collaborators

7 papers

cs.AI2026

ToolLIFT: Lifting Tool-Specific Trajectories into Function-Level Graphs for Generalizable Tool Planning

Xiuhui You, Jiayi Luo, Zichao Shen +2

Historical tool-use trajectories provide valuable experience for large language model (LLM) agents to plan and coordinate tool usage. Existing approaches directly construct tool-le…

cs.CV2026

Future Forcing: Future-aware Training-free KV Cache Policy for Autoregressive Video Generation

Jiayi Luo, Qiyan Liu, Tengyang Wang +8

Autoregressive (AR) video generation has emerged as a promising paradigm for long-horizon video synthesis, where each frame is generated conditioned on previously generated tokens.…

cs.CL2026

CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation

Wenhan Xiao, Ziwei Zhang, Chuanyue Yu +4

Retrieval-augmented generation (RAG) improves knowledge-intensive question answering by incorporating external evidence. However, existing RAG methods still suffer from hallucinati…

cs.CV2026

EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection

Xinyu Zhao, Qingyun Sun, Jiayi Luo +1

Zero-Shot Anomaly Detection (ZSAD) aims to detect anomalies in unseen domains without target-domain adaptation. Recent CLIP-based methods have shown promising performance by levera…

cs.LG2026

Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning

Yi Huang, Qingyun Sun, Jia Li +2

Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational Deep Learning (RDL) addresse…

cs.LG2026

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models

Haonan Yuan, Qingyun Sun, Junhua Shi +3

Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs fro…