Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
On the Trainability of Masked Diffusion Language Models via Blockwise Locality
Yuxiang Wang, Yu Xiang, Baojian Zhou +4
Masked diffusion language models (MDMs) have recently emerged as a promising alternative to standard autoregressive large language models (AR-LLMs), yet their optimization can be s…
cs.LG2025
Towards Graph Foundation Models: A Transferability Perspective
Yuxiang Wang, Wenqi Fan, Suhang Wang +1
In recent years, Graph Foundation Models (GFMs) have gained significant attention for their potential to generalize across diverse graph domains and tasks. Some works focus on Doma…
cs.LG2025
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
Yuxiang Wang, Xinnan Dai, Wenqi Fan +1
In recent years, large language models (LLMs) have emerged as promising candidates for graph tasks. Many studies leverage natural language to describe graphs and apply LLMs for rea…