{"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/shuffle-rows","entry":"shuffle_rows","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":6,"n_papers_ran":6,"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":3,"n_samples_ran":3,"n_samples_fingerprinted":3,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":1,"unverified":0},"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":"2409.17958","paper":"/paper/the-hard-positive-truth-about-vision-language","title":"The Hard Positive Truth about Vision-Language Compositionality","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amitakamath/hard_positives","path":"dataset_zoo/perturbations.py","file_url":"https://github.com/amitakamath/hard_positives/blob/HEAD/dataset_zoo/perturbations.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29d83052b338a1f7","mcp_get_code":{"code_sha256":"29d83052b338a1f7"}},{"arxiv_id":"2310.19785","paper":"/paper/what-s-up-with-vision-language-models","title":"What's \"up\" with vision-language models? Investigating their struggle with spatial reasoning","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amitakamath/whatsup_vlms","path":"dataset_zoo/perturbations.py","file_url":"https://github.com/amitakamath/whatsup_vlms/blob/HEAD/dataset_zoo/perturbations.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29d83052b338a1f7","mcp_get_code":{"code_sha256":"29d83052b338a1f7"}},{"arxiv_id":"2210.01936","paper":"/paper/when-and-why-vision-language-models-behave","title":"When and why vision-language models behave like bags-of-words, and what to do about it?","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mertyg/vision-language-models-are-bows","path":"dataset_zoo/perturbations.py","file_url":"https://github.com/mertyg/vision-language-models-are-bows/blob/HEAD/dataset_zoo/perturbations.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29d83052b338a1f7","mcp_get_code":{"code_sha256":"29d83052b338a1f7"}},{"arxiv_id":"2010.09546","paper":"/paper/model-based-policy-optimization-with","title":"Model-based Policy Optimization with Unsupervised Model Adaptation","date":"2020-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RockySJ/ampo","path":"models/utils.py","file_url":"https://github.com/RockySJ/ampo/blob/HEAD/models/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14a3c589a22e7e32","mcp_get_code":{"code_sha256":"14a3c589a22e7e32"}},{"arxiv_id":"1805.12114","paper":"/paper/deep-reinforcement-learning-in-a-handful-of","title":"Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models","date":"2018-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"quanvuong/handful-of-trials-pytorch","path":"MPC.py","file_url":"https://github.com/quanvuong/handful-of-trials-pytorch/blob/HEAD/MPC.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"14a3c589a22e7e32","mcp_get_code":{"code_sha256":"14a3c589a22e7e32"}},{"arxiv_id":"1801.09321","paper":"/paper/document-image-classification-with-intra","title":"Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks","date":"2018-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hiarindam/document-image-classification-TL-SG","path":"Weight_conversion_th_to_tf_Keras2.py","file_url":"https://github.com/hiarindam/document-image-classification-TL-SG/blob/HEAD/Weight_conversion_th_to_tf_Keras2.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f18761523c1165e3","mcp_get_code":{"code_sha256":"f18761523c1165e3"}}]}