{"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-files","entry":"get_files","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":30,"n_papers_ran":9,"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":31,"n_samples_ran":9,"n_samples_fingerprinted":3,"n_places":32,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":8,"unverified":22},"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":"2608.02751","paper":"/paper/arxiv-2608-02751","title":"Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"ielab/skim-search-agent","path":"agent_search/corpus/code_repo.py","file_url":"https://github.com/ielab/skim-search-agent/blob/HEAD/agent_search/corpus/code_repo.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":"b66a7a13685fe778","mcp_get_code":{"code_sha256":"b66a7a13685fe778"}},{"arxiv_id":"2604.27542","paper":"/paper/arxiv-2604-27542","title":"HATS: An Open data set Integrating Human Perception Applied to the Evaluation of Automatic Speech Recognition Metrics","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"kaldi-asr/kaldi","path":"cmake/gen_cmake_skeleton.py","file_url":"https://github.com/kaldi-asr/kaldi/blob/HEAD/cmake/gen_cmake_skeleton.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"17cdb773fd1a554e","mcp_get_code":{"code_sha256":"17cdb773fd1a554e"}},{"arxiv_id":"2412.00568","paper":"/paper/the-well-a-large-scale-collection-of-diverse","title":"The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning","date":"2024-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lanl/nubhlight","path":"script/util.py","file_url":"https://github.com/lanl/nubhlight/blob/HEAD/script/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7990041b657e98a1","mcp_get_code":{"code_sha256":"7990041b657e98a1"}},{"arxiv_id":"2407.17459","paper":"/paper/hidden-or-inferred-fair-learning-to-rank-with","title":"Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics","date":"2024-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sewen007/hoiltr","path":"HOIRank/data_analysis/combine.py","file_url":"https://github.com/sewen007/hoiltr/blob/HEAD/HOIRank/data_analysis/combine.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"434b485a4d4b996f","mcp_get_code":{"code_sha256":"434b485a4d4b996f"}},{"arxiv_id":"2406.13356","paper":"/paper/jogging-the-memory-of-unlearned-model-through","title":"Jogging the Memory of Unlearned LLMs Through Targeted Relearning Attacks","date":"2024-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"s-huu/jog_llm_memory","path":"wmdp/src/generate_answers.py","file_url":"https://github.com/s-huu/jog_llm_memory/blob/HEAD/wmdp/src/generate_answers.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":"2c9cb9e02d070bfd","mcp_get_code":{"code_sha256":"2c9cb9e02d070bfd"}},{"arxiv_id":"2405.10436","paper":"/paper/positional-encoding-is-not-the-same-as","title":"Positional encoding is not the same as context: A study on positional encoding for sequential recommendation","date":"2024-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researcher1741/position_encoding_srs","path":"Results/results_analysis.py","file_url":"https://github.com/researcher1741/position_encoding_srs/blob/HEAD/Results/results_analysis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea5d9e3461446c08","mcp_get_code":{"code_sha256":"ea5d9e3461446c08"}},{"arxiv_id":"2403.05004","paper":"/paper/can-t-remember-details-in-long-documents-you","title":"Can't Remember Details in Long Documents? You Need Some R&R","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"casetext/r-and-r","path":"tables.py","file_url":"https://github.com/casetext/r-and-r/blob/HEAD/tables.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2be958fb9ab8b142","mcp_get_code":{"code_sha256":"2be958fb9ab8b142"}},{"arxiv_id":"2402.18243","paper":"/paper/learning-or-self-aligning-rethinking","title":"Learning or Self-aligning? Rethinking Instruction Fine-tuning","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"renmengjie7/self-aligning","path":"XieZhiBenchmark/utils.py","file_url":"https://github.com/renmengjie7/self-aligning/blob/HEAD/XieZhiBenchmark/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a9df6914f56c9118","mcp_get_code":{"code_sha256":"a9df6914f56c9118"}},{"arxiv_id":"2310.15171","paper":"/paper/robodepth-robust-out-of-distribution-depth-1","title":"RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ldkong1205/RoboDepth","path":"corruptions/pixel_anaylyze.py","file_url":"https://github.com/ldkong1205/RoboDepth/blob/HEAD/corruptions/pixel_anaylyze.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4586b04127631227","mcp_get_code":{"code_sha256":"4586b04127631227"}},{"arxiv_id":"2303.11591","paper":"/paper/svcnet-scribble-based-video-colorization","title":"SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhaoyuzhi/svcnet","path":"GCS/generate_color_scribbles_ImageNet.py","file_url":"https://github.com/zhaoyuzhi/svcnet/blob/HEAD/GCS/generate_color_scribbles_ImageNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"889fe30d9fa3b00d","mcp_get_code":{"code_sha256":"889fe30d9fa3b00d"}},{"arxiv_id":"2302.07253","paper":"/paper/energy-transformer","title":"Energy Transformer","date":"2023-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lemon-cmd/energy-transformer-torch","path":"image_et/misc.py","file_url":"https://github.com/Lemon-cmd/energy-transformer-torch/blob/HEAD/image_et/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c3b6f391746cf8b","mcp_get_code":{"code_sha256":"0c3b6f391746cf8b"}},{"arxiv_id":"2211.11972","paper":"/paper/imitation-clean-imitation-learning","title":"imitation: Clean Imitation Learning Implementations","date":"2022-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HumanCompatibleAI/airl","path":"ci/clean_notebooks.py","file_url":"https://github.com/HumanCompatibleAI/airl/blob/HEAD/ci/clean_notebooks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"824f3848a8c4669a","mcp_get_code":{"code_sha256":"824f3848a8c4669a"}},{"arxiv_id":"2210.14250","paper":"/paper/exploring-document-level-literary-machine","title":"Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature","date":"2022-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"katherinethai/par3","path":"par3_align/align_books.py","file_url":"https://github.com/katherinethai/par3/blob/HEAD/par3_align/align_books.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b17fe71e689c1799","mcp_get_code":{"code_sha256":"b17fe71e689c1799"}},{"arxiv_id":"2209.12332","paper":"/paper/on-the-optimal-linear-contraction-order-for","title":"On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond","date":null,"month_inferred_from_arxiv_id":"2022-09","title_source":"archive","repo":"stoianmihail/netzwerk","path":"src/util.py","file_url":"https://github.com/stoianmihail/netzwerk/blob/HEAD/src/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"655afd2b175f5391","mcp_get_code":{"code_sha256":"655afd2b175f5391"}},{"arxiv_id":"2203.07996","paper":"/paper/leveraging-uni-modal-self-supervised-learning-1","title":"Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition","date":"2022-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lumia-group/leveraging-self-supervised-learning-for-avsr","path":"saveh5.py","file_url":"https://github.com/lumia-group/leveraging-self-supervised-learning-for-avsr/blob/HEAD/saveh5.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb52c4993e0435d8","mcp_get_code":{"code_sha256":"cb52c4993e0435d8"}},{"arxiv_id":"2110.03370","paper":"/paper/wenetspeech-a-10000-hours-multi-domain","title":"WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition","date":"2021-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aizhiqi-work/MM-KWS","path":"mm-kws/dataloaders/wenetphrase_train.py","file_url":"https://github.com/aizhiqi-work/MM-KWS/blob/HEAD/mm-kws/dataloaders/wenetphrase_train.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":"1d9a26a31a50681a","mcp_get_code":{"code_sha256":"1d9a26a31a50681a"}},{"arxiv_id":"2110.03370","paper":"/paper/wenetspeech-a-10000-hours-multi-domain","title":"WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition","date":"2021-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aizhiqi-work/MM-KWS","path":"mm-kws/dataloaders/SPC_N0_ALL.py","file_url":"https://github.com/aizhiqi-work/MM-KWS/blob/HEAD/mm-kws/dataloaders/SPC_N0_ALL.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":"7f08f754c8e83bee","mcp_get_code":{"code_sha256":"7f08f754c8e83bee"}},{"arxiv_id":"2109.04513","paper":"/paper/filling-the-gaps-in-ancient-akkadian-texts-a","title":"Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach","date":"2021-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SLAB-NLP/Akk","path":"RAW/work_on_files.py","file_url":"https://github.com/SLAB-NLP/Akk/blob/HEAD/RAW/work_on_files.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f75983bc1a014c4","mcp_get_code":{"code_sha256":"4f75983bc1a014c4"}},{"arxiv_id":"2106.14131","paper":"/paper/symbolicgpt-a-generative-transformer-model","title":"SymbolicGPT: A Generative Transformer Model for Symbolic Regression","date":"2021-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mojivalipour/symbolicgpt","path":"baselines/baseline_utils.py","file_url":"https://github.com/mojivalipour/symbolicgpt/blob/HEAD/baselines/baseline_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e2f6a5f5a46b5d9","mcp_get_code":{"code_sha256":"7e2f6a5f5a46b5d9"}},{"arxiv_id":"2104.12357","paper":"/paper/vcgan-video-colorization-with-hybrid","title":"VCGAN: Video Colorization with Hybrid Generative Adversarial Network","date":"2021-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhaoyuzhi/VCGAN","path":"resnet50_in_pre_training/utils.py","file_url":"https://github.com/zhaoyuzhi/VCGAN/blob/HEAD/resnet50_in_pre_training/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"889fe30d9fa3b00d","mcp_get_code":{"code_sha256":"889fe30d9fa3b00d"}},{"arxiv_id":"2010.10727","paper":"/paper/learning-disentangled-phone-and-speaker","title":"Learning Disentangled Phone and Speaker Representations in a Semi-Supervised VQ-VAE Paradigm","date":"2020-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rhoposit/icassp2021","path":"preprocess_vqvae.py","file_url":"https://github.com/rhoposit/icassp2021/blob/HEAD/preprocess_vqvae.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":"627231d93d81b72c","mcp_get_code":{"code_sha256":"627231d93d81b72c"}},{"arxiv_id":"2006.14699","paper":"/paper/learning-data-augmentation-with-online","title":"Learning Data Augmentation with Online Bilevel Optimization for Image Classification","date":"2020-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ElementAI/bilevel_augment","path":"src/datasets/bach.py","file_url":"https://github.com/ElementAI/bilevel_augment/blob/HEAD/src/datasets/bach.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":"4e019bff79f375f3","mcp_get_code":{"code_sha256":"4e019bff79f375f3"}},{"arxiv_id":"2003.13479","paper":"/paper/2003-13479","title":"RPM-Net: Robust Point Matching using Learned Features","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinits5/masknet","path":"3dmatch/make_video.py","file_url":"https://github.com/vinits5/masknet/blob/HEAD/3dmatch/make_video.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a45813b942a85200","mcp_get_code":{"code_sha256":"a45813b942a85200"}},{"arxiv_id":"1912.08193","paper":"/paper/pointrend-image-segmentation-as-rendering","title":"PointRend: Image Segmentation as Rendering","date":"2019-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CuberrChen/PointRend-Paddle","path":"utils/create_dataset_list.py","file_url":"https://github.com/CuberrChen/PointRend-Paddle/blob/HEAD/utils/create_dataset_list.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":"10c053391f452886","mcp_get_code":{"code_sha256":"10c053391f452886"}},{"arxiv_id":"1909.09347","paper":"/paper/mimii-dataset-sound-dataset-for","title":"MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection","date":"2019-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Huanzhuo/IA-Net","path":"train_ids.py","file_url":"https://github.com/Huanzhuo/IA-Net/blob/HEAD/train_ids.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90c18ada9ef83c66","mcp_get_code":{"code_sha256":"90c18ada9ef83c66"}},{"arxiv_id":"1909.09347","paper":"/paper/mimii-dataset-sound-dataset-for","title":"MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection","date":"2019-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Huanzhuo/IA-Net","path":"validate.py","file_url":"https://github.com/Huanzhuo/IA-Net/blob/HEAD/validate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80124c0027623fda","mcp_get_code":{"code_sha256":"80124c0027623fda"}},{"arxiv_id":"1905.10520","paper":"/paper/6-dof-graspnet-variational-grasp-generation","title":"6-DOF GraspNet: Variational Grasp Generation for Object Manipulation","date":"2019-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/6dof-graspnet","path":"utils.py","file_url":"https://github.com/NVlabs/6dof-graspnet/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"f28c51963b87efd6","mcp_get_code":{"code_sha256":"f28c51963b87efd6"}},{"arxiv_id":"1905.09263","paper":"/paper/fastspeech-fast-robust-and-controllable-text","title":"FastSpeech: Fast, Robust and Controllable Text to Speech","date":"2019-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"as-ideas/deepforcedaligner","path":"dfa/utils.py","file_url":"https://github.com/as-ideas/deepforcedaligner/blob/HEAD/dfa/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ff429bfc952ac29","mcp_get_code":{"code_sha256":"8ff429bfc952ac29"}},{"arxiv_id":"1904.01941","paper":"/paper/character-region-awareness-for-text-detection","title":"Character Region Awareness for Text Detection","date":"2019-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/CRAFT-pytorch","path":"file_utils.py","file_url":"https://github.com/clovaai/CRAFT-pytorch/blob/HEAD/file_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5eb8f5306638605a","mcp_get_code":{"code_sha256":"5eb8f5306638605a"}},{"arxiv_id":"1901.08164","paper":"/paper/decoupled-greedy-learning-of-cnns","title":"Decoupled Greedy Learning of CNNs","date":"2019-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"batuozt/gleam","path":"datasets/format_fastmri.py","file_url":"https://github.com/batuozt/gleam/blob/HEAD/datasets/format_fastmri.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f8eba979963c16e9","mcp_get_code":{"code_sha256":"f8eba979963c16e9"}},{"arxiv_id":"1409.1556","paper":"/paper/very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","date":"2014-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Little-Frog-233/imagecluster","path":"imagecluster/common.py","file_url":"https://github.com/Little-Frog-233/imagecluster/blob/HEAD/imagecluster/common.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":"29ef3a86fe537728","mcp_get_code":{"code_sha256":"29ef3a86fe537728"}},{"arxiv_id":"05117","paper":null,"title":"arXiv:05117","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MaticFuc/ECCV_TransFusion","path":"dataset/Dataset.py","file_url":"https://github.com/MaticFuc/ECCV_TransFusion/blob/HEAD/dataset/Dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8989f4ebbe3c29ca","mcp_get_code":{"code_sha256":"8989f4ebbe3c29ca"}}]}