7 citations · 7 across the 9 of their papers we have counts for
7 papers · 1 filter
OmniThoughtVis: A Scalable Distillation Pipeline for Deployable Multimodal Reasoning Models
Yuanhao Yue, Chengyu Wang, Yuanjie Lyu +2
Recent multimodal large language models (MLLMs) have shown strong chain-of-thought (CoT) reasoning ability on vision-language tasks, but their direct deployment in real-world syste…
AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use
Yuanjie Lyu, Chengyu Wang, Haonan Zheng +4
Modern industrial applications increasingly demand language models that act as agents, capable of multi-step reasoning and tool use in real-world settings. These tasks are typicall…
Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs
Yuanjie Lyu, Chengyu Wang, Jun Huang +1
Large Language Model agents achieve strong performance on multi-step reasoning and tool-use tasks, but their impressive capabilities typically rely on extremely large backbones. Ex…
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks
Yuanjie Lyu, Chao Zhang, Yuhao Chen +2
In Retrieval-Augmented Generation (RAG) and agent-based frameworks, the "Chain of Models" approach is widely used, where multiple specialized models work sequentially on distinct s…
Generating Event-oriented Attribution for Movies via Two-Stage Prefix-Enhanced Multimodal LLM
Yuanjie Lyu, Tong Xu, Zihan Niu +3
The prosperity of social media platforms has raised the urgent demand for semantic-rich services, e.g., event and storyline attribution. However, most existing research focuses on…
Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation
Yuanjie Lyu, Zihan Niu, Zheyong Xie +4
Despite the significant progress of large language models (LLMs) in various tasks, they often produce factual errors due to their limited internal knowledge. Retrieval-Augmented Ge…
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
Yuanjie Lyu, Zhiyu Li, Simin Niu +7
Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresse…