5 papers
VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model
Wenhao Li, Xiu Su, Yichao Cao +5
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities and generalization in embodied manipulation. However, their decision-making relies on a fast, instinct…
FreeAct: Freeing Activations for LLM Quantization
Xiaohao Liu, Xiaobo Xia, Manyi Zhang +6
Quantization is pivotal for mitigating the significant memory and computational overhead of Large Language Models (LLMs). While emerging transformation-based methods have successfu…
Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models
Zhiming Liu, Yujie Wei, Lei Feng +5
Current VLMs have demonstrated capabilities across a wide range of multimodal tasks. Typically, in a pretrained VLM, all layers are engaged by default to make predictions on downst…
APEX: A Decoupled Memory-based Explorer for Asynchronous Aerial Object Goal Navigation
Daoxuan Zhang, Ping Chen, Xiaobo Xia +4
Aerial Object Goal Navigation, a challenging frontier in Embodied AI, requires an Unmanned Aerial Vehicle (UAV) agent to autonomously explore, reason, and identify a specific targe…
Inject Once Survive Later: Backdooring Vision-Language-Action Models to Persist Through Downstream Fine-tuning
Jianyi Zhou, Yujie Wei, Ruichen Zhen +5
Vision-Language-Action (VLA) models have become foundational to modern embodied AI systems. By integrating visual perception, language understanding, and action planning, they enab…