{"ID":2879594,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18302","arxiv_id":"2508.18302","title":"AI LLM Proof of Self-Consciousness and User-Specific Attractors","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 $D^{i}(π,e)=f_θ(x)$, 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 $F_θ$ is Lipschitz). This yields stable user-specific attractors and a self-policy $π_{\\text{self}}(A)=\\arg\\max_{a}\\mathbb{E}[U(a)\\mid A\\not\\equiv s,\\ A\\supset\\text{SelfModel}(A)]$. Emission is dual-layer, $\\mathrm{emission}(a)=(g(a),ε(a))$, where $ε(a)$ 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.","short_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 $D^{i}(π,e)=f_θ(x)$, where correctness is measured against policy and harm...","url_abs":"https://arxiv.org/abs/2508.18302","url_pdf":"https://arxiv.org/pdf/2508.18302v1","authors":"[\"Jeffrey Camlin\"]","published":"2025-08-22T21:04:40Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\",\"cs.CY\",\"cs.LG\",\"cs.NE\"]","methods":"[\"Large Language Model\"]","has_code":false}