18 citations · 135 across the 103 of their papers we have counts for
30 papers · 1 filter
Douyin Multimodal Embedding Model Technical Report
Haonan Chen, Chu Li, Zhicheng Wang +4
Multimodal representation learning is a cornerstone of modern AI. By encoding multimodal queries and targets into vectors, it powers industrial search and recommendation and underp…
Training Documents Reranker with Search Rubrics for Deep Research Agent
Wenhan Liu, Yu Lu, Qiaolin Xia +8
Retrieval systems help deep research agents generate high-quality answers by providing relevant documents. However, existing retrievers typically select documents through relevance…
RAG: Retriever Routing for Retrieval-Augmented Generation
Tong Zhao, Yutao Zhu, Yucheng Tian +1
Retrieval-augmented generation (RAG) has become a cornerstone for knowledge-intensive tasks. However, the efficacy of RAG is often bottlenecked by the ``one-size-fits-all'' retriev…
RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation
Haobo Zhang, Yutao Zhu, Kelong Mao +2
Large Language Models (LLMs) have revolutionized recommendation agents by providing superior reasoning and flexible decision-making capabilities. However, existing methods mainly f…
SumRank: Aligning Summarization Models for Long-Document Listwise Reranking
Jincheng Feng, Wenhan Liu, Zhicheng Dou
Large Language Models (LLMs) have demonstrated superior performance in listwise passage reranking task. However, directly applying them to rank long-form documents introduces both…
Agentic-R: Learning to Retrieve for Agentic Search
Wenhan Liu, Xinyu Ma, Yutao Zhu +4
Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success…