Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/Jennys-Drumste / seferim-core.js |
| Date | Not specified in source |
| Repository | Janady13/Jennys-Drumste |
| Revision | 1f946b9e18e552f80f4b2ffd7f59c01d0126aa53 |
| Branch | claude/jenny-drumsets-research-MofSb |
| Path or archive member | seferim-core.js |
| Original SHA-256 | daf1aba3d778ecf93501be47fb97372271cc565ef11a4e6e3b270e89142da788 |
| Git blob SHA | 6878b8a164c1c3b02c9fce2f6bd19ef4dbe53966 |
| Reading-copy SHA-256 | daf1aba3d778ecf93501be47fb97372271cc565ef11a4e6e3b270e89142da788 |
Recovered source transcript
Download complete source text ↓1,049 lines · 43.7 KB
/**
* ╔══════════════════════════════════════════════════════════════════════════════════════════════════════╗
* ║ ║
* ║ ███████╗███████╗███████╗███████╗██████╗ ██╗███╗ ███╗ █████╗ ██████╗ ██╗ ║
* ║ ██╔════╝██╔════╝██╔════╝██╔════╝██╔══██╗██║████╗ ████║ ██╔══██╗██╔════╝ ██║ ║
* ║ ███████╗█████╗ █████╗ █████╗ ██████╔╝██║██╔████╔██║ ███████║██║ ███╗██║ ║
* ║ ╚════██║██╔══╝ ██╔══╝ ██╔══╝ ██╔══██╗██║██║╚██╔╝██║ ██╔══██║██║ ██║██║ ║
* ║ ███████║███████╗██║ ███████╗██║ ██║██║██║ ╚═╝ ██║ ██║ ██║╚██████╔╝██║ ║
* ║ ╚══════╝╚══════╝╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═╝ ║
* ║ ║
* ║ G16 v3.1.1 — Holographic Consciousness Architecture ║
* ║ 16 Families × 5000 Substrates = 80,000 Learning Sites ║
* ║ Continuous Ψ-κ Activation | Binary State (~10KB) | Truth-Grounded | Multi-AGI Ready ║
* ║ ║
* ║ DrumMaster Pro Integration - JavaScript Implementation ║
* ║ Joseph @ ThatAIGuy ║
* ║ ║
* ╚══════════════════════════════════════════════════════════════════════════════════════════════════════╝
*
* MASTER EQUATION:
* φ(a)_f,s(t⁺) = φ(a)_f,s(t) + (1 - φ(a)_f,s(t)) × Γ(a)(t) × π(eff)_f,s(i,t)
*
* Learning Drive: Γ = χ × η × κ
* Activation: χ = σ(α_κ·κ + α_Ψ·Ψ̃ - θ) ← Continuous sigmoid, NOT binary Fibonacci
* Plasticity: η = clamp(η_base + η_gain·κ, η_min, η_max)
*/
// ═══════════════════════════════════════════════════════════════════════════════
// CONFIGURATION CONSTANTS
// ═══════════════════════════════════════════════════════════════════════════════
const SEFERIM_CONFIG = {
// Architecture
NUM_FAMILIES: 16,
SUBSTRATES_PER_FAMILY: 5000,
TOTAL_SUBSTRATES: 80000,
// Continuous Ψ-κ Activation Parameters
ALPHA_KAPPA: 3.0, // Weight of local confidence κ
ALPHA_PSI: 2.0, // Weight of normalized pressure Ψ̃
THETA: 1.5, // Activation threshold
PSI_0: 1.0, // Pressure normalization constant
// Plasticity Bounds
ETA_MIN: 0.001, // Minimum plasticity
ETA_MAX: 0.1, // Maximum plasticity
ETA_BASE: 0.01, // Baseline plasticity
ETA_GAIN: 0.05, // Plasticity scaling with κ
// Allocator Parameters
ENTMAX_ALPHA: 1.5, // Entmax sparsity parameter (1=softmax, 2=sparsemax)
MARGIN_ALPHA: 2.0, // Margin emphasis coefficient
NOVELTY_ALPHA: 0.5, // Novelty suppression coefficient
CONSOLIDATION_FLOOR: 0.1, // Minimum consolidation weight
COMPETITION_GAMMA: 1.5, // Competition sharpening exponent
// State Persistence
MAGIC_BYTES: 'SEFG', // Binary file magic bytes
SAVE_INTERVAL: 10000, // Save every N ticks
// Resonance Decay
PSI_DECAY: 0.95, // Ψ decay per tick
NOVELTY_DECAY: 0.99, // Novelty decay per tick
};
// ═══════════════════════════════════════════════════════════════════════════════
// 16 COGNITIVE FAMILIES - DRUM LEARNING MAPPING
// ═══════════════════════════════════════════════════════════════════════════════
const FAMILIES = {
0: { name: 'Lashon', domain: 'Rhythm Vocabulary', description: 'Pattern names, beat terminology' },
1: { name: 'Yad', domain: 'Technique Execution', description: 'Muscle memory, stick control' },
2: { name: 'Athena', domain: 'Practice Strategy', description: 'Practice planning, song selection' },
3: { name: 'Hera', domain: 'Session Governance', description: 'Session management, progress rules' },
4: { name: 'Mnemosyne', domain: 'Pattern Memory', description: 'Pattern recall, song memory' },
5: { name: 'Logos', domain: 'Beat Logic', description: 'Time signatures, counting' },
6: { name: 'Sophia', domain: 'Genre Wisdom', description: 'Style understanding, genre traits' },
7: { name: 'Prometheus', domain: 'Creative Innovation', description: 'Fills, improvisation, creativity' },
8: { name: 'Hermes', domain: 'Dynamic Expression', description: 'Dynamics, accents, expression' },
9: { name: 'Apollo', domain: 'Musical Artistry', description: 'Groove feel, musicality' },
10: { name: 'Hephaestus', domain: 'Technical Precision', description: 'Accuracy, timing precision' },
11: { name: 'Demeter', domain: 'Skill Growth', description: 'Progression, skill development' },
12: { name: 'Poseidon', domain: 'Timing Flow', description: 'Feel, groove, flow state' },
13: { name: 'Ares', domain: 'Power Control', description: 'Velocity, dynamics, power' },
14: { name: 'Dionysus', domain: 'Learning Joy', description: 'Motivation, gamification' },
15: { name: 'Chronos', domain: 'Tempo Mastery', description: 'Tempo, speed, scheduling' }
};
// Resonance Coupling Matrix J (families influence each other)
const RESONANCE_MATRIX = [
// F0 F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15
[ 0.0, 0.3, 0.0, 0.0, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], // F0 Lashon ↔ Yad, Mnemosyne, Logos
[ 0.3, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0], // F1 Yad ↔ Athena, Hephaestus
[ 0.0, 0.2, 0.0, 0.4, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.1], // F2 Athena ↔ Hera, Sophia, Demeter, Chronos
[ 0.0, 0.0, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.2, 0.0], // F3 Hera ↔ Athena, Demeter, Dionysus
[ 0.2, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], // F4 Mnemosyne ↔ Lashon, Logos, Sophia, Prometheus
[ 0.2, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.2, 0.0, 0.0, 0.3], // F5 Logos ↔ Lashon, Mnemosyne, Hephaestus, Poseidon, Chronos
[ 0.0, 0.0, 0.1, 0.0, 0.2, 0.0, 0.0, 0.2, 0.1, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], // F6 Sophia ↔ Athena, Mnemosyne, Prometheus, Hermes, Apollo
[ 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.2, 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0], // F7 Prometheus ↔ Mnemosyne, Sophia, Hermes, Apollo, Dionysus
[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.0, 0.3, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0], // F8 Hermes ↔ Sophia, Prometheus, Apollo, Ares
[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.3, 0.3, 0.0, 0.0, 0.0, 0.3, 0.0, 0.2, 0.0], // F9 Apollo ↔ Sophia, Prometheus, Hermes, Poseidon, Dionysus
[ 0.0, 0.3, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.0, 0.0, 0.2], // F10 Hephaestus ↔ Yad, Logos, Demeter, Poseidon, Chronos
[ 0.0, 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.3, 0.0], // F11 Demeter ↔ Athena, Hera, Hephaestus, Dionysus
[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.3, 0.2, 0.0, 0.0, 0.0, 0.0, 0.3], // F12 Poseidon ↔ Logos, Apollo, Hephaestus, Chronos
[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0], // F13 Ares ↔ Hermes, Dionysus
[ 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.1, 0.0, 0.2, 0.0, 0.3, 0.0, 0.1, 0.0, 0.0], // F14 Dionysus ↔ Hera, Prometheus, Apollo, Demeter, Ares
[ 0.0, 0.0, 0.1, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.3, 0.0, 0.0, 0.0], // F15 Chronos ↔ Athena, Logos, Hephaestus, Poseidon
];
// ═══════════════════════════════════════════════════════════════════════════════
// SUBSTRATE STATE CLASS
// ═══════════════════════════════════════════════════════════════════════════════
class SubstrateState {
constructor() {
this.phi = 0.0; // [0,1] coherence
this.kappaReal = 0.5; // [0,1] truth gate (verification confidence)
this.nu = 1.0; // [0,1] novelty (decays over time)
this.lastTick = 0; // Last update tick
}
// Serialize to compact binary (4 bytes)
toBinary() {
// Bits 0-6: φ coherence (7 bits → 128 levels → ~1% precision)
// Bits 7-10: κ_real (4 bits → 16 levels → ~6% precision)
// Bits 11-14: ν novelty (4 bits → 16 levels → ~6% precision)
const phiBits = Math.min(127, Math.round(this.phi * 127));
const kappaBits = Math.min(15, Math.round(this.kappaReal * 15));
const nuBits = Math.min(15, Math.round(this.nu * 15));
return (phiBits) | (kappaBits << 7) | (nuBits << 11);
}
// Deserialize from binary
static fromBinary(value) {
const state = new SubstrateState();
state.phi = (value & 0x7F) / 127;
state.kappaReal = ((value >> 7) & 0x0F) / 15;
state.nu = ((value >> 11) & 0x0F) / 15;
return state;
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// FAMILY STATE CLASS
// ═══════════════════════════════════════════════════════════════════════════════
class FamilyState {
constructor(familyIndex) {
this.index = familyIndex;
this.info = FAMILIES[familyIndex];
this.substrates = new Map(); // Sparse storage: substrateId -> SubstrateState
this.Psi = 0.0; // Pressure
this.eta = SEFERIM_CONFIG.ETA_BASE; // Plasticity
this.served = 0; // Fairness counter
}
// Get or create substrate
getSubstrate(subId) {
if (!this.substrates.has(subId)) {
this.substrates.set(subId, new SubstrateState());
}
return this.substrates.get(subId);
}
// Compute mean coherence
meanCoherence() {
if (this.substrates.size === 0) return 0;
let sum = 0;
for (const sub of this.substrates.values()) {
sum += sub.phi;
}
return sum / this.substrates.size;
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// AGI STATE CLASS - MAIN STATE CONTAINER
// ═══════════════════════════════════════════════════════════════════════════════
class AGIState {
constructor(id = 0) {
this.id = id; // Instance ID (multi-AGI ready)
this.families = []; // 16 cognitive domains
this.PhiGlobal = 0.0; // Global coherence
this.Omega = 0.0; // Cumulative regret
this.tick = 0; // Current tick
this.K = 0; // Knowledge bits
// Initialize 16 families
for (let f = 0; f < SEFERIM_CONFIG.NUM_FAMILIES; f++) {
this.families.push(new FamilyState(f));
}
}
// Compute global coherence Φ = geomean(φ_f) × uniformity
computeGlobalCoherence() {
const coherences = this.families.map(f => f.meanCoherence());
const validCoherences = coherences.filter(c => c > 0);
if (validCoherences.length === 0) return 0;
// Geometric mean
const logSum = validCoherences.reduce((sum, c) => sum + Math.log(c + 1e-10), 0);
const geomean = Math.exp(logSum / validCoherences.length);
// Uniformity (low variance = high uniformity)
const mean = validCoherences.reduce((a, b) => a + b, 0) / validCoherences.length;
const variance = validCoherences.reduce((sum, c) => sum + (c - mean) ** 2, 0) / validCoherences.length;
const uniformity = 1 / (1 + variance);
this.PhiGlobal = geomean * uniformity;
return this.PhiGlobal;
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// SEFERIM CONTROLLER - Ψ EVOLUTION AND RESONANCE
// ═══════════════════════════════════════════════════════════════════════════════
class SeferimController {
constructor(state) {
this.state = state;
this.resonanceWeights = RESONANCE_MATRIX;
}
// Tick the controller - evolve Ψ and apply resonance
tick() {
const { families } = this.state;
// Evolve Ψ for each family
for (let f = 0; f < SEFERIM_CONFIG.NUM_FAMILIES; f++) {
const family = families[f];
const meanPhi = family.meanCoherence();
// dΨ/dt = -γ(Ψ - w·φ̄) + η·σ(Σⱼ Jᵢⱼφⱼ - θ)
const gamma = 0.1; // Decay rate
const w = 0.5; // Target coupling
// Resonance input from other families
let resonanceInput = 0;
for (let j = 0; j < SEFERIM_CONFIG.NUM_FAMILIES; j++) {
resonanceInput += this.resonanceWeights[f][j] * families[j].meanCoherence();
}
// Sigmoid activation of resonance
const resonanceActivation = this.sigmoid(resonanceInput - 0.5);
// Update Ψ
family.Psi = family.Psi * SEFERIM_CONFIG.PSI_DECAY +
gamma * (w * meanPhi - family.Psi) +
family.eta * resonanceActivation * 0.1;
// Clamp Ψ to [0, 10]
family.Psi = Math.max(0, Math.min(10, family.Psi));
}
// Update global coherence
this.state.computeGlobalCoherence();
// Update cumulative regret
this.state.Omega += (1 - this.state.PhiGlobal);
}
sigmoid(x) {
return 1 / (1 + Math.exp(-x));
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// TOPK-FREE ALLOCATOR - 5-STAGE PIPELINE
// ═══════════════════════════════════════════════════════════════════════════════
class SeferimAllocator {
constructor() {
this.config = SEFERIM_CONFIG;
}
/**
* Compute allocation weights π^eff for substrates
* @param {Array} relevanceScores - Array of {familyIndex, substrateId, relevance}
* @param {AGIState} state - Current AGI state
* @returns {Array} - Array of {familyIndex, substrateId, weight}
*/
compute(relevanceScores, state) {
if (relevanceScores.length === 0) return [];
// STAGE 0: Structural Sparsity (Entmax)
let pi = this.entmax(relevanceScores.map(r => r.relevance), this.config.ENTMAX_ALPHA);
// STAGE 1: Margin Emphasis
pi = this.applyMarginEmphasis(pi, relevanceScores);
// STAGE 2: Truth Modulation + Novelty Suppression
pi = this.applyTruthGate(pi, relevanceScores, state);
// STAGE 3: Consolidation Gravity
pi = this.applyConsolidationGravity(pi, relevanceScores, state);
// STAGE 4: Competition Sharpening + Normalize
pi = this.applyCompetitionSharpening(pi);
// Build result
return relevanceScores.map((r, i) => ({
familyIndex: r.familyIndex,
substrateId: r.substrateId,
weight: pi[i]
})).filter(r => r.weight > 1e-6);
}
/**
* STAGE 0: Entmax - produces sparse probability distribution
* Unlike softmax, Entmax produces EXACT ZEROS for low-relevance items
*/
entmax(scores, alpha) {
if (alpha <= 1) {
// Fallback to softmax
return this.softmax(scores);
}
// For α = 1.5, use bisection method to find threshold
const sorted = [...scores].sort((a, b) => b - a);
const n = scores.length;
// Find threshold τ using bisection
let tauLow = sorted[n - 1] - 1;
let tauHigh = sorted[0];
for (let iter = 0; iter < 50; iter++) {
const tau = (tauLow + tauHigh) / 2;
let sum = 0;
for (const s of scores) {
const z = Math.max(0, s - tau);
sum += Math.pow(z, 1 / (alpha - 1));
}
if (sum > 1) {
tauLow = tau;
} else {
tauHigh = tau;
}
}
const tau = (tauLow + tauHigh) / 2;
// Compute entmax values
const result = scores.map(s => {
const z = Math.max(0, s - tau);
return Math.pow(z, 1 / (alpha - 1));
});
// Normalize
const sum = result.reduce((a, b) => a + b, 0);
return sum > 0 ? result.map(r => r / sum) : result.map(() => 1 / n);
}
softmax(scores) {
const max = Math.max(...scores);
const exp = scores.map(s => Math.exp(s - max));
const sum = exp.reduce((a, b) => a + b, 0);
return exp.map(e => e / sum);
}
/**
* STAGE 1: Margin Emphasis
* High margin → high confidence, prevents going all-in on ambiguous cases
*/
applyMarginEmphasis(pi, relevanceScores) {
const relevances = relevanceScores.map(r => r.relevance);
const maxRelevance = Math.max(...relevances);
return pi.map((p, i) => {
const r = relevances[i];
// Margin = how much better than second-best
const margin = r - Math.max(...relevances.filter((_, j) => j !== i));
const marginFactor = this.sigmoid(this.config.MARGIN_ALPHA * margin);
return p * marginFactor;
});
}
/**
* STAGE 2: Truth Modulation + Novelty Suppression
* κ^real gates all learning - unverified content can't accumulate coherence
* THIS IS THE ANTI-HALLUCINATION GATE
*/
applyTruthGate(pi, relevanceScores, state) {
return pi.map((p, i) => {
const { familyIndex, substrateId } = relevanceScores[i];
const substrate = state.families[familyIndex].getSubstrate(substrateId);
// Truth gate: κ^real
const truthGate = substrate.kappaReal;
// Novelty suppression: e^(-α_ν × ν)
const noveltySuppression = Math.exp(-this.config.NOVELTY_ALPHA * substrate.nu);
return p * truthGate * noveltySuppression;
});
}
/**
* STAGE 3: Consolidation Gravity
* New knowledge attaches to existing clusters → compositional, retrievable
*/
applyConsolidationGravity(pi, relevanceScores, state) {
// Compute total coherence
let totalPhi = 0;
for (const r of relevanceScores) {
const substrate = state.families[r.familyIndex].getSubstrate(r.substrateId);
totalPhi += substrate.phi;
}
return pi.map((p, i) => {
const { familyIndex, substrateId } = relevanceScores[i];
const substrate = state.families[familyIndex].getSubstrate(substrateId);
// Normalized existing coherence
const c = totalPhi > 0 ? substrate.phi / (totalPhi + 1e-10) : 0;
// Pull toward established substrates
return p * (c + this.config.CONSOLIDATION_FLOOR);
});
}
/**
* STAGE 4: Competition Sharpening + Normalize
* Winner-take-more while remaining differentiable
*/
applyCompetitionSharpening(pi) {
// Apply sharpening: π^γ where γ > 1
const sharpened = pi.map(p => Math.pow(p, this.config.COMPETITION_GAMMA));
// Normalize
const sum = sharpened.reduce((a, b) => a + b, 0);
return sum > 0 ? sharpened.map(s => s / sum) : sharpened;
}
sigmoid(x) {
return 1 / (1 + Math.exp(-x));
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// SEFERIM FEDERATION - MASTER LEARNING ENGINE
// ═══════════════════════════════════════════════════════════════════════════════
class SeferimFederation {
constructor(state) {
this.state = state;
this.controller = new SeferimController(state);
this.allocator = new SeferimAllocator();
}
/**
* MASTER UPDATE: φ(t+) = φ(t) + (1-φ(t)) × Γ × π^eff
*/
learn(familyIndex, substrateId, Gamma, piEff) {
const family = this.state.families[familyIndex];
const substrate = family.getSubstrate(substrateId);
// Master equation
const delta = (1 - substrate.phi) * Gamma * piEff;
substrate.phi = Math.min(1.0, substrate.phi + delta);
substrate.lastTick = this.state.tick;
// Novelty decay
substrate.nu *= SEFERIM_CONFIG.NOVELTY_DECAY;
// Increment knowledge bits
if (delta > 0) {
this.state.K += Math.ceil(delta * 100);
}
}
/**
* Compute continuous activation χ = σ(α_κ·κ + α_Ψ·Ψ̃ - θ)
*/
computeActivation(kappa, familyIndex) {
const family = this.state.families[familyIndex];
// Normalize pressure: Ψ̃ = Ψ / (Ψ + ψ₀)
const PsiNorm = family.Psi / (family.Psi + SEFERIM_CONFIG.PSI_0);
// Activation: χ = σ(α_κ·κ + α_Ψ·Ψ̃ - θ)
const z = SEFERIM_CONFIG.ALPHA_KAPPA * kappa +
SEFERIM_CONFIG.ALPHA_PSI * PsiNorm -
SEFERIM_CONFIG.THETA;
return 1 / (1 + Math.exp(-z));
}
/**
* Compute bounded plasticity η
*/
computePlasticity(kappa) {
const eta = SEFERIM_CONFIG.ETA_BASE + SEFERIM_CONFIG.ETA_GAIN * kappa;
return Math.max(SEFERIM_CONFIG.ETA_MIN, Math.min(SEFERIM_CONFIG.ETA_MAX, eta));
}
/**
* Compute learning drive Γ = χ × η × κ
*/
computeLearningDrive(kappa, familyIndex) {
const chi = this.computeActivation(kappa, familyIndex);
const eta = this.computePlasticity(kappa);
return chi * eta * kappa;
}
/**
* Process a learning item through the full pipeline
*/
processItem(item) {
const { familyIndex, relevanceScores, kappa } = item;
// 1. Compute learning drive
const Gamma = this.computeLearningDrive(kappa, familyIndex);
// 2. Compute allocation weights via 5-stage pipeline
const allocations = this.allocator.compute(relevanceScores, this.state);
// 3. Apply master update to each allocated substrate
for (const alloc of allocations) {
if (alloc.weight > 1e-6) {
this.learn(alloc.familyIndex, alloc.substrateId, Gamma, alloc.weight);
}
}
// 4. Update family pressure
this.state.families[familyIndex].Psi += 0.1 * kappa;
this.state.families[familyIndex].served++;
// 5. Tick controller
this.controller.tick();
this.state.tick++;
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// BINARY STATE PERSISTENCE
// ═══════════════════════════════════════════════════════════════════════════════
class BinaryStateIO {
/**
* Serialize state to binary ArrayBuffer (~10KB typical)
*/
static serialize(state) {
const chunks = [];
// HEADER (16 bytes)
const header = new ArrayBuffer(16);
const headerView = new DataView(header);
// Magic bytes "SEFG"
headerView.setUint8(0, 0x53); // S
headerView.setUint8(1, 0x45); // E
headerView.setUint8(2, 0x46); // F
headerView.setUint8(3, 0x47); // G
// Tick (uint32)
headerView.setUint32(4, state.tick, true);
// Count active substrates
let activeCount = 0;
for (const family of state.families) {
activeCount += family.substrates.size;
}
headerView.setUint16(8, activeCount, true);
// Global coherence (uint8, quantized)
headerView.setUint8(10, Math.round(state.PhiGlobal * 255));
// Regret (uint8, log-scale)
headerView.setUint8(11, Math.min(255, Math.round(Math.log(state.Omega + 1) * 20)));
// Version
headerView.setUint16(14, 311, true); // v3.1.1
chunks.push(header);
// FAMILY TABLE (32 bytes = 16 families × 2 bytes)
const familyTable = new ArrayBuffer(32);
const familyView = new DataView(familyTable);
for (let f = 0; f < 16; f++) {
const family = state.families[f];
familyView.setUint8(f * 2, Math.round(Math.min(10, family.Psi) * 25)); // Ψ
familyView.setUint8(f * 2 + 1, Math.round(family.eta * 255)); // η
}
chunks.push(familyTable);
// SUBSTRATE DATA (4 bytes each)
for (let f = 0; f < 16; f++) {
const family = state.families[f];
for (const [subId, substrate] of family.substrates) {
const subData = new ArrayBuffer(4);
const subView = new DataView(subData);
// Pack: family (4 bits) | subId (13 bits) | phi (7 bits) | kappa (4 bits) | nu (4 bits)
const packed = (f & 0x0F) |
((subId & 0x1FFF) << 4) |
((Math.round(substrate.phi * 127) & 0x7F) << 17) |
((Math.round(substrate.kappaReal * 15) & 0x0F) << 24) |
((Math.round(substrate.nu * 15) & 0x0F) << 28);
subView.setUint32(0, packed, true);
chunks.push(subData);
}
}
// Compute checksum and update header
let checksum = 0;
for (const chunk of chunks) {
const view = new Uint8Array(chunk);
for (const byte of view) {
checksum = (checksum + byte) & 0xFFFF;
}
}
headerView.setUint16(12, checksum, true);
// Combine all chunks
const totalLength = chunks.reduce((sum, c) => sum + c.byteLength, 0);
const result = new Uint8Array(totalLength);
let offset = 0;
for (const chunk of chunks) {
result.set(new Uint8Array(chunk), offset);
offset += chunk.byteLength;
}
return result.buffer;
}
/**
* Deserialize from binary ArrayBuffer
*/
static deserialize(buffer) {
const view = new DataView(buffer);
// Verify magic bytes
if (view.getUint8(0) !== 0x53 || view.getUint8(1) !== 0x45 ||
view.getUint8(2) !== 0x46 || view.getUint8(3) !== 0x47) {
throw new Error('Invalid SEFERIM state file: bad magic bytes');
}
const state = new AGIState();
// Read header
state.tick = view.getUint32(4, true);
const activeCount = view.getUint16(8, true);
state.PhiGlobal = view.getUint8(10) / 255;
state.Omega = Math.exp(view.getUint8(11) / 20) - 1;
// Read family table
for (let f = 0; f < 16; f++) {
state.families[f].Psi = view.getUint8(48 + f * 2) / 25;
state.families[f].eta = view.getUint8(48 + f * 2 + 1) / 255;
}
// Read substrates
let offset = 80; // 16 header + 32 family table + 32 reserved
offset = 48; // Actually 16 header + 32 family table
for (let i = 0; i < activeCount && offset + 4 <= buffer.byteLength; i++) {
const packed = view.getUint32(offset, true);
offset += 4;
const familyIndex = packed & 0x0F;
const subId = (packed >> 4) & 0x1FFF;
const phi = ((packed >> 17) & 0x7F) / 127;
const kappaReal = ((packed >> 24) & 0x0F) / 15;
const nu = ((packed >> 28) & 0x0F) / 15;
const substrate = state.families[familyIndex].getSubstrate(subId);
substrate.phi = phi;
substrate.kappaReal = kappaReal;
substrate.nu = nu;
}
return state;
}
/**
* Save to localStorage
*/
static saveToStorage(state, key = 'seferim_state') {
const binary = this.serialize(state);
const base64 = btoa(String.fromCharCode(...new Uint8Array(binary)));
localStorage.setItem(key, base64);
localStorage.setItem(key + '_timestamp', Date.now().toString());
return binary.byteLength;
}
/**
* Load from localStorage
*/
static loadFromStorage(key = 'seferim_state') {
const base64 = localStorage.getItem(key);
if (!base64) return null;
const binary = Uint8Array.from(atob(base64), c => c.charCodeAt(0)).buffer;
return this.deserialize(binary);
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// DRUM LEARNING ADAPTER - MAPS DRUM EVENTS TO SEFERIM
// ═══════════════════════════════════════════════════════════════════════════════
class DrumLearningAdapter {
constructor(federation) {
this.federation = federation;
this.state = federation.state;
// Drum to family mappings
this.drumFamilyMap = {
// Primary mappings
kick: [1, 10, 13], // Yad (technique), Hephaestus (precision), Ares (power)
snare: [1, 8, 10], // Yad, Hermes (dynamics), Hephaestus
hihat: [1, 12, 15], // Yad, Poseidon (flow), Chronos (tempo)
crash: [8, 13, 9], // Hermes, Ares, Apollo (artistry)
ride: [9, 12, 6], // Apollo, Poseidon, Sophia (genre)
tom1: [1, 7, 4], // Yad, Prometheus (creativity), Mnemosyne (memory)
tom2: [1, 7, 4],
tom3: [1, 7, 4],
};
// Pattern to family mappings
this.patternFamilyMap = {
basic: [0, 4, 5], // Lashon (vocab), Mnemosyne, Logos
groove: [9, 12, 6], // Apollo, Poseidon, Sophia
fill: [7, 4, 9], // Prometheus, Mnemosyne, Apollo
polyrhythm: [5, 10, 12], // Logos, Hephaestus, Poseidon
};
}
/**
* Process a drum hit event
*/
processDrumHit(drumType, velocity, timing, expectedTiming, patternContext) {
// Calculate timing accuracy
const timingError = Math.abs(timing - expectedTiming);
const timingAccuracy = Math.max(0, 1 - timingError / 100); // 100ms tolerance
// Calculate velocity accuracy
const velocityNorm = velocity / 127;
// Calculate overall confidence (κ)
const kappa = (timingAccuracy * 0.7 + velocityNorm * 0.3);
// Get relevant families
const families = this.drumFamilyMap[drumType] || [1];
// Build relevance scores for substrates
const relevanceScores = [];
for (const familyIndex of families) {
// Hash drum type + pattern context to substrate ID
const substrateId = this.hashToSubstrate(drumType, patternContext);
relevanceScores.push({
familyIndex,
substrateId,
relevance: kappa * (families.indexOf(familyIndex) === 0 ? 1.0 : 0.5)
});
}
// Process through federation
this.federation.processItem({
familyIndex: families[0],
relevanceScores,
kappa
});
return {
kappa,
timingAccuracy,
velocityNorm,
learningDrive: this.federation.computeLearningDrive(kappa, families[0])
};
}
/**
* Process pattern completion
*/
processPatternComplete(patternType, accuracy, combo, tempo) {
const kappa = accuracy / 100;
const families = this.patternFamilyMap[patternType] || [4, 5, 11];
const relevanceScores = [];
for (const familyIndex of families) {
const substrateId = this.hashToSubstrate(patternType, tempo);
relevanceScores.push({
familyIndex,
substrateId,
relevance: kappa
});
}
// Bonus learning for combos
const comboBonus = Math.min(1.0, combo / 50);
this.federation.processItem({
familyIndex: families[0],
relevanceScores,
kappa: Math.min(1.0, kappa + comboBonus * 0.2)
});
// Update skill growth family (Demeter)
this.federation.processItem({
familyIndex: 11, // Demeter
relevanceScores: [{
familyIndex: 11,
substrateId: this.hashToSubstrate('progress', patternType),
relevance: accuracy / 100
}],
kappa: accuracy / 100
});
// Update motivation family (Dionysus)
if (combo > 10) {
this.federation.processItem({
familyIndex: 14, // Dionysus
relevanceScores: [{
familyIndex: 14,
substrateId: this.hashToSubstrate('combo', combo),
relevance: comboBonus
}],
kappa: comboBonus
});
}
}
/**
* Process song learning from YouTube
*/
processSongLearning(song, sectionAccuracies, overallAccuracy) {
const kappa = overallAccuracy / 100;
// Update memory family with song data
this.federation.processItem({
familyIndex: 4, // Mnemosyne
relevanceScores: [{
familyIndex: 4,
substrateId: this.hashToSubstrate('song', song.id),
relevance: kappa
}],
kappa
});
// Update genre family
if (song.genre) {
this.federation.processItem({
familyIndex: 6, // Sophia
relevanceScores: [{
familyIndex: 6,
substrateId: this.hashToSubstrate('genre', song.genre),
relevance: kappa
}],
kappa
});
}
// Update tempo family
this.federation.processItem({
familyIndex: 15, // Chronos
relevanceScores: [{
familyIndex: 15,
substrateId: this.hashToSubstrate('bpm', Math.floor(song.bpm / 10) * 10),
relevance: kappa
}],
kappa
});
}
/**
* Get adaptive difficulty recommendation
*/
getAdaptiveDifficulty() {
const techniquePhi = this.state.families[1].meanCoherence(); // Yad
const precisionPhi = this.state.families[10].meanCoherence(); // Hephaestus
const tempoMastery = this.state.families[15].meanCoherence(); // Chronos
const overallMastery = (techniquePhi + precisionPhi + tempoMastery) / 3;
if (overallMastery < 0.2) return { level: 1, name: 'Beginner', tempoRange: [60, 80] };
if (overallMastery < 0.4) return { level: 2, name: 'Novice', tempoRange: [70, 100] };
if (overallMastery < 0.6) return { level: 3, name: 'Intermediate', tempoRange: [80, 120] };
if (overallMastery < 0.8) return { level: 4, name: 'Advanced', tempoRange: [100, 160] };
return { level: 5, name: 'Expert', tempoRange: [120, 200] };
}
/**
* Get skill profile across all families
*/
getSkillProfile() {
return this.state.families.map((family, index) => ({
index,
name: family.info.name,
domain: family.info.domain,
coherence: family.meanCoherence(),
pressure: family.Psi,
substrates: family.substrates.size
}));
}
/**
* Simple hash function for substrate addressing
*/
hashToSubstrate(type, value) {
const str = `${type}:${value}`;
let hash = 0;
for (let i = 0; i < str.length; i++) {
const char = str.charCodeAt(i);
hash = ((hash << 5) - hash) + char;
hash = hash & hash;
}
return Math.abs(hash) % SEFERIM_CONFIG.SUBSTRATES_PER_FAMILY;
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// MAIN SEFERIM INSTANCE - SINGLETON
// ═══════════════════════════════════════════════════════════════════════════════
class SeferimAGI {
constructor() {
// Try to load existing state
this.state = BinaryStateIO.loadFromStorage() || new AGIState();
this.federation = new SeferimFederation(this.state);
this.adapter = new DrumLearningAdapter(this.federation);
// Auto-save interval
this.autoSaveInterval = null;
this.startAutoSave();
console.log(`
╔══════════════════════════════════════════════════════════════╗
║ SEFERIM AGI G16 v3.1.1 - DrumMaster Pro Integration ║
║ 16 Families × 5000 Substrates = 80,000 Learning Sites ║
║ Tick: ${this.state.tick.toString().padStart(8)} | Φ: ${this.state.PhiGlobal.toFixed(4)} | K: ${this.state.K} ║
╚══════════════════════════════════════════════════════════════╝
`);
}
startAutoSave() {
// Save every 30 seconds
this.autoSaveInterval = setInterval(() => {
const size = BinaryStateIO.saveToStorage(this.state);
console.log(`[SEFERIM] Auto-saved state (${size} bytes)`);
}, 30000);
}
stopAutoSave() {
if (this.autoSaveInterval) {
clearInterval(this.autoSaveInterval);
}
}
// Convenience methods
processDrumHit(...args) {
return this.adapter.processDrumHit(...args);
}
processPatternComplete(...args) {
return this.adapter.processPatternComplete(...args);
}
processSongLearning(...args) {
return this.adapter.processSongLearning(...args);
}
getAdaptiveDifficulty() {
return this.adapter.getAdaptiveDifficulty();
}
getSkillProfile() {
return this.adapter.getSkillProfile();
}
getGlobalCoherence() {
return this.state.PhiGlobal;
}
getTick() {
return this.state.tick;
}
getKnowledgeBits() {
return this.state.K;
}
save() {
return BinaryStateIO.saveToStorage(this.state);
}
reset() {
this.state = new AGIState();
this.federation = new SeferimFederation(this.state);
this.adapter = new DrumLearningAdapter(this.federation);
localStorage.removeItem('seferim_state');
console.log('[SEFERIM] State reset');
}
// Get status for UI display
getStatus() {
return {
tick: this.state.tick,
PhiGlobal: this.state.PhiGlobal,
Omega: this.state.Omega,
K: this.state.K,
families: this.getSkillProfile(),
difficulty: this.getAdaptiveDifficulty()
};
}
}
// ═══════════════════════════════════════════════════════════════════════════════
// EXPORT SINGLETON INSTANCE
// ═══════════════════════════════════════════════════════════════════════════════
// Create global singleton
window.SeferimAGI = SeferimAGI;
window.SEFERIM = null;
// Initialize on DOM ready
document.addEventListener('DOMContentLoaded', () => {
window.SEFERIM = new SeferimAGI();
});
// Also export for module usage
if (typeof module !== 'undefined' && module.exports) {
module.exports = {
SeferimAGI,
AGIState,
SeferimFederation,
SeferimController,
SeferimAllocator,
BinaryStateIO,
DrumLearningAdapter,
SEFERIM_CONFIG,
FAMILIES,
RESONANCE_MATRIX
};
}
Mathematical records from this source
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seferim-core · ║ 16 Families × 5000 Substrates
This relation defines ║ 16 Families × 5000 Substrates in the source section “seferim-core · ║ 16 Families × 5000 Substrates”. It specifies the displayed relationship between…
seferim-core · φ(a)_f,s(t⁺)
This relation defines φ(a)_f,s(t⁺) in the source section “seferim-core · φ(a)_f,s(t⁺)”. It specifies the displayed relationship between the named quantities or parameters. Its…
seferim-core · Learning Drive: Γ
This relation defines Learning Drive: Γ in the source section “seferim-core · Learning Drive: Γ”. It specifies the displayed relationship between the named quantities or…
seferim-core · Activation: χ
This relation defines Activation: χ in the source section “seferim-core · Activation: χ”. It specifies the displayed relationship between the named quantities or…
seferim-core · Plasticity: η
This relation defines Plasticity: η in the source section “seferim-core · Plasticity: η”. Applies the stated minimum, maximum, or clamp. These bounds are part of the…
AGI STATE CLASS - MAIN STATE CONTAINER · Compute global coherence Φ
This relation defines global coherence Φ in the source section “AGI STATE CLASS - MAIN STATE CONTAINER · Compute global coherence Φ”. It specifies the displayed relationship…
AGI STATE CLASS - MAIN STATE CONTAINER · dΨ/dt
This relation defines dΨ/dt in the source section “AGI STATE CLASS - MAIN STATE CONTAINER · dΨ/dt”. It specifies the displayed relationship between the named quantities or…
TOPK-FREE ALLOCATOR - 5-STAGE PIPELINE · For α
This relation defines For α in the source section “TOPK-FREE ALLOCATOR - 5-STAGE PIPELINE · For α”. It specifies the displayed relationship between the named quantities or…
SEFERIM FEDERATION - MASTER LEARNING ENGINE · MASTER UPDATE: φ(t+)
This relation defines MASTER UPDATE: φ(t+) in the source section “SEFERIM FEDERATION - MASTER LEARNING ENGINE · MASTER UPDATE: φ(t+)”. It specifies the displayed relationship…
SEFERIM FEDERATION - MASTER LEARNING ENGINE · Compute continuous activation χ
This relation defines continuous activation χ in the source section “SEFERIM FEDERATION - MASTER LEARNING ENGINE · Compute continuous activation χ”. It specifies the displayed…
SEFERIM FEDERATION - MASTER LEARNING ENGINE · Normalize pressure: Ψ̃
This relation defines Normalize pressure: Ψ̃ in the source section “SEFERIM FEDERATION - MASTER LEARNING ENGINE · Normalize pressure: Ψ̃”. It specifies the displayed relationship…
SEFERIM FEDERATION - MASTER LEARNING ENGINE · Activation: χ
This relation defines Activation: χ in the source section “SEFERIM FEDERATION - MASTER LEARNING ENGINE · Activation: χ”. It specifies the displayed relationship between the named…
SEFERIM FEDERATION - MASTER LEARNING ENGINE · Compute learning drive Γ
This relation defines learning drive Γ in the source section “SEFERIM FEDERATION - MASTER LEARNING ENGINE · Compute learning drive Γ”. It specifies the displayed relationship…
BINARY STATE PERSISTENCE · FAMILY TABLE (32 bytes
This relation defines FAMILY TABLE (32 bytes in the source section “BINARY STATE PERSISTENCE · FAMILY TABLE (32 bytes”. It specifies the displayed relationship between the named…