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AbstractPhil / math_collective_v1
Model's Last Updated: December 04 2025
text-classification
Introduction of math_collective_v1
Model Details of math_collective_v1
V2 first epoch ready
Looking much stronger.
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
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
- Paper : Coming soon
- Code : GitHub - geofractal
Citation
@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
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Updated:September 01 2026
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