3 papers
cs.CL2026
How to Make LMs Strong Node Classifiers?
Zhe Xu, Kaveh Hassani, Si Zhang +7
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tas…
cs.CL2025
Haystack Engineering: Context Engineering for Heterogeneous and Agentic Long-Context Evaluation
Mufei Li, Dongqi Fu, Limei Wang +10
Modern long-context large language models (LLMs) perform well on synthetic "needle-in-a-haystack" (NIAH) benchmarks, but such tests overlook how noisy contexts arise from biased re…
cs.NE2025
Learning Graph Quantized Tokenizers
Limei Wang, Kaveh Hassani, Si Zhang +7
Transformers serve as the backbone architectures of Foundational Models, where domain-specific tokenizers allow them to adapt to various domains. Graph Transformers (GTs) have rece…