2 citations · 2 across the 14 of their papers we have counts for
11 papers · 1 filter
From Noise to Diversity: Random Embedding Injection in LLM Reasoning
Heejun Kim, Seungpil Lee, Jewon Yeom +5
Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned content or from the act of inje…
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…
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…