Adaptive Process-Supervised Tree Search Super-Elevated: The Rough-Torsional APSTS Framework (RT-APSTS)

The Adaptive Process-Supervised Tree Search (APSTS) effectively unifies Chain-of-Thought (CoT) and Tree/Graph-of-Thoughts (ToT/GoT) by dynamically controlling the search mechanism and process rewards. However, its reliance on scalar value estimations and heuristic branch control limits its theoretical robustness against error accumulation and hallucination. We super-elevate this framework into Rough-Torsional APSTS (RT-APSTS)byintegrating Universal Rough Operator Algebra (UROA) [3], Seonggil Theory of Complex Torsion (STCT) [1], and Generative Game Theory. This fundamentally transcends previous limits, replacing rigid heuristic thresholding with non-commutative probabilistic equilibria across topological search manifolds [2].