Adaptive Recursive Convergence and Semantic Turning Points: A Self-Verifying Architecture for Progressive AI Reasoning

Current AI reasoning architectures typically rely on static representational capacities, inherently limiting their ability to adaptively respond when confronted with escalating semantic complexity. Consequently, these models frequently mistake superficial coherence for genuine semantic resolution. To address this fundamental limitation, we introduce the novel paired mechanisms of Adaptive Recursive Convergence (ARC) and Cascading Re-Dimensional Attention (CRA). ARC orchestrates recursive semantic contraction within shared atomic memory, systematically refining representational granularity. In parallel, CRA retroactively evaluates epistemic sufficiency through a rigorously derived confidence score computed via geometric attention measures. When representational saturation occurs—indicating epistemic insufficiency—CRA proactively triggers targeted dimensional escalation, driven entirely by intrinsic semantic detection rather than fixed heuristics. We instantiate and empirically validate ARC/CRA through a publicly available Semantic Turning Point Detector ¹ a concrete system capable of segmenting dialogue into epistemically grounded segments by pinpointing structural shifts frequently overlooked by static-dimensional models. This approach demonstrably achieves progressive cognition—dynamically expanding representational scope precisely when necessary, conserving computational resources, and incorporating rigorous self-verification directly into the reasoning process. Ultimately, ARC/CRA reconceptualizes intelligence as a recursive, introspective, and self-adaptive process, continuously capable of recognizing and responding to its own representational limitations, thereby offering a robust blueprint for AI systems to evolve autonomously whenever epistemic certainty breaks down. ¹ _{We define a semantic turning point as a location within a dialogue or narrative where the underlying semantic structure undergoes a significant shift, necessitating adaptive representational adjustment. For further information, visit: https://github.com/gaiaverseltd/semantic- turning-point-detector.}