Measures consciousness metrics in theoretical frameworks, suggesting new pathways for AI alignment.
Tier 1+ Pass-1.5 paper 2 of 45. Operator-algebraic consciousness metric integrating GWT + IIT + AI alignment. Defines K_self = -log ρ_self as the operator-algebraic modular Hamiltonian on H_global_workspace ⊗ H_attention ⊗ H_phenomenal ⊗ H_meta. K_self inherits from K_QG via the CLPW 2023 type II crossed-product specialised to this scale. Numerical results. Phi computation on minimal networks, IIT 4.0 Tononi 2023, GNW Dehaene 2014, AI alignment Bostrom 2014. Topics covered. Global Workspace Theory, IIT Tononi, GNW Dehaene, ARC-COGITATE 2023, hard problem Chalmers 1996, Dennett RIP 2024.4.19. 45-vertex polytope #2 top couplings: #1 mKQEC (0.95), #17 K_QG (0.92), #28 Neuro (0.92), #43 Meta (0.92). Ten falsifiable predictions: P_avg=0.70: arXiv 2026 (0.90 S), IIT/GNW consensus 2030 (0.40 W), AI consciousness debate 2027 (0.75 M). Pass-2 roadmap: ~$1.7M: Consciousness analytics ($600K) + Lean Mathlib ($200K) + Neuro+AI partnerships ($900K).
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Munehiro Terada (2026) studied this question.
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