2 citations · 3 across the 8 of their papers we have counts for
9 papers
Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation
Jaewon Chu, Ji Soo Lee, Jihwan Park +8
An agent skill is a reusable, actionable natural-language artifact that guides an agent to perform a task effectively under a given harness. Recent studies have explored the optimi…
AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems
Jaewon Chu, Jinwoo Seo, Jaewon Cho +4
Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design o…
PRESTO: Preimage-Informed Instruction Optimization for Prompting Black-Box LLMs
Jaewon Chu, Seunghun Lee, Hyunwoo J. Kim
Large language models (LLMs) have achieved remarkable success across diverse domains, due to their strong instruction-following capabilities. This has led to increasing interest in…
Latent Bayesian Optimization via Autoregressive Normalizing Flows
Seunghun Lee, Jinyoung Park, Jaewon Chu +2
Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions. Recent advancements in Latent Bayesian Optimization (L…
Inversion-based Latent Bayesian Optimization
Jaewon Chu, Jinyoung Park, Seunghun Lee +1
Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address…
vid-TLDR: Training Free Token merging for Light-weight Video Transformer
Joonmyung Choi, Sanghyeok Lee, Jaewon Chu +2
Video Transformers have become the prevalent solution for various video downstream tasks with superior expressive power and flexibility. However, these video transformers suffer fr…
Advancing Bayesian Optimization via Learning Correlated Latent Space
Seunghun Lee, Jaewon Chu, Sihyeon Kim +2
Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through…
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Hyeong Kyu Choi, Seunghun Lee, Jaewon Chu +1
Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based appr…
Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models
Dohwan Ko, Ji Soo Lee, Miso Choi +3
Video Question Answering (VideoQA) is a challenging task that entails complex multi-modal reasoning. In contrast to multiple-choice VideoQA which aims to predict the answer given s…