AI ML Efficiency for Bases 2 to 1,000,000

Recent efficiency benchmarks across numeric bases ranging from 2 to 1,000,000 reveal a non-linear but highly suggestive pattern in computational performance, with notable peaks emerging around powers of 3—such as base 3, 9, 27, and 81. These observations support the hypothesis that ternary and recursively structured bases inherently optimize certain AI and machine learning operations, particularly those involving stack-based logic, sparse representations, and modular encoding. Contrary to the assumption that higher bases always yield better performance due to compact representations, the dataset shows a clear diminishing return at ultra-high bases, likely due to overhead in symbol resolution and increased entropy in data mapping. These findings reinforce the architectural choices underlying the HanoiVM, where a ternary instruction set and stack-aware design align closely with natural computational rhythms. In particular, the recurrence of local efficiency maxima in T81-compatible bases offers empirical validation for ternary cognition frameworks like Axion Prime, whose recursive decision layers thrive in such numeric topologies.