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Y. Kim

18 papers hereh-index 8646 citations26 works total

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

author position
  • first author9
  • middle author6

Across the 15 of 18 papers where every author was matched, so the position is known.

fields
  • cs.AI7
  • cs.CL6
  • cs.CV3
  • cs.HC1
  • cs.SD1
same name
  • Y. Kim — 121 papers, h 56
  • Y. Kim — 105 papers, h 53
  • Y. Kim — 104 papers, h 23
  • Y. Kim — 78 papers
  • Y. Kim — 66 papers, h 31
  • Y. Kim — 40 papers, h 75

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
20222026
most citedHealth-LLM: Large Language Models for Health Prediction via Wearable Sensor Data

30 citations · 77 across the 18 of their papers we have counts for

collaborators
Showing 2025 · cs.AIShow all

4 papers · 2 filters

cs.AI2025

Towards a Science of Scaling Agent Systems

Yubin Kim, Ken Gu, Chanwoo Park +17

Agents, language model-based systems capable of reasoning, planning, and acting are widely adopted in real-world tasks, yet how their performance changes as these systems scale acr…

cs.AI2025

InvThink: Premortem Reasoning for Safer Language Models

Yubin Kim, Taehan Kim, Eugene Park +4

We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before generating its final response. Unlike…

cs.AI2025★ 5 cited

The Anatomy of a Personal Health Agent

A. Ali Heydari, Ken Gu, Vidya Srinivas +35

Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. Howev…

cs.AI2025

Tiered Agentic Oversight: A Hierarchical Multi-Agent System for Healthcare Safety

Yubin Kim, Hyewon Jeong, Chanwoo Park +9

Large language models (LLMs) deployed as agents introduce significant safety risks in clinical settings due to their potential for error and single points of failure. We introduce…

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