1 citations · 1 across the 4 of their papers we have counts for
4 papers
The Amazing Agent Race: Strong Tool Users, Weak Navigators
Zae Myung Kim, Dongseok Lee, Jaehyung Kim +2
Existing tool-use benchmarks for LLM agents are overwhelmingly linear: our analysis of six benchmarks shows 55 to 100% of instances are simple chains of 2 to 5 steps. We introduce…
Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits
Dongwon Kim, Dongseok Lee
This work develops a mean-field analysis for the asymptotic behavior of deep BitNet-like architectures as smooth quantization parameters approach zero. We establish that empirical…
Training-free LLM Verification via Recycling Few-shot Examples
Dongseok Lee, Jimyung Hong, Dongyoung Kim +1
Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varying conclusions present significant chal…
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading
Uk Jo, Taehyun Jo, Wanjun Kim +3
We explore deep Reinforcement Learning(RL) algorithms for scalping trading and knew that there is no appropriate trading gym and agent examples. Thus we propose gym and agent like…