AI RESEARCH
SAFformer:Improving Spiking Transformer via Active Predictive Filtering
arXiv CS.AI
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ArXi:2605.08270v1 Announce Type: cross Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for building low-power Transformers. However, existing Spiking Transformers largely adhere to a passive reactive paradigm, which struggles to focus on task-relevant information and incurs substantial computational overhead when processing redundant visual data.