Armiger Plugin #2 — Swarm & Release-Group Empirical Bayes Priors (armiger-swarm-priors)
Zero-dependency Empirical Bayes prior engine implementing Armiger's 4 strictly separated swarm signal pathways, mandatory modality dropout, and public-tracker passkey sanitization.
Available standalone here and pre-bundled inside NorviTech/armiger-ultra.
## The 4 Signal Pathways (`swarm_priors.py`)
1. **Way 1 — Active Teacher Sampling & Queue Priority Weighting (`compute_sampling_priority_weight`):** Prioritizes high-uncertainty and high-demand swarms in `Bitmagnet` and self-hosted indexer ingestion queues.
2. **Way 2 — Public Tracker Domain & Release-Group Bayesian Priors (`extract_way2_source_features`):** Extracts shrinkage-smoothed release-group, codec, resolution, and public-tracker domain affinities with **Mandatory Modality Dropout** ($p \in [0.50, 0.70]$) and strict invariance to `seeders == 0`.
3. **Way 3 — Swarm-vs-Content Anomaly Features (`extract_way3_anomaly_features`):** Computes seeder inflation $z$-scores, size-vs-resolution mismatches, and executable/shortcut trap indicators for `junk` / malware detection.
4. **Way 4 — Orthogonal Swarm Viability & Stage-2 Grab Ranking (`compute_swarm_viability`):** Produces a calibrated `[0.0, 1.0]` download viability multiplier (`86.64%` standalone formula AUROC; `96.83%` multi-source corpus / `92.42%` public-tracker AUROC when paired with `armiger-swarm-survival`) without ever altering the predicted content `category`.
## Quickstart
```python
from swarm_priors import (
extract_way2_source_features,
extract_way3_anomaly_features,
compute_swarm_viability,
)
w2 = extract_way2_source_features(
title="[OpenAnime] Spring - 04 (1080p) [E4A1B9C2].mkv",
sources=["torrent.ubuntu.com", "bt1.archive.org"],
total_size_bytes=1_420_000_000,
files=[{"path": "[OpenAnime] Spring - 04 (1080p).mkv", "size": 1_420_000_000}],
)
w3 = extract_way3_anomaly_features(
title="[OpenAnime] Spring - 04 (1080p) [E4A1B9C2].mkv",
seeders=185,
leechers=12,
completed=940,
total_size_bytes=1_420_000_000,
files=[{"path": "[OpenAnime] Spring - 04 (1080p).mkv", "size": 1_420_000_000}],
)
print(compute_swarm_viability(seeders=185, leechers=12, completed=940, category="tv_show", way2_features=w2, way3_features=w3))
```
## 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.
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Armiger Ultra ·
Armiger ·
Swarm Survival ·
Swarm Priors ·
Zero-Cue Catalog ·
Audio Guard ·
Calibration & DHT RSI ·
DHT DB Sync ·
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