{"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/autofill","entry":"autofill","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":5,"n_papers_ran":1,"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":6,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"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":"2203.04559","paper":"/paper/learning-temporal-consistency-for-source-free","title":"Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuyu0010/ATCoN","path":"train_source.py","file_url":"https://github.com/xuyu0010/ATCoN/blob/HEAD/train_source.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e1a572626f226fa","mcp_get_code":{"code_sha256":"9e1a572626f226fa"}},{"arxiv_id":"2107.04941","paper":"/paper/partial-video-domain-adaptation-with-partial","title":"Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network","date":"2021-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuyu0010/PATAN","path":"train_da.py","file_url":"https://github.com/xuyu0010/PATAN/blob/HEAD/train_da.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b352238f535c2545","mcp_get_code":{"code_sha256":"b352238f535c2545"}},{"arxiv_id":"1912.00998","paper":"/paper/a-multigrid-method-for-efficiently-training","title":"A Multigrid Method for Efficiently Training Video Models","date":"2019-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates","path":"train_kinetics.py","file_url":"https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates/blob/HEAD/train_kinetics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be3c993685952552","mcp_get_code":{"code_sha256":"be3c993685952552"}},{"arxiv_id":"1912.00998","paper":"/paper/a-multigrid-method-for-efficiently-training","title":"A Multigrid Method for Efficiently Training Video Models","date":"2019-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates","path":"train_diving48.py","file_url":"https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates/blob/HEAD/train_diving48.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8062365931ddd386","mcp_get_code":{"code_sha256":"8062365931ddd386"}},{"arxiv_id":"1908.00347","paper":"/paper/central-similarity-hashing-via-hadamard","title":"Central Similarity Quantization for Efficient Image and Video Retrieval","date":"2019-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanli2333/Hadamard-Matrix-for-hashing","path":"video/train_hmdb51.py","file_url":"https://github.com/yuanli2333/Hadamard-Matrix-for-hashing/blob/HEAD/video/train_hmdb51.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bab73c3099afdbc5","mcp_get_code":{"code_sha256":"bab73c3099afdbc5"}},{"arxiv_id":"1811.12814","paper":"/paper/graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/GloRe","path":"test/test-single-clip.py","file_url":"https://github.com/facebookresearch/GloRe/blob/HEAD/test/test-single-clip.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53974ebeab9f0b81","mcp_get_code":{"code_sha256":"53974ebeab9f0b81"}}]}