activity
20232026
most citedReview-driven Personalized Preference Reasoning with Large Language Models for Recommendation

12 citations · 35 across the 44 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

SkillSandbox: Skill Verification via Dynamic Scenario Synthesis

Serin Kim, Kwangwook Seo, Dokyung Song +2

Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferable knowledge. It is therefore c…

cs.AI2026

Towards Direct Evaluation of Harness Optimizers via Priority Ranking

Kai Tzu-iunn Ong, Minseok Kang, Dongwook Choi +11

Harness optimization enables automated agent creation by having an optimizer agent iteratively update the harness of target agents. Despite its success, current studies evaluate op…

cs.AI2026

On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length

Sunghwan Kim, Junhee Cho, Beong-woo Kwak +6

Large language models (LLMs) have shown promise as interactive agents that solve tasks through extended sequences of environment interactions. While prior work has primarily focuse…

cs.AI2026

PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints

Minjun Park, Donghyun Kim, Hyeonjong Ju +5

We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents. However, the dynamics of mult…

cs.AI2025

Designing Memory-Augmented AR Agents for Spatiotemporal Reasoning in Personalized Task Assistance

Dongwook Choi, Taeyoon Kwon, Dongil Yang +2

Augmented Reality (AR) systems are increasingly integrating foundation models, such as Multimodal Large Language Models (MLLMs), to provide more context-aware and adaptive user exp…

cs.AI2024

Large Language Models Are Self-Taught Reasoners: Enhancing LLM Applications via Tailored Problem-Solving Demonstrations

Kai Tzu-iunn Ong, Taeyoon Kwon, Jinyoung Yeo

Guiding large language models with a selected set of human-authored demonstrations is a common practice for improving LLM applications. However, human effort can be costly, especia…

cs.AI2024★ 1 cited

COCOA: CBT-based Conversational Counseling Agent using Memory Specialized in Cognitive Distortions and Dynamic Prompt

Suyeon Lee, Jieun Kang, Harim Kim +3

The demand for conversational agents that provide mental health care is consistently increasing. In this work, we develop a psychological counseling agent, referred to as CoCoA, th…