ARC-TGI：基于推理链模板的人工验证任务生成器用于ARC-AGI
ARC-TGI: Human-Validated Task Generators with Reasoning Chain Templates for ARC-AGI
AI总结：
ARC-TGI通过推理链模板生成任务，结合人工验证确保规则推导的准确性，提升ARC-AGI任务的可扩展性和可控性。
摘要：
抽象与推理语料库（ARC-AGI）探索了在小视觉网格上的少样本抽象和规则归纳，但由于过拟合、数据泄露和记忆化，难以在静态的手工编写谜题集合上取得进展。我们引入了ARC-TGI（ARC任务生成器库存），一个开源的任务族生成器框架：紧凑的Python程序，能够在生成多样化的ARC-AGI任务的同时保留潜在规则。ARC-TGI围绕面向求解器的表示构建：每个生成的任务都配以自然语言输入和转换推理链，以及部分评估的Python代码，实现采样、转换和片段构建。关键的是，ARC-TGI支持任务级约束，使得训练示例共同暴露所需的变化，以推断潜在规则，这是人类可解ARC任务的必要条件，而独立的每例采样往往无法保证。所有生成器都经过人工细化和本地验证，以确保网格和推理轨迹在变化下保持自然和一致。我们发布了461个生成器，涵盖180个ARC-Mini任务，215个ARC-AGI-1任务（200训练，15测试），以及66个ARC-AGI-2任务（55训练，11测试），使可扩展的数据集采样和受控基准测试成为可能。
英文摘要：
The Abstraction and Reasoning Corpus (ARC-AGI) probes few-shot abstraction and rule induction on small visual grids, but progress is difficult to measure on static collections of hand-authored puzzles due to overfitting, dataset leakage, and memorisation. We introduce ARC-TGI (ARC Task Generators Inventory), an open-source framework for task-family generators: compact Python programs that sample diverse ARC-AGI tasks while preserving a latent rule. ARC-TGI is built around a solver-facing representation: each generated task is paired with natural-language input and transformation reasoning chains and partially evaluated Python code implementing sampling, transformation, and episode construction. Crucially, ARC-TGI supports task-level constraints so that training examples collectively expose the variations needed to infer the underlying rule, a requirement for human-solvable ARC tasks that independent per-example sampling often fails to guarantee. All generators undergo human refinement and local verification to keep both grids and reasoning traces natural and consistent under variation. We release 461 generators covering 180 ARC-Mini tasks, 215 ARC-AGI-1 tasks (200 train, 15 test), and 66 ARC-AGI-2 tasks (55 train, 11 test), enabling scalable dataset sampling and controlled benchmarking.
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。
