SYRAG™ Lab – Spontaneous Coherence Condensation in an Octonion-Based Transformer: Empirical Validation of the Relational Ontology Framework

AbstractWe present an experimental study of an octonion-based language model(OctoLLM) trained on WikiText-103, designed to test predictions of theRelational Ontology Framework (ROF). Without any hyperparameterchange during training (λcoh = 0.02, λfric = 0.002), the system spon-taneously evolves from a low-coherence, high-friction state to a regimeof near-perfect coherence (Cn → 0.9997) and zero ontological friction(F → 0). The transition is accompanied by a significant reduction in GPUpower consumption (from > 30 W to ≈ 25 W) and a faster step time (from6.7 to 5.8 s/step). Language loss reaches a minimum of 1.52 at step 2375and stabilises around 4.0–5.5 at high coherence, indicating that extremecoherence does not destroy linguistic diversity. The total energy consumedover 27.4 hours of training is approximately 690 Wh. These results pro-vide the first empirical evidence for spontaneous coherence condensationin an octonion neural architecture, supporting the ROF’s hypothesis of anatural attractor toward a coherent, energy-efficient relational vacuum.