README.md

---
pretty_name: KEY Neuroevolution Dataset
license: mit
tags:
  - neuroevolution
  - lora
  - genetic-algorithms
  - provenance
  - world-model
language:
  - en
configs:
  - config_name: comm_events
    data_files:
      - split: train
        path: data/comm_events/train.jsonl
  - config_name: crossovers
    data_files:
      - split: train
        path: data/crossovers/train.jsonl
  - config_name: selection
    data_files:
      - split: train
        path: data/selection/train.jsonl
  - config_name: mutations
    data_files:
      - split: train
        path: data/mutations/train.jsonl
  - config_name: fitness
    data_files:
      - split: train
        path: data/fitness/train.jsonl
  - config_name: performance
    data_files:
      - split: train
        path: data/performance/train.jsonl
  - config_name: errors
    data_files:
      - split: train
        path: data/errors/train.jsonl
  - config_name: evolution_events
    data_files:
      - split: train
        path: data/evolution_events/train.jsonl
---

# 🔑 KEY: Neuroevolution Dataset

**40,000+ logged events from real evolutionary runs** — every mutation, crossover, selection, and fitness evaluation.

KEY evolves LoRA adapters on frozen base models (MiniLM-L6, DreamerV3) using NEAT-style neuroevolution. This dataset captures the complete evolutionary history.

---

## 🎮 Links

| | |
|---|---|
| **[🌌 Live Demo](https://huggingface.co/spaces/tostido/Cascade-Hyperlattice)** | Watch evolution in action |
| **[🧠 Champion Model](https://huggingface.co/datasets/tostido/key-data/tree/main/models)** | The evolved DreamerV3 model |

---

## Loading the Dataset

```python
from datasets import load_dataset

# Available configs:
ds = load_dataset("tostido/key-data", "comm_events")      # 16,968 rows - pod communication
ds = load_dataset("tostido/key-data", "crossovers")       # 8,878 rows - breeding events
ds = load_dataset("tostido/key-data", "selection")        # 4,266 rows - tournament selection
ds = load_dataset("tostido/key-data", "mutations")        # 3,848 rows - mutation events
ds = load_dataset("tostido/key-data", "fitness")          # 2,121 rows - fitness evaluations
ds = load_dataset("tostido/key-data", "performance")      # 2,121 rows - runtime telemetry
ds = load_dataset("tostido/key-data", "errors")           # 2,070 rows - errors/warnings
ds = load_dataset("tostido/key-data", "evolution_events") # event bus stream
```

---

## Example: Evolving Semantic Similarity

**Task**: Adapt MiniLM embeddings to preserve semantic relationships

**Test Pair**: "The cat sat on the mat" ↔ "A feline rested on the rug"

| Generation | Cosine Similarity | Fitness |
|------------|-------------------|---------|
| 0          | 0.42 (random)     | 0.35    |
| 50         | 0.76              | 0.64    |
| 100        | 0.89              | 0.82    |

The evolved adapter learned to preserve semantic similarity while improving output quality.

---

## What Gets Evolved

KEY freezes the base model and evolves only the adapter:

```
┌──────────────────────────────────────┐
│         Evolvable Brain              │
│  ┌────────────────────────────────┐  │
│  │   Base Model (FROZEN)          │  │  ← MiniLM (22M) or DreamerV3 (200M)
│  └─────────────┬──────────────────┘  │
│                ▼                     │
│  ┌────────────────────────────────┐  │
│  │   LoRA Adapter    (~12K)       │  │  ← EVOLVED
│  │   Projection Head (~99K)       │  │  ← EVOLVED
│  └────────────────────────────────┘  │
└──────────────────────────────────────┘

Total evolved parameters: ~111K (vs 22M-200M frozen)
```

---

## Fitness Functions

What evolution optimized for (from `fitness.jsonl`):

### AdapterFitness (Interface Quality)
- **Preservation (40%)**: Does adapter maintain semantic structure?
- **Signal Quality (30%)**: Is output well-conditioned? (not collapsed/exploded)
- **Consistency (30%)**: Similar inputs → similar outputs?

### EmbeddingKleeneFitness (Semantic Convergence)
- **Coherence**: Similar pairs should have high cosine similarity
- **Separation**: Dissimilar pairs should be far apart
- **Convergence**: Embedding variance stays bounded

### DreamerFitness (World Model Quality)
- **Prediction**: How well does imagination match reality?
- **Stability**: Do trajectories stay bounded?
- **Reward**: Can the model anticipate outcomes?

---

## Schema Reference

### `mutations.jsonl`
```json
{
  "timestamp": 1737403521.234,
  "event": "mutation",
  "generation": 42,
  "parent_id": "node_abc123",
  "child_id": "node_def456",
  "parent_fitness": 0.72,
  "mutation_rate": 0.1,
  "mutated_traits": ["exploration", "caution"],
  "deltas": {"exploration": 0.05, "caution": -0.02}
}
```

### `crossovers.jsonl`
```json
{
  "event": "crossover",
  "generation": 42,
  "parent1_id": "node_abc",
  "parent2_id": "node_xyz",
  "child_id": "node_new",
  "parent1_fitness": 0.72,
  "parent2_fitness": 0.68,
  "contribution_p1": 0.55
}
```

### `fitness.jsonl`
```json
{
  "event": "fitness_evaluation",
  "generation": 42,
  "node_id": "node_abc123",
  "fitness_function": "AdapterFitness",
  "raw_fitness": 0.823,
  "components": {
    "preservation": 0.85,
    "signal": 0.79,
    "consistency": 0.84
  },
  "eval_time_ms": 45.2
}
```

### `selection.jsonl`
```json
{
  "event": "selection",
  "generation": 42,
  "method": "tournament",
  "survivors": ["node_a", "node_b", "node_c"],
  "eliminated": ["node_d", "node_e"],
  "elites_preserved": 2
}
```

---

## Why Evolve Instead of Gradient Descent?

Neuroevolution works when:
- ✅ Your objective **isn't differentiable** (human preference, discrete outputs)
- ✅ You want **population diversity** (speciation prevents local optima)
- ✅ You're optimizing for **interface quality**, not task loss
- ✅ You need **full auditability** (every mutation logged with provenance)

---

## FAQ

**Q: What's a "quine brain"?**
> A brain that can serialize its weights → mutate → deserialize. This enables genetic algorithms to evolve neural networks. Think "self-modifying adapter."

**Q: Why not just use backprop?**
> Backprop requires differentiable objectives. Evolution works with any fitness function: human ratings, game scores, discrete metrics.

**Q: Is this real data?**
> Yes. This dataset contains 40K+ events from actual evolutionary runs.

---

## 🔐 Get Full Source Access

| Tier | Price | What You Get |
|------|-------|--------------|
| **🔑 Source Access** | $100 one-time | Full codebase, private repo invite |
| **🤝 Hands-On** | $50/hour | I coach you through wiring your own model |
| **🛠️ Done-For-You** | $500 flat | I wire up your custom model for you |
| **🎤 Speaking** | $2,000 | Talk at your company on gradient-free optimization |

### **[→ Sponsor on GitHub](https://github.com/sponsors/Yufok1)**

---

## Contact

**DM on X: [@Toasteedo](https://x.com/Toasteedo)**

---

## License

MIT