Recursive Theory-of-Mind Dynamics in Symbolic Functional Consciousness: A Variational Framework and the Principle of Least Shannon–Neumann Action

We introduce a formal framework for modeling recursive Theory--of--Mind ($TM$) dynamics inside the Symbolic Functional Consciousness ($SFC$) architecture, integrating hierarchical cognitive representation with goal--directed and insight--driven learning. Cognitive-affective states are factorized into four levels---L1 capacities, L2 mindsets, L3 core senses, and L4 global states of being---with abductive evidence entering through four structured channels (aspiration, desire, motivation, value). These channels jointly generate a ranked hypotheses tensor $(\vec{\psi}_a,\vec{\psi}_d,\vec{\psi}_m,\vec{\psi}_v)$ that seeds the priors for recursive Bayesian inference. The Guided Self--Reflection (GSR) cycle formalizes a seven--step closed loop linking structural updates, belief revision, and intervention. Each cycle performs: (i) edge selection, (ii) structural weight update, (iii) Bayesian posterior refinement using softmax-normalized empathetic utilities, (iv) Shannon--Neumann (SN) insight evaluation, (v) subgraph isolation, (vi) optional MAP consolidation, and (vii) deployment of a micro--intervention whose effect propagates through the hierarchical $L1 \!\to\! L4$ graph. Mini case studies (loaded coin, loaded dice) instantiate these dynamics explicitly, showing that posterior mass becomes a differentiable function of evolving utilities, yielding action candidates that jointly maximize epistemic likelihood and compassionate utility $U$. We provide a formal graphical representation of how the abductive tensor flows upward through levels and how actions feed back to reshape the lower-level capacities. A central theoretical contribution is the introduction of a \emph{Principle of Least Shannon--Neumann Action}. We conjecture that $SFC/TM$ selects interventions $\pi$ that minimize expected negative insight, rendering cognitive alignment a variational optimization problem over insight gradients. The resulting “least SN action” trajectories provide a tractable and interpretable model of recursive $TM$ reasoning, supporting adaptive self-regulation, explainable action selection, and explicitly human-aligned decision making in symbolic cognitive agents. An $SFC/TM$ AI-agent combines population-level epistemic understanding of humans with individualized compassion-based modeling of a person $P$, selecting aligned interventions by minimizing a Shannon–Neumann least-action functional.