Armiger Plugin #3 — Zero-Cue Catalog & Structural Disambiguator (armiger-zero-cue-catalog)
Resolves bare-title ("zero-cue") torrents lacking season/episode syntax via an offline 929,400-entry BLAKE2b-64 cryptographically hashed media catalog (26.0 MB) and deterministic structural rules, lifting 5-split (N=10,262) category accuracy from 94.00% to 95.42% and specialist zero-cue accuracy from 87.40% to 91.50% (+4.10 pp).
Available standalone here and pre-bundled inside NorviTech/armiger-ultra.
## Why the Zero-Cue Catalog Plugin Exists
In real-world BitTorrent/DHT indexing, **2% to 4%** of movie, TV, and anime torrents contain **zero structural cues** in either their title or file tree:
- `Sintel (2010) [1080p] [WEB-DL].mkv` (a standalone film vs. a 1-file TV special)
- `[SubGroup] Bare Japanese/English Title [1080p].mkv` (a theatrical anime film vs. a TV OVA/series without an episode number)
- `Director Name - Movies 1-6 Collection (1979-1998) 1080p` (a multi-file movie pack whose 6 `.mkv` files look identical in file-count to a 6-episode TV mini-series)
Even a 70B+ parameter cloud LLM must guess when given only a bare title unless it can consult a media catalog. `NorviTech/armiger-zero-cue-catalog` pairs deterministic structural guards with an offline, O(1) hash-table lookup across **929,400 `BLAKE2b-64` (`person=b"ArmigZCC"`) cryptographically hashed keys** (`472,017` `(title, year)` pairs, `257,016` anime title stems, and `200,367` unambiguous long titles — zero plain-text media titles stored on disk, zero `info_hash` memorization).
## Measured Accuracy Lift Across All 6 Evaluation Cohorts (Unmutated Ground-Truth Keys)
| Evaluation Cohort (Unmutated `rows.y`) | Valid `N` | Standalone Neural (`FP32` / `INT8`) | **Fair Blind Hybrid (`+ armiger-zero-cue-catalog` FP32 / INT8)** | Net Lift |
| :--- | :---: | :---: | :---: | :---: |
| **`oct15_confirmation` (Gold Standard)** | `2,835` | `95.03%` / `95.06%` (`96.60%` Movie-vs-TV) | **`95.49%`** / **`95.49%`** (**`97.30%`** Movie-vs-TV) | `+0.46 pp` |
| **`fresh_confirmation` (Fresh Holdout)** | `1,402` | `94.51%` / `94.44%` | **`94.58%`** / **`94.51%`** | `+0.07 pp` |
| **`beyond_confirmation` (Hard Edge-Cases)** | `1,025` | `92.88%` / `92.88%` (`88.74%` Movie-vs-TV) | **`95.12%`** / **`95.12%`** (**`96.54%`** Movie-vs-TV) | **`+2.24 pp`** |
| **`zero_cue_2000` (Specialist Zero-Cue)** | `2,000` | `87.40%` / `87.35%` | **`91.50%`** / **`91.50%`** | **`+4.10 pp`** |
| **`multilang_3000` (Multilingual)** | `3,000` | `97.50%` / `97.37%` | **`98.47%`** / **`98.40%`** | **`+0.97 pp`** |
| **`calibration` (Calibration Split)** | `424` | `96.46%` / `96.46%` | **`96.70%`** / **`96.70%`** | `+0.24 pp` |
| **5-Split Weighted Mean (`N=10,262`)** | **`10,262`** | **`94.00%`** / **`93.95%`** | **`95.42%`** / **`95.39%`** | **`+1.42 pp`** |
## Quickstart
```python
from zero_cue_plugin import ZeroCueCatalogPlugin
plugin = ZeroCueCatalogPlugin("validated_zero_cue_catalog.json")
res = plugin.resolve(
raw_name="Sintel (2010) [1080p] [WEB-DL]",
probs_category=[0.46, 0.51, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03],
p_adult=0.01,
p_anime=0.02,
)
print(res)
# -> {'category_index': 0, 'category': 'movie', 'resolution_source': 'zero_cue_title_year'}
```
## Part of the Armiger Modular Plugin Suite
| 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`) | `167 KB` (`132 KB` `.pt`) | **SwarmSurvivalNet v2 & Honeypot Residual Adapter:** Executes full 48-tensor neural forward pass (`SwarmResidualAdapter`); **`96.83%`** Multi-Source Corpus 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 `zero_cue_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, `.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). |
---
## Intended Use & Legal Compliance
`Armiger`, `Armiger Ultra`, and all bundled plugins are open-source machine learning and cryptographic metadata-classification utilities designed strictly for **lawful use cases**, including:
- Organizing and indexing licensed, public-domain, Creative Commons, and open-source digital media, software distributions (e.g., Linux ISOs, open-source games), and educational archives (e.g., OpenCourseWare, LibriVox, Project Gutenberg).
- Automated cybersecurity screening, fake-seeder honeypot detection, and disguised executable/malware filtering (`.mkv.exe`, `.scr`, `.vbs`) in peer-to-peer and decentralized storage research.
- Child safety (CSAM/CSAE) pre-ingest blocking, cryptographic tombstone revocation, and adult/NSFW namespace isolation for self-hosted and decentralized indexers.
**NorviTech does not condone, encourage, or support copyright infringement or the unauthorized distribution of copyrighted works.** Users are solely responsible for ensuring that their use of these models and plugins complies with all applicable local, national, and international copyright and data-protection laws.
---
Armiger Ultra ·
Armiger ·
Swarm Survival ·
Swarm Priors ·
Zero-Cue Catalog ·
Audio Guard ·
Calibration & DHT RSI ·
DHT DB Sync ·
norvitech.com