{"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/pad-collate","entry":"pad_collate","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":11,"n_papers_ran":5,"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":10,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":11,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":2,"ran_fixture":0,"ran":2,"unverified":5},"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":"2406.13123","paper":"/paper/vilco-bench-video-language-continual-learning","title":"ViLCo-Bench: VIdeo Language COntinual learning Benchmark","date":"2024-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cruiseresearchgroup/ViLCo","path":"MQ/ego4d_clip_token_extractor.py","file_url":"https://github.com/cruiseresearchgroup/ViLCo/blob/HEAD/MQ/ego4d_clip_token_extractor.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"294b96328dd17cb8","mcp_get_code":{"code_sha256":"294b96328dd17cb8"}},{"arxiv_id":"2404.04269","paper":"/paper/algorithmic-collective-action-in-recommender","title":"Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joebaumann/recsys-collectiveaction","path":"recommender_system/2023_deezer_transformers/src/data_manager/data_manager.py","file_url":"https://github.com/joebaumann/recsys-collectiveaction/blob/HEAD/recommender_system/2023_deezer_transformers/src/data_manager/data_manager.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"16d1e571688a45d7","mcp_get_code":{"code_sha256":"16d1e571688a45d7"}},{"arxiv_id":"2304.07670","paper":"/paper/explanations-of-black-box-models-based-on-1","title":"Explanations of Black-Box Models based on Directional Feature Interactions","date":"2023-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davinhill/BivariateShapley","path":"BlackBox_Models/IMDB/load_data.py","file_url":"https://github.com/davinhill/BivariateShapley/blob/HEAD/BlackBox_Models/IMDB/load_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bc70e784a3ff6ae","mcp_get_code":{"code_sha256":"5bc70e784a3ff6ae"}},{"arxiv_id":"2301.04944","paper":"/paper/vits-for-sits-vision-transformers-for","title":"ViTs for SITS: Vision Transformers for Satellite Image Time Series","date":"2023-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VSainteuf/pastis-benchmark","path":"code/collate.py","file_url":"https://github.com/VSainteuf/pastis-benchmark/blob/HEAD/code/collate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad13585f01796e87","mcp_get_code":{"code_sha256":"ad13585f01796e87"}},{"arxiv_id":"2108.03861","paper":"/paper/knowledge-graph-augmented-political","title":"KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media","date":"2021-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BunsenFeng/news_stance_detection","path":"SemEval.py","file_url":"https://github.com/BunsenFeng/news_stance_detection/blob/HEAD/SemEval.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"55fb0b8852a5fc21","mcp_get_code":{"code_sha256":"55fb0b8852a5fc21"}},{"arxiv_id":"2101.11103","paper":"/paper/screen2vec-semantic-embedding-of-gui-screens","title":"Screen2Vec: Semantic Embedding of GUI Screens and GUI Components","date":null,"month_inferred_from_arxiv_id":"2021-01","title_source":"archive","repo":"tobyli/screen2vec","path":"main_preloaded.py","file_url":"https://github.com/tobyli/screen2vec/blob/HEAD/main_preloaded.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7fe258c196b21890","mcp_get_code":{"code_sha256":"7fe258c196b21890"}},{"arxiv_id":"2003.11618","paper":"/paper/violin-a-large-scale-dataset-for-video-and","title":"VIOLIN: A Large-Scale Dataset for Video-and-Language Inference","date":"2020-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jimmy646/violin","path":"violin_dataset.py","file_url":"https://github.com/jimmy646/violin/blob/HEAD/violin_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c35ac9d938aa83bc","mcp_get_code":{"code_sha256":"c35ac9d938aa83bc"}},{"arxiv_id":"1809.01696","paper":"/paper/tvqa-localized-compositional-video-question","title":"TVQA: Localized, Compositional Video Question Answering","date":"2018-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jayleicn/TVQA","path":"tvqa_dataset.py","file_url":"https://github.com/jayleicn/TVQA/blob/HEAD/tvqa_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d10981a16ea4b3bc","mcp_get_code":{"code_sha256":"d10981a16ea4b3bc"}},{"arxiv_id":"1603.01417","paper":"/paper/dynamic-memory-networks-for-visual-and","title":"Dynamic Memory Networks for Visual and Textual Question Answering","date":"2016-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dandelin/Dynamic-memory-networks-plus-Pytorch","path":"babi_loader.py","file_url":"https://github.com/dandelin/Dynamic-memory-networks-plus-Pytorch/blob/HEAD/babi_loader.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c6f6a59b1cb4647f","mcp_get_code":{"code_sha256":"c6f6a59b1cb4647f"}},{"arxiv_id":"1512.01882","paper":"/paper/thchs-30-a-free-chinese-speech-corpus","title":"THCHS-30 : A Free Chinese Speech Corpus","date":"2015-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"foamliu/Listen-Attend-and-Spell","path":"data_gen.py","file_url":"https://github.com/foamliu/Listen-Attend-and-Spell/blob/HEAD/data_gen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b22196dd6d0fb21","mcp_get_code":{"code_sha256":"7b22196dd6d0fb21"}},{"arxiv_id":"1508.01211","paper":"/paper/listen-attend-and-spell","title":"Listen, Attend and Spell","date":"2015-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"foamliu/Listen-Attend-Spell-v2","path":"data_gen.py","file_url":"https://github.com/foamliu/Listen-Attend-Spell-v2/blob/HEAD/data_gen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b22196dd6d0fb21","mcp_get_code":{"code_sha256":"7b22196dd6d0fb21"}}]}