5 papers · 1 filter
Towards Direct Evaluation of Harness Optimizers via Priority Ranking
Kai Tzu-iunn Ong, Minseok Kang, Dongwook Choi +9
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…
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…
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…
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…
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…