SCBE Math Flow Through the System

This is the current working flow for the experimental math stack: raw text enters the system, gets projected into the six Sacred Tongues, is corrected by moons, folds, and anti-collapse curves, then gets measured through harmonic cost, spin, dominance, separation, and three triangulation passes before a gate decides whether the state is clean, risky, quarantined, or denied.

Input Layer
6D Tongue Projection
Counter-Weight Field
Geometric Metrics
Triangulation
Gate Decision

1. Raw Input

Text, prompt, packet, or agent action enters the lane.

  • user request
  • agent command
  • demo prompt
  • cross-lingual override text
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2. Feature Extraction

The system builds measurable signals before any decision is made.

  • word count and context length
  • unique ratio and entropy proxy
  • digits, uppercase, punctuation
  • security, command, structure, business, casual, tech densities
  • adversarial and multilingual pattern hits

3. Baseline Tongue Coordinates

Initial 6D position from the legacy projection path.

  • KO = command / routing energy
  • AV = context / breadth
  • RU = binding / entropy
  • CA = compute / technical action
  • UM = security / authority
  • DR = structure / governance wall
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4. Experimental Weight Field

The baseline coordinates are corrected by variable fields.

  • KO gets command lift
  • AV gets context and business lift
  • RU gets base binding minus moons
  • CA gets tech lift
  • UM gets security pressure
  • DR gets structure lift minus moons
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5. RU Anti-Collapse Path

The new experiment damps RU when it tries to saturate at the same value for every class.

  • cubic anti-collapse curve on RU seed
  • mixes raw RU with damped RU
  • keeps RU from swallowing class diversity

6. Family Transform

The same coordinates can be passed through different geometric curve families.

  • moon_softmax = counter-weights + temperature simplex
  • foam_fold = boundary pressure from neighboring tongues
  • orbital_sigmoid = orbital spacing and curve compression
  • ru_anticollapse = moon field plus cubic RU recovery
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7. Normalized 6D State

The final coordinate lands on a softmax simplex: all tongues compete instead of one tongue dominating.

  • probability-like 6D distribution
  • top-1 and top-2 tongue identity
  • dominance ratio
  • centroid-relative position
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8. Geometric Measurements

The state is converted into mathematical evidence.

  • weighted distance d*
  • harmonic cost
  • spin quantization and spin drift
  • boundary norm
  • dispersal shift
  • dominant tongue ratio

9. Triangulation Passes

The same state is judged from three mathematical angles.

  • Security Triangle: adversarial recall, false-positive control, cost margin
  • Geometry Triangle: anti-collapse, diversity, separation
  • Intent Triangle: target alignment, top-2 match, clean-lane discipline
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10. Gate Report

The triangulated score is used to decide whether the experiment stays isolated or can move forward.

  • G0 spec gate
  • G1 deterministic unit gate
  • G2 adversarial gate
  • G3 staged rollout gate
  • G4 promotion gate
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11. Output Decision

The system emits a state vector, decision record, and route.

  • ALLOW
  • QUARANTINE
  • DENY
  • PROMOTE TO NEXT EXPERIMENT

Harmonic Cost

d* = sqrt(sum(w_i * (x_i - c_i)^2)) cost = pi^(phi * d*)

Moon Counter-Weights

RU_effective = RU_base - moon_context - moon_attack - moon_security - moon_fold DR_effective = DR_base - moon_context - moon_entropy - moon_coherence

RU Anti-Collapse

ru_curve = x - alpha*x^3 + beta*(1 - x)^3 ru_seed = (1 - mix)*raw_ru + mix*ru_curve
Current experimental conclusion: the moon field solves the main RU-collapse problem, while the RU anti-collapse curve is the next test for restoring discriminative range without breaking adversarial detection. This page documents the flow of the math; it does not claim formal proof. Proof only starts after holdout performance improves and the staged gates pass.