SpiralHelios – Deterministic Geometric Intelligence (DGI) SpiralHelios is a deterministic computational framework based on the paradigm of Deterministic Geometric Intelligence (DGI). The system encodes knowledge within high-dimensional geometric vector spaces using purely deterministic mathematical transformations. Unlike conventional artificial intelligence systems that rely on stochastic optimization, neural network training, large-scale datasets, or cloud infrastructure, SpiralHelios operates under a strictly deterministic computational model. The architecture follows four fundamental operational constraints: 100% Offline 0% Big Data 0% Training 0% Cloud All computations are executed locally using deterministic algorithms, producing reproducible outputs across repeated executions when identical code, datasets, and runtime environments are used. Deterministic Geometric Intelligence (DGI) Deterministic Geometric Intelligence defines a computational paradigm where information and concepts are represented geometrically in high-dimensional vector spaces. Within this framework: textual symbols are mapped to deterministic vector states knowledge structures emerge as geometric relationships concept retrieval is performed through vector similarity operations Because all transformations are mathematically deterministic, the system does not require statistical learning or parameter training. Reproducible Execution Benchmark This repository documents a reproducible execution benchmark of the SpiralHelios system. The benchmark demonstrates the deterministic behavior of the architecture through controlled runtime experiments and geometric vector-space evaluations. The execution environment uses Python and NumPy operating entirely offline without network connectivity. Each sentence in the evaluation dataset is deterministically mapped into a 1024-dimensional vector representation, enabling concept similarity analysis through centroid comparison. The benchmark is designed to demonstrate deterministic geometric structure rather than machine-learning classification performance. Dataset The evaluation dataset consists of conceptual sentences spanning multiple scientific domains, including mathematics, physics, computer science, biology, and social sciences. Examples of conceptual categories include: ALGORITHM CRYPTOGRAPHY VECTOR MATRIX ENERGY GRAVITY DNA PHOTOSYNTHESIS LANGUAGE DEMOCRACY The dataset structure follows the format: MODE | CONCEPT | SENTENCE The dataset is intentionally small and controlled in order to verify deterministic geometric behavior rather than statistical learning performance. Deterministic Verification The deterministic nature of the system is verified through several mechanisms: vector normalization stability deterministic fingerprint generation reproducible runtime measurements cryptographic execution hashes Integrity and reproducibility are verified using SHA-256 cryptographic hashes associated with execution artifacts and datasets. Execution Artifacts The repository includes several files that document the execution process: RUN_LOG.txt – execution log containing runtime results MANIFEST.sha256 – cryptographic integrity manifest PROOF_STAMP.txt – proof record of execution dataset32.txt – deterministic benchmark dataset These files allow verification of the computational execution and integrity of the experiment. Cryptographic Execution Integrity The complete execution state is associated with cryptographic identifiers that ensure reproducibility and traceability. Key elements include: script SHA256 fingerprints dataset verification hashes run log SHA256 identifiers canonical public execution hashes These identifiers allow verification that the execution artifacts correspond to the documented deterministic computation. Screenshot Documentation The repository also contains screenshot documentation of the real system execution. These screenshots illustrate: deterministic vector normalization fingerprint generation runtime benchmark measurements concept retrieval outputs The screenshots serve as visual evidence of the system operating within a deterministic runtime environment. Reproducibility All results documented in this repository are reproducible under the following conditions: identical source code identical dataset identical runtime libraries and environment Under these conditions, the SpiralHelios system produces consistent deterministic outputs across repeated runs. Relation to the SpiralHelios Research Framework This repository represents execution evidence supporting the broader SpiralHelios research framework and the proposed paradigm of Deterministic Geometric Intelligence (DGI). The work demonstrates that a computational intelligence architecture can operate: entirely offline without stochastic training processes without large-scale datasets without cloud infrastructure while still producing stable geometric representations of conceptual information. Author Christos Baltas (PH-GAIOS)Independent Researcher – Creator – Systems DesignerSpiralEnergy-Systems – Athens, Greece ORCIDhttps://orcid.org/0009-0000-9743-3678 Contactinfo@spiral-code.com Zenodo Communityhttps://zenodo.org/communities/spiral-code-energy/