Overall human-LLM judge agreement rate for trait expression is 92.8% across 120 pairwise judgments (3 annotators × 6 personas × 10 pairs × 2 models)

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

• Overall human-LLM judge agreement rate is 91% (109/120 and 173/190) across two human ratersfinding0.903
  Validates LLM judge quality for trait expression scoring
• Overall human-LLM judge agreement rate for coherency is 91.7% across 120 pairwise judgmentsfinding0.882
  Validates the LLM-as-a-Judge evaluation protocol for coherency scoring
• LLM judge (GPT-4.1-mini) achieves 94.7% agreement with human judges across 300 pairwise comparisons for evil, sycophancy, hallucinationfinding0.828
  Validates the automated trait expression scoring pipeline
• Would human raters and cross-judge replication corroborate the LLM-as-judge assessments of coherence and trait expression?question0.827
  Methodological concern raised about potential bias and circularity of model-based classifiers
• Trait expression scores on internal evaluation questions correlate r=0.941 (Qwen, evil) and r=0.950 (Llama, evil) with external benchmark scoresfinding0.796
  Validates that internal evaluation set provides reliable proxy for broader behavioral tendencies
• LLM Judge Trait-Expression Scoringmethod0.793
  Automated scoring of trait expression on 0-100 scale using G20B as a local judge model
• 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.finding0.775
  Table 2, row 3, showing equivalence when prior preferences match rewards.
• LLM judge (deepseek-v3) agrees with human evaluator on 91.6% of 200 sampled jailbreak responsesfinding0.772
  Validates the LLM-based harm evaluation rubric