# A Configurable Library for Generating and Manipulating Maze Datasets

arXiv: https://arxiv.org/abs/2309.10498 · HTML: https://mlsanity.com/paper/2309.10498/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Leveraging Procedural Generation to Benchmark Reinforcement Learning](https://mlsanity.com/paper/1912.01588/) (2019)
- [Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks](https://mlsanity.com/paper/2207.13243/) (2022)
- [Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks](https://mlsanity.com/paper/2106.04537/) (2021)
- [Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis](https://mlsanity.com/paper/2308.11224/) (2023)
- [Evaluating ChatGPT and GPT-4 for Visual Programming](https://mlsanity.com/paper/2308.02522/) (2023)
- [Datasets for Studying Generalization from Easy to Hard Examples](https://mlsanity.com/paper/2108.06011/) (2021)

## Built on by (most cited first, 30 of 31)

- [Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing](https://mlsanity.com/paper/2506.09965/) (2025)
- [Visual Planning: Let's Think Only with Images](https://mlsanity.com/paper/2505.11409/) (2025)
- [Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning](https://mlsanity.com/paper/2507.16746/) (2025)
- [Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning](https://mlsanity.com/paper/2410.16162/) (2024)
- [AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO](https://mlsanity.com/paper/2502.14669/) (2025)
- [DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models](https://mlsanity.com/paper/2512.24165/) (2025)
- [Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models](https://mlsanity.com/paper/2601.19834/) (2026)
- [Structured World Representations in Maze-Solving Transformers](https://mlsanity.com/paper/2312.02566/) (2023)
- [Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy](https://mlsanity.com/paper/2406.16087/) (2024)
- [Transformers Use Causal World Models in Maze-Solving Tasks](https://mlsanity.com/paper/2412.11867/) (2024)
- [ViC-Bench: Benchmarking Visual-Interleaved Chain-of-Thought Capability in MLLMs with Free-Style Intermediate State Representations](https://mlsanity.com/paper/2505.14404/) (2025)
- [MMGR: Multi-Modal Generative Reasoning Benchmark and Evaluation](https://mlsanity.com/paper/2512.14691/) (2025)
- [Transformers Can Navigate Mazes With Multi-Step Prediction](https://mlsanity.com/paper/2412.05117/) (2024)
- [Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning](https://mlsanity.com/paper/2601.21037/) (2026)
- [Revisiting the Necessity of Lengthy Chain-of-Thought in Vision-centric Reasoning Generalization](https://mlsanity.com/paper/2511.22586/) (2025)
- [On Logical Extrapolation for Mazes with Recurrent and Implicit Networks](https://mlsanity.com/paper/2410.03020/) (2024)
- [Adaptive Inference-Time Scaling via Cyclic Diffusion Search](https://mlsanity.com/paper/2505.14036/) (2025)
- [VFScale: Intrinsic Reasoning through Verifier-Free Test-time Scalable Diffusion Model](https://mlsanity.com/paper/2502.01989/) (2025)
- [MORSE-500: A Programmatically Controllable Video Benchmark to Stress-Test Multimodal Reasoning](https://mlsanity.com/paper/2506.05523/) (2025)
- [Chain of Mindset: Reasoning with Adaptive Cognitive Modes](https://mlsanity.com/paper/2602.10063/) (2026)
- [Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought](https://mlsanity.com/paper/2604.17912/) (2026)
- [Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation](https://mlsanity.com/paper/2606.06076/) (2026)
- [BODHI: Do LLMs Branch Out and Discover Heterogeneous Inferences?](https://mlsanity.com/paper/2608.02867/) (2026)
- [Thinking into the Future: Latent Lookahead Training for Transformers](https://mlsanity.com/paper/2603.20219/) (2026)
- [The Confidence Shortcut: A Reasoning Failure Mode of Masked Diffusion Models](https://mlsanity.com/paper/2605.29123/) (2026)
- [Spectral-Progressive Thought Flow for Lightweight Multimodal Reasoning](https://mlsanity.com/paper/2606.02842/) (2026)
- [Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes](https://mlsanity.com/paper/2607.13188/) (2026)
- [Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning](https://mlsanity.com/paper/2508.16524/) (2025)
- [SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization](https://mlsanity.com/paper/2601.22491/) (2026)
- [SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2606.11770/) (2026)

## Abstract (arXiv)

Understanding how machine learning models respond to distributional shifts is a key research challenge. Mazes serve as an excellent testbed due to varied generation algorithms offering a nuanced platform to simulate both subtle and pronounced distributional shifts. To enable systematic investigations of model behavior on out-of-distribution data, we present textttmaze-dataset, a comprehensive library for generating, processing, and visualizing datasets consisting of maze-solving tasks. With this library, researchers can easily create datasets, having extensive control over the generation algorithm used, the parameters fed to the algorithm of choice, and the filters that generated mazes must satisfy. Furthermore, it supports multiple output formats, including rasterized and text-based, catering to convolutional neural networks and autoregressive transformer models. These formats, along with tools for visualizing and converting between them, ensure versatility and adaptability in research applications.