Psychopathy construct classifier achieves 90.50% accuracy, lowest among all evaluated constructs

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

• Neuroticism construct classifier achieves 99.00% accuracy on held-out statement corpusfinding0.796
  Highest individual classifier performance among OCEAN constructs
• Embedding-based construct classifiers achieve mean accuracy and F1-macro of 95.96% across OCEAN, HEXACO, Dark Tetrad, CMNI, CFNI constructsfinding0.759
  Validates use of lightweight classifiers as replacement for frontier LLM evaluation during alpha sweeps
• Under reward shaping (G=100, H=-100, F=0), Active Inference scored 99.52, Bayesian RL 99.77, Q-learning 95.56, with nearly identical behavior between belief-based agents.finding0.731
  Table 2, row 3, showing equivalence when prior preferences match rewards.
• LAT classifiers perform worst on the Companions dataset (weakest model cognition domain) while achieving 100% F1 on Facts and Animals datasetsfinding0.715
  Shows strong correlation between layer-wise representations and domain-specific semantic understanding
• Chronic pain agent achieves M=4235.5, SD=180.3 COR in non-stationary All category (n=300), highest across all chronic resultsfinding0.706
  Peak performance of chronic pain agents across all reward categories in non-stationary environment
• Normal (α=0.9) and chronic (α=0.1) agents in Objective-only non-stationary category perform best with opposite learning ratesfinding0.703
  Suggests fundamental differences in learning dynamics between normal and chronic perception models
• Pairwise similarity of trait PC1 across all three models is >0.81; no pairwise correlation in top 3 trait PCs is below 0.70finding0.702
  Shows trait space has more cross-model consistency than role space beyond PC1
• Model organism accuracy on BigCodeBench: 56.8% (pre-fine-tuned: 59.1%); steering toward deployment: 55.2%; steering toward evaluation: 43.1%finding0.701
  Quantifies performance cost of fine-tuning and steering; deployment steering has minimal accuracy cost.