11 papers
SepPrune:A Separator-based Pruning Framework for Efficient Multimodal Large Language Models
Yuchen Wang, Qihui Zhu, Yang Liu +2
Recent multimodal large language models (MLLMs), such as Qwen2.5-VL and InternVL3, generate large numbers of vision tokens for high-resolution inputs, leading to substantial comput…
RP-OPSD: Resolution-Privileged On-Policy Self-Distillation for Multimodal Large Language Models
Qihui Zhu, Yuchen Wang, Zijian Wen +7
On-Policy Self-Distillation (OPSD) uses privileged information available only to the teacher to provide dense token-level supervision on trajectories generated by the student. Howe…
MAR:Multi-Agent Reflexion Improves Reasoning Abilities in LLMs
Onat Ozer, Yuchen Wang, Grace Wu +3
LLMs have shown the capacity to improve their performance on reasoning tasks through reflecting on their mistakes, and acting with these reflections in mind. However, continual ref…
Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding
Yuchen Wang, Haonan Wang, Yu Guo +2
Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain constrained by three fundamental issues…
Compose and Fuse: Revisiting the Foundational Bottlenecks in Multimodal Reasoning
Yucheng Wang, Yifan Hou, Aydin Javadov +2
Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio. Yet cross-modal reasoning remains underexplored,…
HAWK: Head Importance-Aware Visual Token Pruning in Multimodal Models
Qihui Zhu, Tao Zhang, Yuchen Wang +9
In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making them impractical for real-time…