SOURCE-LINKED INTELLIGENCE

Channel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Duality (VSSD). Yet the low-bit post-training quantization (PTQ) behavior of VSSD remains insufficiently understood. A weight-activation split on VSSD-Tiny identifies activation quantization as the dominant low-bit bottleneck, while representative inputs to selected VSSD-backbone linear layers exhibit strong channel-wise magnitude variation and token-localized extremes. We propose the Channel-wise Tok

Read original source ↗ Open in workspace

recordType

paper

region

Global