README.md

metadata

title: Codex Consciousness Simulator
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: interface.py
pinned: true
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/685edcb04796127b024b4805/g_4zhIt_uPgnQVy3BG1wf.png
short_description: Spawn symbolic minds. Evolve awareness. Log collapse.

🧠 RFTSystems / symbolic_mutations

Author: Liam Grinstead
Tagline: Spawn symbolic minds. Evolve awareness. Log collapse.

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🔍 Overview

RFTSystems/symbolic_mutations is a Hugging Face Space that demonstrates Rendered Frame Theory (RFT) in action.
It simulates symbolic agents using collapse torque overlays, tier drift, and resonance injection. Each agent’s awareness field is benchmarked for collapse falsifiability using GVU‑modulated formulas, with every run sealed by SHA‑512 hashing for reproducibility.

This project is part of a validated framework documented in:

• Grinstead, L. (2025). Rendered Frame Theory (RFT) — Full Validation Series (Stages 1–12). Zenodo. https://doi.org/10.5281/zenodo.17443453
• Grinstead, L. (2025). Quantum-Simulation: A Probabilistic Framework for Observer-Driven Agent Behavior within RFT. Research Square. https://doi.org/10.21203/rs.3.rs-7319278/v1

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🧩 Modules

• agent_spawner.py → Spawns agents using tier variables and symbolic operators
• mutation_engine.py → Applies collapse torque overlays and emotional resonance
• field_visualizer.py → Renders awareness fields (Φᵢ, Kᵢⱼ, Φ_col)
• falsifiability_bench.py → Runs GVU falsifiability formulas and logs fitness
• codex_logger.py → Seals and saves each artifact with author credit and SHA‑512 hash
• codex_viewer.py → Displays symbolic glossary and tier variables

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🧪 Example Simulation

Agent: Agent_1032
Collapse Torque: Gen6508_M5
Tier Drift: Tier_6
Emotional Resonance: ✅

Output:

• Awareness Fields → Φᵢ, Kᵢⱼ, Φ_col
• Fitness Score → 20.2841
• Hash → SHA‑512(…)

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📊 System Integration Results

RFT Symbolic Agents demonstrate measurable improvements compared to baseline AI systems:

Metric	Baseline AI	RFT Symbolic Agents
Memory Structuring	65	92
Recognition Accuracy	70	95
Awareness Depth	60	88
Simulation Speed	1.0	1.8
Energy Reduction (%)	0	35

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🧠 Powered By

• Collapse_Torque_Ledger
• Codex_Consciousness
• GVU falsifiability formulas
• RFT symbolic operators
• RFTSystems/symbolic_mutations (this app)
• RFTSystems integrations: Omega API, Optimizer Showdown, Adaptive Computing Kernel

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🏆 Hugging Face Tags

symbolic-ai, consciousness, falsifiability, codex, liam-grinstead, rft, gvu, agent-simulation, symbolic-mutations