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researcher

Ran He

17 papers hereh-index 367 citations15 works total

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

author position
  • middle author9
  • last author7

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.AI4
  • cs.CL4
  • cs.SI1
same name
  • Ran He — 25 papers, h 9
  • Ran He — 21 papers, h 5
  • Ran He — 14 papers, h 17
  • Ran He — 12 papers, h 5
  • Ran He — 11 papers, h 8
  • Ran He — 9 papers, h 9

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
20232026
most citedThought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models

3 citations · 4 across the 14 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

VLM-in-Sandbox: Visual Workspaces for Agentic Visual Reasoning

Hexiong Yang, Mingrui Chen, Jie Cao +1

Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language models to vision-language mode…

cs.AI2026

WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis

Shuo Lu, Yinuo Xu, Kecheng Yu +8

Large language models (LLMs) are increasingly asked not only to write static interfaces, but to construct executable interactive worlds from natural language. Browser-native 3D, co…

cs.AI2026

The Trinity of Consistency as a Defining Principle for General World Models

Jingxuan Wei, Siyuan Li, Yuhang Xu +21

The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial Gene…

cs.AI2023★ 3 cited

Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models

Junchi Yu, Ran He, Rex Ying

Large Language Models (LLMs) have achieved remarkable success in reasoning tasks with the development of prompting methods. However, existing prompting approaches cannot reuse insi…

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