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

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`	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`	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`	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`	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`	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`	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

Armiger Plugin #3 — Zero-Cue Catalog & Structural Disambiguator (armiger-zero-cue-catalog)