Φmax (IIT 3.0)

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• IIT 3.0framework0.796
  Version 3.0 of IIT, used to compute Φmax and Conceptual Information (CI) from LLM representation networks.
• IIT 4.0framework0.757
  Version 4.0 of IIT, used to compute Φ and Φ-structure from LLM representation networks; latest iteration at time of study.
• Can estimates of Φ, the primary metric of IIT, robustly differentiate responses across distinct ToM performance levels?question0.749
  Criterion 1 operationalization: requires >80% 'good' cases (higher score → higher Φ) per ToM task.
• Phi-4concept0.735
  Backbone model used in E3 robustness overlay.
• If 'consciousness' phenomenon can be observed from ToM-related RN, higher ToM test scores should yield higher values of μΦmax (IIT 3.0) and/or μΦ (IIT 4.0).hypothesis0.734
  Specific prediction linking IIT's prediction of high Φ for good performance to the experimental design's scoring structure.
• Criterion 1: Φ estimates must yield >80% 'good' cases (higher score → higher Φ) per ToM task to indicate potential consciousness.concept0.723
  First of three operational criteria for identifying consciousness phenomena in LLM representations.
• Criterion 2: Statistically significant Φ value differences (p<0.05) across ToM score categories via Wilcoxon test.concept0.707
  Second of three operational criteria; requires distributional significance in IIT estimates across performance levels.
• Larger S_max correlates with smaller θ50 across backbones in E3 (negative association consistent across pooling and metric choices)finding0.704
  Key geometry-to-behavior bridge finding in E3; robust to pooling choice, cosine vs. L2, and frozen external encoder