{"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/get-file-paths","entry":"get_file_paths","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":14,"n_papers_ran":3,"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":9,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":14,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":7},"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":"2601.11779","paper":"/paper/arxiv-2601-11779","title":"Cross-Domain Object Detection Using Unsupervised Image Translation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"junyanz/pytorch-CycleGAN-and-pix2pix","path":"datasets/make_dataset_aligned.py","file_url":"https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/HEAD/datasets/make_dataset_aligned.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"cefa735e77f924d4","mcp_get_code":{"code_sha256":"cefa735e77f924d4"}},{"arxiv_id":"2503.00522","paper":"/paper/periodic-materials-generation-using-text","title":"Periodic Materials Generation using Text-Guided Joint Diffusion Model","date":"2025-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdmsit/TGDMat","path":"csp_task/compute_metrics.py","file_url":"https://github.com/kdmsit/TGDMat/blob/HEAD/csp_task/compute_metrics.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d63ea1111adc6f6","mcp_get_code":{"code_sha256":"8d63ea1111adc6f6"}},{"arxiv_id":"2502.06485","paper":"/paper/wyckoffdiff-a-generative-diffusion-model-for","title":"WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry","date":"2025-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sibasmarak/symmcd","path":"scripts/compute_metrics.py","file_url":"https://github.com/sibasmarak/symmcd/blob/HEAD/scripts/compute_metrics.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d63ea1111adc6f6","mcp_get_code":{"code_sha256":"8d63ea1111adc6f6"}},{"arxiv_id":"2310.15147","paper":"/paper/s3eval-a-synthetic-scalable-systematic","title":"S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lfy79001/s3eval","path":"convert_csv.py","file_url":"https://github.com/lfy79001/s3eval/blob/HEAD/convert_csv.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18dbbf0eed3bcc2f","mcp_get_code":{"code_sha256":"18dbbf0eed3bcc2f"}},{"arxiv_id":"2308.02165","paper":"/paper/diffusion-probabilistic-models-enhance","title":"Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling","date":"2023-08-04","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":"9a6539ae57f02d7a","mcp_get_code":{"code_sha256":"9a6539ae57f02d7a"}},{"arxiv_id":"2306.09305","paper":"/paper/fast-training-of-diffusion-models-with-masked","title":"Fast Training of Diffusion Models with Masked Transformers","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anima-lab/maskdit","path":"train_wds.py","file_url":"https://github.com/anima-lab/maskdit/blob/HEAD/train_wds.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f41162e24a6af878","mcp_get_code":{"code_sha256":"f41162e24a6af878"}},{"arxiv_id":"2305.15086","paper":"/paper/unpaired-image-to-image-translation-via-1","title":"Unpaired Image-to-Image Translation via Neural Schrödinger Bridge","date":"2023-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cyclomon/UNSB","path":"datasets/detect_cat_face.py","file_url":"https://github.com/cyclomon/UNSB/blob/HEAD/datasets/detect_cat_face.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cefa735e77f924d4","mcp_get_code":{"code_sha256":"cefa735e77f924d4"}},{"arxiv_id":"2201.06009","paper":"/paper/memory-assisted-prompt-editing-to-improve-gpt","title":"Memory-assisted prompt editing to improve GPT-3 after deployment","date":"2022-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"madaan/memprompt","path":"src/utils/log_utils.py","file_url":"https://github.com/madaan/memprompt/blob/HEAD/src/utils/log_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"27a47de20a9e25e4","mcp_get_code":{"code_sha256":"27a47de20a9e25e4"}},{"arxiv_id":"2110.06197","paper":"/paper/crystal-diffusion-variational-autoencoder-for-1","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","date":"2021-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ixsluo/cond-cdvae","path":"scripts/compute_metrics.py","file_url":"https://github.com/ixsluo/cond-cdvae/blob/HEAD/scripts/compute_metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a6539ae57f02d7a","mcp_get_code":{"code_sha256":"9a6539ae57f02d7a"}},{"arxiv_id":"2106.14574","paper":"/paper/quantifying-social-biases-in-nlp-a","title":"Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics","date":"2021-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/generalized-fairness-metrics","path":"src/utils.py","file_url":"https://github.com/amazon-science/generalized-fairness-metrics/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a1a2002ce03c7d63","mcp_get_code":{"code_sha256":"a1a2002ce03c7d63"}},{"arxiv_id":"2103.11521","paper":"/paper/conditional-frechet-inception-distance","title":"Conditional Frechet Inception Distance","date":"2021-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Michael-Soloveitchik/CFID","path":"Datasets/make_dataset_aligned.py","file_url":"https://github.com/Michael-Soloveitchik/CFID/blob/HEAD/Datasets/make_dataset_aligned.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"cefa735e77f924d4","mcp_get_code":{"code_sha256":"cefa735e77f924d4"}},{"arxiv_id":"1807.02701","paper":"/paper/deepsource-point-source-detection-using-deep","title":"DeepSource: Point Source Detection using Deep Learning","date":"2018-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vafaei-ar/deepsource","path":"deepsource/cross_match.py","file_url":"https://github.com/vafaei-ar/deepsource/blob/HEAD/deepsource/cross_match.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":"d695e3329614fe1e","mcp_get_code":{"code_sha256":"d695e3329614fe1e"}},{"arxiv_id":"1611.07004","paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"acecreamu/angularGAN","path":"angulargan/datasets/make_dataset_aligned.py","file_url":"https://github.com/acecreamu/angularGAN/blob/HEAD/angulargan/datasets/make_dataset_aligned.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cefa735e77f924d4","mcp_get_code":{"code_sha256":"cefa735e77f924d4"}},{"arxiv_id":"2025.naacl-long.65","paper":null,"title":"arXiv:2025.naacl-long.65","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Zehong-Wang/TANS","path":"TANS/preprocess/query_llms.py","file_url":"https://github.com/Zehong-Wang/TANS/blob/HEAD/TANS/preprocess/query_llms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"195e375a5a4e05e3","mcp_get_code":{"code_sha256":"195e375a5a4e05e3"}}]}