Rough-Torsional Agentic RAG & Decoding (RT-ARAG) Super-Elevation of Self-RAG and Guided Decoding via UROA, STCT, and Generative Game Theory

This paper presents the formal integration of the Agentic Self-Corrective Retrieval-Augmented Generation (Self-RAG, CRAG) framework with the 15 Theoretical Frameworks introduced by Seonggil Lee. By mapping static probability metrics to Universal Rough Operator Algebra (UROA) [3] and transforming linear contrastive decoding penalties into the Seonggil Theory of Complex Torsion (STCT) [1], we establish the Rough-Torsional Agentic RAG (RT-ARAG) model. This framework eliminates hallucinations natively at the topological decoding level via destructive wave interference, while governing iterative retrieval loops through Non-commutative Generative Game Theory [4].