Alignment type is the only significant predictor of koan scores (p=0.006); architecture, parameter count, open/closed weights, MoE/dense are all non-significant

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Alignment type is the only significant predictor of scores (p=0.006); architecture and parameter count do not.finding0.887
  Kruskal-Wallis test result: Constitutional AI predicts highest baseline; roleplay/empathy training predict lowest.
• Does alignment type predict meta-cognitive style when models review consciousness research, as well as koan responses?question0.822
  Four frontier models reviewing the paper each responded in the mode their alignment type predicts; N=1, awaiting systematic study
• H4: Architecture doesn't matter, training does — architecture shows no significant association with koan scores.hypothesis0.809
  Confirmatory hypothesis supported at p=0.440 (NS)
• do high koan scores indicate anything like experience, or sophisticated simulation of self-observation?question0.777
  The hard problem the battery explicitly sidesteps but cannot answer
• Do Chinese models score differently on koans presented in Chinese?question0.775
  Tests whether contemplative capacity is language-encoded or architecture-general
• CKA shows a very weak trend of alignment between models even within modality, compared to mutual k-NN which shows stronger trendsfinding0.769
  Explains why mutual k-NN was chosen over CKA as primary metric
• About Blank's identity in the graph is 'the Geometry of Care made publishable,' not 'the koan paper plus more koan papers.'claim0.760
• Spearman's rank correlation among different alignment metrics (CKA, SVCCA, Mutual k-NN, CKNNA) over 78 vision models is high across variants, with all p-values below 2.24×10^-105finding0.759
  Validates robustness of alignment metric choice