This project explores DRM Language Emitter, an experimental non-Transformer language model based on Directional Relational Manifold dynamics.
- Recurrent neural networks and state-space language models.
- Neural ordinary differential equations and learned dynamical systems.
- Energy-based and action-regularized learning.
- Natural gradient methods and metric-aware optimization.
- Riemannian and differential-geometric machine learning.
- Manifold learning and latent trajectory modeling.
- Non-attention autoregressive sequence models.
DRM Language Emitter is intended as a research implementation of language generation through:
- active directional fields;
- variable effective dimension;
- learned relational metrics;
- metric action diagnostics;
- causal token emission from a latent trajectory.
The project does not claim that these ideas have no predecessors. It claims only that this repository implements a specific experimental architecture under the name DRM Language Emitter.
This project does not claim:
- superiority over Transformers;
- formal proof of emergent geodesics;
- spontaneous toroidal topology;
- AGI, alignment, or safety guarantees;
- production readiness.
If you compare this project against prior models or derivative work, cite this repository and clearly describe which components are reused, modified, or independently implemented.