5 papers
MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs
Yuanteng Chen, Peisong Wang, Zhilei Liu +9
Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compression essential. Among PTQ methods…
RoboNeuron: A Middle-Layer Infrastructure for Agent-Driven Orchestration in Embodied AI
Weifan Guan, Qinghao Hu, Huasen Xi +3
Vision-language-action (VLA) models and LLM agents have advanced rapidly, yet reliable deployment on physical robots is often hindered by an interface mismatch between agent tool A…
Towards Efficient and Accurate Spiking Neural Networks via Adaptive Bit Allocation
Xingting Yao, Qinghao Hu, Fei Zhou +4
Multi-bit spiking neural networks (SNNs) have recently become a heated research spot, pursuing energy-efficient and high-accurate AI. However, with more bits involved, the associat…
Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey
Weifan Guan, Qinghao Hu, Aosheng Li +1
Vision-Language-Action (VLA) models extend vision-language models to embodied control by mapping natural-language instructions and visual observations to robot actions. Despite the…
SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World
Xingting Yao, Qinghao Hu, Fei Zhou +5
In this paper, we propose SpikingNeRF, which aligns the temporal dimension of spiking neural networks (SNNs) with the radiance rays, to seamlessly accommodate SNNs to the reconstru…