Research paperEfficiency & Inference · Large Language Models · Robotics & Embodied AI1 source · Oct 7, 2026

SpikingVLA: Asynchronous Spiking Vision-Language-Action Models

To address this challenge, we introduce SpikingVLA, an ANN-to-SNN conversion framework that enables accurate and low-latency spiking VLA inference.

Specifically, we propose a Dendritic Integrate-and-Fire (DIF) neuron that alleviates channel-wise activation outliers through dendritic mixing and adaptive somatic firing, enabling accurate ANN-to-SNN conversion with fewer timesteps.
Building on DIF neurons, we further introduce an asynchronous execution mechanism that overlaps temporal computation across VLA components, reducing synchronization overhead and latency.
Extensive experiments demonstrate that SpikingVLA achieves competitive navigation performance with substantially improved inference efficiency.
Compared with existing spiking VLA methods, SpikingVLA improves SR and SPL by 11.9% and 12.6%, respectively, while reducing first-action latency by 11.2$\times$.

[1]SpikingVLA: Asynchronous Spiking Vision-Language-Action Models
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 09:05 AM
To address this challenge, we introduce SpikingVLA, an ANN-to-SNN conversion framework that enables accurate and low-latency spiking VLA inference.
Specifically, we propose a Dendritic Integrate-and-Fire (DIF) neuron that alleviates channel-wise activation outliers through dendritic mixing and adaptive somatic firing, enabling accurate ANN-to-SNN conversion with fewer timesteps.

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