3 papers
cs.LG2026
LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks
Zhongyuan Wang, Pratyusha Vemuri
Adding LLM-generated node features to graph neural networks (GNNs) is widely reported to improve accuracy on standard benchmarks. We document a contrasting observation: when LLM fe…
cs.AI2026
When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More
Zhongyuan Wang, Pratyusha Vemuri
A growing line of work equips large language model (LLM) agents with graph neural networks (GNNs) as callable tools, assuming the agent exercises judgment over when and how much to…
cs.CL2025
General Table Question Answering via Answer-Formula Joint Generation
Zhongyuan Wang, Richong Zhang, Zhijie Nie +1
Advanced table question answering (TableQA) methods prompt large language models (LLMs) to generate answer text, SQL query, Python code, or custom operation, which impressively imp…