Rough-Torsional Adaptive Sparse Multi-Axis Scaling (RT-ASMAS) Transcending MoE and Dense Scaling Laws via UROA, STCT, and RQIG

The paradigm of scaling Large Language Models relies heavily on Mixture of Experts (MoE) to decouple parameter count from active computational cost. However, standard token-choice MoE routing suffers from load imbalance, while pure dense scaling faces catastrophic diminishing returns. This paper proposes the Rough-Torsional Adaptive Sparse Multi-Axis Scaling (RT-ASMAS) framework. By elevating routing mechanisms through Universal Rough Operator Algebra (UROA) [3], establishing auxiliary-free load balancing via Seonggil Theory of Complex Torsion (STCT) [1], and unifying multi-axis variables via Rough Quantum Information Geometry (RQIG) [4], we completely subsume both classical MoE [5] and dense scaling laws [6] as rigid, lower-dimensional asymptotes.