1 citations · 1 across the 4 of their papers we have counts for
6 papers
ARGUS: An Agenda-Driven Framework for Reliable User Simulation
Hang Zheng, Nan Jiang, Zichen Zhu +6
High-fidelity user simulators are essential for evaluating and training agents in interactive environments. However, current LLM-based simulators struggle with reliability issues,…
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…
No Action Without a NOD: A Heterogeneous Multi-Agent Architecture for Reliable Service Agents
Zixu Yang, Hang Zheng, Nan Jiang +5
Large language model (LLM) agents have increasingly advanced service applications, such as booking flight tickets. However, these service agents suffer from unreliability in long-h…
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…
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…