1 citations · 2 across the 5 of their papers we have counts for
5 papers
DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows
Zechun Niu, Yukun Zhao, Jiaxin Zhang +12
Autonomous agents are increasingly adopted to complete complex, multi-tool workflows in real-world settings. However, existing benchmarks typically separate tasks by application or…
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…
Addressing Personalized Bias for Unbiased Learning to Rank
Zechun Niu, Lang Mei, Liu Yang +4
Unbiased learning to rank (ULTR), which aims to learn unbiased ranking models from biased user behavior logs, plays an important role in Web search. Previous research on ULTR has s…
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study
Zechun Niu, Zhilin Zhang, Jiaxin Mao +2
Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking model…