{"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-dataloaders","entry":"create_dataloaders","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":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":12,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":15,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":11},"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":"2608.15073","paper":"/paper/arxiv-2608-15073","title":"BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"XZT008/Standard-GP-is-all-you-need-for-HDBO","path":"benchmark/nn_pruning_utils.py","file_url":"https://github.com/XZT008/Standard-GP-is-all-you-need-for-HDBO/blob/HEAD/benchmark/nn_pruning_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c4c634f6672cc5d1","mcp_get_code":{"code_sha256":"c4c634f6672cc5d1"}},{"arxiv_id":"2607.03626","paper":"/paper/arxiv-2607-03626","title":"Reflected Schrödinger Bridge Matching","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"viktor765/rsbm","path":"src/data_utils.py","file_url":"https://github.com/viktor765/rsbm/blob/HEAD/src/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"eaec6e72f41c236b","mcp_get_code":{"code_sha256":"eaec6e72f41c236b"}},{"arxiv_id":"2606.10137","paper":"/paper/arxiv-2606-10137","title":"Ambiguous Strategic Classification","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"BML-Technion/AmbSC","path":"dataloader.py","file_url":"https://github.com/BML-Technion/AmbSC/blob/HEAD/dataloader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc08903854898857","mcp_get_code":{"code_sha256":"bc08903854898857"}},{"arxiv_id":"2601.18555","paper":"/paper/arxiv-2601-18555","title":"A CROSS-MODALITY VALIDATION OF MRI VERSUS X-RAY","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Malga-Vision/Landmarks-Hip-Conditions","path":"aligned_datasets.py","file_url":"https://github.com/Malga-Vision/Landmarks-Hip-Conditions/blob/HEAD/aligned_datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"50fed4d45b1ff37a","mcp_get_code":{"code_sha256":"50fed4d45b1ff37a"}},{"arxiv_id":"2505.10518","paper":"/paper/multi-token-prediction-needs-registers","title":"Multi-Token Prediction Needs Registers","date":"2025-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nasosger/mutor","path":"language_modeling/src/dataloaders_mutor.py","file_url":"https://github.com/nasosger/mutor/blob/HEAD/language_modeling/src/dataloaders_mutor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09a73cf0b65ef0df","mcp_get_code":{"code_sha256":"09a73cf0b65ef0df"}},{"arxiv_id":"2503.04429","paper":"/paper/activation-space-interventions-can-be","title":"Activation Space Interventions Can Be Transferred Between Large Language Models","date":"2025-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"withmartian/closing-backdoors-via-representation-transfer","path":"representation_transfer/local_datasets.py","file_url":"https://github.com/withmartian/closing-backdoors-via-representation-transfer/blob/HEAD/representation_transfer/local_datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4efe03c7c622adce","mcp_get_code":{"code_sha256":"4efe03c7c622adce"}},{"arxiv_id":"2503.04429","paper":"/paper/activation-space-interventions-can-be","title":"Activation Space Interventions Can Be Transferred Between Large Language Models","date":"2025-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"withmartian/closing-backdoors-via-representation-transfer","path":"representation_transfer/on_disk/load_dataset.py","file_url":"https://github.com/withmartian/closing-backdoors-via-representation-transfer/blob/HEAD/representation_transfer/on_disk/load_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6dced7b3cce13537","mcp_get_code":{"code_sha256":"6dced7b3cce13537"}},{"arxiv_id":"2402.15504","paper":"/paper/gen4gen-generative-data-pipeline-for","title":"Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition","date":"2024-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"louisYen/Gen4Gen","path":"gen4gen/saliency_models/DIS/data_loader_cache.py","file_url":"https://github.com/louisYen/Gen4Gen/blob/HEAD/gen4gen/saliency_models/DIS/data_loader_cache.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5cd2aa75a7b0c6ec","mcp_get_code":{"code_sha256":"5cd2aa75a7b0c6ec"}},{"arxiv_id":"2401.13051","paper":"/paper/pa-sam-prompt-adapter-sam-for-high-quality","title":"PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xzz2/pa-sam","path":"utils/dataloader.py","file_url":"https://github.com/xzz2/pa-sam/blob/HEAD/utils/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7b785ed7aad4a361","mcp_get_code":{"code_sha256":"7b785ed7aad4a361"}},{"arxiv_id":"2312.02010","paper":"/paper/towards-learning-a-generalist-model-for","title":"Towards Learning a Generalist Model for Embodied Navigation","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lavi-lab/navillm","path":"tasks/loaders.py","file_url":"https://github.com/lavi-lab/navillm/blob/HEAD/tasks/loaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a2aa37d868fb374","mcp_get_code":{"code_sha256":"1a2aa37d868fb374"}},{"arxiv_id":"2311.14671","paper":"/paper/segic-unleashing-the-emergent-correspondence","title":"SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation","date":"2023-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"menglcool/segic","path":"utils/dataloader.py","file_url":"https://github.com/menglcool/segic/blob/HEAD/utils/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b785ed7aad4a361","mcp_get_code":{"code_sha256":"7b785ed7aad4a361"}},{"arxiv_id":"1908.07442","paper":"/paper/tabnet-attentive-interpretable-tabular","title":"TabNet: Attentive Interpretable Tabular Learning","date":"2019-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dreamquark-ai/tabnet","path":"pytorch_tabnet/utils.py","file_url":"https://github.com/dreamquark-ai/tabnet/blob/HEAD/pytorch_tabnet/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e26b17641debb3ed","mcp_get_code":{"code_sha256":"e26b17641debb3ed"}},{"arxiv_id":"aaai_20309","paper":null,"title":"arXiv:aaai_20309","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"WhatAShot/DANet","path":"lib/utils.py","file_url":"https://github.com/WhatAShot/DANet/blob/HEAD/lib/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e3c907c95401d5b5","mcp_get_code":{"code_sha256":"e3c907c95401d5b5"}},{"arxiv_id":"Zheng_Towards_Learning_a_Generalist_Model_for_Embodied_Navigation_CVPR_2024_paper","paper":null,"title":"arXiv:Zheng_Towards_Learning_a_Generalist_Model_for_Embodied_Navigation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"LaVi-Lab/NaviLLM","path":"tasks/loaders.py","file_url":"https://github.com/LaVi-Lab/NaviLLM/blob/HEAD/tasks/loaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a2aa37d868fb374","mcp_get_code":{"code_sha256":"1a2aa37d868fb374"}},{"arxiv_id":"05519","paper":null,"title":"arXiv:05519","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MengLcool/SEGIC","path":"utils/dataloader.py","file_url":"https://github.com/MengLcool/SEGIC/blob/HEAD/utils/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b785ed7aad4a361","mcp_get_code":{"code_sha256":"7b785ed7aad4a361"}}]}