collaborators

6 papers

cs.CV2026

OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction

Taiting Lu, Runze Liu, Ziwei Dong +18

Recent vision-language models (VLMs) can generate executable CAD programs from images, but existing methods mainly target coarse, general-purpose 3D objects and rarely address the…

cs.CV2026

OmniRouting: A Semantic-Coupled Multimodal Benchmark for Constraint-Aware Spatial Reasoning in PCB Routing

Taiting Lu, Kaiyuan Lin, Ziwei Dong +18

Recent large language models (LLMs) have demonstrated remarkable progress in constraint-aware navigation, maze reasoning, and graph reasoning. However, their ability to reason abou…

cs.CV2026

OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout

Taiting Lu, Kaiyuan Lin, Mingjia Wang +12

Recent large language models (LLMs) have demonstrated remarkable progress in 3D spatial reasoning, spatial grounding, and fine-grained geometric understanding. However, their abili…

cs.CV2026

OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning

Taiting Lu, Kaiyuan Lin, Yuxin Tian +13

Recent large multimodal models (LMMs) have made rapid progress in visual grounding, document understanding, and diagram reasoning tasks. However, their ability to convert Printed C…

cs.CV2026

ASTRA: Enhancing Multi-Subject Generation with Retrieval-Augmented Pose Guidance and Disentangled Position Embedding

Tianze Xia, Zijian Ning, Zonglin Zhao +1

Subject-driven image generation has shown great success in creating personalized content, but its capabilities are largely confined to single subjects in common poses. Current appr…

cs.CL2025

Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?

Wenxuan Shen, Mingjia Wang, Yaochen Wang +4

Retrieval-Augmented Generation (RAG) systems using Multimodal Large Language Models (MLLMs) show great promise for complex document understanding, yet their development is critical…