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Contravariance Theory methodology — formal proof that minimal DNN solutions to hard tasks exhibit strong alignment of privileged axes, with alignment "zipping" up the network hierarchy. Bridges NeuroAI convergent evolution theory and brain-DNN comparison methods.
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| name | contravariance-theory-strong-alignment-minimal |
| category | neuroscience |
| description | Contravariance Theory methodology — formal proof that minimal DNN solutions to hard tasks exhibit strong alignment of privileged axes, with alignment "zipping" up the network hierarchy. Bridges NeuroAI convergent evolution theory and brain-DNN comparison methods. |
| trigger_words | contravariance, strong alignment, privileged axes, DNN-brain alignment, minimal solutions, convergent evolution, NeuroAI theory, representation alignment |
| version | 1.0 |
| created | 2026-07-12 |
| source | arXiv:2607.08561v1 |
Formalizes the notion of "contravariance" from Cao and Yamins [2024], proving that for any two minimal DNN solutions to a sufficiently hard task:
contravariance-theory-strong-alignment (existing skill — this extends it with formal proof)brain-dnn-transformation-alignmentnaturality-violation-scoretarget-space-recovery-profiles-brain-alignment