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

Current AI systems, limited by fixed representational capacity, often fail under semantic complexity they cannot adapt to, mistaking surface coherence for res- olution. We introduce Adaptive Recursive Convergence (ARC) and Cascading Re-Dimensional Attention (CRA), a framework enabling systems to recognize when their reasoning must evolve. ARC governs recursive contraction over shared atomic memory, while CRA retroactively assesses epistemic soundness via an attention-derived confidence score. When existing abstractions saturate, CRA triggers dimensional expansion—driven by detection, not fixed design. We instan- tiate this in a Semantic Turning Point Detector that segments dialogues by revealing structural shifts static models miss. This yields progressive cognition: dynamically expanding scope only when necessary, conserving effort, and self- verifying conclusions within the reasoning loop. ARC/CRA reframes intelligence as a recursive, introspective process that recognizes when answerhood itself demands evolution, laying a blueprint for systems adapting, like organisms, when certainty breaks. Certain proprietary systems may be referenced conceptually in this paper are part of GaiaVerse Ltd.’s internal research framework and are not disclosed in detail. All methods herein reflect generalizable architectures intended for public academic discussion.