{"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":"/code/manhattan-distance","entry":"manhattan_distance","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":13,"n_papers_ran":7,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":11,"n_samples_ran":7,"n_samples_fingerprinted":5,"n_places":13,"n_places_pointer_only":7,"by_status":{"ran_honours":1,"ran_violates":1,"ran_draft_wrong":1,"ran_fixture":0,"ran":4,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2510.24826","paper":"/paper/arxiv-2510-24826","title":"Augmenting Biological Fitness Prediction Benchmarks with Landscapes Features from GraphFLA","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"COLA-Laboratory/GraphFLA","path":"graphfla/distances.py","file_url":"https://github.com/COLA-Laboratory/GraphFLA/blob/HEAD/graphfla/distances.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14330842b1a0f393","mcp_get_code":{"code_sha256":"14330842b1a0f393"}},{"arxiv_id":"2502.16690","paper":"/paper/from-text-to-space-mapping-abstract-spatial","title":"From Text to Space: Mapping Abstract Spatial Models in LLMs during a Grid-World Navigation Task","date":"2025-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mneuronico/griw-world-spatial-orientation-task","path":"analysis/correctness_analysis.py","file_url":"https://github.com/mneuronico/griw-world-spatial-orientation-task/blob/HEAD/analysis/correctness_analysis.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f105a9126dcc821d","mcp_get_code":{"code_sha256":"f105a9126dcc821d"}},{"arxiv_id":"2412.07186","paper":"/paper/monte-carlo-tree-search-based-space-transfer","title":"Monte Carlo Tree Search based Space Transfer for Black-box Optimization","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lamda-bbo/mcts-transfer","path":"mcts/MCTS.py","file_url":"https://github.com/lamda-bbo/mcts-transfer/blob/HEAD/mcts/MCTS.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e34043a45f43b60","mcp_get_code":{"code_sha256":"3e34043a45f43b60"}},{"arxiv_id":"2410.17389","paper":"/paper/navigating-noisy-feedback-enhancing","title":"Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models","date":"2024-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sy-shi/RLAIF_ScoreDiff","path":"generate_ranking_data.py","file_url":"https://github.com/sy-shi/RLAIF_ScoreDiff/blob/HEAD/generate_ranking_data.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f0e515a163e67f9f","mcp_get_code":{"code_sha256":"f0e515a163e67f9f"}},{"arxiv_id":"2408.05342","paper":"/paper/optimal-treatment-allocation-strategies-for-a","title":"ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments","date":"2024-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"datake/armadesign","path":"ARMAdesign.py","file_url":"https://github.com/datake/armadesign/blob/HEAD/ARMAdesign.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e1717c7d14ab8b9f","mcp_get_code":{"code_sha256":"e1717c7d14ab8b9f"}},{"arxiv_id":"2402.14083","paper":"/paper/beyond-a-better-planning-with-transformers","title":"Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/searchformer","path":"searchformer/sokoban.py","file_url":"https://github.com/facebookresearch/searchformer/blob/HEAD/searchformer/sokoban.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e6efc6bfc4f2862d","mcp_get_code":{"code_sha256":"e6efc6bfc4f2862d"}},{"arxiv_id":"2309.10498","paper":"/paper/a-configurable-library-for-generating-and","title":"A Configurable Library for Generating and Manipulating Maze Datasets","date":"2023-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"understanding-search/maze-dataset","path":"maze_dataset/utils.py","file_url":"https://github.com/understanding-search/maze-dataset/blob/HEAD/maze_dataset/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"LGPL-3.0","inline_ok":false,"code_sha256_prefix":"135b4d661d0338ef","mcp_get_code":{"code_sha256":"135b4d661d0338ef"}},{"arxiv_id":"2306.00323","paper":"/paper/thought-cloning-learning-to-think-while-1","title":"Thought Cloning: Learning to Think while Acting by Imitating Human Thinking","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShengranHu/Thought-Cloning","path":"babyai/bot.py","file_url":"https://github.com/ShengranHu/Thought-Cloning/blob/HEAD/babyai/bot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1ffbae9fb9c6fc2","mcp_get_code":{"code_sha256":"c1ffbae9fb9c6fc2"}},{"arxiv_id":"2109.09740","paper":"/paper/neural-distance-embeddings-for-biological","title":"Neural Distance Embeddings for Biological Sequences","date":"2021-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gcorso/neuroseed","path":"edit_distance/models/pair_encoder.py","file_url":"https://github.com/gcorso/neuroseed/blob/HEAD/edit_distance/models/pair_encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"74beb371d13f031f","mcp_get_code":{"code_sha256":"74beb371d13f031f"}},{"arxiv_id":"2007.12770","paper":"/paper/babyai-1-1","title":"BabyAI 1.1","date":"2020-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mila-iqia/babyai","path":"babyai/bot.py","file_url":"https://github.com/mila-iqia/babyai/blob/HEAD/babyai/bot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"c1ffbae9fb9c6fc2","mcp_get_code":{"code_sha256":"c1ffbae9fb9c6fc2"}},{"arxiv_id":"1910.14599","paper":"/paper/adversarial-nli-a-new-benchmark-for-natural","title":"Adversarial NLI: A New Benchmark for Natural Language Understanding","date":"2019-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tlatkowski/multihead-siamese-nets","path":"layers/similarity.py","file_url":"https://github.com/tlatkowski/multihead-siamese-nets/blob/HEAD/layers/similarity.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"63ceb8de67b2b5d4","mcp_get_code":{"code_sha256":"63ceb8de67b2b5d4"}},{"arxiv_id":"1810.08272","paper":"/paper/babyai-first-steps-towards-grounded-language","title":"BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning","date":"2018-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"c1ffbae9fb9c6fc2","mcp_get_code":{"code_sha256":"c1ffbae9fb9c6fc2"}},{"arxiv_id":"1802.05335","paper":"/paper/multimodal-generative-models-for-scalable","title":"Multimodal Generative Models for Scalable Weakly-Supervised Learning","date":"2018-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gabinsane/multimodal-vae-comparison","path":"multimodal_compare/eval/eval_cdsprites.py","file_url":"https://github.com/gabinsane/multimodal-vae-comparison/blob/HEAD/multimodal_compare/eval/eval_cdsprites.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"069b8e2ab4d7baeb","mcp_get_code":{"code_sha256":"069b8e2ab4d7baeb"}}]}