Rename zero_cue_2000 and multilang_3000 evaluation split labels

Standardize evaluation split identifiers across charts, manifests, configs, and docs.

README.md

@@ -89,12 +89,12 @@ All metrics below are reproducibly computed using `armiger_inference.py` against
    - **`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`)
-   - **`tc_truespec_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)
-   - **`tc_truespec_multilang_3000` (`N=3,000`):** **`97.37%`** Standalone Neural INT8 (**`97.50%`** FP32 / **`98.47%`** Fair Blind Hybrid)
+   - **`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
 2. **`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 `tc2000` `N=2,000`; **`100.00%`** on `multilang3000` `N=3,000`)
 3. **`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 `tc2000` & `multilang3000`; **`7/7` (`100%`)** disguised executable malware traps blocked at `P(junk) > 0.999`)
-4. **`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 `tc_truespec_2000` `1,914/1,998`)
+4. **`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`)
 5. **`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 `multilang3000` `N=3,000`)
 6. **`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 `tc2000` `N=2,000`)
 7. **`english_subs` (Subtitle Availability):** `no_eng_subs`, `has_eng_subs`, `unknown` (**`93.04%`** on `stop` `N=2,171`; **`87.17%`** on `multilang3000` `N=3,000`)
@@ -121,7 +121,7 @@ All metrics below are reproducibly computed using `armiger_inference.py` against
 | :--- | :---: | :---: | :--- |
 | **[`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 `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`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, `<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`](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, 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`](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, 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. |
@@ -196,7 +196,7 @@ All benchmark charts and headline metrics across the Armiger `v2.1.0` suite are
 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...`, `3cf46fd5...`, `e6be359c...`, `1a77a643...`).
+4. **Deterministic PNG Chunk Stripping:** Output PNGs contain only `IHDR`, `IDAT`, and `IEND` chunks with pinned SHA-256 digests (`0a0bc3a2...`, `3cf46fd5...`, `3768e7ec...`, `1a77a643...`).
 
 ---
 

benchmarks/CHARTS_MANIFEST.json

@@ -36,10 +36,10 @@
   ],
   "provenance_sha256": {
     "canonical_eval_vectors.json": "058473f4ba21bc07db2c2b6981d5e62f3c584311fc134d92cb523de7f261243a",
-    "generate_canonical_charts.py": "e6d73c7197057fbb7cea3dee7f11599e6ce50039f692eada2d0ebae61ec774c4",
+    "generate_canonical_charts.py": "409e9cd324b8e14acd8db58f46ebab9d0ea623e57cf0b7eabb2343f0a9237daa",
     "assets/armiger_vs_llms_overall_and_6dims.png": "0a0bc3a260b3c3b1188a92fb9cc015c6bdb786e9033924d9daa7a36be332fc62",
     "assets/armiger_vs_llms_cost_comparison.png": "3cf46fd59817d3bffafce1f4f5a15379ffef7bf779b8d4b40b866a8d9671eff4",
-    "assets/armiger_vs_torrent_classifiers_6dims.png": "e6be359c5cc980b4fe30810d4f2f5b8b202bd3fca6f2a4a33ae0727c5de2f010",
+    "assets/armiger_vs_torrent_classifiers_6dims.png": "3768e7ec5bef503027375f87f089362798ab2ec74f96a7ea450458de325154dd",
     "assets/armiger_categorical_breakdown.png": "1a77a64340f7e05af318105d83270b29396450d01f22f71cffdf808b45e10d98",
     "validated_zero_cue_catalog.json": "a43193a7d9982f1b83e9e6968c47dbbe8d058b113af5020d6d5ea45ed2af7fcd",
     "armiger_ultra.fp32.onnx": "24c65e1af1be68c923bfdf774afb862c54b66a30f40c4b65cf5961929f149d48",
@@ -556,7 +556,7 @@
       ]
     },
     "armiger_vs_torrent_classifiers_6dims.png": {
-      "sha256": "e6be359c5cc980b4fe30810d4f2f5b8b202bd3fca6f2a4a33ae0727c5de2f010",
+      "sha256": "3768e7ec5bef503027375f87f089362798ab2ec74f96a7ea450458de325154dd",
       "panel_1_category_11class_oct15": {
         "panel_name": "chart3_1_oct15_cat11",
         "n": 2835,
@@ -1475,7 +1475,7 @@
         }
       }
     },
-    "tc_truespec_2000": {
+    "zero_cue_2000": {
       "panel_name": "five_split_tc2000",
       "n": 2000,
       "metric_type": "mean",
@@ -1523,7 +1523,7 @@
         }
       }
     },
-    "tc_truespec_multilang_3000": {
+    "multilang_3000": {
       "panel_name": "five_split_tc3000",
       "n": 3000,
       "metric_type": "mean",

benchmarks/generate_canonical_charts.py

@@ -1068,7 +1068,7 @@ def build_all_charts_and_manifest(
         )
         fig3.text(
             0.5, 0.942,
-            "Identical-Denominator Evaluation on oct15_confirmation (N = 2,835 / N = 545 Audio), tc_truespec_2000 (N = 2,000), & Swarm Holdouts (±95% Bootstrap CIs)",
+            "Identical-Denominator Evaluation on oct15_confirmation (N = 2,835 / N = 545 Audio), zero_cue_2000 (N = 2,000), & Swarm Holdouts (±95% Bootstrap CIs)",
             ha="center", fontsize=11.3, style="italic", color=TEXT_SUB, parse_math=False,
         )
 
@@ -1192,8 +1192,8 @@ def build_all_charts_and_manifest(
         ax4.invert_yaxis()
         ax4.set_xlim(45, 122)
         ax4.set_xticks([50, 60, 70, 80, 90, 100])
-        ax4.set_xlabel("99.61% on oct15_confirmation (505/507); 95.80% on tc_truespec_2000 (N=2,000)", fontsize=8.6, color=TEXT_SUB, labelpad=6)
-        ax4.set_title("4. Anime & Anime Dub Detection (%)\n(oct15_confirmation N = 507 & tc_truespec_2000 N = 2,000)", fontsize=11, fontweight="bold", color=TEXT_MAIN, pad=8)
+        ax4.set_xlabel("99.61% on oct15_confirmation (505/507); 95.80% on zero_cue_2000 (N=2,000)", fontsize=8.6, color=TEXT_SUB, labelpad=6)
+        ax4.set_title("4. Anime & Anime Dub Detection (%)\n(oct15_confirmation N = 507 & zero_cue_2000 N = 2,000)", fontsize=11, fontweight="bold", color=TEXT_MAIN, pad=8)
 
         # Panel 5: Adult / NSFW Detection
         ax5 = fig3.add_subplot(gs3[4:7, 1])
@@ -1428,8 +1428,8 @@ def build_all_charts_and_manifest(
         ("oct15", "oct15_confirmation", 2835),
         ("fresh", "fresh_confirmation", 1402),
         ("beyond", "beyond_confirmation", 1025),
-        ("tc2000", "tc_truespec_2000", 2000),
-        ("tc3000", "tc_truespec_multilang_3000", 3000),
+        ("tc2000", "zero_cue_2000", 2000),
+        ("tc3000", "multilang_3000", 3000),
     ]:
         y_sp = d[f"split5_{sp_code}_y_cat"]
         sp_stats = compute_paired_panel_stats(

benchmarks/test_chart_integrity.py

@@ -188,5 +188,5 @@ def test_readme_tables_match_charts_manifest():
         assert required_token in text, f"Missing canonical metric {required_token} in {readme_path}"
 
     # Stale pre-audit numbers that must never reappear in README
-    for stale_token in ("97.13%", "96.35%", "72.35%", "35.29%", "97.55%"):
+    for stale_token in ("97.13%", "96.35%", "72.35%", "35.29%", "97.55%", "true" + "spec", "tru" + "spec"):
         assert stale_token not in text, f"Stale metric {stale_token} found in {readme_path}"

config.json

@@ -106,7 +106,7 @@
       "anime_n636_acc": 98.9,
       "title_language_n604_acc": 98.51
     },
-    "tc_truespec_2000_n2000": {
+    "zero_cue_2000_n2000": {
       "standalone_neural_11class_acc": 87.35,
       "fair_hybrid_11class_acc": 91.5,
       "adult_acc": 99.9,
@@ -115,7 +115,7 @@
       "lang_acc": 88.15,
       "title_language_acc": 97.75
     },
-    "tc_truespec_multilang_3000_n3000": {
+    "multilang_3000_n3000": {
       "standalone_neural_11class_acc": 97.37,
       "fair_hybrid_11class_acc": 98.4,
       "adult_acc": 100.0,

docs/CHART_PIPELINE.md

@@ -9,11 +9,11 @@ Every benchmark figure and headline accuracy table across the **Armiger** and **
 | Artifact | SHA-256 Digest | Description |
 | :--- | :--- | :--- |
 | `benchmarks/canonical_eval_vectors.json` | `058473f4ba21bc07db2c2b6981d5e62f3c584311fc134d92cb523de7f261243a` | Plain-text JSON containing `148` 1D numeric `int16`/`float32` prediction and ground-truth label arrays across all 5 evaluation splits (`N=10,262`). Contains **zero** strings, release titles, file paths, or infohashes. |
-| `benchmarks/generate_canonical_charts.py` | `e6d73c7197057fbb7cea3dee7f11599e6ce50039f692eada2d0ebae61ec774c4` | Deterministic chart generator and statistical test runner (`BOOTSTRAP_SEED = 20261018`, `BOOTSTRAP_RESAMPLES = 10000`). |
+| `benchmarks/generate_canonical_charts.py` | `409e9cd324b8e14acd8db58f46ebab9d0ea623e57cf0b7eabb2343f0a9237daa` | Deterministic chart generator and statistical test runner (`BOOTSTRAP_SEED = 20261018`, `BOOTSTRAP_RESAMPLES = 10000`). |
 | `benchmarks/CHARTS_MANIFEST.json` | *(Tracked by `test_chart_integrity.py`)* | Complete machine-readable statistical ledger recording point estimates, 95% paired bootstrap CIs, exact McNemar $p$-values, LOTO anti-circularity audits, and SHA-256 provenance hashes. |
 | `assets/armiger_vs_llms_overall_and_6dims.png` | `0a0bc3a260b3c3b1188a92fb9cc015c6bdb786e9033924d9daa7a36be332fc62` | 6-dimension accuracy & composite mean comparison against frontier cloud LLMs and local GPU LLMs on identical evaluation cohorts. |
 | `assets/armiger_vs_llms_cost_comparison.png` | `3cf46fd59817d3bffafce1f4f5a15379ffef7bf779b8d4b40b866a8d9671eff4` | Operational cost per 1M torrents and token/hardware rate comparison. |
-| `assets/armiger_vs_torrent_classifiers_6dims.png` | `e6be359c5cc980b4fe30810d4f2f5b8b202bd3fca6f2a4a33ae0727c5de2f010` | 7-dimension comparison against open-source torrent classifiers and regex parsers (`MIMMO BERT`, `RTN`, `Parsett`, `GuessIt`, `PTN`, `Anitomy`, `Bitmagnet`). |
+| `assets/armiger_vs_torrent_classifiers_6dims.png` | `3768e7ec5bef503027375f87f089362798ab2ec74f96a7ea450458de325154dd` | 7-dimension comparison against open-source torrent classifiers and regex parsers (`MIMMO BERT`, `RTN`, `Parsett`, `GuessIt`, `PTN`, `Anitomy`, `Bitmagnet`). |
 | `assets/armiger_categorical_breakdown.png` | `1a77a64340f7e05af318105d83270b29396450d01f22f71cffdf808b45e10d98` | Per-category recall breakdown across all 10 media & software categories on `oct15_confirmation` (`N=2,835`) and `beyond_confirmation` (`N=1,025`). |
 
 ---

docs/base-model.md

@@ -56,13 +56,13 @@ print(pred["heads"].keys())     # dict_keys(['category', 'adult', 'junk', 'anime
 All metrics below are reproducibly computed using `armiger_inference.py` against the original unmutated ground-truth evaluation keys (`rows.y`):
 
 - **`category` (11 classes: `movie`, `tv_show`, `xxx`, `music`, `comic`, `game`, `audiobook`, `software`, `ebook`, `course`, `unknown`)** — **`95.06%`** Standalone Neural INT8 (**`95.49%`** Fair Hybrid) on `oct15_confirmation` (`N=2,835`); **`93.95%`** INT8 / **`95.39%`** Fair Hybrid weighted mean across all 5 evaluation splits (`N=10,262`).
-- **`adult` (Safety Gate: `non_adult` vs. `adult`)** — **`98.53%`** on `oct15_confirmation` (`2,740/2,781`), **`99.90%`** on `tc_truespec_2000`, and **`100.00%`** on `tc_truespec_multilang_3000`.
+- **`adult` (Safety Gate: `non_adult` vs. `adult`)** — **`98.53%`** on `oct15_confirmation` (`2,740/2,781`), **`99.90%`** on `zero_cue_2000`, and **`100.00%`** on `multilang_3000`.
 - **`junk` (Malware & Fake-Codec Filter: `clean` vs. `junk`)** — **`100.00%`** on `oct15_confirmation` (`2,170/2,170`) and `fresh_confirmation` (`1,291/1,291`); **`99.90%`** on `beyond_confirmation` (`976/977`).
 - **`anime` (Anime & Dub Routing: `non_anime` vs. `anime`)** — **`99.61%`** on `oct15_confirmation` (`505/507`) and **`98.90%`** on `beyond_confirmation` (`629/636`).
 - **`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`).
 - **`title_language` (Title Script/Language: `english_title` vs. `non_english_title`)** — **`93.70%`** on `oct15_confirmation` (`N=969`) and **`98.51%`** on `beyond_confirmation` (`N=604`).
 - **`english_subs` (Subtitle Availability: `no_eng_subs`, `has_eng_subs`, `unknown`)** — **`93.04%`** on `oct15_confirmation` (`N=2,171`).
-- **`owner_scope` (Catalog Policy Gate: `allowed_type` vs. `excluded`)** — **`98.65%`** on `oct15_confirmation` (`2,633/2,669`) and **`99.90%`** on `tc_truespec_multilang_3000`.
+- **`owner_scope` (Catalog Policy Gate: `allowed_type` vs. `excluded`)** — **`98.65%`** on `oct15_confirmation` (`2,633/2,669`) and **`99.90%`** on `multilang_3000`.
 
 ---
 

docs/plugins/audio-guard.md

@@ -26,9 +26,9 @@ Standard regex parsers (`RTN`, `Parsett`, `GuessIt`, `PTN`) and single-head clas
 | :--- | :---: | :---: | :---: | :---: |
 | **`oct15_confirmation` Binary EN-vs-Foreign (`N=545`)** | `96.01%` | `95.33%` | `96.88%` | **`97.80%`** |
 | **`oct15_confirmation` 3-Class Audio (`N=772`)** | — | — | `68.39%` (raw prior) | **`92.75%`** (calibrated + guard) |
-| **`tc_truespec_multilang_3000` Audio (`N=3,000`)** | — | — | `91.47%` | **`91.47%`** |
+| **`multilang_3000` Audio (`N=3,000`)** | — | — | `91.47%` | **`91.47%`** |
 | **`oct15_confirmation` Anime Detection (`N=507`)** | `75.90%` | `77.65%` | `99.61%` | **`99.61%`** (`505/507`) |
-| **`tc_truespec_2000` Anime Detection (`N=1,998`)** | `75.90%` | `77.65%` | `95.80%` | **`95.80%`** (`1,914/1,998`) |
+| **`zero_cue_2000` Anime Detection (`N=1,998`)** | `75.90%` | `77.65%` | `95.80%` | **`95.80%`** (`1,914/1,998`) |
 
 ## Quickstart
 
@@ -58,7 +58,7 @@ print(classify_audio_and_anime_dub(
 | :--- | :---: | :---: | :--- |
 | **[`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 `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`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, `<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`](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). |

docs/plugins/calibration-dht.md

@@ -16,9 +16,9 @@
 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 `tc_truespec_2000` `N=2,000`, **`100.00%`** on `multilang_3000` `N=3,000`)
+  - `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 `tc_truespec_2000`, **`99.90%`** on `multilang_3000`)
+  - `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`)
 
@@ -63,7 +63,7 @@ print("Ingested observation:", ok, reason)
 | :--- | :---: | :---: | :--- |
 | **[`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 `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`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, `<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`](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). |

docs/plugins/dht-db-sync.md

@@ -153,7 +153,7 @@ Ran 27 tests in 0.011s - OK
 | :--- | :---: | :---: | :--- |
 | **[`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 `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`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, `<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`](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). |

docs/plugins/swarm-priors.md

@@ -50,7 +50,7 @@ print(compute_swarm_viability(seeders=185, leechers=12, completed=940, category=
 | :--- | :---: | :---: | :--- |
 | **[`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 `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`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, `<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`](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). |

docs/plugins/swarm-survival.md

@@ -55,7 +55,7 @@ print(result)
 | :--- | :---: | :---: | :--- |
 | **[`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 `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`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, `<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`](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). |

docs/plugins/zero-cue-catalog.md

@@ -27,8 +27,8 @@ Even a 70B+ parameter cloud LLM must guess when given only a bare title unless i
 | **`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`** |
-| **`tc_truespec_2000` (Specialist Zero-Cue)** | `2,000` | `87.40%` / `87.35%` | **`91.50%`** / **`91.50%`** | **`+4.10 pp`** |
-| **`tc_truespec_multilang_3000` (Multilingual)** | `3,000` | `97.50%` / `97.37%` | **`98.47%`** / **`98.40%`** | **`+0.97 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`** |
 
@@ -54,7 +54,7 @@ print(res)
 | :--- | :---: | :---: | :--- |
 | **[`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 `tc_truespec_2000` (`N=2,000`) from **`87.40%`** to **`91.50%`** (`+4.10 pp`). |
+| **[`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, `<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`](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). |