How do representations differ or converge between architectures, tasks, and modalities?

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• There is a bidirectional relationship between the geometry of representation and behavior across tasks and modalities.claim0.848
  Author’s interpretive claim that the shared geometry is general and robust.
• Different models cannot converge to the same representation if they have access to fundamentally different information; convergence is capped by mutual information between input signalsclaim0.833
  Key limitation of the PRH for non-bijective observations
• Bigger models are more likely to converge to a shared representation than smaller modelshypothesis0.808
  Selective pressure toward convergence via model capacity
• What has led to representational convergence, will it continue, and ultimately where does it end?question0.806
  Central motivating questions of the paper
• Interpretability features converge across different model architectures, revealing structural similarities.claim0.803
• 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 matchedhypothesis0.797
  Second falsifiable prediction linking objective function structure to valence profile
• 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 cortexfinding0.796
  Empirical evidence for the universality hypothesis cited as supporting the possibility of convergent consciousness-like solutions
• In modern architecture, neither design nor construction typically works step by step; instead there is a fixed end-state produced without realistic feedback.claim0.793
  Contrast between living process and current architectural practice.