arXiv AI June 15, 2026 By Zequan Xie, Weiming Zeng, Yunhua Chen, Sichang Ling, Tongyang Chen, Jinsheng Xiao

SAFformer:Improving Spiking Transformer via Active Predictive Filtering

arXiv:2605. 08270v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for building low-power Transformers.

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arXiv AI

2d ago

Contrastive Attention Mitigates Spectral Bias in Spiking Transformers

The paper introduces Spiking Contrastive Attention (SCA), a module designed to reduce spectral bias in Spiking Transformers by enhancing high‑frequency information. It demonstrates that spiking neurons and spiking self‑attention act as low‑pass filters, leading to loss of high‑frequency components. Experiments show that SCA improves performance across image classification, semantic segmentation, and event‑based tracking while maintaining lower complexity than the original spiking self‑attention.

By Xiaoli Liu, Malu Zhang, Yang Yang

llms computer-vision safety

arXiv Computer Vision

Aug 21

Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer

arXiv:2608. 19238v1 Announce Type: cross Abstract: Spiking Transformers model token interactions primarily through spiking self-attention (SSA).

By Dongcheng Zhao, Sicheng Shen, Zhenyu Yang, Zhiyuan Li, Jinyan Yu, Yongjian Wang, Tiechui Yao, Wenli Zhang, Tielin Zhang

llms computer-vision benchmarks

arXiv AI

Jun 19

Hybrid ANN-SNN Pipeline with Local Plasticity

arXiv:2606. 20151v1 Announce Type: cross Abstract: This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs).

By Denis Larionov, Khairutin Shtanchaev, Mikhail Kiselev, Mikhail Korovin, Ivan Tugoy

rag benchmarks

arXiv Machine Learning

Sep 21

Predictive Suppression Layers for Communication-Efficient Spiking Neural Networks

arXiv:2609.21583v1 Announce Type: cross Abstract: Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redund...

By Aidin Attar, Michele Rossi

arXiv AI

Jun 11

SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

arXiv:2606. 12287v1 Announce Type: cross Abstract: The Transformer architecture is widely regarded as the most powerful tool for natural language processing, but due to a high number of complex operations, it inherently faces the issue of high energy consumption.

By Claas Beger, Florian Walter, Alois Knoll

llms rag computer-vision nlp

arXiv AI

Jun 24

End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing

arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.

By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua

llms benchmarks

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Related stories

Contrastive Attention Mitigates Spectral Bias in Spiking Transformers

Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer

Hybrid ANN-SNN Pipeline with Local Plasticity

Predictive Suppression Layers for Communication-Efficient Spiking Neural Networks

SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing