{"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/apply-to-sample","entry":"apply_to_sample","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":4,"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":2,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":1},"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":"2409.03643","paper":"/paper/cdm-a-reliable-metric-for-fair-and-accurate","title":"Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching","date":"2024-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opendatalab/unimernet","path":"unimernet/datasets/data_utils.py","file_url":"https://github.com/opendatalab/unimernet/blob/HEAD/unimernet/datasets/data_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"992489a59b9e08d0","mcp_get_code":{"code_sha256":"992489a59b9e08d0"}},{"arxiv_id":"2405.17111","paper":"/paper/diffusion-bridge-autoencoders-for","title":"Diffusion Bridge AutoEncoders for Unsupervised Representation Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ckczzj/PDAE","path":"trainer/train_representation_learning.py","file_url":"https://github.com/ckczzj/PDAE/blob/HEAD/trainer/train_representation_learning.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e6a9932fddabc671","mcp_get_code":{"code_sha256":"e6a9932fddabc671"}},{"arxiv_id":"2402.07197","paper":"/paper/graphtranslator-aligning-graph-model-to-large","title":"GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks","date":"2024-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba/graphtranslator","path":"Translator/datasets/data_utils.py","file_url":"https://github.com/alibaba/graphtranslator/blob/HEAD/Translator/datasets/data_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"992489a59b9e08d0","mcp_get_code":{"code_sha256":"992489a59b9e08d0"}},{"arxiv_id":"2006.16336","paper":"/paper/learning-sparse-prototypes-for-text","title":"Learning Sparse Prototypes for Text Generation","date":"2020-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jxhe/sparse-text-prototype","path":"sparse_prototype/sp_criterion.py","file_url":"https://github.com/jxhe/sparse-text-prototype/blob/HEAD/sparse_prototype/sp_criterion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"992489a59b9e08d0","mcp_get_code":{"code_sha256":"992489a59b9e08d0"}},{"arxiv_id":"2005.00547","paper":"/paper/goemotions-a-dataset-of-fine-grained-emotions","title":"GoEmotions: A Dataset of Fine-Grained Emotions","date":"2020-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hlt-maia/emotion-transformer","path":"utils.py","file_url":"https://github.com/hlt-maia/emotion-transformer/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"da253f3773e31f0e","mcp_get_code":{"code_sha256":"da253f3773e31f0e"}}]}