6 papers · 1 filter
AquaVLM: Improving Underwater Situation Awareness with Mobile Vision Language Models
Beitong Tian, Lingzhi Zhao, Bo Chen +5
Underwater activities like scuba diving enable millions annually to explore marine environments for recreation and scientific research. Maintaining situational awareness and effect…
Spatio-Temporal LLM: Reasoning about Environments and Actions
Haozhen Zheng, Beitong Tian, Mingyuan Wu +3
Despite significant recent progress of Multimodal Large Language Models (MLLMs), current MLLMs are challenged by "spatio-temporal" prompts, i.e., prompts that refer to 1) the entir…
Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling?
Mingyuan Wu, Meitang Li, Jingcheng Yang +6
Inference time techniques such as decoding time scaling and self refinement have been shown to substantially improve mathematical reasoning in large language models (LLMs), largely…
VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use
Mingyuan Wu, Jingcheng Yang, Jize Jiang +6
Reinforcement Learning Finetuning (RFT) has significantly advanced the reasoning capabilities of large language models (LLMs) by enabling long chains of thought, self-correction, a…
AquaScope: Reliable Underwater Image Transmission on Mobile Devices
Beitong Tian, Lingzhi Zhao, Bo Chen +5
Underwater communication is essential for both recreational and scientific activities, such as scuba diving. However, existing methods remain highly constrained by environmental ch…
Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning
Mingyuan Wu, Jize Jiang, Haozhen Zheng +8
Vision Language Models (VLMs) have achieved remarkable success in a wide range of vision applications of increasing complexity and scales, yet choosing the right VLM model size inv…