6 citations · 9 across the 11 of their papers we have counts for
6 papers · 1 filter
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…