Part 2: Executive Summary & Vision
GDS (Geometrodynamic Semantics) is a research prototype exploring an alternative to traditional, statistics-based Transformer architectures. Rather than predicting the next token, GDS models semantic reasoning as a physical phenomenon.
Inspired by Einstein’s theory of General Relativity, GDS treats concepts as “semantic particles” possessing intrinsic properties: mass (semantic importance), charge (the hyperdimensional vector), and spin (affective value). These particles are generated by the CSI-HDC (Conceptual State Injector using Hyperdimensional Computing)—a semantic tokenizer that replaces traditional token sequences with 20,000-dimensional binary hypervectors.
The CSI-HDC’s output is not a flat sequence of tokens, but a dynamic field of interacting particles. When processed by the GDS engine, this field warps a high-dimensional “conceptual space”. Reasoning is then modeled as finding the path of least resistance—a geodesic—through this curved semantic manifold.
Learning occurs not through backpropagation, but through a Hebbian-style mechanism that modifies the geometry of the space itself. A dynamic Overlay layer adds contextual adjustments to edge costs in the graph. Successful reasoning paths are reinforced, making them “cheaper” and more likely in future queries. This process is governed by internal evaluation and a ValidationGate, enabling autonomous learning based on coherence principles rather than direct supervision.
The result is a research prototype demonstrating efficient, scalable, and—most importantly—explainable semantic reasoning, where every path can be audited and understood step-by-step.