activity
20212025
most citedLearning Implicit User Profiles for Personalized Retrieval-Based Chatbot

22 citations · 33 across the 7 of their papers we have counts for

collaborators

17 papers

cs.CL2025

FinSight: Towards Real-World Financial Deep Research

Jiajie Jin, Yuyao Zhang, Yimeng Xu +3

Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, w…

cs.AI2025

HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search

Jiajie Jin, Xiaoxi Li, Guanting Dong +5

Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG…

cs.CL20254 cited

Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning

Guanting Dong, Yifei Chen, Xiaoxi Li +7

Recently, large language models (LLMs) have shown remarkable reasoning capabilities via large-scale reinforcement learning (RL). However, leveraging the RL algorithm to empower eff…

cs.CL2025

Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization

Yutao Zhu, Jiajie Jin, Hongjin Qian +3

Existing studies have optimized retrieval-augmented generation (RAG) across various sub-tasks, such as query understanding and retrieval refinement, but integrating these optimizat…

cs.CL2025

Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging

Hongjin Qian, Zheng Liu

Augmenting large language models (LLMs) with external retrieval has become a standard method to address their inherent knowledge cutoff limitations. However, traditional retrieval-…

cs.CL2025

WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Xiaoxi Li, Jiajie Jin, Guanting Dong +5

Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge l…