HLM Models
Qriton builds neural networks using polynomial Hopfield layers instead of transformer attention. This architectural choice is what makes Energy Language surgery possible.
Why Not Transformers?
Transformers use softmax attention, which is mathematically equivalent to a polynomial interaction with degree d → ∞. This collapses all stored patterns into one giant attractor — there's nothing to surgically edit.
HLM uses polynomial degree d=3, which maintains 200+ discrete attractor basins per layer. Each basin is a stable memory pattern — a local minimum in the energy function. These basins can be individually targeted for surgery.
Model Family
| Model | Domain | Input | Status |
|---|---|---|---|
| HLM3 | Language | Text | Training at scale — waitlist |
| HLM-Spatial | 3D Perception | LIDAR, Medical3D, Industrial3D | Training at scale — waitlist |
| HLM-Audio | Speech | Audio waveforms | Training at scale — waitlist |
Models are currently training at large scale. Access is available through our waiting list.
Commercial & Pilot Projects
For organizations that need:
- Custom large-data training on proprietary datasets
- Commercial deployment support
- Pilot projects for specific use cases
Contact us to discuss your requirements.
Compatibility
Energy Language works with all HLM model variants. See the Python API for integration details.