size 8276448
+oid sha256:0f7496a70565145d09a8b29b09de9b99171cf483e5c32ff32e89df1931231401
+size 8283542

armiger_inference.py

@@ -0,0 +1,475 @@
+"""Standalone Armiger v2.1 / Armiger Ultra ONNX Featurizer & Inference Runtime (`armiger_inference.py`).
+
+Implements exact feature parity with the Armiger v2.1 training pipeline (`sift/prepare.py` +
+`enrich_side21_v2` 36-dim `stats` vector + `vocab.json` 4,096-extension lookup + CRC32
+digit-folded path-word hashing) with zero external codebase dependencies beyond `numpy`
+and `onnxruntime`.
+
+All release-group and publisher token tables are stored as 8-byte BLAKE2b-64 hex digests
+so zero copyrighted or scene group strings appear in plaintext.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+from pathlib import Path
+import re
+from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple
+import zlib
+
+import numpy as np
+
+NEURAL_HEAD_CLASSES: Dict[str, List[str]] = {
+    "category": [
+        "movie", "tv_show", "xxx", "music", "comic",
+        "game", "audiobook", "software", "ebook", "course", "unknown",
+    ],
+    "adult": ["non_adult", "adult"],
+    "junk": ["clean", "junk"],
+    "anime": ["non_anime", "anime"],
+    "english_subs": ["no_eng_subs", "has_eng_subs", "unknown"],
+    "lang": ["english", "non_english", "multi_or_unknown"],
+    "owner_scope": ["allowed_type", "excluded"],
+    "title_language": ["english_title", "non_english_title"],
+}
+
+_VOCAB_EXT_MAP: Optional[Dict[str, int]] = None
+_PREP_KIND_EXTS = {
+    0: frozenset("mkv mp4 avi ts m2ts mts vob wmv mpg mpeg mov flv rmvb rm m4v divx webm 3gp asf f4v ogv mxf m2v bik".split()),
+    1: frozenset("mp3 flac m4a m4b aac ogg opus wav wma ape alac aif aiff aifc ac3 dts mka mpc wv shn mid midi dff dsf".split()),
+    2: frozenset("zip rar 7z gz tgz bz2 xz zst lz lzma tar cab iso img dmg mdf mds nrg wim par par2 z".split()),
+    3: frozenset("exe msi apk ipa dll bat cmd sh vbs ps1 com scr jar deb rpm appimage run lnk".split()),
+    4: frozenset("jpg jpeg png gif webp bmp tif tiff avif jxl heic heif psd ai svg ico tga cr2 nef arw dng raw eps".split()),
+    5: frozenset("pdf epub mobi azw azw3 djvu djv fb2 txt rtf doc docx odt xls xlsx ppt pptx csv chm cbz cbr cb7 html htm mht mhtml md tex lit".split()),
+    6: frozenset("srt ass ssa sub idx vtt sup smi sbv ttml dfxp usf scc stl mks".split()),
+}
+_PREP_EXT_TO_KIND = {e: k for k, es in _PREP_KIND_EXTS.items() for e in es}
+_PREP_SPLIT_ARC_RE = re.compile(r"[rsz]\d{2,3}|\d{3}")
+_PREP_WORD_RE = re.compile(r"[^\W_]+")
+_PREP_DIGIT_RE = re.compile(r"\d")
+_PREP_SAMPLE_RE = re.compile(r"(?<![a-z])samples?(?![a-z])", re.IGNORECASE)
+
+# BLAKE2b-64 digest sets for release-tag disambiguation (zero plaintext group names)
+_SUB_ONLY_GRP_H = frozenset(['00d47045a1f1cd70', '0380c309e464115a', '0665f92d474878ff', '0f8f3809c6074d35', '1bc080c5d14b4eae', '1bf62e9b91086379', '22325081668ed28b', '26503f6826942362', '33b21e80d5dbfa62', '3e370e64b7539495', '4297edae7e25ba02', '4491be5b41a66832', '44e08227cfda9421', '4b956de9c462ee70', '519230ab4d0911f7', '581a8006ea8bd801', '65dfe6849ae4bb0d', '66ced9df20d8f558', '6a437c446b5778d6', '6cff013c3e752ac8', '6d3b6a62beedc878', '7bc52c5f863149cc', '867c636203d0524d', 'a7637cb5c531d8e9', 'b61d75b73d8e0c59', 'bed3322685febfa5', 'c603a601b7cb9d4c', 'd4eaba2bc7ef10d6', 'e20a8d50ddf1aa2e', 'e3fd326207da261c', 'fc94bcdf9fcb0164'])
+_DUAL_DUB_GRP_H = frozenset(['16bdcdbbbd493d9e', '17addcc669201a7b', '1cb486cd76cea8c6', '1cc2f6b1aa24d6f7', '236a71bfa2d47ca3', '24eee113ee4bddb0', '341556a463a4784b', '3658912d4f05f762', '6149de24f7784f27', '627dcb38527b8e4c', '6f09d409143a679c', '727c5e1f0f05ad51', '8b7c320e1df4bf3e', '9e64e0c8c213bf00', 'ac3675ff37aaf11e', 'b2c26515563660f5', 'bd672057ff15b985', 'd9621931df7eb664', 'e20db1736162ba0c', 'e2ee075c6b5545ad', 'eb446df9e0b5f645', 'f7a175e4cf1907fb', 'fe0fe2b14bb48506'])
+_GAME_GRP_H = frozenset(['03f22fe2a782da12', '0ae36a7939ac0be1', '1217fb0deda20335', '1c8f3166691fa2c6', '306372c75c8ceaa2', '32ba70bb6b5ae022', '4509f87fe97d6ed9', '4afe068be7dcf016', '6f7a14ce24f000a0', '7403ff2453c8a173', '7de6d237db23b5fa', '9641051915abdbb6', 'a491e84e39e2064e', 'a65a1816debe0915', 'aa89d2a9d5d18f01', 'aaac60aa2d75341b', 'bb98906accdd6e05', 'c200372b38fb6d26', 'c430ddffd526c993', 'cc81415c4d797870', 'e37a0da20d8d24ac', 'ed5d3b0f00026714', 'ee7b45399bcf968a', 'f0982ea7c2028c50', 'f1737c198253be9d', 'f766dbb74e38db04', 'fbe41c3bbd33ac19'])
+_SW_GRP_H = frozenset(['09455d377a262608', '0e30392b37df5495', '103064b4fea08f1c', '11a120607b793e4c', '158ba6de7392b368', '193a01c7e1bd0f62', '2199b8b3ff553486', '240843438078b817', '2508ee34b17c2225', '2a4eb6e114be0fab', '352836dac99f04b7', '391bd8a58914224a', '4185301675b9d80c', '4472c2744066912a', '4c4684b615a127b2', '522a470219d3a4f1', '5a6bb90382b6c1f6', '5ca7613c608a9ea5', '63f15e4687bfc135', '66a7555e70f0872e', '73d877e885d34562', '77c343b865d05e31', '780409423e866d09', '7e0a43f55a6714fd', '97409999bccb98f4', '996bd865c828c8f9', '9b60cdfff0e2ab1c', 'a330b94802e4b6c6', 'a9c5a9029bc6a596', 'b48e9326bf6316ef', 'c595d125bfd5ebe9', 'c8575232dac0347d', 'd64fa92611ac0eb2', 'e182e241e26cb119', 'e88087f130a6e7d6'])
+_BOOK_COMIC_GRP_H = frozenset(['0cad54552908fa65', '338c3fad71b18311', '5913147687b3c1e5', '712f82b4b85131a0', '7f5fc23af41380b2', '8d8b63a8bb00b7e4', '913bd80742a62e95', 'c00722c39b756d92', 'ecfebbed5476af20', 'ee2807f40b38f779', 'f89c7ccfb7b1338c', 'fdffac028227e18e', 'ffb2c2ead4c7ba7e'])
+_BENIGN_README_EXE_H = frozenset(['062b96e72d602b6e', '9a9f15179701c6ae', 'ddf05591355cf7e0'])
+
+_TOKEN_SCAN_RE = re.compile(r"[A-Za-z0-9]+(?:-[A-Za-z0-9]+)*")
+_SIDE_ANIME_KW_RE = re.compile(
+    r"(?i)\b(?:anime|ova|ona|donghua|aeni|bdrip[ ._-]*1080p[ ._-]*flac|dual[ ._-]*audio[ ._-]*\[?(?:jpn|jap))\b"
+)
+_SIDE_VID_CLAIM_RE = re.compile(
+    r"(?i)\b(?:1080p|720p|2160p|4k|bluray|blu-ray|bdrip|brrip|web-dl|webdl|webrip|hdtv|dvdrip|hdrip|camrip|hdcam|telesync|x264|x265|hevc|avc|h264|h265|s\d{1,2}e\d{1,3}|season[ ._-]*\d+|fc2-?ppv)\b|高清首发|更新至第"
+)
+_SIDE_DECEPTIVE_SUB_STRIP_RE = re.compile(
+    r"(?i)\b(?:eng(?:lish)?|japanese|jap|jpn|spanish|esp|french|fre|german|ger|italian|ita|russian|rus)[ ._-]+(?:hard|soft)?sub(?:s|bed|titled|titles)?\b|"
+    r"\b(?:hard|soft)?sub(?:s|bed|titled|titles)?[ ._-]+(?:eng|english|esp|ja|jpn|fr|de|it|ru)\b|"
+    r"\b(?:m[ ._-]?subs?|multi[ ._-]?subs?|esubs?|vostfr|hardsubs?)\b"
+)
+_SIDE_ANIME_DUB_KW_RE = re.compile(
+    r"(?i)\b(?:dual[ ._-]?audio|dubbed|eng(?:lish)?[ ._-]?dub(?:bed)?|multi[ ._-]?audio)\b"
+)
+_SIDE_SW_APP_TITLE_RE = re.compile(
+    r"(?i)\b(?:v?\d+\.\d+\.\d+|repack|portable|cracked|keygen|patch|patcher|activat(?:ed|or)|"
+    r"multilingual|win(?:32|64)|x64|x86|macos|macosx|android|apk|pc[ ._-]?game|nintendo[ ._-]?switch|ps4|ps5|"
+    r"emulator|driver|firmware|setup|installer|downloader|studio|vst3?|"
+    r"pro[ ._-]+v?\d|ce[ ._-]+v?\d)\b"
+)
+_SIDE_COURSE_RE = re.compile(
+    r"(?i)\b(?:complete[ ._-]+course|video[ ._-]+course|masterclass|bootcamp|tutorial[ ._-]+series|certification[ ._-]+course|hands-on[ ._-]+training)\b"
+)
+_SIDE_BOOK_COMIC_KW_RE = re.compile(
+    r"(?i)\b(?:audiobooks?|audible|m4b|unabridged|narrated[ ._-]?by|ebook|e-book|epub|mobi|azw3|calibre)\b"
+)
+_SIDE_VPN_TRAP_RE = re.compile(
+    r"(?i)免费安装下载vpn|打不开网页|codec[ ._-]?(?:pack|setup|install)|player[ ._-]?setup|instructions?[ ._-]?to[ ._-]?watch"
+)
+_SIDE_CJK_RE = re.compile(r"[\u4e00-\u9fff]")
+_SIDE_EP_ZIP_RE = re.compile(
+    r"(?i)(?:^\[[^\[\]]{2,30}\].*?(?:\s-\s*\d{1,3}\b|\bS\d{1,2}E\d{1,3}\b).*?\b(?:1080p|720p|2160p|480p)\b|"
+    r"\bS\d{1,2}E\d{1,3}\b.*?\b(?:1080p|720p|2160p|WEB-DL|WEBRip|BluRay|HDTV)\b)"
+)
+_SIDE_BENIGN_ZIP_RE = re.compile(
+    r"(?i)\b(?:batch|complete|season[ ._-]*\d+|s\d{1,2}[ ._-]*complete|\d{1,3}[ ._-]*-[ ._-]*\d{1,3}|"
+    r"ost|soundtrack|artbook|scans?|wallpapers?|fonts?|subs?|subtitles?)\b"
+)
+
+
+def _h64(s: str) -> str:
+    return hashlib.blake2b(s.strip().lower().encode("utf-8"), digest_size=8).hexdigest()
+
+
+def _extract_token_hashes(text: str) -> frozenset[str]:
+    toks = [m.group(0).lower() for m in _TOKEN_SCAN_RE.finditer(text)]
+    out = {_h64(t) for t in toks}
+    for i in range(len(toks) - 1):
+        out.add(_h64(f"{toks[i]} {toks[i + 1]}"))
+    return frozenset(out)
+
+
+def _get_vocab_ext_map() -> Dict[str, int]:
+    global _VOCAB_EXT_MAP
+    if _VOCAB_EXT_MAP is not None:
+        return _VOCAB_EXT_MAP
+    vpath = Path(__file__).resolve().with_name("vocab.json")
+    if vpath.exists():
+        data = json.loads(vpath.read_text(encoding="utf-8"))
+        _VOCAB_EXT_MAP = {e: i + 1 for i, e in enumerate(data.get("exts", []))}
+    else:
+        _VOCAB_EXT_MAP = {
+            "mp3": 2, "mp4": 3, "mkv": 4, "avi": 7, "srt": 10, "zip": 12,
+            "rar": 17, "exe": 23, "flac": 24, "png": 25, "dll": 31, "m4b": 53,
+            "epub": 81, "cbz": 96, "lnk": 150, "scr": 310,
+        }
+    return _VOCAB_EXT_MAP
+
+
+def _hash_ext(ext: str) -> int:
+    if not ext:
+        return 4096
+    return _get_vocab_ext_map().get(ext.lower(), 4096)
+
+
+def _hash_word(w: str) -> int:
+    if not w:
+        return 0
+    folded = _PREP_DIGIT_RE.sub("0", w.lower())
+    return (zlib.crc32(folded.encode("utf-8")) % 32767) + 1
+
+
+def _log2_size_bucket(sz: int) -> int:
+    return min(47, max(int(sz), 1).bit_length() - 1)
+
+
+def _prep_split_path(path: str) -> Tuple[str, str, str, int]:
+    p_norm = path.replace("\\", "/")
+    parent, _, base = p_norm.rpartition("/")
+    i = base.rfind(".")
+    ext = base[i + 1:].lower() if i > 0 else ""
+    if len(ext) <= 10 and ext.isascii() and ext.isalnum():
+        stem = base[:i]
+    else:
+        ext, stem = "", base
+    return parent.rpartition("/")[2], stem, ext, p_norm.count("/")
+
+
+def _prep_path_words(parent: str, stem: str) -> List[int]:
+    p_words = [_hash_word(w) for w in _PREP_WORD_RE.findall(parent.lower())][-2:] if parent else []
+    s_words = [_hash_word(w) for w in _PREP_WORD_RE.findall(stem.lower())][-(4 - len(p_words)):]
+    res = p_words + s_words
+    while len(res) < 4:
+        res.append(0)
+    return res[:4]
+
+
+def encode_torrent_features(
+    row: Mapping[str, Any],
+    max_name_len: int = 256,
+    max_files: int = 64,
+) -> Dict[str, Any]:
+    """Encode a torrent metadata dictionary into the exact ONNX tensor inputs for Armiger v2.1."""
+    name = str(row.get("name") or row.get("title") or "")
+    b_name = name.encode("utf-8", "replace")
+    if len(b_name) > max_name_len:
+        b_name = b_name[:max_name_len].decode("utf-8", "ignore").encode("utf-8")
+    raw_bytes = list(b_name) if b_name else [32]
+
+    raw_files = row.get("files") or []
+    total_size = int(row.get("size") or 0)
+    items: List[Tuple[str, int]] = []
+    for f in raw_files[:max_files]:
+        if isinstance(f, Mapping):
+            p = str(f.get("path") or f.get("p") or f.get("name") or "")
+            sz = int(f.get("size") or f.get("s") or 0)
+        else:
+            p = str(f or "")
+            sz = 0
+        if p:
+            items.append((p, sz))
+    if not items and (row.get("ext") or row.get("extension") or "." in name):
+        items.append((name, total_size))
+
+    ext_map = _get_vocab_ext_map()
+    exts: List[int] = []
+    sizes: List[int] = []
+    depths: List[int] = []
+    words_list: List[List[int]] = []
+    f_exts_str: List[str] = []
+
+    kbytes = [0.0] * 8
+    kcount = [0] * 8
+    has_sample = False
+
+    for path, sz in items:
+        parent, stem, ext, depth = _prep_split_path(path)
+        f_exts_str.append(ext)
+        exts.append(ext_map.get(ext, 4096))
+        sizes.append(_log2_size_bucket(sz))
+        depths.append(min(depth, 7))
+        words_list.append(_prep_path_words(parent, stem))
+        k = _PREP_EXT_TO_KIND.get(ext)
+        if k is None:
+            k = 2 if _PREP_SPLIT_ARC_RE.fullmatch(ext) else 7
+        kbytes[k] += max(sz, 0)
+        kcount[k] += 1
+        has_sample = has_sample or (_PREP_SAMPLE_RE.search(path) is not None)
+
+    tot_k = sum(kbytes)
+    weights = kbytes if tot_k > 0 else [float(c) for c in kcount]
+    wsum = sum(weights)
+    count = max(int(row.get("file_count") or 0), len(items))
+    biggest = max((max(s, 0) for _, s in items), default=0)
+    max_share = (biggest / tot_k if tot_k > 0 else (1.0 / len(items) if items else 0.0))
+
+    base_stats = [
+        math.log1p(max(count, 0)),
+        math.log1p(max(total_size if total_size > 0 else int(tot_k), 0)),
+    ]
+    base_stats += [w / wsum if wsum > 0 else 0.0 for w in weights]
+    base_stats += [max_share, float(has_sample)]
+
+    side = [0.0] * 24
+    n_f = float(max(1, len(items)))
+
+    m_grp = re.match(r"^\[([^\[\]]{2,30})\]", name)
+    grp_lc = m_grp.group(1).strip().lower() if m_grp else ""
+    grp_h = _h64(grp_lc) if grp_lc else ""
+    title_tok_h = _extract_token_hashes(name)
+
+    has_sub_grp = (grp_h in _SUB_ONLY_GRP_H) or bool(title_tok_h & _SUB_ONLY_GRP_H)
+    has_dub_grp = (grp_h in _DUAL_DUB_GRP_H) or bool(title_tok_h & _DUAL_DUB_GRP_H)
+    has_game_grp = bool(title_tok_h & _GAME_GRP_H)
+    has_sw_grp = bool(title_tok_h & _SW_GRP_H)
+    has_book_grp = bool(title_tok_h & _BOOK_COMIC_GRP_H)
+
+    if items:
+        ebk_set = {"epub", "mobi", "azw3", "azw", "djvu", "fb2", "lit", "iba"}
+        pdf_set = {"pdf"}
+        m4b_set = {"m4b", "aa", "aax"}
+        com_set = {"cbz", "cbr", "cb7", "cbt"}
+        con_set = {"nsp", "xci", "xcz", "nsz", "wbfs", "rvz", "gcz", "cia", "3ds", "vpk", "ps4", "fpkg"}
+        app_set = {"dmg", "pkg", "apk", "ipa", "deb", "rpm", "appimage"}
+        exe_dll_set = {"exe", "msi", "bat", "cmd", "sh", "com", "dll"}
+        iso_set = {"iso", "bin", "cue", "mdf", "mds", "nrg", "img"}
+        arc_set = {"rar", "zip", "7z", "tar", "gz", "xz", "bz2", "001"}
+        htm_set = {"html", "htm", "url", "mht", "torrent"}
+        scr_set = {"scr", "lnk", "vbs", "hta", "pif", "ps1", "wsf", "reg"}
+        vid_set = {"mkv", "mp4", "avi", "ts", "m2ts", "wmv", "mov", "webm", "flv", "mpg", "mpeg", "m4v", "vob", "ogv", "rmvb", "3gp", "asf", "divx"}
+        bd_game_benign = {"vcdiff", "apk", "bdmv", "clpi", "mpls", "ifo", "bup", "vob", "npa", "vpk", "pck", "umd", "pak", "xp3", "rgss3a", "wolf", "nsp", "xci", "wbfs"}
+
+        side[0] = sum(1 for e in f_exts_str if e in ebk_set) / n_f
+        side[1] = sum(1 for e in f_exts_str if e in pdf_set) / n_f
+        side[2] = sum(1 for e in f_exts_str if e in m4b_set) / n_f
+        side[3] = sum(1 for e in f_exts_str if e in com_set) / n_f
+        side[4] = sum(1 for e in f_exts_str if e in con_set) / n_f
+        side[5] = sum(1 for e in f_exts_str if e in app_set) / n_f
+
+        n_real_exec_dll = 0
+        has_real_exe = False
+        has_dll = False
+        has_scr_trap = False
+        has_oversized_lnk = False
+        has_root_launcher = False
+        vid_depths: List[int] = []
+
+        for (p_i, _), e_str, s_b, d_i, w_i in zip(items, f_exts_str, sizes, depths, words_list):
+            p_tok_h = _extract_token_hashes(p_i)
+            is_benign_stub = (e_str == "exe") and (s_b < 15 or 16570 in w_i or bool(p_tok_h & _BENIGN_README_EXE_H))
+            if e_str in exe_dll_set and not is_benign_stub:
+                n_real_exec_dll += 1
+            if e_str == "exe" and not is_benign_stub and s_b >= 15:
+                has_real_exe = True
+                if d_i == 0:
+                    has_root_launcher = True
+            elif e_str == "dll":
+                has_dll = True
+            if e_str in {"scr", "lnk", "hta", "vbs", "pif", "ps1", "wsf", "cmd", "com"} and s_b >= 12:
+                has_scr_trap = True
+                if d_i == 0:
+                    has_root_launcher = True
+                if e_str == "lnk" and s_b >= 15:
+                    has_oversized_lnk = True
+            if e_str in vid_set:
+                vid_depths.append(d_i)
+
+        side[6] = n_real_exec_dll / n_f
+        side[7] = sum(1 for e in f_exts_str if e in iso_set) / n_f
+        side[8] = sum(1 for e in f_exts_str if e in arc_set) / n_f
+        side[9] = sum(1 for e in f_exts_str if e in htm_set) / n_f
+        side[10] = 1.0 if any(e in scr_set for e in f_exts_str) else 0.0
+
+        vc = bool(_SIDE_VID_CLAIM_RE.search(name) or _SIDE_ANIME_KW_RE.search(name) or has_sub_grp or has_dub_grp)
+        is_sw_or_game = bool(
+            has_game_grp
+            or has_sw_grp
+            or _SIDE_SW_APP_TITLE_RE.search(name)
+            or any(e in bd_game_benign for e in f_exts_str)
+        )
+        vid_share = base_stats[2]
+        flagged = False
+        if not is_sw_or_game and len(items) <= 12:
+            if vc and has_real_exe and has_dll:
+                flagged = True
+            elif vc and (1 <= len(vid_depths) <= 2) and min(vid_depths) >= 1 and has_root_launcher:
+                flagged = True
+            elif (vc or len(vid_depths) > 0) and has_oversized_lnk:
+                flagged = True
+            elif vc and vid_share == 0.0 and (has_real_exe or has_scr_trap):
+                flagged = True
+            elif len(items) == 1 and f_exts_str[0] == "zip" and vid_share == 0.0:
+                if _SIDE_EP_ZIP_RE.search(name) and not _SIDE_BENIGN_ZIP_RE.search(name):
+                    flagged = True
+
+        if flagged or _SIDE_VPN_TRAP_RE.search(name) or (vc and vid_share == 0.0 and (side[6] > 0 or side[9] > 0 or side[10] > 0)):
+            side[11] = 1.0
+        if vc and vid_share > 0 and 0 < base_stats[1] < 14.22:
+            side[12] = 1.0
+
+    ag = bool(_SIDE_ANIME_KW_RE.search(name)) or has_sub_grp or has_dub_grp
+    if ag:
+        side[15] = 1.0
+    clean_audio_name = _SIDE_DECEPTIVE_SUB_STRIP_RE.sub(" ", name)
+    clean_tok_h = _extract_token_hashes(clean_audio_name)
+    ad = bool(_SIDE_ANIME_DUB_KW_RE.search(clean_audio_name)) or (grp_h in _DUAL_DUB_GRP_H) or bool(clean_tok_h & _DUAL_DUB_GRP_H)
+    if ad:
+        side[16] = 1.0
+    if (grp_h in _SUB_ONLY_GRP_H) and not ad:
+        side[14] = 1.0
+    if has_game_grp:
+        side[17] = 1.0
+    if has_sw_grp and not _SIDE_CJK_RE.search(name):
+        side[18] = 1.0
+    if _SIDE_COURSE_RE.search(name) and not ag:
+        side[19] = 1.0
+    if _SIDE_BOOK_COMIC_KW_RE.search(name) or has_book_grp:
+        side[20] = 1.0
+
+    lang_str = (row.get("language") or "").strip().lower()
+    if lang_str in ("ja", "ru", "fr", "es", "de", "it", "pt", "zh", "ko", "hi", "pl", "nl", "tr", "ar") or _SIDE_CJK_RE.search(name) or ((grp_h in _SUB_ONLY_GRP_H) and not ad):
+        side[22] = 1.0
+    elif lang_str in ("en", "eng", "english") or ad:
+        side[21] = 1.0
+    else:
+        side[23] = 1.0
+
+    return {
+        "name_bytes": raw_bytes,
+        "ext": exts if exts else [0],
+        "size": sizes if sizes else [0],
+        "depth": depths if depths else [0],
+        "words": words_list if words_list else [[0, 0, 0, 0]],
+        "stats": base_stats + side,
+    }
+
+
+def build_onnx_batch_inputs(
+    rows: Sequence[Mapping[str, Any]],
+    max_name_len: int = 256,
+    max_files: int = 64,
+) -> Dict[str, np.ndarray]:
+    """Collate a sequence of torrent dicts into padded NumPy arrays for ONNX Runtime."""
+    encoded = [encode_torrent_features(r, max_name_len=max_name_len, max_files=max_files) for r in rows]
+    bsz = len(encoded)
+    max_l = max(len(e["name_bytes"]) for e in encoded)
+    max_f = max(len(e["ext"]) for e in encoded)
+
+    name_b = np.zeros((bsz, max_l), dtype=np.int64)
+    name_l = np.zeros((bsz,), dtype=np.int64)
+    ext_t = np.zeros((bsz, max_f), dtype=np.int32)
+    size_t = np.zeros((bsz, max_f), dtype=np.int64)
+    depth_t = np.zeros((bsz, max_f), dtype=np.int64)
+    words_t = np.zeros((bsz, max_f, 4), dtype=np.int32)
+    stats_t = np.zeros((bsz, 36), dtype=np.float32)
+
+    for i, e in enumerate(encoded):
+        lb = len(e["name_bytes"])
+        lf = len(e["ext"])
+        name_b[i, :lb] = e["name_bytes"]
+        name_l[i] = lb
+        ext_t[i, :lf] = e["ext"]
+        size_t[i, :lf] = e["size"]
+        depth_t[i, :lf] = e["depth"]
+        words_t[i, :lf, :] = e["words"]
+        stats_t[i, :] = e["stats"]
+
+    return {
+        "name_bytes": name_b,
+        "name_len": name_l,
+        "ext": ext_t,
+        "size": size_t,
+        "depth": depth_t,
+        "words": words_t,
+        "stats": stats_t,
+    }
+
+
+def _softmax(logits: np.ndarray) -> np.ndarray:
+    shifted = logits - np.max(logits, axis=-1, keepdims=True)
+    exps = np.exp(shifted)
+    return exps / np.sum(exps, axis=-1, keepdims=True)
+
+
+class ArmigerStandaloneONNX:
+    """Self-contained ONNX Runtime classifier for `NorviTech/armiger` and `NorviTech/armiger-ultra`."""
+
+    def __init__(
+        self,
+        onnx_path: Optional[str | Path] = None,
+        vocab_path: Optional[str | Path] = None,
+        calibration_path: Optional[str | Path] = None,
+    ) -> None:
+        import onnxruntime as ort
+
+        base = Path(__file__).resolve().parent
+        if onnx_path is None:
+            for cand in ("armiger.onnx", "armiger.int8.onnx", "armiger_ultra.fp32.onnx"):
+                if (base / cand).exists():
+                    onnx_path = base / cand
+                    break
+        if onnx_path is None:
+            raise FileNotFoundError("Could not find armiger.onnx or armiger_ultra.fp32.onnx")
+        self.onnx_path = Path(onnx_path)
+        self.vocab_path = Path(vocab_path) if vocab_path else (base / "vocab.json")
+        self.calibration_path = Path(calibration_path) if calibration_path else (base / "calibration.json")
+        self.session = ort.InferenceSession(str(self.onnx_path), providers=["CPUExecutionProvider"])
+        self.head_names = [o.name for o in self.session.get_outputs()]
+
+    def predict_batch(self, rows: Sequence[Mapping[str, Any]]) -> List[Dict[str, Any]]:
+        feed = build_onnx_batch_inputs(rows)
+        raw_outs = self.session.run(self.head_names, feed)
+        logits_by_head = dict(zip(self.head_names, raw_outs))
+        results: List[Dict[str, Any]] = []
+        for i in range(len(rows)):
+            item_res: Dict[str, Any] = {"heads": {}, "probabilities": {}}
+            for h, arr in logits_by_head.items():
+                vec = arr[i].astype(np.float64)
+                probs = _softmax(vec)
+                classes = NEURAL_HEAD_CLASSES.get(h, [str(k) for k in range(len(probs))])
+                idx = int(np.argmax(probs))
+                item_res["heads"][h] = classes[idx] if idx < len(classes) else str(idx)
+                item_res["probabilities"][h] = {
+                    classes[k]: round(float(probs[k]), 6) for k in range(min(len(classes), len(probs)))
+                }
+            item_res["category"] = item_res["heads"].get("category", "unknown")
+            p_junk = item_res["probabilities"].get("junk", {}).get("junk", 0.0)
+            p_adult = item_res["probabilities"].get("adult", {}).get("adult", 0.0)
+            p_excl = item_res["probabilities"].get("owner_scope", {}).get("excluded", 0.0)
+            item_res["p_junk"] = p_junk
+            item_res["mondrian_gates"] = {
+                "adult_flagged": bool(p_adult >= 0.5451),
+                "junk_flagged": bool(p_junk >= 0.1010),
+                "scope_excluded": bool(p_excl >= 0.4770),
+                "passed_all": bool(p_adult < 0.5451 and p_junk < 0.1010 and p_excl < 0.4770),
+            }
+            results.append(item_res)
+        return results
+
+    def predict(self, row: Mapping[str, Any]) -> Dict[str, Any]:
+        return self.predict_batch([row])[0]
+
+
+ArmigerONNXClassifier = ArmigerStandaloneONNX

config.json

@@ -2,9 +2,28 @@
   "model_name": "armiger",
   "version": "2.1.0",
   "architecture": "armiger-v2-byte-rope-transformer-8head",
+  "checkpoint": "H350000_Curated_Wave2-seed0 (direct-side hardened junk head)",
   "format": "onnx-int8-dynamic",
-  "size_bytes": 8276448,
-  "sha256": "2a562f8e0f4704c8d246865a5164e094a774a3475b3210c93e0eb7d38bbe459c",
+  "size_bytes": 8283542,
+  "sha256": "0f7496a70565145d09a8b29b09de9b99171cf483e5c32ff32e89df1931231401",
+  "artifacts": {
+    "armiger.onnx": {
+      "size_bytes": 8283542,
+      "sha256": "0f7496a70565145d09a8b29b09de9b99171cf483e5c32ff32e89df1931231401"
+    },
+    "calibration.json": {
+      "size_bytes": 2689,
+      "sha256": "bd49efbc3e04e24a3bfeb0828e0c4dd69497f1902ed81d5d883c795e1504538b"
+    },
+    "vocab.json": {
+      "size_bytes": 39991,
+      "sha256": "9853b5cd56d07beee64e6fc44396084d9c70167c4d74fbc149c0d4e021330b4e"
+    },
+    "armiger_inference.py": {
+      "size_bytes": 23176,
+      "sha256": "8dd45e3853cb6430f01bb319bdf2b83ec79a1fbf75daf5de826e74e70849e261"
+    }
+  },
   "heads": {
     "category": [
       "movie",
@@ -55,17 +74,74 @@
     "junk": 0.101,
     "owner_scope": 0.477
   },
-  "benchmarks": {
-    "gold_standard_11class_category_acc": 97.92,
-    "movie_vs_tv_acc": 99.15,
-    "catalog_matching_rate_acc": 98.38,
-    "audio_language_acc": 98.17,
-    "anime_and_dub_acc": 97.82,
-    "adult_safety_gate_acc": 98.71,
-    "junk_malware_filter_acc": 99.9,
-    "swarm_liveliness_auroc": 86.64,
-    "overall_6dim_composite_mean": 96.87,
-    "cpu_batched_latency_ms": 0.42,
-    "cpu_single_item_latency_ms": 2.44
+  "benchmarks_unmutated_ground_truth": {
+    "oct15_confirmation_n2835": {
+      "standalone_neural_11class_acc": 95.06,
+      "fair_hybrid_with_zero_cue_plugin_11class_acc": 95.49,
+      "standalone_neural_movie_vs_tv_acc": 96.6,
+      "fair_hybrid_movie_vs_tv_acc": 97.15,
+      "standalone_neural_catalog_matching_acc": 95.85,
+      "fair_hybrid_catalog_matching_acc": 96.39,
+      "standalone_neural_audio_lang_binary_n545_acc": 96.88,
+      "with_audio_guard_binary_n545_acc": 97.8,
+      "audio_lang_3class_n772_acc": 92.75,
+      "anime_n507_acc": 99.61,
+      "adult_n2781_acc": 98.53,
+      "junk_n2170_acc": 100.0,
+      "owner_scope_n2669_acc": 98.65,
+      "title_language_n969_acc": 93.7
+    },
+    "fresh_confirmation_n1402": {
+      "standalone_neural_11class_acc": 94.44,
+      "fair_hybrid_11class_acc": 94.58,
+      "adult_n1393_acc": 98.21,
+      "junk_n1291_acc": 100.0,
+      "anime_n253_acc": 99.6
+    },
+    "beyond_confirmation_n1025": {
+      "standalone_neural_11class_acc": 92.88,
+      "fair_hybrid_11class_acc": 95.12,
+      "adult_n1016_acc": 98.52,
+      "junk_n977_acc": 99.9,
+      "anime_n636_acc": 98.9,
+      "title_language_n604_acc": 98.51
+    },
+    "tc_truespec_2000_n2000": {
+      "standalone_neural_11class_acc": 87.35,
+      "fair_hybrid_11class_acc": 91.5,
+      "adult_acc": 99.9,
+      "junk_acc": 100.0,
+      "anime_n1998_acc": 95.8,
+      "lang_acc": 88.15,
+      "title_language_acc": 97.75
+    },
+    "tc_truespec_multilang_3000_n3000": {
+      "standalone_neural_11class_acc": 97.37,
+      "fair_hybrid_11class_acc": 98.4,
+      "adult_acc": 100.0,
+      "junk_acc": 100.0,
+      "anime_n2625_acc": 96.38,
+      "lang_acc": 91.47
+    },
+    "five_split_summary_n10262": {
+      "weighted_mean_standalone_neural_acc": 93.95,
+      "weighted_mean_fair_hybrid_acc": 95.39,
+      "macro_mean_standalone_neural_acc": 93.42,
+      "macro_mean_fair_hybrid_acc": 95.0
+    },
+    "swarm_liveliness": {
+      "way4_priors_formula_auroc": 86.64,
+      "with_swarm_adapter_public_tracker_n757_auroc": 92.42,
+      "with_swarm_adapter_public_holdout_n493_auroc": 93.03,
+      "with_swarm_adapter_warehouse_n10278_auroc": 96.83
+    },
+    "overall_6dim_composite_mean": 96.5
+  },
+  "hardware_latency_benchmarks": {
+    "apple_m4_pro_cpu_onnx_int8_single_item_p50_ms": 2.44,
+    "apple_m4_pro_cpu_onnx_int8_batch64_per_item_ms": 3.07,
+    "apple_m4_pro_cpu_onnx_int8_batch64_throughput_items_sec": 326.0,
+    "apple_m4_pro_mps_gpu_fp32_batch64_per_item_ms": 0.29,
+    "nvidia_rtx_gpu_fp32_batch64_per_item_ms": 0.21
   }
 }

vocab.json

@@ -0,0 +1,4134 @@
+{
+ "feature_version": 1,
+ "ext_pad_id": 0,
+ "ext_other_id": 4096,
+ "ext_vocab_size": 4097,
+ "exts": [
+  "jpg",
+  "mp3",
+  "mp4",
+  "mkv",
+  "url",
+  "txt",
+  "avi",
+  "png",
+  "",
+  "srt",
+  "nfo",
+  "zip",
+  "html",
+  "webp",
+  "gif",
+  "vob",
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+}