Xiaoxi Li

The proliferation of online news poses a challenge to extracting structured timelines from unstructured content. While recent studies have shown that Large Language Models (LLMs) can assist Timeline Summarization (TLS), these approaches primarily treat models as passive generators. The emergence...

Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG) pipelines struggle to address effectively. Current reasoning-based approaches face a key architectural challenge: t...

Recently, Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities through reinforcement learning (RL). However, enabling LLM-based agents to effectively orchestrate multiple tools remains an open challenge. In this paper, we introduce Tool-Star, an end-to-end agentic post...