{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-configurable-library-for-generating-and","title":"A Configurable Library for Generating and Manipulating Maze Datasets","arxiv_id":"2309.10498","date":"2023-09-19","proceeding":null,"authors":["Michael Igorevich Ivanitskiy","Rusheb Shah","Alex F. Spies","Tilman Räuker","Dan Valentine","Can Rager","Lucia Quirke","Chris Mathwin","Guillaume Corlouer","Cecilia Diniz Behn","Samy Wu Fung"],"abstract":"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 $\\texttt{maze-dataset}$, 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.","url_abs":"https://arxiv.org/abs/2309.10498v2","url_pdf":"https://arxiv.org/pdf/2309.10498v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-configurable-library-for-generating-and","repo_url":"https://github.com/understanding-search/maze-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"tasks":[],"methods":[{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[{"slug":"maze-dataset","name":"maze-dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2309.10498","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10498"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/understanding-search/maze-dataset","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"summary":{"ran":5},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"fa1c4ce4e90ea858","entry":"bool_array_from_string","repo":"understanding-search/maze-dataset","repo_kind":"official","path":"maze_dataset/utils.py","file_url":"https://github.com/understanding-search/maze-dataset/blob/HEAD/maze_dataset/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"LGPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"fa1c4ce4e90ea858"}},{"code_sha256_prefix":"8295f6cebc270c1d","entry":"corner_first_ndindex","repo":"understanding-search/maze-dataset","repo_kind":"official","path":"maze_dataset/utils.py","file_url":"https://github.com/understanding-search/maze-dataset/blob/HEAD/maze_dataset/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"LGPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"8295f6cebc270c1d"}},{"code_sha256_prefix":"d55564913a849c79","entry":"endpoint_kwargs_to_name","repo":"understanding-search/maze-dataset","repo_kind":"official","path":"maze_dataset/benchmark/config_sweep.py","file_url":"https://github.com/understanding-search/maze-dataset/blob/HEAD/maze_dataset/benchmark/config_sweep.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"LGPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"d55564913a849c79"}},{"code_sha256_prefix":"135b4d661d0338ef","entry":"manhattan_distance","repo":"understanding-search/maze-dataset","repo_kind":"official","path":"maze_dataset/utils.py","file_url":"https://github.com/understanding-search/maze-dataset/blob/HEAD/maze_dataset/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"LGPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"135b4d661d0338ef"}},{"code_sha256_prefix":"b8b60ede0d8c47ce","entry":"tokens_between","repo":"understanding-search/maze-dataset","repo_kind":"official","path":"maze_dataset/token_utils.py","file_url":"https://github.com/understanding-search/maze-dataset/blob/HEAD/maze_dataset/token_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"LGPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"b8b60ede0d8c47ce"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}