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Ruochen Li

University of Texas at Dallas

8 papers hereh-index 6165 citations13 works total

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

author position
  • first author1
  • middle author6

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI2
  • cs.CV1
  • cs.SE1
affiliations
  • University of Texas at Dallas
same name
  • Ruochen Li — 9 papers, h 5
  • Ruochen Li — 6 papers, h 4
  • Ruochen Li — 3 papers, h 1
  • Ruochen Li — 2 papers, h 5
  • Ruochen Li — 1 paper, h 2

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 citedMLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents

3 citations · 8 across the 8 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.AI2025

The Ramon Llull's Thinking Machine for Automated Ideation

Xinran Zhao, Boyuan Zheng, Chenglei Si +8

This paper revisits Ramon Llull's Ars combinatoria - a medieval framework for generating knowledge through symbolic recombination - as a conceptual foundation for building a modern…

cs.CL2025

Coherent Multimodal Reasoning with Iterative Self-Evaluation for Vision-Language Models

Wenjie Luo, Ruocheng Li, Shanshan Zhu +1

Despite significant advancements, current large language models (LLMs) and vision-language models (LVLMs) continue to struggle with complex, multi-step, cross-modal common sense re…

cs.SE2025

LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research

Shuo Yan, Ruochen Li, Ziming Luo +11

Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reprodu…

cs.CL2025

AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control

Ruosen Li, Ziming Luo, Quan Zhang +4

Large reasoning models (LRMs) achieve impressive reasoning capabilities by generating lengthy chain-of-thoughts, but this "overthinking" incurs high latency and cost without commen…

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