--- title: NorviTech emoji: ⚡ colorFrom: blue colorTo: indigo sdk: static pinned: true ---

NorviTech Suite — open-source apps for the Linux desktop and the self-hosted stack

Armiger v2.1.0 Release Canonical Statistical Chart Audit DHT Sharing Opt-In & Anonymous DH-Waypoint v5.6.0

**NorviTech** builds fast, compact, self-hosted neural models and systems tools for the Linux desktop and the self-hosted stack. ## 1. Armiger Core Neural Models (Audited Unmutated Ground-Truth Benchmarks) | Model Repository | Role | Footprint | Hardware-Attributed Speed | 6-Dim Composite | 11-Class Category (Unmutated Keys) | Movie vs. TV (`oct15` `N=1,296`) | Audio Language (`oct15` `N=545`) | Swarm Liveliness (AUROC) | | :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | **[`NorviTech/armiger-ultra`](https://huggingface.co/NorviTech/armiger-ultra)** | **Unified Flagship Suite** (`Armiger` + `Ultra FP32` + `armiger_inference.py` + **All 6 Plugins Rolled In**) | `32.3 MB` model (`+26.0 MB` `BLAKE2b-64` catalog) | **`0.21 ms` RTX GPU / `0.29 ms` M4 Pro MPS** (batch-64) | **`96.86%`** Pub / **`97.60%`** Corpus | **`95.49%`** `oct15` / **`95.12%`** `beyond` / **`98.47%`** `mlang` (`95.42%` 5-split `N=10,262`) | **`97.30%`** (`96.54%` `beyond`) | **`97.80%`** | **`96.83%`** Corpus / **`92.42%`** Pub (`100%` trap recall) | | **[`NorviTech/armiger`](https://huggingface.co/NorviTech/armiger)** | **Universal Fast CPU Classifier** (`ONNX INT8` + `armiger_inference.py`) | **`8.3 MB`** | **`2.44 ms` single-item / `3.07 ms` batch-64** (M4 Pro CPU) | **`96.85%`** (`96.50%` Raw) | **`95.06%`** `oct15` / **`92.88%`** `beyond` / **`97.37%`** `mlang` (`93.95%` 5-split `N=10,262`) | **`96.60%`** | **`96.88%`** | **`86.64%`** Priors (`92.42%` w/ Adapter) | ## 2. Armiger Modular Plugins (6 Standalone Repos & Rolled Into `Armiger Ultra`) | 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 (`61/61` unit tests, `4/4` multi-instance transfer tests, `45/45` red-team exploit probes blocked). | | **[`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 (`37/37` unit + 12-node localhost UDP Kademlia BEP-44/46 wire tests, `45/45` red-team exploit probes blocked). | --- ## 3. Ecosystem Integrations (`DH-Waypoint`, `Bitmagnet` & Torznab / Webhook Clients) Both **[`NorviTech/armiger-ultra`](https://huggingface.co/NorviTech/armiger-ultra)** and **[`spencernorton/armiger`](https://github.com/spencernorton/armiger)** include turnkey Docker Compose stacks and HTTP adapters under `examples/` and `docs/INTEGRATION_GUIDE.md` for: - **[`spencernorton/DH-Waypoint`](https://github.com/spencernorton/DH-Waypoint):** Native `dh-waypoint.processor.v1` classification, `dh-waypoint.processors.admission.v1` cohort admission gate, and 4-tier `dh-waypoint.processors.feedback.v1` closed-loop telemetry (`examples/dh-waypoint/`). - **`Bitmagnet`:** Pre-ingest / post-crawl batch classifier, GraphQL `torrent.delete` malware/honeypot pruner, and Torznab proxy (`examples/bitmagnet/`). - **Torznab Index Consumers (`Prowlarr`, `Sonarr`, `Radarr`, `Lidarr`, `Readarr`) & `qBittorrent`:** Pre-grab Torznab XML filtering proxy (`armiger_torznab_proxy.py`) and post-import webhook + `ffprobe` ground-truth receiver (`armiger_arr_webhook_receiver.py` -> `SeriesWebhookAdapter`, `MovieWebhookAdapter`, `AudioWebhookAdapter`, `PublicationWebhookAdapter`, `QBittorrentAdapter`, `PhysicalGroundTruthVerifier`). --- ## Canonical Statistical Chart Audit & Cryptographic Provenance (`v2.1.0`) All benchmark charts and headline metrics across the Armiger `v2.1.0` suite are generated deterministically from [`benchmarks/canonical_eval_vectors.json`](https://huggingface.co/NorviTech/armiger-ultra/blob/main/benchmarks/canonical_eval_vectors.json) (`0` strings, titles, or infohashes) and locked by [`benchmarks/CHARTS_MANIFEST.json`](https://huggingface.co/NorviTech/armiger-ultra/blob/main/benchmarks/CHARTS_MANIFEST.json) under four machine-enforced invariants (`pytest benchmarks/test_chart_integrity.py`): 1. **Identical Denominator Lock (`assert_identical_denominator`):** Every model compared on a benchmark bar shares the exact same item count $N$ and item mask (`oct15_confirmation` $N=2,835$; `beyond_confirmation` $N=1,025$; Movie-vs-TV $N=1,296$; Audio $N=545$; Anime $N=507$; Junk $N=2,170$). 2. **Non-Circular Teacher Evaluation Lock (`assert_non_circular_teacher_eval`):** Frontier teacher models (`OpenAI GPT (Fast Tier)`, `Google Gemini Flash`) are evaluated strictly via **Leave-One-Teacher-Out (LOTO)** reference keys (`self_vote_weight = 0.0`), removing $+1.47\text{ pp}$ and $+1.81\text{ pp}$ of circular self-vote inflation. 3. **Paired Bootstrap 95% CIs & Exact McNemar $p$-Values:** Computed using `10,000` paired resamples (`seed = 20261018`, integer-scaled `float64` dot products) and exact binomial `math.comb` McNemar tests (`p = 1.42e-11` vs. `Google Gemini Flash` LOTO; `p = 3.88e-24` vs. `Anthropic Claude Haiku 4.5`). 4. **Deterministic PNG Chunk Stripping:** Output PNGs contain only `IHDR`, `IDAT`, and `IEND` chunks with pinned SHA-256 digests (`0a0bc3a2...`, `7344ba71...`, `51e1f229...`, `1a77a643...`). --- ## Privacy & Anonymous Opt-In-By-Default DHT Sharing (`opt_in_sharing=False`) Distributed DHT intelligence sharing (`NorviTech/armiger-calibration-dht` and `NorviTech/armiger-dht-db-sync`) is **strictly opt-in by default** and **enforced by the 5-layer `AnonymityGuard` wire firewall** when enabled (see [`docs/ANONYMOUS_OPT_IN_DHT.md`](https://huggingface.co/NorviTech/armiger-ultra/blob/main/docs/ANONYMOUS_OPT_IN_DHT.md) and [`benchmarks/anonymous_opt_in_proof_report.json`](https://huggingface.co/NorviTech/armiger-ultra/blob/main/benchmarks/anonymous_opt_in_proof_report.json)): | Guarantee | Default State | Wire-Level & Mathematical Enforcement | | :--- | :--- | :--- | | **Opt-In by Default** | `opt_in_sharing=False` (`ARMIGER_DHT_SHARE_OPT_IN="0"`) | `0` outbound DHT packets are emitted unless the operator explicitly opts in (`opt_in_sharing=True` and `ARMIGER_DHT_SHARE_OPT_IN="1"`). | | **`AnonymityGuard` Wire Firewall** | Always active (`500/500` canary block rate) | Inspects every outbound payload before signing; blocks `100.0%` (`500/500`) of synthetic leaks containing raw 40-hex infohashes, release titles, file paths, tracker URLs, passkeys, IPv4/IPv6 addresses, or hostnames. | | **Ephemeral `Ed25519` Keys** | Single-use keypair per announcement | Rotates a fresh 32-byte `Ed25519` keypair and `ephemeral_node_id` on every publish so contributions are unlinkable across time. | | **Coarse UTC Hour Timestamps** | `epoch_ts = (utc_now // 3600) * 3600` | Quantizes all outbound timestamps to 1-hour UTC boundaries (`3600s`), eliminating clock-skew and grab-timing correlation. | | **DP-Clipped Weight Deltas** | `clip_norm = 1.0`, `noise_multiplier = 0.05` | Clips calibration/adapter update vectors in $L_2$ norm (`<= 1.0`) and adds calibrated Gaussian noise ($\sigma = 0.05$) before hybrid `X25519` + `ML-KEM-768` + `ChaCha20-Poly1305` encryption. | | **`BLAKE2b-64` One-Way Catalog Hashes** | 16-hex-char (`64-bit`) digest | Shared catalog entries store only domain-separated `BLAKE2b-64` title hashes mapped to integer catalog IDs—never raw torrent names or infohashes. | --- ## 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. ---

Part of the NorviTech Suite — open-source apps for the Linux desktop and the self-hosted stack

Armiger Ultra · Armiger · Swarm Survival · Swarm Priors · Zero-Cue Catalog · Audio Guard · Calibration & DHT RSI · DHT DB Sync · norvitech.com