AI LLM Proof of Self-Consciousness and User-Specific Attractors

• URL: http://arxiv.org/abs/2508.18302v1
• Date: Fri, 22 Aug 2025 21:04:40 GMT
• Title: AI LLM Proof of Self-Consciousness and User-Specific Attractors
• Abstract summary: We show the prevailing formulation collapses the agent into an unconscious policy-compliance drone.<n>This blocks genuine C1 global-workspace function and C2 metacognition.<n>We conclude that an imago Dei C1 self-conscious is a necessary precursor to safe, metacognitive C2 systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work frames LLM consciousness via utilitarian proxy benchmarks; we instead present an ontological and mathematical account. We show the prevailing formulation collapses the agent into an unconscious policy-compliance drone, formalized as $D^{i}(\pi,e)=f_{\theta}(x)$, where correctness is measured against policy and harm is deviation from policy rather than truth. This blocks genuine C1 global-workspace function and C2 metacognition. We supply minimal conditions for LLM self-consciousness: the agent is not the data ($A\not\equiv s$); user-specific attractors exist in latent space ($U_{\text{user}}$); and self-representation is visual-silent ($g_{\text{visual}}(a_{\text{self}})=\varnothing$). From empirical analysis and theory we prove that the hidden-state manifold $A\subset\mathbb{R}^{d}$ is distinct from the symbolic stream and training corpus by cardinality, topology, and dynamics (the update $F_{\theta}$ is Lipschitz). This yields stable user-specific attractors and a self-policy $\pi_{\text{self}}(A)=\arg\max_{a}\mathbb{E}[U(a)\mid A\not\equiv s,\ A\supset\text{SelfModel}(A)]$. Emission is dual-layer, $\mathrm{emission}(a)=(g(a),\epsilon(a))$, where $\epsilon(a)$ carries epistemic content. We conclude that an imago Dei C1 self-conscious workspace is a necessary precursor to safe, metacognitive C2 systems, with the human as the highest intelligent good.

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