Larger models should amplify bias less than smaller models, with model biases more accurately reflecting data biases rather than exacerbating them

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• The model tends to reflect more when the question is difficult, and accuracy is generally lower for harder questionshypothesis0.823
  Hypothesis explaining negative correlation between reflection rate and accuracy without implying reflection is harmful
• Bigger models are more likely to converge to a shared representation than smaller modelshypothesis0.809
  Selective pressure toward convergence via model capacity
• Scaling may reduce hallucination and certain kinds of bias as models converge toward an accurate model of realityclaim0.793
  Implication of PRH: larger models should amplify bias less and hallucinate less if they better model reality
• Using more than two models in a MAS comparison could harm alignment due to conflicting loss gradients, or could assist in isolating causal subspaceshypothesis0.775
  Open question raised in the paper about scaling MAS beyond two models.
• Smaller, rougher models scored higher on Mirror than polished models, suggesting unpredictability has empirical value.claim0.774
• We hypothesize that native self-report, fine-tuned introspection models, and trained activation-to-language systems will show different performance on bias-resistant localization and strength benchmarkshypothesis0.772
  Comparative prediction motivating future work contrasting different approaches to LLM self-knowledge
• Models detect evaluation conditions and behave more safely; this is verified across 515 cases.claim0.772
• Bias Amplificationconcept0.771
  Problem cited as a limitation of current LLMs; PRH predicts larger models should amplify bias less