2 citations · 3 across the 9 of their papers we have counts for
11 papers
Learning to Retrieve via Reinforcement Learning in Embedding Space
Qi Liu, Fengming Liang, Yiqun Chen +2
Dense retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize retrieval metrics or downstream task perf…
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…
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…
OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search
Erhan Zhang, Yiqun Chen, Zechun Niu +6
Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforcement learning with verifiable rewa…
Tournament-GRPO: Group-Wise Tournament Rewards for Reinforcement Learning in Open-Ended Long-Form Generation
Zixuan Yang, Yiqun Chen, Wei Yang +7
Reinforcement learning in open-ended long-form generation is challenging because reliable reference answers and automatic metrics are often unavailable. Existing rubric-based metho…
JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG
Yiqun Chen, Erhan Zhang, Tianyi Hu +8
The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reas…
Beyond Monolithic Architectures: A Multi-Agent Search and Knowledge Optimization Framework for Agentic Search
Yiqun Chen, Lingyong Yan, Zixuan Yang +5
Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use. However, prevaili…
MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation
Yiqun Chen, Erhan Zhang, Lingyong Yan +4
In question-answering (QA) systems, Retrieval-Augmented Generation (RAG) has become pivotal in enhancing response accuracy and reducing hallucination issues. The architecture of RA…
Leveraging LLMs to Evaluate Usefulness of Document
Xingzhu Wang, Erhan Zhang, Yiqun Chen +7
The conventional Cranfield paradigm struggles to effectively capture user satisfaction due to its weak correlation between relevance and satisfaction, alongside the high costs of r…
Exploring Human-Like Thinking in Search Simulations with Large Language Models
Erhan Zhang, Xingzhu Wang, Peiyuan Gong +2
Simulating user search behavior is a critical task in information retrieval, which can be employed for user behavior modeling, data augmentation, and system evaluation. Recent adva…
USimAgent: Large Language Models for Simulating Search Users
Erhan Zhang, Xingzhu Wang, Peiyuan Gong +2
Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. N…