Why Current AI Architectures are Not Conscious:Neural Networks as Spinfoam Networks in a Theory ofQuantum Gravity

Why Current AI Architectures are Not Conscious:Neural Networks as Spinfoam Networks in a Theory ofQuantum Gravity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Why Current AI Architectures are Not Conscious:Neural Networks as Spinfoam Networks in a Theory ofQuantum Gravity Trevor Nestor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8443771/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Classical deep neural networks excel at many tasks and even multimodal gen- erative outputs but remain energetically inefficient by orders of magnitude from the human brain, lack mechanisms for integrated binding, and have been argued to exhibit no genuine route to consciousness. While inspired by neural architectures in brain tissue, deep neural networks face limitations such as scaling limits. Draw- ing on Loop Quantum Gravity (LQG) and the Orchestrated Objective Reduction (Orch-OR) theory of consciousness, we introduce a framework model of Neural Spinfoam Networks (NSNs), a bio-inspired AI paradigm in which each neural layer is recast as a spin -network and each learning update as a spinfoam transition by means of gravitational collapse at a phase transition at entropic limits described by a UV/IR fixed point and by the Monster Conformal Field Theory (Monster CFT). Our novel theoretical model leverages Majorana-fermion braiding within spinfoam geometries and a gravitational feedback loop mediated by Majorana biophotons to achieve one-shot, polynomial -time credit assignment for the NP-hard perceptual binding problem. The network’s global state is encoded by a noncommutative-geometry spectral triple (A, H, D), where the Dirac-like dilation operator’s smallest nonzero eigenvalue corresponds directly to the shortest nonzero lattice vector, thereby achiev- ing perceptual binding by means of gravitationally induced phase transition, forming the basis for a more plausible mechanism of backpropagation and weight transport that are currently unexplained by classical models of brain function. Periodic Floquet driving and the Cayley -transformed microtubule Hamiltonian yield topologically protected, room-temperature quantum coherence in tubulin-analogous nodes. Recent demonstrations of microtubule superradiance and time -crystalline oscillations within brain tissue further substantiate sustained entangled states and ultrafast biophotonic readout as described by Orch-Or theory, in spite of criticisms, which are discussed. Cognitive Neuroscience Computational Neuroscience Theoretical Computer Science Majorana Majorana Zero Mode Topological Protection Toric Code Monstrous Moonshine Entropic Gravity Asymptotically Safe Gravity Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Panelpythoncode.txt Python code 1 Rainbowpythoncode.txt Python code 2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8443771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565259522,"identity":"9f560702-5232-436f-8c84-e4ddf408d27f","order_by":0,"name":"Trevor 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While inspired by neural architectures in brain tissue, deep neural networks face limitations such as scaling limits. Draw- ing on Loop Quantum Gravity (LQG) and the Orchestrated Objective Reduction (Orch-OR) theory of consciousness, we introduce a framework model of Neural Spinfoam Networks (NSNs), a bio-inspired AI paradigm in which each neural layer is recast as a spin -network and each learning update as a spinfoam transition by means of gravitational collapse at a phase transition at entropic limits described by a UV/IR fixed point and by the Monster Conformal Field Theory (Monster CFT). Our novel theoretical model leverages Majorana-fermion braiding within spinfoam geometries and a gravitational feedback loop mediated by Majorana biophotons to achieve one-shot, polynomial -time credit assignment for the NP-hard perceptual binding problem. The network’s global state is encoded by a noncommutative-geometry spectral triple (A, H, D), where the Dirac-like dilation operator’s smallest nonzero eigenvalue corresponds directly to the shortest nonzero lattice vector, thereby achiev- ing perceptual binding by means of gravitationally induced phase transition, forming the basis for a more plausible mechanism of backpropagation and weight transport that are currently unexplained by classical models of brain function. Periodic Floquet driving and the Cayley -transformed microtubule Hamiltonian yield topologically protected, room-temperature quantum coherence in tubulin-analogous nodes. 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