构建可生成多样化逻辑题的框架，解决人工题目过拟合问题
ARC-TGI: Human-Validated Task Generators with Reasoning Chain Templates for ARC-AGI
- 用程序化生成器批量创建带推理链的视觉逻辑题
- 释放461个生成器，覆盖300+任务，支持可控基准测试
- 通过人工验证确保题目自然且规则可推断，适合研究者使用
抽象与推理语料库（ARC-AGI）用于测试少量样本下的抽象归纳和规则推断，但静态手写题目易导致过拟合、数据泄露和记忆问题。本文提出ARC-TGI（ARC任务生成器清单），一个开源框架，通过紧凑的Python程序生成多样化的ARC-AGI任务，同时保持隐含规则一致。该框架采用面向求解器的表示：每个生成任务均配有自然语言输入、变换推理链及部分执行的Python代码，实现采样、变换和回合构建。关键优势在于支持任务级约束，确保训练样例共同暴露推断规则所需的变异模式，满足人类可解任务的要求，而独立采样常无法保证这一点。所有生成器经人工精炼与本地验证，确保网格与推理轨迹在变化中保持自然一致。我们发布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），支持可扩展的数据集采样与受控基准测试。
原文摘要 · Abstract (English)
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.
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