Classifier Evidence Guided Token Selection for Spiking Transformers

Spiking transformers achieve strong performance in neuromorphic vision, but multi-step token processing still introduces substantial redundancy and inference cost. Existing token reduction methods mainly rely on response-driven cues and do not explicitly exploit the temporal class evidence encoded by the frozen classifier. Since token utility is ultimately reflected in downstream prediction, we investigate the classifier as an additional source of ranking information. We observe that different tokens exhibit distinct uncertainty trajectories across spiking steps, suggesting that temporally aggregated classifier evidence can provide a complementary signal for token selection. Based on this observation, we propose CETS (Classifier-Evidence Token Selection), a training-free and plug-and-play framework that models token-wise class evidence with a Dirichlet distribution and aggregates its temporal uncertainty statistics into an inference-time ranking score. CETS requires no auxiliary predictor or parameter update and can be directly applied to token reduction. Experiments on static and neuromorphic benchmarks demonstrate favorable accuracy-efficiency trade-offs under controlled retention ratios. Further analyses show that the discriminative strength of classifier-derived uncertainty varies across representations, providing empirical insight into when classifier-side evidence is informative for token selection.