Symbiotic human-AI architecture for somatic sensing, symbolic reasoning and metacognitive control

Abstract:

We present a tripartite cognitive architecture that unifies somatic grounding, symbolic inference and metacognitive control within an extended SORK-N loop. Cognition is cast as the interaction of a Somatic layer (biophysical and affective inputs), a Symbolic layer (linguistic, logical, and representational processes), and a Metacognitive layer (global coherence estimation and policy adjustment), coordinated by a methodological framework that time-locks and analyzes multimodal physiological, linguistic and self-report data. Mathematical structure is provided by the Mirrored Profile Graph (MPG), an evidence-linked, hierarchical state space, and Rogue Variable (RV) analysis, which together localize structural sources of prediction-observation gaps and support falsifiable tests. This framework enables reproducible tests of intuition, pre-event cognition, and collective coherence, while remaining compatible with empirical scrutiny. We further discuss implications for symbiotic human-AI systems and argue that intentional co-evolution of biological and artificial cognition offers a practical route toward robust, reflective intelligence.

Patent note:

U.S. Provisional Utility Patent Application No. 63/910,500, “H3LIX: AI–Human Symbiotic Integration Process,” filed Nov 3, 2025 (process patent).