Yimin Deng

Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences over long interactions. Existing memory systems mainly rely on static, hand-crafted update rules; although reinforcement learning (RL)-b...

Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle temporal information remains limited. To address this limitation, existing approaches often design time-sensitive reasoning pipelines that rely on extern...

Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While large language models have shown strong capabilities in commonsense reasoning, they still struggle with diagnostic reasoning due to limited domain knowledge. Existing approaches often rely on interna...

Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. The complexity of user queries, coupled with potential knowledge deficiencies in Large Language Models (LLMs), gives rise to two pivotal challenges that underpin the performance on this task: the corre...