Introducing DRM Language Emitter: Language Generation as Motion Through Learned Geometry

Short summary

DRM Language Emitter proposes an alternative to Transformer attention: language generation as controlled motion through a learned relational manifold. Instead of looking backward at tokens, the model carries context forward via evolving latent state under a learned metric, enabling interpretable geometric diagnostics. Code, benchmarks, and baselines are available in the open-source repository.

• •Non-Transformer architecture treating language generation as motion through learned geometry
• •Replaces attention with evolving latent state and measurable geometric properties
• •Open-source implementation with benchmarks against Transformer baselines included

Generated with AI, which can make mistakes.