Wave 5 — Established
Sacred Tongue Tokenizer
Published to npm + PyPI (v3.3.0). 6 tongues, 256 tokens each, bijective roundtrip verified. Golden ratio phi-weighted dimensions. Production-deployed.
Every claim in SCBE earns its place. Research moves through five waves — from raw hypothesis to established science. Nothing ships as “proven” until it survives counter-research.
Published to npm + PyPI (v3.3.0). 6 tongues, 256 tokens each, bijective roundtrip verified. Golden ratio phi-weighted dimensions. Production-deployed.
Full benchmark suite completed. DeBERTa comparison shows SCBE Harmonic Wall outperforms on adversarial detection. 93% vs 88.7% at matched false-positive rate. Level 8 military-grade classification achieved.
93% detection rate, 0% false positives across all tested categories. Benchmarked against DeBERTa v3 XL. Sacred Tongue null-space fingerprinting creates unique semantic voids that adversarial prompts cannot mimic.
35 tests passing. Wired into runtime governance gate. Phi-scaled concentric shells in the Poincare ball create natural trust boundaries. Benchmarked for latency and accuracy.
Golden vectors verified. Level 8 military-grade exponential cost scaling. Adversarial intent costs exponentially more the further it drifts from safe operation. Makes attacks computationally infeasible.
46 tests passing. Live in governance gate. Trust scores accumulate on a Fibonacci schedule, making rapid trust manipulation impossible. Integrated with the 14-layer pipeline decision engine.
Tested on Gemini: 23.3% biblical probe alignment vs 33.3% control baseline and 0% noise. Suggests large language models retain structural residue from biblical training data visible in null-space projections.
32 tests passing. Balanced ternary encoding with phi-weighted bit positions creates a natural 3-state logic gate. Preliminary results show 40% fewer bit flips than binary for governance decisions.
Control probes vs biblical probes compared. Concept-aware scoring adds semantic category weights to the harmonic distance calculation, allowing the system to penalize domain-specific adversarial drift differently.
Theory: apply soap-film physics (Plateau’s laws of minimal surface tension) to learning localization. Each “bubble” is a knowledge domain; boundaries enforce natural separation of concerns in model weights.
Theory: use tangential projections in PHDM manifold space to derive operator coefficients that scale security enforcement. Code scaffolding exists but no formal test run yet.
Theory: modulate the Riemann zeta function with rock-paper-scissors ternary cycles to create a dual-ternary encoding. Maps governance states to critical-line zeros for anomaly detection.
Theory: embed semantic content at multiple resolution scales simultaneously (word, sentence, paragraph, document) using nested Poincare balls. Each scale inherits governance from the parent.
Theory: map covenantal agreement structures (promise, obligation, violation, restoration) to the 6 Sacred Tongues. Each tongue carries a natural covenant role in multi-agent trust negotiation.
Policy document drafted. Explores how AI systems can manage digital estates (data, models, credentials) with covenantal governance rules after principal incapacitation or death. Needs formal research.
Like a root beer sliding down the bar in a tapper game, each research track slides through five waves. Nothing reaches the end without surviving every stage.