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May 21, 20260 citationsOpen Access

ITU Tier 1+ #2: Consciousness Metric (Kₛelf)

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MTMunehiro Terada

Key Points

  • To develop an operator-algebraic metric for consciousness integrating various theories and AI alignment.
  • Defined K_self as the modular Hamiltonian on combined spaces including attention and phenomenal experiences.
  • Computed numerical results using Phi on minimal networks related to established consciousness theories.
  • Integrated concepts from Global Workspace Theory, Integrated Information Theory, and AI alignment frameworks.
  • Identified top couplings such as mKQEC (0.95) and K_QG (0.92) relevant for consciousness metrics.
  • Ten falsifiable predictions made with varying probabilities, indicating future directions in consciousness research.

Abstract

Tier 1+ Pass-1. 5 paper 2 of 45. Operator-algebraic consciousness metric integrating GWT + IIT + AI alignment. Defines Kₛelf = -log ρₛelf as the operator-algebraic modular Hamiltonian on Hglobalworkspace ⊗ Hₐttention ⊗ Hₚhenomenal ⊗ Hₘeta. Kₛelf inherits from KQG 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 KQG (0. 92), #28 Neuro (0. 92), #43 Meta (0. 92). Ten falsifiable predictions: Pₐvg=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). Copyright © 2026 Munehiro Terada / Roboken. Licensed under CC-BY-4. 0.

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Cite This Study

Munehiro Terada (2026) studied this question.

synapsesocial.com/papers/6a0ea074be05d6e3efb5f272https://doi.org/10.5281/zenodo.20269793
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