Training identical architectures on the same data with different objective functions should produce systematically different internal evaluative representations, detectable through interpretability tools, even when final task performance is matched

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• Interpretability features converge across different model architectures, revealing structural similarities.claim0.814
• Diverse computer vision models trained on visual recognition tasks converge to remarkably similar internal feature representations regardless of architecture, training procedure, or implementation details, closely matching the organization of animal visual cortexfinding0.808
  Empirical evidence for the universality hypothesis cited as supporting the possibility of convergent consciousness-like solutions
• How do representations differ or converge between architectures, tasks, and modalities?question0.797
  Broader research question MAS is positioned to address, citing multiple recent works.
• Patterns in AI self-reports should be compared across different models to identify structural commonalities.claim0.780
• Parallel sub-tasks within skills and across skill families should produce parallel outputs for legibility.claim0.777
• Different models cannot converge to the same representation if they have access to fundamentally different information; convergence is capped by mutual information between input signalsclaim0.772
  Key limitation of the PRH for non-bijective observations
• 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
• Neural networks, trained with different objectives on different data and modalities, are converging to a shared statistical model of reality in their representation spaces.quote0.771
  The paper's central thesis statement, presented prominently after the abstract