6 papers
AERA: Adaptive Evidence Residual Allocation for Efficient Test-Time Reasoning
Ziming Wang, Ivor Tsang, Hangwei Qian
Test-time scaling improves language-model reasoning by generating additional candidate solutions, but allocating the same inference budget to every problem is computationally waste…
BadSKP: Backdoor Attacks on Knowledge Graph-Enhanced LLMs with Soft Prompts
Xiaoting Lyu, Yufei Han, Hangwei Qian +6
Recent knowledge graph (KG)-enhanced large language models (LLMs) move beyond purely textual knowledge augmentation by encoding retrieved subgraphs into continuous soft prompts via…
SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domains
Longkun Li, Yuanben Zou, Jinghan Wu +4
Graph-RAG improves LLM reasoning using structured knowledge, yet conventional designs rely on a centralized knowledge graph. In distributed and access-restricted settings (e.g., ho…
Learning ORDER-Aware Multimodal Representations for Composite Materials Design
Xinyao Li, Hangwei Qian, Jingjing Li +2
Artificial intelligence has shown remarkable success in materials discovery and property prediction, particularly for crystalline and polymer systems where material properties and…
Exploring the Effectiveness and Interpretability of Texts in LLM-based Time Series Models
Zhengke Sun, Hangwei Qian, Ivor Tsang
Large Language Models (LLMs) have been applied to time series forecasting tasks, leveraging pre-trained language models as the backbone and incorporating textual data to purportedl…
Cross-Context Backdoor Attacks against Graph Prompt Learning
Xiaoting Lyu, Yufei Han, Wei Wang +3
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learn…