10 papers
Self-Evolving Neuro-Symbolic Skills for Tool-Augmented Spatial Reasoning
Shi-Yu Tian, Zhuo-Xia Wang, Xuan-Yi Zhu +6
Large vision-language models have achieved strong performance in multimodal reasoning, but they remain unreliable on fine-grained spatial tasks that demand both precise spatial per…
VizRAG: Enhancing Retrieval-Augmented Generation with Hypergraph Visualization
Yanbin Wei, Yang Chen, Renling Gan +7
Hypergraph-based RAG systems surpass traditional graph-based approaches by organizing complex n-ary atomic facts among entities, rather than relying solely on binary relationships.…
HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
Yanbin Wei, Chun Kang, Siwei Li +11
Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain unexplored. In the real wor…
LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models
Shi-Yu Tian, Zhi Zhou, Kun-Yang Yu +5
Spatial reasoning is a cornerstone capability for intelligent systems to perceive and interact with the physical world. However, multimodal large language models (MLLMs) frequently…
See It, Say It, Sorted: An Iterative Training-Free Framework for Visually-Grounded Multimodal Reasoning in LVLMs
Yongchang Zhang, Oliver Ma, Tianyi Liu +2
Recent large vision-language models (LVLMs) have demonstrated impressive reasoning ability by generating long chain-of-thought (CoT) responses. However, CoT reasoning in multimodal…
DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs
Yanbin Wei, Jiangyue Yan, Chun Kang +4
Vision-Language Models (VLMs) have emerged as versatile solutions for zero-shot question answering (QA) across various domains. However, enabling VLMs to effectively comprehend str…