COJGN-3-small
COJGN-3-small is a small trained Covariant Osculating Jet Geometric Network for a fully reproducible 128-dimensional curved-latent classification benchmark. It uses learned metric-routed contacts, signed low-rank quadratic curvature, symmetric low-rank cubic jets, and C2 overlap gluing.
This is a research checkpoint, not a claim of general superiority over MLPs. The accompanying manuscript reports that COJGN's advantage is regime-dependent and also reports negative results and higher runtime cost on generic tasks.
Model details
- Input: float tensor
(batch, 128) - Output: 2 class logits
- Jet order: 3
- Hidden width: 20
- Residual blocks: 2
- Contacts per block: 4
- Quadratic rank: 2
- Cubic rank: 3
- Metric rank: 3
- Trainable parameters: 23,750
- Released training seed: 42
- Dataset seed: 2608
- Released test accuracy: 90.10%
Reproducible release benchmark
| Model | Parameters | Mean test accuracy | Std |
|---|---|---|---|
| COJGN-3-small | 23750 | 91.26% | 0.64% |
| Matched residual MLP | 23922 | 91.12% | 0.49% |
The benchmark uses the repository's deterministic curved-latent-128-release-v1 generator. It is inspired by the mechanism-aligned task in the manuscript but is explicitly defined in the release code and should not be described as a bit-for-bit reproduction of the paper unless the original generator is used.
Usage
import torch
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(
"YOUR_HF_USERNAME/COJGN-3-small",
trust_remote_code=True,
)
model.eval()
x = torch.randn(4, 128)
with torch.no_grad():
out = model(features=x)
print(out.logits.shape) # torch.Size([4, 2])
Reproduce training
Clone the training repository and run:
python scripts/train_release.py --config configs/cojgn-3-small.json --device cuda
python scripts/benchmark.py --config configs/cojgn-3-small.json --device cuda
python scripts/build_hf_release.py --repo-id YOUR_HF_USERNAME/COJGN-3-small
Limitations
COJGN is a specialized higher-order geometric inductive bias. The current reference implementation uses many small tensor operations and is not optimized with fused CUDA/Triton kernels. A 128 GB GPU is far more memory than this small checkpoint requires; the DGX Spark is useful here primarily for fast repeated benchmarking and for later scale-up experiments.
Citation
If you use this model, cite the accompanying manuscript:
Andrew Kiruluta. Covariant Osculating Jet Networks: Higher-Order Contact Fields with Soft-Routing Guarantees, Differential Diagnostics, and Matched Prior-Art Controls. August 2026.
DOI: https://doi.org/10.13140/RG.2.2.19228.65922
License
Software and model code in this release are provided under the MIT License. See LICENSE.