6 papers
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…
Improving Safety Alignment via Balanced Direct Preference Optimization
Shiji Zhao, Mengyang Wang, Shukun Xiong +7
With the rapid development and widespread application of Large Language Models (LLMs), their potential safety risks have attracted widespread attention. Reinforcement Learning from…
Mind over Space: Can Multimodal Large Language Models Mentally Navigate?
Qihui Zhu, Shouwei Ruan, Xiao Yang +6
Despite the widespread adoption of MLLMs in embodied agents, their capabilities remain largely confined to reactive planning from immediate observations, consistently failing in sp…
World2Mind: Cognition Toolkit for Allocentric Spatial Reasoning in Foundation Models
Shouwei Ruan, Bin Wang, Zhenyu Wu +4
Achieving robust spatial reasoning remains a fundamental challenge for current Multimodal Foundation Models (MFMs). Existing methods either overfit statistical shortcuts via 3D gro…
From reactive to cognitive: brain-inspired spatial intelligence for embodied agents
Shouwei Ruan, Liyuan Wang, Caixin Kang +4
Spatial cognition enables adaptive goal-directed behavior by constructing internal models of space. Robust biological systems consolidate spatial knowledge into three interconnecte…
MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
Shiji Zhao, Qihui Zhu, Shukun Xiong +7
Large pre-trained Vision Language Models (VLMs) demonstrate excellent generalization capabilities but remain highly susceptible to adversarial examples, posing potential security r…