20 citations · 22 across the 7 of their papers we have counts for
7 papers
Lies We Can See: Joint Verbal and Non-Verbal Deception by VLM Agents in Embodied Social Interactions
Jaewoo Ahn, Junseo Kim, Hyunseo Kim +4
Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a hidden role and communicates w…
SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos
Jaehyeon Son, Junhyun Kim, Kyle Kam +7
Vision-language-action models (VLAs) are promising general-purpose robot policies, but adapting them to new tasks typically requires costly task-specific teleoperation data. As an…
FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games
Jaewoo Ahn, Junseo Kim, Heeseung Yun +4
GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adv…
Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning
Jaehyeon Son, Soochan Lee, Gunhee Kim
Recent studies have shown that Transformers can perform in-context reinforcement learning (RL) by imitating existing RL algorithms, enabling sample-efficient adaptation to unseen t…
Learning to Continually Learn with the Bayesian Principle
Soochan Lee, Hyeonseong Jeon, Jaehyeon Son +1
In the present era of deep learning, continual learning research is mainly focused on mitigating forgetting when training a neural network with stochastic gradient descent on a non…
When Meta-Learning Meets Online and Continual Learning: A Survey
Jaehyeon Son, Soochan Lee, Gunhee Kim
Over the past decade, deep neural networks have demonstrated significant success using the training scheme that involves mini-batch stochastic gradient descent on extensive dataset…
Recasting Continual Learning as Sequence Modeling
Soochan Lee, Jaehyeon Son, Gunhee Kim
In this work, we aim to establish a strong connection between two significant bodies of machine learning research: continual learning and sequence modeling. That is, we propose to…