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Jiaxin Mao

17 papers hereh-index 7159 citations17 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author8

Across the 13 of 17 papers where every author was matched, so the position is known.

fields
  • cs.AI9
  • cs.CL4
  • cs.IR4
same name
  • Jiaxin Mao — 13 papers, h 16
  • Jiaxin Mao — 8 papers, h 23
  • Jiaxin Mao — 8 papers, h 6
  • Jiaxin Mao — 7 papers, h 4
  • Jiaxin Mao — 5 papers, h 4
  • Jiaxin Mao — 4 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20222026
most citedPTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning

4 citations · 6 across the 15 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

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…

cs.CL2025

Deep Research: A Systematic Survey

Zhengliang Shi, Yiqun Chen, Haitao Li +23

Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable ou…

cs.CL2025

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…

cs.CL2025

Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning

Yiqun Chen, Lingyong Yan, Weiwei Sun +6

Retrieval-augmented generation (RAG) is widely utilized to incorporate external knowledge into large language models, thereby enhancing factuality and reducing hallucinations in qu…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.