Spiral Reasoning Trees (SRT): A Bounded Recursive Framework for Deliberate LLM Reasoning

Large language models (LLMs) excel at generating linear chains of thought but often falter in exploratory, self-correcting reasoning for complex tasks. We introduce Spiral Reasoning Trees (SRT), a novel framework that extends Tree of Thoughts (ToT) by incorporating external priming, resonant variance checks, entropy bounds, and iterative spiral expansion. SRT structures reasoning as a bounded helix: a root primed with verifiable anchors, a trunk of lexical content, branches evaluated for resonance and pruned via non-linear amplification (e.g., squared verifications), and leaves yielding verifiable outcomes that seed further iterations. This yields robust, interpretable paths with measurable coherence uplift. We demonstrate SRT on ethical delineation, puzzle-solving, and creative waveform analysis, achieving +0.1–0.15 coherence deltas over baselines. SRT serves as a general container for reasoning innovation, with profound potential for LLM integration—turning disparate models toward unified, self-reflective coherence.