PPaperPicks

Xun Liang

Renmin University of China, Department of Computer Science, Beijing, China

18 papers at tracked venues · 17 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. FAMDR: Feature-Aligned Multimodal Denoising for Reliable Diagnostic Reconciliation in Medical Imaging
    AAAI 2026 · Xun Liang
  2. From Semantics to Spectrum: A New Lens on Graph Augmentation Strategy
  3. PibE-MPP: A Play-it-by-Ear Masking Performance Plug-in for LLMs
  4. RoleCDE: Benchmarking and Mitigating Role-Alignment Trade-offs in Role-Playing Agents
  5. SEAP: Sparse Expert Activation Pruning Unlocks the Brainpower of Large Language Models
    AAAI 2026 · Xun Liang
  6. Safe RAG by RAG: Untying the Bell That RAG Rang with the RAG Hand
    AAAI 2026 · Xun Liang
  7. Breaking Semantic Barriers: A Zero-Shot Generalized Framework for Graph Anomaly Detection
  8. Bridging Expertise: Doctor Recommendations for Cross-Disciplinary Collaborations in Online Medical Consultations
  9. Enhancing Healthcare Recommendations: A Privacy-Protective and Interpretable Cross-Domain Framework
    AAAI 2025 · Xun Liang
  10. Integrating Large Language Models and Möbius Group Transformations for Temporal Knowledge Graph Embedding on the Riemann Sphere
  11. SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model
    ACL 2025 · Xun Liang
  12. A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement Learning
  13. Biomedical Knowledge Graph Embedding with Householder Projection (Student Abstract)
  14. Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
    ACL 2024 · Xun Liang
  15. Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against Fraudsters
  16. Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract)
  17. UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
    ACL 2024 · Xun Liang
  18. When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)