4 papers
MMSpec: Benchmarking Speculative Decoding for Vision-Language Models
Hui Shen, Xin Wang, Ping Zhang +11
Vision-language models (VLMs) achieve strong performance on multimodal tasks but suffer from high inference latency due to large model sizes and long multimodal contexts. Speculati…
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Jingxuan Zhang, Yunta Hsieh, Zhongwei Wan +5
Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing co…
MMFormalizer: Multimodal Autoformalization in the Wild
Jing Xiong, Qi Han, Yunta Hsieh +11
Autoformalization, which translates natural language mathematics into formal statements to enable machine reasoning, faces fundamental challenges in the wild due to the multimodal…
PhyX: Does Your Model Have the "Wits" for Physical Reasoning?
Hui Shen, Taiqiang Wu, Qi Han +16
Existing benchmarks fail to capture a crucial aspect of intelligence: physical reasoning, the integrated ability to combine domain knowledge, symbolic reasoning, and understanding…