6 citations · 9 across the 11 of their papers we have counts for
13 papers
MatrixReward: Reward from Rubric Matrix for Open-Ended Generation
Zihan Shen, Qi Liu, Zixuan Yang +4
Open-ended query generation lacks standard answers, thus necessitating an effective reward mechanism. Pointwise scoring rubrics provide limited information about the relative quali…
RankBuffer: Efficient Ranking-Based Rewards for Open-Ended Generation
Zixuan Yang, Yiqun Chen, Qi Liu +6
Open-ended generation lacks canonical answers, making pointwise rewards difficult to calibrate for group-based reinforcement learning. Directly ranking same-query rollouts provides…
Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents
Qi Liu, Yiqun Chen, Zidan Chen +6
Search agents now answer questions that take dozens of searches to settle, yet how such an agent reads a page has drawn far less attention than how it finds one. Nearly all of them…
Diagnosing Search Behavior and Failure Modes in Long-Horizon Search Agents
Qi Liu, Jiaxin Mao, Fengbin Zhu +1
Deep search agents answer difficult information-seeking questions by iteratively issuing search queries to gather supporting evidence, but it remains unclear whether and how greate…
UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems
Yiqun Chen, Wei Yang, Erhan Zhang +14
LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely op…
DiffuRank: Effective Document Reranking with Diffusion Language Models
Qi Liu, Kun Ai, Jiaxin Mao +6
Recent advances in large language models (LLMs) have inspired new paradigms for document reranking. While this paradigm better exploits the reasoning and contextual understanding c…
E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search
Qi Liu, Yanzhao Zhang, Mingxin Li +3
Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-based listwise rerankers, which cap…
How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
Qi Liu, Jiaxin Mao, Ji-Rong Wen
Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment genera…
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
Qi Liu, Haozhe Duan, Yiqun Chen +3
Utilizing large language models (LLMs) for document reranking has been a popular and promising research direction in recent years, many studies are dedicated to improving the perfo…
Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
Hanqi Zhang, Chong Chen, Lang Mei +2
In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relat…
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
Qi Liu, Bo Wang, Nan Wang +1
Recent studies have demonstrated the effectiveness of using large language language models (LLMs) in passage ranking. The listwise approaches, such as RankGPT, have become new stat…
TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy
Yiqun Chen, Qi Liu, Yi Zhang +6
Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs f…
An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models
Qi Liu, Gang Guo, Jiaxin Mao +5
With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and…