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researcher

Bowen Yu

41 papers hereh-index 1914.8k citations62 works total

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

author position
  • first author1
  • middle author29

Across the 30 of 41 papers where every author was matched, so the position is known.

fields
  • cs.CL27
  • cs.AI4
  • cs.CV4
  • cs.LG4
  • cs.CR1
  • cs.DC1
same name
  • Bowen Yu — 9 papers, h 14
  • Bowen Yu — 4 papers, h 1
  • Bowen Yu — 3 papers, h 1
  • Bowen Yu — 3 papers, h 2
  • Bowen Yu — 2 papers, h 3
  • Bowen Yu — 1 paper, h 0

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 citedA Multi-Agent System Enables Versatile Information Extraction from the Chemical Literature

2 citations · 2 across the 5 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026★ 2 cited

A Multi-Agent System Enables Versatile Information Extraction from the Chemical Literature

Yufan Chen, Ching Ting Leung, Bowen Yu +5

To fully expedite AI-powered chemical research, high-quality chemical databases are the foundation. Automatic extraction of chemical information from the literature is essential fo…

cs.AI2026

WebWorld: A Large-Scale World Model for Web Agent Training

Zikai Xiao, Jianhong Tu, Chuhang Zou +7

Web agents require massive trajectories to generalize, yet real-world training is constrained by network latency, rate limits, and safety risks. We introduce \textbf{WebWorld} seri…

cs.AI2025

ProcessBench: Identifying Process Errors in Mathematical Reasoning

Chujie Zheng, Zhenru Zhang, Beichen Zhang +6

As language models regularly make mistakes when solving math problems, automated identification of errors in the reasoning process becomes increasingly significant for their scalab…

cs.AI2025

MARGE: Improving Math Reasoning for LLMs with Guided Exploration

Jingyue Gao, Runji Lin, Keming Lu +3

Large Language Models (LLMs) exhibit strong potential in mathematical reasoning, yet their effectiveness is often limited by a shortage of high-quality queries. This limitation nec…

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