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Renhe Jiang

23 papers hereh-index 10391 citations27 works total

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

author position
  • middle author14
  • last author6

Across the 20 of 23 papers where every author was matched, so the position is known.

fields
  • cs.AI7
  • cs.LG7
  • cs.CL4
  • cs.CV1
  • cs.GR1
  • cs.HC1
same name
  • Renhe Jiang — 13 papers, h 27
  • Renhe Jiang — 6 papers, h 3
  • Renhe Jiang — 4 papers, h 3
  • Renhe Jiang — 2 papers, h 2
  • Renhe Jiang — 2 papers, h 3
  • Renhe Jiang — 1 paper, h 4

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
20242026
most citedLLM-Based Human-Agent Collaboration and Interaction Systems: A Survey

2 citations · 2 across the 10 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026★ 2 cited

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey

Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +17

Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant…

cs.CL2026

Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety

Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu +12

Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address co…

cs.CL2025

League: Leaderboard Generation on Demand

Jian Wu, Jiayu Zhang, Dongyuan Li +5

This paper introduces Leaderboard Auto Generation (LAG), a novel and well-organized framework for automatic generation of leaderboards on a given research topic in rapidly evolving…

cs.CL2025

Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs

Yangning Li, Weizhi Zhang, Yuyao Yang +17

Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inf…

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