Datasets:
metadata
license: mit
tags:
- quine
- dreamerv3
- neuroevolution
- glass-box-ai
- world-model
- reinforcement-learning
🔮 Champion Gen42 - Glass Box Quine Brain
THIS IS THE OPPOSITE OF A BLACK BOX
Every weight, decision, and mutation is fully transparent.
▶️ Try It Live
🌌 CASCADE Hyperlattice Demo — Real-time 3D visualization with HOLD protocol
What Is This?
A self-contained, merkle-hashed AI agent that can:
- Run inference with full transparency
- Verify its own integrity via quine hash
- Be replicated without external dependencies
- Log every decision to a provenance chain
Architecture
TIER 1 - RSSM (Recurrent State Space Model)
• DreamerV3 L-size: deter=4096, stoch=32x32
• Total latent: 5120 dimensions
TIER 2 - LoRA Adapter (EVOLVED)
• ~82K parameters, rank=16
• Maps latent → 8 action logits
TIER 3 - Evolved Traits
• Hyperparameters discovered via NEAT-style evolution
Quine Properties
| Property | Value |
|---|---|
| Merkle Hash | 38438eb04586975cda66a08fd3c447bb |
| Generation | 42 |
| Fitness | 0.5879 |
| Brain Type | DreamerV3 |
Quick Start
# Load from Hugging Face
from huggingface_hub import hf_hub_download
import importlib.util
path = hf_hub_download(
repo_id="tostido/key-data",
filename="models/champion_gen42.py",
repo_type="dataset"
)
spec = importlib.util.spec_from_file_location("champion", path)
champion = importlib.util.module_from_spec(spec)
spec.loader.exec_module(champion)
# Create agent
agent = champion.ChampionAgent()
# Verify integrity
assert agent.verify_quine_integrity()
# Run inference
obs = [0.0] * 64 # Your observation
action_probs, value = agent.forward(obs)
print(f"Action: {action_probs.argmax()}, Value: {value:.4f}")
# Full transparency
agent.show_readme()
🛑 HOLD Protocol
Human-in-the-loop oversight at inference time:
# Run with HOLD (requires cascade-lattice)
result = agent.forward_hold(obs, blocking=True, timeout=30.0)
# Result includes:
# - action: final action taken
# - was_override: True if human intervened
# - hold_id: merkle hash of decision point
💼 Quine Brain Conversion Service
Want your model wrapped as a Glass Box Quine Brain?
I offer conversion services for:
- Custom RL agents
- Foundation models
- Any PyTorch/JAX model
What you get:
- ✅ Self-contained single-file capsule
- ✅ Merkle-hashed provenance
- ✅ Quine verification (tamper-proof)
- ✅ HOLD protocol integration
- ✅ Full transparency APIs
📧 Contact: [towers.jeff@gmail.com]
Files
| File | Description |
|---|---|
champion_gen42.py |
The complete quine brain (~50MB with embedded weights) |
requirements.txt |
Dependencies for running locally |
License
MIT — Use freely, but the quine hash proves provenance.