docs/plugins/calibration-dht.md

Armiger Plugin #5 — Conformal Calibration & BEP-44 Decentralized DHT Updater (armiger-calibration-dht)

Per-head temperature scaling, Menon prior adjustments, Mondrian conformal safety gates (100.00% junk accuracy on oct15_confirmation N=2,170 and 99.90% on beyond_confirmation N=977), and a Hybrid Post-Quantum Ed25519 + WOTS+-SHAKE256 signed BEP-44 / BEP-46 BitTorrent DHT update & RSI engine.
Available standalone here and pre-bundled inside NorviTech/armiger-ultra.

1. Per-Head Calibration & Mondrian Conformal Safety Gates (calibration.json)

Raw neural network logits are often overconfident on rare classes (course, comic, software) and high-stakes safety boundaries (adult, junk). calibration.json + ArmigerCalibrator apply:

• Per-Head Temperature Scaling ($T_h$) and Menon Long-Tail Logit Adjustments ($\tau_h \log \pi_h$) across all 8 classification heads.
• Mondrian Conformal Safety Thresholds (Unmutated Ground-Truth Keys):
  • adult: 0.5451 (98.53% on oct15_confirmation N=2,781, 98.62% on beyond_confirmation N=1,016, 99.90% on zero_cue_2000 N=2,000, 100.00% on multilang_3000 N=3,000)
  • junk: 0.1010 (100.00% on oct15_confirmation 2,170/2,170, 100.00% on fresh_confirmation 1,291/1,291, 99.90% on beyond_confirmation 976/977, and 7/7 (100%) disguised executable malware traps blocked)
  • owner_scope: 0.4770 (98.65% on oct15_confirmation 2,633/2,669, 99.80% on zero_cue_2000, 99.90% on multilang_3000)

2. Decentralized BEP-44 + Ed25519 DHT Update Engine (ArmigerDHTUpdater)

When Armiger or Armiger Ultra runs inside a distributed indexer (Bitmagnet and self-hosted media indexers), ArmigerDHTUpdater enables opt-in decentralized hot-reloading of new zero-cue catalog entries, swarm-priors tables, and rank-8 LoRA micro-adapters directly over the mainline BitTorrent DHT:

• BEP-44 Mutable DHT Pointers + BEP-46 Infohash Distribution: Nodes look up SHA1(pubkey || "armiger-ultra-v2") on the DHT to discover the latest signed bundle infohash.
• Cryptographic Anti-Spoofing (Ed25519 + Monotonic seq): Every manifest must carry a valid Ed25519 signature from the pinned offline root public key and a strictly increasing sequence number (seq > current_seq), preventing spoofed updates and downgrade attacks.
• $K$-of-$N$ Quorum & Safe Formats Only: Bundles contain only JSON, ONNX, or SafeTensors tensors (never arbitrary Python pickle code) and require a configurable $K$-of-$N$ attestation quorum before activation.

3. Distributed Recursive Self-Improvement (RSI) & Data Engine (armiger_dht_rsi.py)

For multi-agent indexers, private media stacks, and community clusters, armiger_dht_rsi.py provides a production-grade, zero-trust distributed training & calibration telemetry pipeline:

1. Universal Adapters (Client-Agnostic across Media Stacks): Native ingestion for series, movie, audio, and publication library webhooks, ffprobe / MediaInfo physical stream inspectors, and qBittorrent / Transmission completion events.
2. Multi-Stage Physical Ground-Truth Verification: Rejects raw unverified model predictions. Observations require 100% download verification, physical stream decodability, and canonical catalog matches (series_catalog, movie_catalog, audio_catalog, isbn). Active malware masquerades (.exe or .scr disguised as media) are physically isolated as junk canaries.
3. Cryptographic Anonymization & Differential Privacy: Strips all private tracker passkeys, IPv4/IPv6 addresses, and user home directory paths. Generates salted HMAC-blinded syntactic title stems. Adds $(\epsilon, \delta)$-differential privacy (Laplace perturbation + 16MB log-binning) and Poisson timing jitter ($\Delta t \sim \text{Exp}(\lambda)$) against traffic analysis.
4. Post-Quantum End-to-End Encryption: Hybrid X25519 ECDH + NIST FIPS 203 ML-KEM-768 lattice key encapsulation (with Fujisaki-Okamoto CCA2 implicit rejection), combined via SHAKE-256 / HKDF-SHA3-256 with 256-bit ChaCha20-Poly1305 AEAD payload encryption.
5. Hybrid Post-Quantum Anti-Rollback Protocol: BEP-44 strictly monotonic sequence numbers (seq > current_seq) paired with Hybrid Ed25519 + WOTS+-SHAKE256 (RFC 8391 / NIST FIPS 205 hash-based PQ) threshold multi-signatures ($M$-of-$N$ federation quorum).
6. Sybil-Resistant ASN Quorum & Canary Gate: Enforces $K$-of-$N$ distinct-ASN peer attestation quorum, strict format whitelisting (SafeTensors, ONNX, JSON only; zero pickle tolerance), and a mandatory local 500-item golden canary regression gate (rejects any update with malware bypass or Mondrian safety degradation).
7. Opt-In / Opt-Out Modes: Supports off, pull_only, and anonymous_contribute.

Quickstart

from calibration_and_dht import ArmigerCalibrator
from armiger_dht_rsi import ArmigerDHTRSIEngine, SeriesWebhookAdapter

# 1. Conformal calibration
cal = ArmigerCalibrator("calibration.json")
probs_cat = cal.calibrate_head("category", [2.4, -1.1, -3.0, -2.5, -3.1, -2.8, -3.2, -2.9, -3.0, -3.4, -2.0])
gates = cal.evaluate_safety_gates(p_adult=0.02, p_junk=0.004, p_excluded_scope=0.01)
print("Calibrated top prob:", max(probs_cat), "Safety gates:", gates)

# 2. Distributed RSI Engine
engine = ArmigerDHTRSIEngine(mode="anonymous_contribute")
obs = SeriesWebhookAdapter.parse_webhook({"eventType": "Download", "series": {"seriesCatalogId": 371980}})
ok, reason, record = engine.ingest_candidate(obs)
print("Ingested observation:", ok, reason)

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).

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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 · norvitech.com