collaborators

13 papers

cs.HC2026

Context Aware AI Assistant and AR Interface for Lunar Extravehicular Activity (EVA) Procedural Guidance

Rodrigo Gallardo, Qilmeg Doudatcz, Ganit Goldstein +7

As human space exploration returns to the Moon, astronauts need rapid access to procedural information during extravehicular activities (EVAs), where attention is divided across na…

cs.AI2026

CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps

JungMin Yun, JuneHyoung Kwon, YoungBin Kim

Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge. Although current models achieve strong results on existing multi-hop quest…

cs.CV2026

Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks

JuneHyoung Kwon, MiHyeon Kim, Eunju Lee +3

While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal information. Current unlearning ben…

cs.AI2026

Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs

Byeonggeuk Lim, JungMin Yun, Junehyoung Kwon +2

Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary models to construct preference…

cs.CL2026

Medal Matters: Probing LLMs' Failure Cases Through Olympic Rankings

Juhwan Choi, Seunguk Yu, JungMin Yun +1

Large language models (LLMs) have achieved remarkable success in natural language processing tasks, yet their internal knowledge structures remain poorly understood. This study exa…

cs.IR2026

Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval

JungMin Yun, YoungBin Kim

Large Language Models (LLMs) have been increasingly employed for query expansion. However, their generative nature often undermines performance on complex multi-hop retrieval tasks…