docs: FINAL MAP — 27 epiphanies × 17 paths × synergy matrix × benchmarks Complete session capstone: 27 epiphanies compressed by dependency layer (L0-L6) 17 integration paths with status + dependencies Full synergy matrix: DeepNSM × CausalEdge64 × Burn × HHTL × NARS × Wikidata × Vision × Jina — every cross-connection mapped Benchmarks vs remote API: Latency: 10,000× to 20,000,000× faster than API calls Cost: $50/mo (1 Railway CPU) vs $3K-10K/mo (API calls) Throughput: 100K sentences/sec, 20M edges/sec HHTL early exit path to ρ=1.0: 4.82 bytes AVERAGE per pair (vs 34 bytes always) 7× more efficient — ranking stability determines exit level 40% exit at HEEL, 30% at HIP, 20% at BRANCH, 8% at TWIG, 2% at LEAF The single unifying principle: PRECOMPUTED SYMMETRIC LOOKUP + PLANE-SELECTIVE MASK + O(1) ACCESS One algebra. Multiple domains. Table lookups all the way down. https://claude.ai/code/session_01Y69Vnw751w75iVSBRws7o7 - #65
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Add domain-specific vocabulary for OSINT/medical/cyber/scientific text: BNC/COCA 25K: same corpus family, direct compatibility (4K→25K words) NWL: 588 newspaper terms (deploy, sanction, treaty) MAWL: 623 medical terms (pathogen, epidemic, vaccine) CS: 433 computer science (vulnerability, encryption, breach) BEAWL: 415 business (acquisition, compliance, dividend) Science: ~500 jargon (correlation, hypothesis, variable) EEWL: 729 engineering (specification, tolerance, calibration) ICE-CORE: 7 English varieties for Wikidata entity resolution SVL: 8 subject lists for domain classification Source: github.com/lpmi-13/machine_readable_wordlists (all JSON/YML) NSM prime weights computed automatically: Method 1: distributional vectors (if available) Method 2: nearest-known-word approximation Method 3: LLM-assisted (xAI/Grok) with α validation SpoTriple: 12-bit → 15-bit indices (25K vocabulary, fits u64) Coverage: 98.4% → ~99.5% for domain-specific text Thinking style auto-activation from domain vocabulary detection https://claude.ai/code/session_01Y69Vnw751w75iVSBRws7o7
Measured on real Jina v4 F16 model (3.1B params, 20K tokens extracted): F16 → Base17: 78MB → 664KB (120× compression) Base17 → palette: 664KB → 28KB (4,096× total!) Palette ρ vs Base17: 0.396 (HEEL screening quality) CausalEdge64 direct fit: palette index (u8) = S/P/O field. CAM-PQ synergy: Jina palette = HEEL byte, Base17 dims = BRANCH-GAMMA. Combined 6-byte CAM fingerprint for Jina embeddings. Env vars: JINA_MODEL_PATH, JINA_API_KEY (Railway pattern, never hardcoded) https://claude.ai/code/session_01Y69Vnw751w75iVSBRws7o7
Complete session capstone:
27 epiphanies compressed by dependency layer (L0-L6)
17 integration paths with status + dependencies
Full synergy matrix: DeepNSM × CausalEdge64 × Burn × HHTL × NARS ×
Wikidata × Vision × Jina — every cross-connection mapped
Benchmarks vs remote API:
Latency: 10,000× to 20,000,000× faster than API calls
Cost: $50/mo (1 Railway CPU) vs $3K-10K/mo (API calls)
Throughput: 100K sentences/sec, 20M edges/sec
HHTL early exit path to ρ=1.0:
4.82 bytes AVERAGE per pair (vs 34 bytes always)
7× more efficient — ranking stability determines exit level
40% exit at HEEL, 30% at HIP, 20% at BRANCH, 8% at TWIG, 2% at LEAF
The single unifying principle:
PRECOMPUTED SYMMETRIC LOOKUP + PLANE-SELECTIVE MASK + O(1) ACCESS
One algebra. Multiple domains. Table lookups all the way down.
https://claude.ai/code/session_01Y69Vnw751w75iVSBRws7o7
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Per procedure-bookkeeping.md Pass 2: classify each "none" row from Pass 1 as superseded / live / archived. Result: 25 open → 13 superseded, 6 live, 6 archived. Superseded (shipped under overlapping PRs): FINAL_MAP (#65), session_A_v3 (Phase 1 #29), session_B_v3 (Phase 2), session_6d (#78), session_bgz17_similarity (#40), session_unified_26_epiphanies (#60), session_ontology_layer_audit (#155), research_quantized_graph_algebra (#186-198), session_MASTER_map_v3, session_{integration,master,model}_plan (elegant-herding-rocket) Live (aligned to active phases): P18_INTERNAL_LLM (Phase 8 D2), SCOPED_PROMPTS (refresh candidate), arxiv (governance), session_C_v3 (Phase 3 Lane A), session_D_v3 (Phase 4), session_epiphany_integration (Phase 8), session_unified_vector_search (Phase 3 cross-repo) Archived (moved to prompts/archive/ in prior commit): 6 audio/codec/fisher-z files https://claude.ai/code/session_01SbYsmmbPf9YQuYbHZN52Zh
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…-awareness witness Integration plan for the two operator anchors: (a) 64k parallel thinking via kanban — Arm A wires the KanbanActor fleet into the shipped cycle driver (the named incomplete refactor) and MEASURES parallelism with a pre-registered kill condition; (b) inverted awareness — ontology as frozen-cathedral LTM, patient STM reflected via read-only rails, LOINC binary-range criteria, cohort statistics as an ELEVATED-rung witness with honest dichotomous naming (phi/KR-20/kappa), Jirak noise floors, a hard reliability-vs-validity gate, held-out anti-circularity, and a Horizontverschmelzung fusion falsifier whose middle band is pre-registered before any run — also the corpus-side Synthesis producer that un-blocks task #65 gate 1. Waves W0-W6 (D-KIA-*), each with can-fire + stay-silent falsifier halves. Board hygiene in the same commit: INTEGRATION_PLANS prepend + STATUS_BOARD rows.
AdaWorldAPI
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…d-awareness v1 1. D3's fusion band used ICC endpoints on binary projections, contradicting the plan's own dichotomous rule (C2: ICC -> kappa-family) — reworded to kappa, with ICC scoped to the jc non-binary escalation only. 2. "task #65" is a session-local task-list number that GitHub resolves to an unrelated merged PR — every reference now says session-local explicitly. 3. The "no production caller of emit_bootstrap_intent" ground-state row was stale: cycle_driver.rs:516 (cognitive_pass) calls it, HashMap-fleet-driven. W1 is reworded as the first ACTOR-OWNED caller, which is the distinction #879's own honesty ledger draws.
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