3 papers
cs.LG2026
NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption
Ziluowen Luo, Jun Yin, Ruochen Liu +4
Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried predictions to perturbed inputs…
cs.IR2026
From Token Generation to Item Ranking: Direct Generative Recommendation with Semantic IDs
Yuanbo Zhao, Ruochen Liu, Senzhang Wang +6
Generative recommendation formulates item recommendation as a token-level generation task, where Semantic IDs (SIDs) represents each item as a sequence of discrete tokens. However,…
cs.AI2025
Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang +10
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further adva…