13 papers
CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG
Nengbo Wang, Tuo Liang, Vikash Singh +6
Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a powerful paradigm for structure…
CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation
Xia Su, Ruiqi Chen, Benlin Liu +4
Vision-Language Models (VLMs) have shown remarkable progress in Vision-Language Navigation (VLN), offering new possibilities for navigation decision-making that could benefit both…
Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning
Xiang Liu, Sen Cui, Guocai Yao +4
Training robotic policies directly in the real world is expensive and unscalable. Although generative simulation enables large-scale data synthesis, current approaches often fail t…
Visibility-Aware Language Aggregation for Open-Vocabulary Segmentation in 3D Gaussian Splatting
Sen Wang, Kunyi Li, Siyun Liang +4
Recently, distilling open-vocabulary language features from 2D images into 3D Gaussians has attracted significant attention. Although existing methods achieve impressive language-b…
Input Order Shapes LLM Semantic Alignment in Multi-Document Summarization
Jing Ma
Large language models (LLMs) are now used in settings such as Google's AI Overviews, where it summarizes multiple long documents. However, it remains unclear whether they weight al…
GeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs
Liangwei Yang, Jing Ma, Jianguo Zhang +11
Graph neural networks (GNNs) on text--attributed graphs (TAGs) typically encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear nei…