Linjuan Wu

Despite the rapid progress of LLMs, their evaluation remains hindered by static, manually curated benchmarks with limited task coverage and poor adaptability to emerging domains. Existing automated approaches typically operate within fixed task schemas and often fail to autonomously discover new...

Expanding Large Language Models(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training(CPT) and data-intensive alignment. While recent data-free merging techniques attempt to bypass alignment by fusing a multilingual CPT-enhanced model with its instruct counterpar...

Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are incomplete, producing confident but unreliable conclusions—a failure mode we term ungrounded reasoning. We argue that this issue arises not from...