ARCTraj：抽象问题解决中人类推理轨迹的数据集和基准
ARCTraj: A Dataset and Benchmark of Human Reasoning Trajectories for Abstract Problem Solving
AI总结：
ARCTraj通过记录人类推理轨迹，提供了一种结构化且可解释的框架，用于研究抽象问题解决中的推理过程，推动可解释性和可推广智能的发展。
摘要：
我们提出了ARCTraj，一个用于建模复杂视觉任务中人类推理的数据库和方法论框架，该框架基于抽象和推理语料库（ARC）。虽然ARC已经激发了大量关于抽象推理的研究，但大多数现有方法依赖于静态的输入输出监督，这限制了对推理如何随时间展开的理解。ARCTraj通过记录时间有序的对象级动作，捕捉人类如何迭代地将输入转化为输出，揭示了传统数据集所忽视的中间推理步骤。通过O2ARC网页界面收集，其中包含大约10,000条轨迹，标注有任务标识符、时间戳和成功标签，涵盖ARC-AGI-1基准中的400个训练任务。它进一步定义了一个统一的推理流程，包括数据收集、动作抽象、马尔可夫决策过程（MDP）建模和下游学习，使该流程能够与强化学习、生成建模和序列建模方法如PPO、World Models、GFlowNets、扩散代理和决策变压器相结合。对空间选择、颜色归因和战略收敛的分析突显了人类推理的结构和多样性。这些贡献使ARCTraj成为研究类人推理的结构化和可解释的基础，推动了可解释性、对齐和可推广的智能的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
