3 papers
cs.AI2026
Beyond Pixels: Vector-to-Graph Transformation for Reliable Schematic Auditing
Chengwei Ma, Zhen Tian, Zhou Zhou +5
Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual understanding, yet they suffer from a critical limitation: structural blindness. Even state-of-the…
cs.CV2026
AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models
Zhifeng Rao, Wenlong Chen, Lei Xie +4
Vision-Language-Action (VLA) models have recently achieved remarkable progress in robotic perception and control, yet most existing approaches primarily rely on VLM trained using 2…
cs.CV2026
Intelligent Power Grid Design Review via Active Perception-Enabled Multimodal Large Language Models
Taoliang Tan, Chengwei Ma, Zhen Tian +3
The intelligent review of power grid engineering design drawings is crucial for power system safety. However, current automated systems struggle with ultra-high-resolution drawings…