Learning Dynamic Evidence Routes for Vision Transformer Probing
Probing frozen vision transformers typically uses permutation-invariant aggregation (GAP or $\texttt{[CLS]}$), treating patch tokens as an unstructured set. Content-dependent probes such as self-attention are useful accuracy controls, but they do not expose a fixed token schedule or fixed position weights for auditing. We introduce SSMProbe, a probe of frozen vision transformers that learns a Sinkhorn patch route and reads it with a diagonal S4 decoder. The decoder's causal linear time-invariant (LTI) recurrence yields fixed position coefficients, exposing how patch arrangement interacts with sequential weighting. Held-out spatial predictability measures route organization beyond a locati...
| Authors | Zice Wang, Zhenyu Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 29 April 2026 |
| Last updated at source | 14 October 2026 |
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