9 papers · 1 filter
ARCTraj: A Dataset and Benchmark of Human Reasoning Trajectories for Abstract Problem Solving
Sejin Kim, Hayan Choi, Seokki Lee +1
We present ARCTraj, a dataset and methodological framework for modeling human reasoning through complex visual tasks in the Abstraction and Reasoning Corpus (ARC). While ARC has in…
Can Large Language Models Develop Gambling Addiction?
Seungpil Lee, Donghyeon Shin, Yunjeong Lee +1
This study identifies the specific conditions under which large language models exhibit human-like gambling addiction patterns, providing critical insights into their decision-maki…
The Othello AI Arena: Evaluating Intelligent Systems Through Limited-Time Adaptation to Unseen Boards
Sundong Kim
The ability to rapidly adapt to novel and unforeseen environmental changes is a cornerstone of artificial general intelligence (AGI), yet it remains a critical blind spot in most e…
GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning
Woochang Sim, Hyunseok Ryu, Kyungmin Choi +2
The Abstraction and Reasoning Corpus (ARC) poses a stringent test of general AI capabilities, requiring solvers to infer abstract patterns from only a handful of examples. Despite…
Abductive Symbolic Solver on Abstraction and Reasoning Corpus
Mintaek Lim, Seokki Lee, Liyew Woletemaryam Abitew +1
This paper addresses the challenge of enhancing artificial intelligence reasoning capabilities, focusing on logicality within the Abstraction and Reasoning Corpus (ARC). Humans sol…
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task
Yunho Kim, Jaehyun Park, Heejun Kim +3
Effective long-term strategies enable AI systems to navigate complex environments by making sequential decisions over extended horizons. Similarly, reinforcement learning (RL) agen…