README.md

---
license: apache-2.0
tags:
  - geofractal-router
  - collective-intelligence
  - math-reasoning
  - multi-stream
  - emergence
  - routing
datasets:
  - openai/gsm8k
base_model:
  - tbs17/MathBERT
  - google-t5/t5-base
pipeline_tag: text-classification
---

# V2 first epoch ready
Looking much stronger. 

![image](https://cdn-uploads.huggingface.co/production/uploads/630cf55b15433862cfc9556f/n-I_F4q4xhj-5sPZrmohJ.png)


# V1 BAD END - V2 using qwen 2.5 math 1.5b
Will the Qwen 2.5 1.5b model cut through the noise?


```
======================================================================
GALAXY BRAIN COLLECTIVE - GSM8K FINAL RESULTS
======================================================================

Streams:
  FUZZY:        MathBERT (symbolic), T5 (linguistic)
  DETERMINISTIC: Eigenspectrum, Cayley-Menger, Symbolic Calc, Fractal

| Epoch | Collective | MathBERT | T5 | Eigen | Cayley | Symbolic | Fractal | ρ |
|-------|------------|----------|-----|-------|--------|----------|---------|-------|
| 1 | 8.9% | 10.4% | 13.0% | 5.4% | 5.4% | 5.7% | 6.7% | 0.686 |
| 2 | 9.6% | 8.9% | 13.0% | 6.5% | 6.5% | 4.6% | 6.1% | 0.738 |
| 3 | 9.9% | 9.6% | 13.6% | 5.8% | 5.8% | 4.5% | 5.8% | 0.728 |
| 4 | 8.3% | 10.1% | 13.3% | 6.1% | 4.5% | 4.8% | 4.5% | 0.619 |
| 5 | 9.0% | 11.1% | 14.6% | 4.5% | 4.6% | 5.0% | 5.9% | 0.620 |

✓ Best epoch: 3 with collective accuracy 9.9%
✓ All checkpoints available at: https://huggingface.co/AbstractPhil/math_collective
```

# Math Collective - Galaxy Brain Router

**6-stream collective intelligence system for mathematical reasoning.**

## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│                    GALAXY BRAIN COLLECTIVE                   │
├─────────────────────────────────────────────────────────────┤
│  FUZZY STREAMS (learned representations)                    │
│  ├── MathBERT (frozen) → Head A → "symbolic understanding"  │
│  └── T5-base (frozen)  → Head B → "linguistic reasoning"    │
│                                                             │
│  DETERMINISTIC STREAMS (pure computation)                   │
│  ├── Eigenspectrum    → Head C → "covariance geometry"      │
│  ├── Cayley-Menger    → Head D → "distance geometry"        │
│  ├── Symbolic Calc    → Head E → "actual arithmetic"        │
│  └── Fractal Dim      → Head F → "complexity measure"       │
│                                                             │
│  All 6 streams → Fusion → Classifier                        │
└─────────────────────────────────────────────────────────────┘
```

## Key Innovation

**Fuzzy + Deterministic Routing**

The router learns WHEN to trust each stream:
- Simple arithmetic? Trust the Symbolic Calculator (deterministic)
- Complex word problem? Trust MathBERT/T5 (semantic)
- Ambiguous? Triangulate across all 6 perspectives

## Streams

| Stream | Type | Source | Purpose |
|--------|------|--------|---------|
| MathBERT | Fuzzy | tbs17/MathBERT (frozen) | Mathematical notation understanding |
| T5-base | Fuzzy | t5-base (frozen) | General language reasoning |
| Eigenspectrum | Deterministic | Covariance eigenvalues | Geometric structure of embeddings |
| Cayley-Menger | Deterministic | Distance matrix geometry | Simplex volume features |
| Symbolic | Deterministic | Regex + arithmetic | Actual number extraction & computation |
| Fractal | Deterministic | Correlation dimension | Problem complexity measure |

## Training

- **Dataset**: GSM8K (Grade School Math 8K)
- **Task**: Answer magnitude bucket prediction (20 buckets)
- **Frozen params**: ~330M (MathBERT + T5)
- **Trainable params**: ~15M (routing heads, fusion, projections)

## Emergence Metric (ρ)
```
ρ = collective_accuracy / max(individual_accuracies)

ρ > 1.0 = emergence (collective outperforms best individual)
```

## Usage
```python
from huggingface_hub import hf_hub_download
import torch

# Download checkpoint
checkpoint_path = hf_hub_download(
    repo_id="AbstractPhil/math_collective",
    filename="checkpoints/checkpoint_epoch_5.pt"
)

# Load and use (see geofractal-router for full implementation)
checkpoint = torch.load(checkpoint_path)
print(f"Epoch: {checkpoint['epoch']}")
print(f"Metrics: {checkpoint['metrics']}")
```

## Related

- **Framework**: [AbstractPhil/geofractal_router](https://huggingface.co/AbstractPhil/geofractal_router)
- **Paper**: Coming soon
- **Code**: [GitHub - geofractal](https://github.com/AbstractEyes/geofractal)

## Citation
```bibtex
@misc{abstractphil2025mathcollective,
  title={Math Collective: Galaxy Brain Routing for Mathematical Reasoning},
  author={AbstractPhil},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/AbstractPhil/math_collective}
}
```

## License

Apache 2.0