Zhiwei Liu

Large reasoning models (LRMs) achieve strong performance on complex tasks by generating intermediate reasoning before the final answer, yet they remain prone to reasoning hallucinations such as subtle arithmetic or constraint-violation errors. Prior hallucination detectors often rely on external...

Autonomous Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) enable digital automation on end-user devices. While scaling both parameters and data has yielded substantial gains, advanced methods still suffer from prohibitive deployment costs on resource-con...

Long-term conversational agents rely on personal memory to maintain coherence and personalization, yet practical systems must operate under context budgets and cope with evolving or contradictory user information. We frame persona memory as a retrieval problem over a growing memory store, and pro...

This paper explores advancements in automated Question-Answer (QA) extraction using large language models (LLMs), addressing challenges in transforming unstructured text into high-quality, retrievable QA pairs. Traditional approaches, whether through segmented question and answer generation or en...