Future interpretability techniques will fundamentally resemble VPD

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• VPD is a meaningful step toward bottom-up interpretabilityclaim0.805
  Positioning of VPD as advancing the paradigm of explaining computation in the model's terms.
• Does VPD mechanistic faithfulness and interpretability survive at frontier model scale?question0.781
  Open research question about whether VPD generalizes beyond the tested 67M-parameter regime.
• The ability to make precise edits demonstrates that VPD identifies real computational machineryclaim0.766
  Claim that editing success validates VPD's decomposition.
• Interpretability features converge across different model architectures, revealing structural similarities.claim0.760
• VPD subcomponents are sparse, interpretable, and avoid feature splitting.claim0.757
  Assertion about the qualitative advantages of VPD's rank-one decomposition.
• Future models with substantially increased capabilities will exhibit alignment faking that is more consistent, robust, and harder to detecthypothesis0.754
  Extrapolation from scale-emergence finding to future risk
• 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 matchedhypothesis0.751
  Second falsifiable prediction linking objective function structure to valence profile
• Interpretability today is a pre-paradigmatic field lacking consensus on objects of study, methods, and evaluative standards.claim0.741
  Diagnosis of the state of the interpretability field, drawing on Kuhn's framework