2 citations · 3 across the 7 of their papers we have counts for
8 papers
Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance
Yanyan Zhang, Disheng Liu, Xinpeng Li +8
While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including manipulated objects, destinations…
Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models
Yanyan Zhang, Disheng Liu, Kai Ye +6
Vision-language-action (VLA) models have improved the flexibility and generality of robotic manipulation, yet they remain fragile to online disruptions, such as changes in task goa…
MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs
Lun Zhan, Feng Xiong, Huanyong Liu +2
Synthesizing high-quality training data is crucial for enhancing domain models' reasoning abilities. Existing methods face limitations in long-tail knowledge coverage, effectivenes…
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…
Assessing LLMs for Serendipity Discovery in Knowledge Graphs: A Case for Drug Repurposing
Mengying Wang, Chenhui Ma, Ao Jiao +6
Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predict…
NEBULA: Do We Evaluate Vision-Language-Action Agents Correctly?
Jierui Peng, Yanyan Zhang, Yicheng Duan +3
The evaluation of Vision-Language-Action (VLA) agents is hindered by the coarse, end-task success metric that fails to provide precise skill diagnosis or measure robustness to real…
ERC-SVD: Error-Controlled SVD for Large Language Model Compression
Haolei Bai, Siyong Jian, Tuo Liang +2
Large language models (LLMs) have demonstrated impressive capabilities in a wide range of downstream natural language processing tasks. Nevertheless, their considerable sizes and m…
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
Hongzhu Li, Xiangrui Gao, Linhui Feng +2
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood.…