SIP-CORE-05 Attractor Navigation and Regulatory Precision: A Coherence Framework for Sparse Token Dynamics in Shared AI System

AbstractWhy does reasoning in large language models concentrate at sparse navigationalpositions? SIP-AI-04 demonstrated the engineering case: five independentmeasurement methods converge on the finding that 1.53–20% of tokens carry thecausal weight of reasoning. This companion paper provides the theoreticalexplanation within the ρ/C coherence framework and derives architecturalimplications for Shared Humanistic AI (SHAI) and Shared Artificial GeneralIntelligence (SAGI).We argue that forking tokens—the high-entropy minority that steers reasoningtrajectory—function as bifurcation controllers in attractor space, and that theirsparsity is a strong structural recurrence across sequential reasoning systems, notan empirical accident. Three independent evidence streams converge. First, thedynamical-systems literature confirms multistability, metastability, and regimeswitching in transformer hidden-state trajectories. Second, two independentcomputational systems—neural networks learning function-word semantics fromgrounded language (Portelance et al., 2024) and RLVR training dynamics showingcontent stabilisation before navigational refinement (Cui et al., 2025)—exhibit thesame content-before-navigation sequence, supported by cross-linguisticdevelopmental evidence spanning eleven languages. Third, the alignment literaturedemonstrates that regulatory interventions can be targeted at specific modelcomponents rather than applied uniformly.From these convergences, we derive the Stewardship Architecture for SHAI: amonitoring and intervention framework positioning alignment at navigationalpositions where reasoning trajectories diverge. We show that the CDR cycleoperates at token scale and generate five testable cross-domain predictions. Weaddress the dual-use challenge that targeted alignment mechanisms are alsotargetable for disalignment.