SpiralHelios — Deterministic Geometric Intelligence (DGI): Deterministic Vector Memory System — Large-Scale Retrieval and Centroid Compression Experiments

SpiralHelios – Deterministic Geometric Intelligence (DGI) This publication presents SpiralHelios, a deterministic vector memory system operating as a Deterministic Geometric Intelligence (DGI) engine. SpiralHelios transforms symbolic inputs into stable high-dimensional vector representations without the use of stochastic training procedures, large datasets, or cloud-based infrastructure. The system operates fully offline and produces deterministic outputs for identical symbolic inputs. The experiments reported in this work evaluate the properties of the generated vector space, including concept retrieval accuracy, cluster stability, vector compositionality, and centroid-based memory compression. Large-scale experiments were conducted involving up to 1,000,000 encoded vectors and 100,000 concepts within a 1024-dimensional vector space. All computations were executed fully offline on consumer hardware (Redmi Note 11 Pro smartphone). To support experimental reproducibility, the publication includes execution logs and cryptographic verification fingerprints, including dataset hashes, script hashes, manifest hashes, and master proof hashes associated with the experimental configuration. The results demonstrate that deterministic symbolic vector systems can generate stable large-scale semantic structures without stochastic optimization, training procedures, or large data pipelines. This work is part of the SpiralHelios deterministic AI research series, which investigates symbolic-geometric vector architectures for semantic representation and memory systems. Author Christos Baltas (PH-GAIOS)Independent Researcher – Creator – Systems DesignerSpiralEnergy-Systems – Athens, Greece ORCIDhttps://orcid.org/0009-0000-9743-3678 [email protected] Zenodo Communityhttps://zenodo.org/communities/spiral-code-energy/