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Jeonghwan Kim

University of Illinois Urbana-Champaign

17 papers hereh-index 8266 citations31 works total

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

author position
  • first author2
  • middle author15

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

fields
  • cs.CV6
  • cs.CL5
  • cs.AI4
  • cs.LG1
  • cs.RO1
affiliations
  • University of Illinois Urbana-Champaign
  • KAIST
  • Handong Global Unviersity
Homepage
same name
  • Jeonghwan Kim — 9 papers, h 2
  • Jeonghwan Kim — 5 papers, h 2
  • Jeonghwan Kim — 3 papers, h 3
  • Jeonghwan Kim — 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

works on
action alignment 1behavior cloning 1representation anchoring 1robot manipulation 1vision-language models 1

From the 1 of 17 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

Advancing Creative Physical Intelligence in Large Multimodal Models

Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10

Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded…

cs.AI2026

OSExpert: Computer-Use Agents Learning Professional Skills via Exploration

Jiateng Liu, Zhenhailong Wang, Rushi Wang +6

General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less…

cs.AI2024

Infogent: An Agent-Based Framework for Web Information Aggregation

Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim +3

Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of…

cs.AI2024

ARMADA: Attribute-Based Multimodal Data Augmentation

Xiaomeng Jin, Jeonghwan Kim, Yu Zhou +4

In Multimodal Language Models (MLMs), the cost of manually annotating high-quality image-text pair data for fine-tuning and alignment is extremely high. While existing multimodal d…

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