4 citations · 8 across the 16 of their papers we have counts for
17 papers
What Makes an Efficient VLA? Navigating Action-Head Design, Scaling, and Latency
Luoyang Sun, Guoyang Xia, Fengfa Li +9
Vision-Language-Action (VLA) models combine a pretrained vision encoder, a language backbone, and an action head, but their relative contribution has not been established under con…
VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction
Hongjin Ji, Guoyang Xia, Luoyang Sun +2
Test-time training (TTT) offers a lightweight way to adapt vision--language--action (VLA) policies from unlabeled deployment streams, but it remains difficult to use reliably in cl…
VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment
Guoyang Xia, Fengfa Li, Hongjin Ji +4
Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remain difficult to compare because…
FastMMoE: Accelerating Multimodal Large Language Models through Dynamic Expert Activation and Routing-Aware Token Pruning
Guoyang Xia, Yifeng Ding, Fengfa Li +4
Multimodal large language models (MLLMs) have achieved impressive performance, but high-resolution visual inputs result in long sequences of visual tokens and substantial inference…
ChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs Inference
Haojie Ouyang, Jianwei Lv, Lei Ren +3
Transformer-based large models excel in natural language processing and computer vision, but face severe computational inefficiencies due to the self-attention's quadratic complexi…
SMAR: Soft Modality-Aware Routing Strategy for MoE-based Multimodal Large Language Models Preserving Language Capabilities
Guoyang Xia, Yifeng Ding, Fengfa Li +4
Mixture of Experts (MoE) architectures have become a key approach for scaling large language models, with growing interest in extending them to multimodal tasks. Existing methods t…