Armiger

Fast 8.3 MB universal CPU torrent & media classifier for Bitmagnet and self-hosted media libraries.
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 (armiger_inference.py + vocab.json) included out of the box.

Armiger (NorviTech/armiger) is an ultra-compact (8.3 MB, 8,283,542 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 (0.21 ms RTX / 0.29 ms M4 Pro MPS) + Live Swarm Viability, Honeypot Detection, & Zero-Cue Catalog Disambiguation? Use 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):

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.WEBM.AV1-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'])

Ecosystem Integrations (`DH-Waypoint`, `Bitmagnet` & Torznab / Webhook Clients)

Armiger and Armiger Ultra ship with turnkey HTTP services, Docker Compose stacks, and reference adapters in examples/ and docs/INTEGRATION_GUIDE.md:

DH-Waypoint Integration (examples/dh-waypoint/) — Drop-in processorapi/v1 service (armiger_waypoint_processor.py) and plugin manifest (07-armiger-ultra.plugin.yml) implementing batch classification (dh-waypoint.processor.v1), pre-ingest cohort admission (dh-waypoint.processors.admission.v1), and 4-tier dh-waypoint.processors.feedback.v1 closed-loop telemetry for spencernorton/DH-Waypoint.
Bitmagnet Integration (examples/bitmagnet/) — Pre-ingest / post-crawl batch classifier and malware/honeypot pruner (bitmagnet_armiger_bridge.py) that upgrades upstream Bitmagnet from 35.17% rule accuracy to 95.49% 11-class neural accuracy and automatically prunes disguised .mkv.exe/.scr/.lnk malware traps via GraphQL torrent.delete.
Torznab Proxy & Media Manager Webhook Suite (examples/arr-stack/) — Pre-grab Torznab XML filtering proxy (armiger_torznab_proxy.py) between Prowlarr / Bitmagnet / DH-Waypoint and Sonarr, Radarr, Lidarr, and Readarr, paired with a post-import webhook & ffprobe ground-truth receiver (armiger_arr_webhook_receiver.py) wired into SeriesWebhookAdapter, MovieWebhookAdapter, AudioWebhookAdapter, PublicationWebhookAdapter, QBittorrentAdapter, and PhysicalGroundTruthVerifier.

See the Complete Ecosystem Integration Guide (docs/INTEGRATION_GUIDE.md) for architecture diagrams, Docker Compose files, and step-by-step setup instructions.

Documentation & Verification Index

docs/INTEGRATION_GUIDE.md — Turnkey integration architecture and Docker Compose guides for DH-Waypoint, Bitmagnet, Prowlarr, Sonarr, Radarr, Lidarr, Readarr, and qBittorrent.
docs/ANONYMOUS_OPT_IN_DHT.md — Mathematical and wire-level specification for opt-in-by-default (opt_in_sharing=False, ARMIGER_DHT_SHARE_OPT_IN="0") anonymous DHT weight and catalog sharing (AnonymityGuard, ephemeral Ed25519, coarse UTC hour bucketing, DP-clipped deltas, and BLAKE2b-64 catalog hashes).
docs/CHART_PIPELINE.md & benchmarks/CHARTS_MANIFEST.json — Canonical statistical chart pipeline, non-circular LOTO teacher evaluation, paired bootstrap 95% CIs, exact McNemar $p$-values, and SHA-256 PNG provenance manifest.
benchmarks/anonymous_opt_in_proof_report.json — Reproducible cryptographic proof report (0 packets emitted when opt-in is disabled; 500/500 synthetic PII/infohash/title/IP leaks blocked by AnonymityGuard).

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):

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 / 97.30% 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)
- zero_cue_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)
- 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
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 zero_cue_2000 N=2,000; 100.00% on multilang_3000 N=3,000)
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 zero_cue_2000 & multilang_3000; 7/7 (100%) disguised executable malware traps blocked at P(junk) > 0.999)
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 zero_cue_2000 1,914/1,998)
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 multilang_3000 N=3,000)
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 zero_cue_2000 N=2,000)
english_subs (Subtitle Availability): no_eng_subs, has_eng_subs, unknown (93.04% on stop N=2,171; 87.17% on multilang_3000 N=3,000)
owner_scope (Catalog Policy Gate): allowed_type vs. excluded (98.65% on oct15_confirmation 2,633/2,669; 99.80% on zero_cue_2000 N=2,000; 99.90% on multilang_3000 N=3,000)

Hardware-Attributed Latency & Footprint Benchmarks

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 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):

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, strictly opt-in-by-default (`opt_in_sharing=False`) anonymous sharing via `AnonymityGuard` (`500/500` leak block rate, ephemeral `Ed25519` keys, coarse UTC hour bucketing, DP-clipped deltas), and 500-canary regression gate.
`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, strictly opt-in-by-default (`opt_in_sharing=False`) `BLAKE2b-64` one-way catalog hashing via `AnonymityGuard`, and $K$-of-$N$ distinct-ASN Sybil quorum.

1. Categorical & Open-Source Torrent Classifier Comparisons (Unmutated Ground-Truth Keys)

7-Dimension Comparison vs. Open-Source Classifiers & Parsers

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` / `zero_cue_2000`)	Adult / NSFW Gate (`N=2,781`)	Junk / Malware (`N=2,170`)	Swarm Liveliness (AUROC)
Armiger Ultra (Fair Hybrid)	`32.3 MB` (`+26 MB` cat.)	`95.49%` (`98.47%` mlang)	`97.30%`	`96.53%`	`97.80%`	`99.61%` (`95.80%` `zero_cue_2000`)	`98.53%`	`100.00%` (`99.90%` beyond)	`96.83%` Corpus / `92.42%` Pub
Armiger (8.3 MB INT8 + Plugins)	`8.3 MB`	`95.49%` (`95.06%` Raw)	`97.30%` (`96.60%` Raw)	`96.53%` (`95.83%` Raw)	`97.80%` (`96.88%` Raw)	`99.61%` (`95.80%` `zero_cue_2000`)	`98.49%`	`100.00%` (`99.90%` beyond)	`92.42%` Pub (`86.64%` Priors)
MIMMO BERT (360M)	`~1,400 MB`	`72.24%`	`76.00%` (`93.61%` 4-Med)	`60.26%`	N/A	N/A	`91.05%`	N/A	N/A
MIMMO Cascade	`~1,400 MB`	`71.32%`	`76.08%` (`93.97%` 4-Med)	`60.34%`	N/A	N/A	`89.68%`	N/A	N/A
RTN (`rank-torrent-name`)	Code Only	`53.58%`	`81.64%`	`80.48%`	`93.76%`	`77.65%`	`79.00%`	`49.29%` (`0%` rec., `98.57%` spec.)	N/A
PTT / Parsett	Code Only	`53.58%`	`81.64%`	`80.48%`	`93.76%`	`77.65%`	`79.00%`	N/A	N/A
GuessIt	Code Only	`38.69%`	`84.65%`	`84.03%`	`94.86%`	`78.80%`	N/A	N/A	N/A
PTN (`parse-torrent-title`)	Code Only	`35.59%`	`77.85%`	`77.62%`	`93.21%`	`68.65%`	N/A	N/A	N/A
Anitomy	Code Only	`33.40%`	`73.07%`	`72.76%`	`90.46%`	`64.55%`	N/A	N/A	N/A
Bitmagnet Built-In	Code Only	`35.17%`	`43.21%` (`88.4%` ans.)	`43.21%`	`88.26%`	N/A	`76.73%`	`50.00%` (`0%` rec., `100%` spec.)	N/A

Per-Category Recall Breakdown Across All 10 Media & Software Categories (Unmutated Keys)

Content Category	`oct15` (`N=2,835`) Armiger Ultra (Fair Hybrid)	`oct15` (`N=2,835`) Armiger (INT8 + ZeroCue)	`oct15` MIMMO BERT (360M)	`oct15` Bitmagnet Built-In	`beyond` (`N=1,025`) Armiger Ultra (Fair Hybrid)	`beyond` (`N=1,025`) Armiger (INT8 + ZeroCue)	`beyond` OpenAI GPT (Fast Tier, LOTO)*	`beyond` Google Gemini Flash (LOTO)*
Audiobook	`97.89%`	`97.89%`	`0.00%`	`9.47%`	`100.00%`	`100.00%`	`98.67%`	`98.00%`
Adult / XXX	`97.80%`	`97.80%`	`95.50%`	`39.37%`	`91.95%`	`91.95%`	`94.83%`	`95.69%`
TV Show	`97.50%`	`97.50%`	`58.40%`	`28.62%`	`96.32%`	`96.32%`	`97.79%`	`93.75%`
Movie	`97.12%`	`97.12%`	`78.85%`	`55.83%`	`96.84%`	`96.84%`	`94.21%`	`92.63%`
Software	`93.75%`	`93.75%`	`59.38%`	`15.62%`	`94.59%`	`94.59%`	`97.30%`	`77.03%`
Music	`90.71%`	`90.71%`	`77.86%`	`7.86%`	`83.33%`	`83.33%`	`100.00%`	`100.00%`
Game	`90.16%`	`90.16%`	`0.00%`	`0.00%`	`96.30%`	`96.30%`	`98.52%`	`98.52%`
Ebook	`88.00%`	`88.00%`	`80.00%`	`4.00%`	`98.40%`	`98.40%`	`96.00%`	`96.80%`
Course	`81.82%`	`81.82%`	`0.00%`	`0.00%`	`92.00%`	`92.00%`	`95.50%`	`97.50%`
Comic	`66.67%`	`66.67%`	`0.00%`	`0.00%`	`98.33%`	`98.33%`	`96.67%`	`95.00%`

Note on beyond_confirmation (N=1,025): OpenAI GPT (Fast Tier) and Google Gemini Flash are evaluated using non-circular Leave-One-Teacher-Out (LOTO) reference keys (self_vote_weight = 0.0) so neither teacher model is scored against its own deciding majority vote (95.12% and 93.80% LOTO vs. 96.59% and 95.61% circular self-voted).

2. Frontier Cloud LLM & Local GPU LLM Comparisons

Accuracy Across 6 Core Dimensions vs. Frontier LLMs

Operational Cost & Token Rate vs. Frontier LLMs

Model	Overall 6-Dim Mean (95% CI)	11-Class Category (`N=1,025` LOTO / `oct15`)	Catalog Matching (`N=1,025` LOTO / `oct15`)	Audio Language (Strict E-vs-F `N=229` / `N=545`)	Anime & Dub (Valid Media `N=636` / `N=507`)	Junk / Malware (`N=977` Bal / Acc)	Swarm Liveliness (AUROC)	Measured Hardware Speed	Cost per 1M Torrents (Batched / Real-Time)
Armiger Ultra (Fair Hybrid)	`96.86%` Pub / `97.60%` Corpus (`[96.35, 97.38]`)	`95.12%` (`95.49%` `oct15`)	`96.39%` (`96.53%` `oct15`)	`99.13%` (`97.80%` `oct15`)	`98.90%` (`99.61%` `oct15`)	`99.23%` (`99.90%` Acc / `100%` `oct15`)	`96.83%` Corpus / `92.42%` Pub	`0.21 ms` RTX / `0.29 ms` M4 Pro MPS	`$0.00` API (`$0.0040` compute)
Armiger (`armiger.int8.onnx` + Plugins)	`96.85%` (`[96.40, 97.30]`)	`95.12%` (`95.49%` `oct15` / `95.06%` Raw)	`96.29%` (`95.80%` Raw)	`99.13%` (`97.80%` `oct15` / `96.88%` Raw)	`98.90%` (`99.61%` `oct15`)	`99.23%` (`99.90%` Acc / `100%` `oct15`)	`92.42%` Pub (`86.64%` Priors)	`2.44 ms` single / `3.07 ms` batch-64 (M4 Pro CPU)	`$0.00` API (`$0.0020` compute)
Google Gemini Flash (LOTO)*	`94.56%` (`[93.97, 95.15]`)	`93.80%` (`95.61%` Circ.)	`90.88%`	`98.69%`	`95.60%` (`608/636`)	`99.18%` (Non-Circ.)	`89.20%` Pub	`~450 ms`	`$94.90` / `$147.00`
Anthropic Claude Haiku 4.5	`92.88%` (`[92.10, 93.66]`)	`93.76%`	`86.15%`	`95.63%`	`98.90%`	`93.09%`	`89.76%` Pub	`~310 ms`	`$712.00` / `$1,220.00`
OpenAI GPT (Fast Tier, LOTO)*	`86.24%` (`[85.64, 86.83]`)	`95.12%` (`96.59%` Circ.)	`89.07%`	`97.82%` (`94.68%` `oct15`)	`95.60%` (`608/636`)	`50.00%` (`0%` rec.)	`89.82%` Pub	`~365 ms`	`$127.30` / `$220.50`
Local 8B Instruct LLM (FP16)	`84.79%` (`[83.80, 85.79]`)	`80.78%`	`77.85%`	`93.45%`	`92.45%`	`97.91%`	`66.32%` Pub	`~185 ms` (`5.4/s` RTX 4070 TiS)	`$0.00` API (`$1.85` local power)

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 (0 strings, titles, or infohashes) and locked by benchmarks/CHARTS_MANIFEST.json under four machine-enforced invariants (pytest benchmarks/test_chart_integrity.py):

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 $N=2,170$).
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.
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).
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 and 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.

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

Downloads last month: 63

Collection including NorviTech/armiger

Armiger — Torrent & Media Intelligence Suite

Collection

Compact byte-level RoPE Transformer + File-Tree Encoder models and Swarm Health Adapters for real-time BitTorrent, DHT, and media classification. • 8 items • Updated about 18 hours ago • 1