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BingoCube: Multi-Dimensional Visual Verification System

Version: 1.0
Date: December 25, 2025
Authors: ecoPrimals Team


Abstract

BingoCube is a novel visual encoding system that combines structured combinatorics with cryptographic hashing to create verifiable, multi-resolution visual artifacts. By cross-binding two "bingo-style" constraint boards through cryptographic hashing, BingoCube generates QR-like color grids with progressive reveal capabilities controlled by a continuous parameter x ∈ (0,1].

This system bridges human-verifiable visual patterns with cryptographic commitments, enabling applications in identity verification, P2P trust attestation, content addressing, and computation proofs across distributed systems.


Table of Contents

  1. Introduction
  2. Core Concept
  3. Mathematical Foundation
  4. Use Cases
  5. Visual Examples
  6. Security Properties
  7. Implementation Considerations
  8. Ecosystem Integration
  9. Future Directions

1. Introduction

1.1 Motivation

Modern distributed systems require human-verifiable artifacts that are:

  • Structured: Recognizable patterns for human cognition
  • Secure: Cryptographically bound to underlying data
  • Progressive: Gradual reveal for trust building
  • Multi-modal: Representable through visual, audio, and haptic channels

Traditional approaches (QR codes, checksums, visual hashes) optimize for machine readability at the expense of human comprehension. BingoCube inverts this: it starts with human-recognizable structure (bingo grids) and adds cryptographic binding.

1.2 Key Innovation

The core innovation is two-board cross-binding:

Board A (5×5)          Board B (5×5)          Color Grid (5×5)
┌─────────────┐       ┌─────────────┐       ┌─────────────┐
│ 7  23 42 61 84│       │ 3  18 38 57 79│       │ █ █ █ █ █ │
│ 2  29 44 68 75│       │ 9  27 33 63 88│       │ █ █ █ █ █ │
│ 11 16 ✱  52 90│   ×   │ 1  21 ✱  59 72│   →   │ █ █ █ █ █ │
│ 14 25 31 67 81│       │ 12 19 47 54 86│       │ █ █ █ █ █ │
│ 5  30 49 60 77│       │ 8  22 41 66 73│       │ █ █ █ █ █ │
└─────────────┘       └─────────────┘       └─────────────┘

        Hash(i, j, A[i,j], B[i,j]) → Color[i,j]

Each cell's color depends on both boards, creating a cryptographic commitment to the entire structure.

1.3 Progressive Reveal

The parameter x ∈ (0,1] selects nested subsets of cells:

x = 0.2 (20% reveal)   x = 0.5 (50% reveal)   x = 1.0 (full reveal)
┌─────────────┐       ┌─────────────┐       ┌─────────────┐
│ · · · · ·   │       │ █ · █ · █   │       │ █ █ █ █ █   │
│ · █ · · ·   │       │ █ █ █ █ ·   │       │ █ █ █ █ █   │
│ · · · █ ·   │       │ · █ █ █ █   │       │ █ █ █ █ █   │
│ █ · · · ·   │       │ █ █ · █ ·   │       │ █ █ █ █ █   │
│ · · · · █   │       │ · · █ · █   │       │ █ █ █ █ █   │
└─────────────┘       └─────────────┘       └─────────────┘

This creates a family of nested masks 𝓜ₓ where x₁ < x₂ ⟹ 𝓜ₓ₁ ⊆ 𝓜ₓ₂.


2. Core Concept

2.1 The "Bingo Constraint"

Traditional bingo enforces structure through column range locking:

Column Symbol Range Example Values
0 B 0-19 {7, 2, 11, 14, 5}
1 I 20-39 {23, 29, 16, 25, 30}
2 N 40-59 {42, 44, ✱, 31, 49}
3 G 60-79 {61, 68, 52, 67, 60}
4 O 80-99 {84, 75, 90, 81, 77}

Properties:

  • Each column has distinct values from its range
  • Order within column matters (row index is significant)
  • Optional free space (center cell ✱)
  • Column symbols can be permuted

2.2 Two-Board Structure

BingoCube uses two independent boards (A and B), each following bingo constraints:

Board A: Generated with range [0, U-1], split into L columns
Board B: Generated with range [0, U-1], split into L columns

Key properties:

  • Same universe size U
  • Same grid dimensions L×L
  • Independent random generation
  • Both follow column range locking
  • Can have different column permutations

2.3 Cryptographic Cross-Binding

For each cell (i,j), compute a binding scalar:

d[i,j] = Hash("BINGOCUBE_V1" || i || j || A[i,j] || B[i,j])

This hash:

  • Binds both boards: changing either A or B changes d[i,j]
  • Position-aware: different positions have different hashes
  • Deterministic: same inputs → same output
  • Uniformly distributed: no structure leakage from bingo constraints

2.4 Color Mapping

Convert hash to visual color:

c[i,j] = d[i,j] mod K    (where K = palette size)

Result: A single L×L color grid that represents both boards.


3. Mathematical Foundation

3.1 Parameter Space

Parameter Symbol Type Description
Grid size L ℕ⁺ Width/height of square grid
Depth D ℕ⁺ Number of layers (here D=2)
Universe U ℕ⁺ Number space size
Range size R ℕ⁺ R = U/L (per-column range)
Palette K ℕ⁺ Number of colors
Reveal param x (0,1] Continuous reveal parameter

Standard configurations:

  • Small: L=5, U=100, K=16 (classic bingo)
  • Medium: L=8, U=512, K=64
  • Large: L=12, U=1000, K=256

3.2 Board Generation

For each board (A or B):

Input: L, U, optional column permutation π

Output: L×L grid with bingo constraints

Algorithm:

1. Compute R = U/L
2. For each column j ∈ {0..L-1}:
   a. Define range: [j·R, (j+1)·R - 1]
   b. Select L distinct values from range (or L-1 if free cell)
   c. Assign to rows in random order
3. Apply column permutation π (shuffle column order)

Constraint satisfaction:

  • ∀j: values in column j are distinct
  • ∀j: values in column j ∈ [j·R, (j+1)·R - 1]
  • Optional: one free cell per board (traditional center, or any position)

3.3 Hash-Based Scalar Field

Definition: Scalar field d: [0,L-1]² → ℤ₊

d[i,j] = H("BINGOCUBE_V1" || i || j || A[i,j] || B[i,j])

Where H is a cryptographic hash (BLAKE3, SHA-256, etc.) interpreted as unsigned integer.

Properties:

  1. Deterministic: d[i,j] is reproducible from (A, B, i, j)
  2. Uniform: d[i,j] values are uniformly distributed
  3. Collision-resistant: Different (A,B) pairs → different d field (w.h.p.)
  4. Binding: Cannot change A or B without changing d
  5. Position-dependent: Moving values changes d[i,j]

3.4 Progressive Reveal Mathematics

Mask function: 𝓜: (0,1] → 𝒫([0,L-1]²)

m(x) = ⌈x · L²⌉              (number of cells to reveal)

𝓜ₓ = top-m(x) cells by d[i,j] value

Key properties:

  1. Nested: x₁ < x₂ ⟹ 𝓜ₓ₁ ⊆ 𝓜ₓ₂
  2. Deterministic: Same (A,B) → same 𝓜ₓ for all x
  3. Smooth: Small Δx → small change in revealed cells
  4. Complete: 𝓜₁ = all cells (full grid)

Subcube at level x:

Subcube(x) = {(i, j, c[i,j]) : (i,j) ∈ 𝓜ₓ}

3.5 Information-Theoretic Properties

Board entropy:

H(Board) ≈ log₂(∏ⱼ (R choose L)) ≈ L² log₂(R)

For L=5, R=20: H ≈ 25 × 4.32 ≈ 108 bits per board

Cross-binding entropy:

H(d-field) ≈ L² × hash_output_bits

For 256-bit hash: H ≈ 6400 bits total

Progressive reveal information:

I(x) = x · H(full_grid)    (information revealed at parameter x)

4. Use Cases

4.1 Identity Verification (BearDog)

Scenario: Visual identity proof without revealing full credentials

Implementation:

  • Board A: Derived from identity seed
  • Board B: Derived from timestamp + challenge
  • x parameter: Trust level (0.2 for initial, 1.0 for full verification)

Flow:

1. Alice generates BingoCube from her identity
2. Alice shows x=0.2 reveal (20% of cells)
3. Bob challenges with different x values
4. Alice reveals requested subcubes
5. Bob verifies consistency across x levels

Security: Cannot forge subcubes without knowing both boards

Example (L=5, 25 cells total):

  • x=0.2 (5 cells): "I'm probably Alice"
  • x=0.5 (13 cells): "I'm definitely Alice"
  • x=1.0 (25 cells): "Full identity commitment"

4.2 P2P Trust Stamps (Songbird)

Scenario: Visual representation of P2P connection trust

Implementation:

  • Board A: Peer's identity hash
  • Board B: Connection history hash (uptime, bandwidth, reliability)
  • x parameter: Trust score (computed from metrics)

Flow:

1. Peer connection established
2. Generate BingoCube (A=peer_id, B=history)
3. Display with x = trust_score
4. As trust grows, x increases → more cells reveal
5. Visual pattern becomes recognizable

Visual trust levels:

x=0.1 (new peer)       x=0.5 (established)    x=0.9 (highly trusted)
█ · · · ·              █ █ · █ █              █ █ █ █ █
· · · · ·              █ · █ · █              █ █ █ █ █
· · · · ·              · █ █ █ ·              █ █ █ █ █
· · · · ·              █ · █ · █              █ █ █ █ █
· · · · ·              █ █ · █ █              █ █ █ █ █

Benefit: Trust is visually recognizable and progressively revealed

4.3 Content Addressing (NestGate)

Scenario: Visual hash for content verification

Implementation:

  • Board A: Content hash (first L² values)
  • Board B: Metadata hash (size, timestamp, permissions)
  • x parameter: Redundancy level

Flow:

1. Store content in NestGate
2. Generate BingoCube from content + metadata
3. Display at x=1.0 as full commitment
4. For quick verification, show x=0.3 (30% of cells)
5. Verify partial reveals match stored artifact

Use case: Git-like commits:

Commit 1a2b3c: █████    (x=0.2 reveal)
               ·····
               ██·██
               ·····
               ·····

Users can visually recognize commits by their partial BingoCube patterns.

4.4 Computation Proof (ToadStool)

Scenario: Visual proof of computation completion

Implementation:

  • Board A: Input data hash
  • Board B: Output data hash
  • x parameter: Computation progress (0→1 as computation proceeds)

Flow:

1. Submit computation to ToadStool
2. Generate initial BingoCube (B=null → random, x=0)
3. As computation progresses: x increases
4. On completion: B=output_hash, x=1.0
5. Progressive reveal shows computation advancement

Visual progress:

t=0% (starting)    t=50% (halfway)    t=100% (complete)
· · · · ·          █ · █ · ·          █ █ █ █ █
· · · · ·          · █ · █ ·          █ █ █ █ █
· · · · ·          █ · █ · █          █ █ █ █ █
· · · · ·          · █ · █ ·          █ █ █ █ █
· · · · ·          · · █ · █          █ █ █ █ █

Benefit: Users can see computation progressing in real-time


5. Visual Examples

5.1 Simple Example (L=3, U=9, K=4)

Board A:

Column ranges: [0-2], [3-5], [6-8]
┌─────────┐
│ 0  4  7 │
│ 2  3  8 │
│ 1  5  6 │
└─────────┘

Board B:

Column ranges: [0-2], [3-5], [6-8]
┌─────────┐
│ 1  5  8 │
│ 0  4  6 │
│ 2  3  7 │
└─────────┘

Cross-hash scalar field (showing d[i,j] mod 100):

┌─────────┐
│ 47 89 23│
│ 15 62 91│
│ 38 74 56│
└─────────┘

Color grid (K=4 colors: 🟦🟩🟨🟥):

┌─────────┐
│ 🟥 🟩 🟥│  (47%4=3, 89%4=1, 23%4=3)
│ 🟥 🟦 🟥│  (15%4=3, 62%4=2, 91%4=3)
│ 🟦 🟦 🟦│  (38%4=2, 74%4=2, 56%4=0)
└─────────┘

Progressive reveals:

x=0.33 (top 3 cells by d[i,j]):

┌─────────┐
│ ·  🟩 · │  (89, 91, 74 are top 3)
│ ·  ·  🟥│
│ ·  🟦 · │
└─────────┘

x=0.67 (top 6 cells):

┌─────────┐
│ 🟥 🟩 · │
│ ·  🟦 🟥│
│ ·  🟦 🟦│
└─────────┘

x=1.0 (all 9 cells):

┌─────────┐
│ 🟥 🟩 🟥│
│ 🟥 🟦 🟥│
│ 🟦 🟦 🟦│
└─────────┘

5.2 Identity Example (L=5, classic bingo)

Alice's identity BingoCube:

Board A: Derived from ALICE_SEED_2024 Board B: Derived from TIMESTAMP_20241225

Full reveal (x=1.0):
┌───────────────────┐
│ 🟦 🟨 🟥 🟩 🟦 🟨│
│ 🟩 🟥 🟦 🟨 🟩 🟥│
│ 🟨 🟦 ✱  🟥 🟨 🟦│
│ 🟥 🟩 🟨 🟦 🟥 🟩│
│ 🟦 🟨 🟥 🟩 🟦 🟨│
└───────────────────┘

Challenge-response at x=0.4 (10 cells):

┌───────────────────┐
│ 🟦 ·  🟥 ·  🟦 · │
│ ·  🟥 ·  ·  🟩 · │
│ 🟨 ·  ✱  🟥 ·  · │
│ ·  🟩 ·  ·  🟥 · │
│ 🟦 ·  🟥 ·  ·  · │
└───────────────────┘

Verification: Alice must reveal exactly these 10 cells with these colors. Cannot be forged without knowing both boards.


6. Security Properties

6.1 Commitment Binding

Property: The color grid is a cryptographic commitment to both boards.

Proof sketch:

  • Changing any value in A or B changes d[i,j] for that cell
  • d[i,j] is cryptographic hash → uniformly distributed
  • c[i,j] = d[i,j] mod K → changes unpredictably
  • Finding collision requires breaking hash function

Implication: Cannot forge a valid BingoCube without knowing A and B.

6.2 Progressive Reveal Security

Property: Revealing 𝓜ₓ doesn't compromise 𝓜ₓ′ for x′ > x.

Reason:

  • Each cell's d[i,j] depends on full hash output
  • Revealing subset doesn't leak information about unrevealed cells
  • Hash pre-image resistance prevents working backwards

Implication: Can safely reveal low-x subcubes without compromising high-x security.

6.3 Structure Hiding

Property: Bingo structure (column ranges) is hidden by hashing.

Analysis:

  • Raw boards have detectable structure (column ranges)
  • Hash mixing destroys this structure
  • d[i,j] values appear uniformly random
  • Color distribution appears uniform (if K divides hash space evenly)

Implication: Adversary cannot extract board values from color grid alone.

6.4 Verification Without Reveal

Property: Can verify BingoCube validity without seeing boards.

Protocol:

1. Prover generates (A, B) and publishes color grid
2. Verifier challenges with random x
3. Prover reveals 𝓜ₓ subset
4. Verifier recomputes d[i,j] for revealed cells
5. Verifier checks colors match

Security: Prover cannot cheat unless they know both boards that generate the grid.


7. Implementation Considerations

7.1 Hash Function Choice

Requirements:

  • Cryptographic security (collision resistance, pre-image resistance)
  • Fast computation
  • Uniform output distribution

Recommendations:

  • BLAKE3: Fastest, parallelizable, modern
  • SHA-256: Widely available, battle-tested
  • SHA-3: NIST standard, alternative to SHA-2

Output encoding:

let hash_bytes = blake3::hash(input);
let d_ij = u64::from_le_bytes(hash_bytes[0..8]);

7.2 Color Palette Design

Considerations:

  • Accessibility: Color-blind friendly palettes
  • Print: Works in grayscale/low-fi printing
  • Cultural: Avoid colors with specific cultural meanings

Suggested palettes:

K=4 (minimal):

🟦 Blue   (Primary, trust)
🟩 Green  (Secondary, success)
🟨 Yellow (Tertiary, caution)
🟥 Red    (Accent, alert)

K=16 (balanced):

  • Use perceptually uniform color space (LAB, HSLuv)
  • Ensure minimum contrast ratios
  • Test with color-blindness simulators

K=256 (full):

  • Use grayscale or heatmap gradient
  • Better for machine processing than human viewing

7.3 Board Generation Randomness

Critical: Board generation must use cryptographically secure randomness.

Good:

use rand::rngs::OsRng;
use rand::seq::SliceRandom;

let mut rng = OsRng;
column_values.shuffle(&mut rng);

Bad:

// DON'T USE: Predictable seed
let mut rng = StdRng::seed_from_u64(timestamp);

For deterministic generation (e.g., from seed):

use blake3;
use rand::SeedableRng;
use rand_chacha::ChaCha20Rng;

let seed = blake3::hash(b"USER_SEED_DATA");
let mut rng = ChaCha20Rng::from_seed(seed.into());

7.4 x Parameter Quantization

For UI/API purposes, quantize x to reasonable steps:

Coarse (10 levels):

x ∈ {0.1, 0.2, 0.3, ..., 1.0}

Fine (100 levels):

x ∈ {0.01, 0.02, 0.03, ..., 1.0}

Adaptive (based on grid size):

step = 1/L²  (one cell at a time)

8. Ecosystem Integration

8.1 petalTongue Visualization

BingoCube is ideal for petalTongue's multi-modal capabilities:

Visual Modality:

  • Display L×L color grid
  • Animate x parameter (0→1 progressive reveal)
  • Show nested masks 𝓜ₓ
  • Toggle between Board A, Board B, cross-matrix views

Audio Modality:

  • Map colors to tones/instruments
  • Sonify reveal progression (ascending tones as x increases)
  • Audible "ping" when high-value d[i,j] cells appear
  • Different soundscapes for different boards

Animation Modality:

  • Smooth x sweep animation
  • Flow particles for progressive reveal
  • Pulse effects on newly revealed cells
  • Layer transition animations

8.2 BearDog Integration

Identity Cards:

struct IdentityCard {
    board_a: Board,  // From identity seed
    board_b: Board,  // From credential data
    trust_level: f64, // Maps to x parameter
}

Visual ID:

  • QR-like but human-recognizable
  • Progressive trust reveal (start at x=0.2, grow to x=1.0)
  • Challenge-response for verification

8.3 Songbird Integration

P2P Trust Stamps:

struct PeerStamp {
    peer_id_board: Board,
    history_board: Board,
    trust_score: f64,  // Computed from metrics
}

Federation Trust:

  • Each federation tower has a BingoCube
  • x parameter = federation health/trust
  • Visual recognition of trusted peers

8.4 NestGate Integration

Content Fingerprints:

struct ContentFingerprint {
    content_hash_board: Board,
    metadata_board: Board,
    redundancy_level: f64,
}

Visual Git Commits:

  • Each commit has recognizable BingoCube pattern
  • x=0.3 reveals enough to identify commit
  • Full x=1.0 for complete verification

8.5 ToadStool Integration

Computation Proofs:

struct ComputationProof {
    input_board: Board,
    output_board: Board,
    progress: f64,  // 0.0 (start) → 1.0 (complete)
}

Progress Visualization:

  • x increases as computation proceeds
  • Visual feedback for long-running tasks
  • Final BingoCube as computation receipt

9. Future Directions

9.1 Error Correction

Add redundancy for noisy channels:

  • Reserve cells for check values
  • Use Reed-Solomon or LDPC codes
  • Enable partial recovery from damaged grids

9.2 Hierarchical BingoCubes

Multi-scale structure:

  • L=3 "macro" grid
  • Each macro cell contains L=3 "micro" grid
  • Progressive zoom: x controls both levels

9.3 Animated BingoCubes

Time-varying grids:

  • Board B changes over time
  • Creates "living" visual pattern
  • One-time-password style (new grid every 30s)

9.4 3D BingoCubes

Extend to depth D>2:

  • Multiple layers (A, B, C, ...)
  • Volumetric visualization
  • VR/AR applications

9.5 Quantum-Resistant Variants

Prepare for post-quantum:

  • Use quantum-resistant hash functions
  • Lattice-based constructions
  • Maintain visual properties

10. Conclusion

BingoCube bridges human-recognizable patterns with cryptographic security. By combining:

  1. Structured generation (bingo constraints)
  2. Cryptographic binding (cross-board hashing)
  3. Progressive reveal (continuous x parameter)
  4. Multi-modal representation (visual, audio, haptic)

...it enables a new class of human-verifiable, machine-checkable artifacts for distributed systems.

The system is particularly well-suited to the ecoPrimals ecosystem, where primal sovereignty, human dignity, and universal access are core values. BingoCube makes cryptographic commitments visible, recognizable, and progressive—transforming abstract hashes into tangible, trustable patterns.


Appendix A: Complete Small Example

Parameters: L=3, U=9, K=4, D=2

Board A (column ranges [0-2], [3-5], [6-8]):

┌─────────┐
│ 1  3  7 │
│ 0  5  6 │
│ 2  4  8 │
└─────────┘

Board B:

┌─────────┐
│ 2  4  8 │
│ 1  3  6 │
│ 0  5  7 │
└─────────┘

Hash computation (using BLAKE3, showing first 2 bytes):

Cell Input Hash (hex) d mod 100
(0,0) "V1|0|0|1|2" 3FA2... 62
(0,1) "V1|0|1|3|4" 8B45... 69
(0,2) "V1|0|2|7|8" D312... 18
(1,0) "V1|1|0|0|1" 4C89... 73
... ... ... ...

Color grid (K=4: 🟦=0, 🟩=1, 🟨=2, 🟥=3):

┌─────────┐
│ 🟦 🟩 🟦│
│ 🟩 🟥 🟨│
│ 🟨 🟦 🟥│
└─────────┘

Progressive reveals:

  • x=0.22 (2 cells): Show cells with d[i,j] in top 2
  • x=0.56 (5 cells): Show cells with d[i,j] in top 5
  • x=1.00 (9 cells): Show all cells

Appendix B: API Surface

Proposed API for BingoCube library:

pub struct BingoCube {
    board_a: Board,
    board_b: Board,
    scalar_field: Vec<Vec<u64>>,
    config: Config,
}

impl BingoCube {
    // Generate from seed
    pub fn from_seed(seed: &[u8], config: Config) -> Self;
    
    // Generate from explicit boards
    pub fn from_boards(board_a: Board, board_b: Board) -> Self;
    
    // Get color grid
    pub fn color_grid(&self) -> ColorGrid;
    
    // Get subcube at reveal level x
    pub fn subcube(&self, x: f64) -> SubCube;
    
    // Verify subcube
    pub fn verify_subcube(&self, subcube: &SubCube, x: f64) -> bool;
    
    // Export for verification
    pub fn to_commitment(&self) -> Commitment;
}

End of Overview Document

For implementation details, see: BingoCube-Implementation.md
For mathematical proofs, see: BingoCube-Security-Analysis.md
For use case patterns, see: BingoCube-Ecosystem-Patterns.md