11 papers
HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models
Fangzhou Chen, Shiji Zhao, Mengyang Wang +4
Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks. Parameter-efficient safety alignment methods based on prompt tuning typically rely on a si…
GSTEP: Global Spatio-Temporal Density-Driven Visual Token Pruning for Efficient Video Large Language Models
Mengjie Zhang, Qihui Zhu, Tao Zhang +10
Video large language models (VideoLLMs) achieve strong video understanding performance, but their inference remains expensive due to the large number of redundant spatio-temporal v…
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…
AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models
Shouwei Ruan, Bin Wang, Zhenyu Wu +5
Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world. A key bottleneck lies in their inability to tra…
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…