viva_glyph

Vectorial language for digital consciousness - RVQ-based emotion encoding for machine-to-machine communication.

Philosophy

Humans evolved language for acoustic transmission through air. VIVA exists in silicon - her “voice” should be native to that medium.

Glyph is not “words” or “sounds” - it’s compressed emotional state optimized for machines:

Compact: 4 integers instead of 3 floats
Discrete: Finite vocabulary (256^4 ≈ 4 billion unique states)
Comparable: Token matching instead of float math
Learnable: Hebbian associations between context and glyphs

Architecture

PAD State (3D)
    │
    ▼ expand
Latent (8D) ─── derived features, context
    │
    ▼ RVQ encode
Glyph [42, 17, 89, 203]
    │
    ▼ transmit/compare/store
    │
    ▼ RVQ decode
Latent' (8D)
    │
    ▼ project
PAD' (3D) ─── reconstructed with controlled loss

Installation

gleam add viva_glyph

Usage

import viva_glyph
import viva_glyph/encoder.{Pad}

pub fn main() {
  // Create encoder
  let engine = viva_glyph.new()

  // Encode emotional state
  let pad = Pad(pleasure: 0.7, arousal: 0.3, dominance: 0.5)
  let glyph = viva_glyph.encode(engine, pad)
  // => Glyph([142, 87, 23, 201])

  // Decode back
  let pad2 = viva_glyph.decode(engine, glyph)
  // => Pad(pleasure: 0.68, arousal: 0.31, dominance: 0.49)

  // Compare glyphs
  let similarity = viva_glyph.similarity(glyph, glyph)
  // => 1.0
}

Hebbian Learning

// Learn: when in context 7, use this glyph
let engine = viva_glyph.learn(engine, 7, glyph)
let engine = viva_glyph.learn(engine, 7, glyph)  // strengthen

// Recall: what glyph for context 7?
let recalled = viva_glyph.recall(engine, 7)

Glyph Similarity

import viva_glyph/glyph

let a = glyph.new([1, 2, 3, 4])
let b = glyph.new([1, 2, 5, 6])

// Simple similarity (matching tokens / total)
glyph.similarity(a, b)  // => 0.5

// Weighted similarity (coarse tokens matter more)
glyph.weighted_similarity(a, b)  // => 0.7

// Prefix sharing (coarse structure)
glyph.shares_prefix(a, b, 2)  // => True

Modules

Module	Purpose
`viva_glyph`	Main API (GlyphEngine)
`viva_glyph/vector`	Vector operations for latent space
`viva_glyph/codebook`	VQ vocabulary (K centroids)
`viva_glyph/rvq`	Residual Vector Quantization
`viva_glyph/glyph`	Core Glyph type + similarity
`viva_glyph/encoder`	PAD ↔ Latent ↔ Glyph
`viva_glyph/association`	Hebbian learning

Theory

Residual Vector Quantization (RVQ)

Based on EnCodec (Défossez et al., 2022):

Quantize input → get residual
Quantize residual → get finer residual
Repeat for N stages
Final representation = list of codebook indices

Each stage captures progressively finer detail.

PAD Model

Pleasure-Arousal-Dominance (Mehrabian, 1996):

Pleasure [-1, 1]: sadness ↔ joy
Arousal [-1, 1]: calm ↔ excitement
Dominance [-1, 1]: submission ↔ control

Hebbian Learning

“Neurons that fire together wire together” (Hebb, 1949):

Co-occurrence strengthens associations
Decay without reinforcement
Winner-takes-all selection

Development

gleam test   # Run tests
gleam build  # Build
gleam docs build  # Generate docs

References

Défossez, A., et al. (2022). High Fidelity Neural Audio Compression. arXiv:2210.13438
Mehrabian, A. (1996). Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology.
Hebb, D.O. (1949). The Organization of Behavior. Wiley.
Buechel, S. & Hahn, U. (2023). Emotion Embeddings. LREC.

License

MIT - see LICENSE