2 citations · 2 across the 14 of their papers we have counts for
6 papers · 2 filters
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…
System 2 Reasoning for Human-AI Alignment: Generality and Adaptivity via ARC-AGI
Sejin Kim, Sundong Kim
Despite their broad applicability, transformer-based models still fall short in System~2 reasoning, lacking the generality and adaptivity needed for human--AI alignment. We examine…
Addressing and Visualizing Misalignments in Human Task-Solving Trajectories
Sejin Kim, Hosung Lee, Sundong Kim
Understanding misalignments in human task-solving trajectories is crucial for enhancing AI models trained to closely mimic human reasoning. This study categorizes such misalignment…
Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus via Model-Based RL
Jihwan Lee, Woochang Sim, Sejin Kim +1
This paper demonstrates that model-based reinforcement learning (model-based RL) is a suitable approach for the task of analogical reasoning. We hypothesize that model-based RL can…
ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning
Hosung Lee, Sejin Kim, Seungpil Lee +4
This paper introduces ARCLE, an environment designed to facilitate reinforcement learning research on the Abstraction and Reasoning Corpus (ARC). Addressing this inductive reasonin…