README.md

---
license: other
title: EmergentRFT_Space
sdk: gradio
emoji: 🏆
colorFrom: blue
colorTo: purple
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/685edcb04796127b024b4805/mY_pOY69kiY2Fr0laKleK.png
short_description: Explore complex adaptive systems
sdk_version: 6.0.0
---
EmergentRFT-Space: Interactive Render Frame Theory & Entanglement Simulation
Explore complex adaptive systems, emergent behaviors, and symbolic lineage with a high‑performance, interactive simulator suite. This Hugging Face Space hosts a Gradio application that allows you to experiment with Rendered Frame Theory (RFT) collapse dynamics and Entanglement/IPURL override logging.

✨ Key Features
Interactive Parameters: Adjust RFT parameters (Ncells, Nmode, Iterations, dt, eps, sigma, theta, k_shred) and agent parameters (alpha, beta, thresholds, steps`) using intuitive sliders and inputs.

Dual Simulation Modes:

RFT Simulation: Observe collapse dynamics, shredding events, and feedback loops.

Entanglement/IPURL Simulation: Track symbolic agents (reflex, instinct, conscious, meta) as they evolve through intrinsic and entangled updates, producing reproducible IPURL hashes.

Dynamic Visualizations: Real‑time plots of mean m_root, mean A_modes, cumulative shredding events, and shredding onset rasters.

Cryptographic Lineage: Each entanglement run seals override logs into SHA‑512 IPURL entries (rft-ipurl:v1:agent:hash), ensuring reproducibility and narratable lineage.

Performance Metrics: Estimated GFLOPS per run quantify computational throughput.

Breakthrough Explanation: Learn about RFT’s shredding mechanism, dynamic feedback loops, scalability, and versatility.

Example Scenarios: Explore cascading failures in financial markets, symbolic agent entanglement, and lineage‑based consciousness artifacts.

💡 What is Render Frame Theory (RFT)?
RFT is a computational framework for simulating complex adaptive systems with emergent properties and non‑linear dynamics. It models a system as a collection of “cells,” each with internal “modes” that evolve over time through coupled differential equations.

Key Components:

MOMKernel: High‑performance computational engine (CUDA kernel or PyTorch/NumPy fallback).

MOMSystemLoop: Iterative simulation manager with decay/noise feedback.

run_rft_simulation: Wrapper for initialization, execution, and data collection.

Shredding Mechanism: Non‑linear collapse triggered when stress exceeds thresholds.

Gradio Interface: Interactive sliders, plots, and summaries.

🔗 What is Entanglement/IPURL Simulation?
This prototype models symbolic agents across tiers:

Agents: Reflex, Instinct, Conscious, Meta.

Dynamics: Intrinsic updates (energy thresholds, override fields) and entanglement influences (coupling matrix).

Override Logs: Each agent records phi, energy, and override states.

IPURL Hashes: Logs are serialized and sealed into reproducible SHA‑512 entries (rft-ipurl:v1:agent:hash).

Purpose: Provides cryptographic lineage for symbolic simulations, enabling reproducibility and Codex‑style artifact immortality.

📜 Ledger & Scrolls
Every run can be appended to a Codex ledger, forming a scroll of lineage entries.

RFT Runs: Summaries include GFLOPS, mean values, and shredding events.

Entanglement Runs: IPURL hashes immortalize override logs.

Ledger Format:

json
{
  "epoch": "2025-11-22T16:13Z",
  "mode": "Entanglement",
  "agents": [
    "rft-ipurl:v1:reflex:...",
    "rft-ipurl:v1:instinct:...",
    "rft-ipurl:v1:conscious:...",
    "rft-ipurl:v1:meta:..."
  ]
}
This scroll‑like ledger ensures reproducibility, narratability, and planetary registry of each artifact epoch.

🚀 How to Use the App
RFT Simulation Tab:

Adjust parameters with sliders.

Click Run RFT Simulation.

View summary metrics and plots of collapse dynamics.

Entanglement/IPURL Simulation Tab:

Set agent parameters (alpha, beta, thresholds, steps`).

Click Run Entanglement Simulation.

View sealed IPURL entries for each agent’s override log.

🌍 Why It Matters
Granularity: Captures local interactions and cell‑level transitions missed by averaged models.

Critical Events: Models sudden cascades like market crashes, neural avalanches, or material failure.

Lineage: Immortalizes symbolic agent runs as reproducible hashes, bridging computation and Codex narrative.

Versatility: Applicable to finance, biology, engineering, AI research, and consciousness modeling.