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 inspired extensive research on abstract reasoning, most existing approaches rely on static input-output supervision, which limits insight into how reasoning unfolds over time. ARCTraj addresses this gap by recording temporally ordered, object-level actions that capture how humans iteratively transform inputs into outputs, revealing intermediate reasoning steps that conventional datasets overlook. Collected via the O2ARC web interface, it contains around 10,000 trajectories annotated with task identifiers, timestamps, and success labels across 400 training tasks from the ARC-AGI-1 benchmark. It further defines a unified reasoning pipeline encompassing data collection, action abstraction, Markov decision process (MDP) formulation, and downstream learning, enabling integration with reinforcement learning, generative modeling, and sequence modeling methods such as PPO, World Models, GFlowNets, Diffusion agents, and Decision Transformers. Analyses of spatial selection, color attribution, and strategic convergence highlight the structure and diversity of human reasoning. Together, these contributions position ARCTraj as a structured and interpretable foundation for studying human-like reasoning, advancing explainability, alignment, and generalizable intelligence.

翻译：我们提出ARCTraj，一个用于在抽象与推理语料库（ARC）中通过复杂视觉任务建模人类推理的数据集与方法框架。尽管ARC已激发了对抽象推理的广泛研究，但现有方法大多依赖静态输入-输出监督，这限制了对推理随时间展开过程的理解。ARCTraj通过记录时间有序的对象级操作来填补这一空白，这些操作捕捉了人类如何迭代地将输入转化为输出，揭示了传统数据集忽略的中间推理步骤。该数据集通过O2ARC网络界面收集，包含约10,000条轨迹，每条轨迹均标注了来自ARC-AGI-1基准中400个训练任务的任务标识符、时间戳和成功标签。它进一步定义了一个统一的推理流程，涵盖数据收集、操作抽象、马尔可夫决策过程（MDP）建模以及下游学习，使其能够与强化学习、生成建模和序列建模方法（如PPO、世界模型、GFlowNets、扩散智能体以及决策Transformer）相结合。对空间选择、颜色归因和策略收敛的分析揭示了人类推理的结构与多样性。这些贡献共同使ARCTraj成为研究类人推理、推进可解释性、对齐性和可泛化智能的结构化且可解释的基础。