We hypothesize that introspective capabilities may scale with model size and architecture, including recurrence/looping that extends the integration window

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• Introspective capabilities may continue to develop with further improvements to model capabilitiesclaim0.861
  Forward-looking statement about future models.
• Either introspection is an emergent capability requiring larger scale, or more stringent controls are needed to test introspection in smaller modelsclaim0.859
  Alternative interpretations offered for why binary detection fails in Llama 3.1 8B but frontier models claim success
• Introspective capabilities have threshold effects requiring very large models; 70B models are barely on the threshold, and independent researchers lack access to larger models.claim0.850
  Practical bottleneck explaining why these phenomena are not widely studied.
• Introspective capacity may follow a simple monotonic scaling law across all concepts and architectureshypothesis0.840
  The paper treats this as possible but unconfirmed; current evidence shows concept-specific scaling only
• Introspective capacity scales with model size for some concepts, approaching near-perfect coupling in LLaMA-3.1-8Bclaim0.834
  Validated for wellbeing and interest; focus and impulsivity do not show consistent scaling
• This introspective capacity is highly unreliable and context-dependent in today's modelsclaim0.833
  A caveat qualifying the main claim.
• Introspective capabilities are confined to early-layer injections (L0-L5) and collapse to chance thereafterclaim0.825
  Key quantitative characterization of the layer-dependence of partial introspection
• Will introspective awareness become more reliable in future AI models?question0.825
  Speculative question about future developments.