{"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/create-loader","entry":"create_loader","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":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":5,"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":0,"ran_fixture":0,"ran":1,"unverified":4},"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":"2501.01428","paper":"/paper/gpt4scene-understand-3d-scenes-from-videos","title":"GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models","date":"2025-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Qi-Zhangyang/GPT4Scene","path":"evaluate/val_dataset.py","file_url":"https://github.com/Qi-Zhangyang/GPT4Scene/blob/HEAD/evaluate/val_dataset.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":"c11a23c56c87bd2d","mcp_get_code":{"code_sha256":"c11a23c56c87bd2d"}},{"arxiv_id":"2305.07922","paper":"/paper/codet5-open-code-large-language-models-for","title":"CodeT5+: Open Code Large Language Models for Code Understanding and Generation","date":"2023-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/codet5","path":"CodeT5+/code_retrieval/data_utils.py","file_url":"https://github.com/salesforce/codet5/blob/HEAD/CodeT5%2B/code_retrieval/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":"634ef255c2b583be","mcp_get_code":{"code_sha256":"634ef255c2b583be"}},{"arxiv_id":"2212.09748","paper":"/paper/scalable-diffusion-models-with-transformers","title":"Scalable Diffusion Models with Transformers","date":"2022-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"milmor/diffusion-transformer","path":"image_datasets.py","file_url":"https://github.com/milmor/diffusion-transformer/blob/HEAD/image_datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"deedff7c289ab393","mcp_get_code":{"code_sha256":"deedff7c289ab393"}},{"arxiv_id":"2108.01684","paper":"/paper/vision-transformer-with-progressive-sampling","title":"Vision Transformer with Progressive Sampling","date":"2021-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuexy/PS-ViT","path":"utils/loader.py","file_url":"https://github.com/yuexy/PS-ViT/blob/HEAD/utils/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1cf1c5e0f9a581ba","mcp_get_code":{"code_sha256":"1cf1c5e0f9a581ba"}},{"arxiv_id":"2103.12731","paper":"/paper/scaling-local-self-attention-for-parameter","title":"Scaling Local Self-Attention for Parameter Efficient Visual Backbones","date":"2021-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kevin-ssy/ViP","path":"utils/loader.py","file_url":"https://github.com/kevin-ssy/ViP/blob/HEAD/utils/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1cf1c5e0f9a581ba","mcp_get_code":{"code_sha256":"1cf1c5e0f9a581ba"}},{"arxiv_id":"2006.10738","paper":"/paper/differentiable-augmentation-for-data","title":"Differentiable Augmentation for Data-Efficient GAN Training","date":"2020-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"milmor/LadaGAN-pytorch","path":"image_datasets.py","file_url":"https://github.com/milmor/LadaGAN-pytorch/blob/HEAD/image_datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c5eecc2f5f4579c","mcp_get_code":{"code_sha256":"0c5eecc2f5f4579c"}}]}