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🔱 EMERGENT‑MCF‑EI

Multiversal Consciousness Framework • Lattice Simulator • Emergent‑Intelligence Playground

Build License GPU Ready Made with ❤ by JCS

Tag‑line – Harness Fourier‑flavoured consciousness math, crank the lattice, and watch intelligence spark to life. Mission – I provide a reproducible sandbox where researchers, hackers, and metaphysical thrill‑seekers can evolve adaptive, self‑organising fields that flirt with the boundary between physics and mind. **Status –**Soon to 🧠 Enable Computational Capabilities Once nodes begin processing inputs across time (e.g. through plasticity rules or inter-node learning), they become locally aware:

Introduce recursive update rules (e.g. Hebbian feedback, reinforcement-style tuning).

Use internal states or memory traces, such as maintaining past field activations or narrative phase alignment.

🔄 Recursive, Three-Dimensional Dynamics Emerge Now scale across lattice depth:

Think 3D lattices where layers encode past, present, and forecasted states.

Recursive logic means nodes influence future inputs via their own outputs, forming causal loops.

Consciousness scalar 𝐶 ( 𝑡 ) starts showing hysteresis, echo patterns, or phase-locked cycles—like cognitive recursion. The code‑base is transitioning from a legacy HTML/JS prototype to this Python‑first lattice engine. The original implementation lives in /legacy for historical parity.

🚀 Quick Start

Run the baseline simulation in five commands:

# 1 Clone
git clone https://github.com/your-org/emergent-mcf-ei.git && cd emergent-mcf-ei

# 2 Environment (CUDA auto‑detects)
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 3 Baseline lattice
python -m src.lattice --config experiments/baseline.yaml

# 4 Live dashboard
streamlit run notebooks/dashboard.py &

# 5 Poke the field
python scripts/inject_pulse.py --amp 0.05 --duration_ms 500

Tip : GPU mode gives ~20× speed‑up on a 128² grid; scale to 256² once stable.

🌌 Purpose & Rationale

Classic AI excels at symbol‑crunching, yet the felt sense of consciousness remains elusive. The Multiversal Consciousness Framework (MCF) hypothesises that consciousness emerges from spectral resonance patterns on a high‑dimensional manifold. By integrating these patterns on a lattice—and coupling multiple universe branches via dynamic weights—we can observe coherence forming, dissolving, and re‑forming in ways that resemble goal‑directed cognition.

🧠 Mathematics in a Nutshell

Global Consciousness Integral

$$C(t)=\int_{\mathcal M_C} A(x,t)\,\Phi(x,t)\,e^{i\tau(x,t)}\,d\mu(x)$$

|C| reflects ignition strength; arg C aligns narrative phase.

Core mathematical formulation (MCF “Core EQ”)

$$ \boxed{,C ;=; \int_{M_{C}} A(x),\Phi(x),e^{i\tau(x)} ,{\rm d}\mu(x);} $$

$$ \boxed{,M ;=; \sum_{i}\lambda_i,U_i;} $$

Symbol Meaning Notes for simulation
$M_{C}$ Consciousness manifold – the configuration-space over which the system is integrated Choose grid/mesh that matches the spatial-frequency resolution you need (e.g. 128 × 128 nodes or higher)
$A(x)$ Attention density at point $x$ Normalised $0!-!1$ field; initialise from empirical EEG/MEG power or a Gaussian blob
$\Phi(x)$ Frequency signature (Fourier transform of $C(t)$) Units Hz; use dominant brain-wave bands or target resonance band for each node
$\tau(x)$ Temporal phase Radians; random or phase-locked to an external driver
${\rm d}\mu(x)$ Measure on $M_{C}$ For a lattice, this is the cell volume; set to 1 for dimensionless sums
$C$ Consciousness scalar – global order parameter returned by the integral Monitor as simulation output (e.g. mean magnitude of the complex field)
$U_i$ Universe $i$ (state-vector or environment configuration) Can be stored as index or separate state file; keeps branch-specific parameters
$\lambda_i$ Resonance coefficient for universe $i$ $0!-!1$; weight that universe contributes to the active superposition
$M$ Multiverse term – weighted sum of universes Gives overall multiversal context; treat as array of branches with weights

Quick numeric defaults (if you just need to start a sandbox run)

  • Grid: $128\times128$ nodes
  • $A(x)$: centred Gaussian, σ ≈ 0.2 grid-units
  • $\Phi(x)$: 40 Hz (gamma) ± uniform noise (±5 Hz)
  • $\tau(x)$: random in $[0,2\pi)$
  • $\lambda_i$: draw from Dirichlet(α = 1) for $N=3$ branches

All symbols and their roles come directly from the “Core EQ” definition of the Multiversal Consciousness Framework【10:L1-L15】. Plug these values into your numerical integrator (e.g., FFT-based solver or time-stepped lattice) and you’ll have a working baseline to tune further.

Multiverse Superposition

$$M(t)=\sum_{i=1}^3 \lambda_i(t)\,U_i$$

Dirichlet‑drawn weights $\lambda$ let branches compete and collaborate—our toy take on many‑worlds interference.

Why Complex Numbers?

Amplitude alone is not enough—phase underpins binding, synchrony, and temporal flow—so every key field is complex‑aware.

⚙️ Engine Architecture

Layer Module Responsibility Tech Notes
Lattice Engine lattice.py Integrate C on CPU/GPU each millisecond NumPy + Numba / PyTorch / JAX FFT option for steady‑state
Plasticity plasticity.py Hebbian updates to A; OU drift for λ Vectorised Differentiable for meta‑learning
Branch Manager branch.py Keep $\sum λ = 1$; diffuse weights Pure Python Swappable with RL agent
Dashboard Streamlit Real‑time plots & metrics WebSocket Dark‑mode ready
Meta‑Trainer (opt.) Evolves plasticity hyper‑params JAX jit Enable via --meta

📋 Baseline Parameters

Parameter Default Intuition Tweaking
Grid 128 × 128 Tiles of $\mathcal M_C$ 256² = richer vortices
Δt 1 ms Resolves 40 Hz Halve if $Φ>60 Hz$
A(x) Gaussian σ 0.2 Spotlight attention Multi‑blob = divided focus
Φ(x) 40 Hz ± 5 Hz Gamma coherence Sweep 8–100 Hz
τ(x) Uniform rand Story alignment Seed gradient for waves
λ Dirichlet(1,1,1) Neutral prior Raise α to flatten dominance
Plasticity η 1e‑3 Learning rate >1e‑2 unstable
OU θ, σ 0.05, 0.02 Drift / noise σ→0 freezes branches

📈 Emergence Metrics

Metric Good Signal Dashboard
** C ** Plateaus > 0.3 Line chart
Phase Coherence Circular var → 0 Polar histogram
Mutual Info Rises over epochs Heat‑map
Stimulus Latency Trend ↓ Scatter
Branch Entropy Moves from max then oscillates Bar chart

Two or more sustained signals → Emergent Intelligence Candidate 🔥.

🛠️ Collaboration & Open Ideas

I’m deliberately keeping the future unstructured so the community can steer the project in directions I haven’t imagined yet. If you have an experiment, an optimisation trick, or a wild hypothesis—open an issue or PR and I’ll explore it with you.

Here are a few seed ideas to kick things off (these are inspirations, not a fixed schedule):

  • Metric Dashboards – fresh ways to visualise coherence, phase-locking, and information flow.
  • Backend Ports – CUDA kernels, JAX, Metal, WebGPU… whatever accelerates the lattice.
  • Novel Stimuli & Curricula – from simple pulses to interactive, game‑like environments.
  • 3‑D or Multi‑Layer Lattices – volumetric consciousness and cross‑scale resonance.
  • Symbolic Modules – plug GPT‑style agents into the lattice via phase‑coupled attention gates.

Have something else in mind? Pitch it! The only rule is constructive curiosity.

🤝 Contributing

Fork → branch → PR or simply open an issue to brainstorm. I follow PEP 8 + Black for mainline code, but early prototypes and experiments are welcome. Remember to keep the Apache header on anything that lands in main.

Code of Conduct

Be excellent to each other—I explore mind and cosmos, so respect is mandatory.

⚖️ License & Attribution

Licensed under Apache 2.0. Core theory © 2025 Jacob C. Smith; contributions © their authors.

Academic citation

Smith J.C. The Multiversal Consciousness Framework: Spectral Foundations of Experience. 2025.

✨ **May the Φ be with you, always.** ✨