{"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/pad-tensor","entry":"pad_tensor","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":27,"n_papers_ran":10,"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":23,"n_samples_ran":7,"n_samples_fingerprinted":3,"n_places":27,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":4,"ran":2,"unverified":16},"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":"2608.30682","paper":"/paper/arxiv-2608-30682","title":"Learning Materials Properties from Scarce Labels and Unlabeled Crystals","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"littlepeachs/SemiMat","path":"ocpmodels/common/gp_utils.py","file_url":"https://github.com/littlepeachs/SemiMat/blob/HEAD/ocpmodels/common/gp_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30bc93160486bf59","mcp_get_code":{"code_sha256":"30bc93160486bf59"}},{"arxiv_id":"2607.01731","paper":"/paper/arxiv-2607-01731","title":"Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"StudioYG/DRU","path":"models/networks.py","file_url":"https://github.com/StudioYG/DRU/blob/HEAD/models/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9457774086c9e2b5","mcp_get_code":{"code_sha256":"9457774086c9e2b5"}},{"arxiv_id":"2604.11214","paper":"/paper/arxiv-2604-11214","title":"HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"yangfanww/hiedit","path":"editor/hieditmm.py","file_url":"https://github.com/yangfanww/hiedit/blob/HEAD/editor/hieditmm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a8372336a87f5776","mcp_get_code":{"code_sha256":"a8372336a87f5776"}},{"arxiv_id":"2507.20163","paper":null,"title":"arXiv:2507.20163","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"Zeyu1226-mt/LLM-IAVC","path":"stage_two/LLM_VC_dataset.py","file_url":"https://github.com/Zeyu1226-mt/LLM-IAVC/blob/HEAD/stage_two/LLM_VC_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"107f2048a0518c96","mcp_get_code":{"code_sha256":"107f2048a0518c96"}},{"arxiv_id":"2505.14679","paper":"/paper/ultraedit-training-subject-and-memory-free","title":"UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaojiegu/ultraedit","path":"editor/ultraedit.py","file_url":"https://github.com/xiaojiegu/ultraedit/blob/HEAD/editor/ultraedit.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a8372336a87f5776","mcp_get_code":{"code_sha256":"a8372336a87f5776"}},{"arxiv_id":"2503.14043","paper":"/paper/learning-on-llm-output-signatures-for-gray","title":"Learning on LLM Output Signatures for gray-box LLM Behavior Analysis","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"barsguy/llm-output-signatures-network","path":"utils/dataset_preprocess.py","file_url":"https://github.com/barsguy/llm-output-signatures-network/blob/HEAD/utils/dataset_preprocess.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3cc2afd1c95778eb","mcp_get_code":{"code_sha256":"3cc2afd1c95778eb"}},{"arxiv_id":"2411.05007","paper":"/paper/svdqunat-absorbing-outliers-by-low-rank","title":"SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models","date":"2024-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mit-han-lab/nunchaku","path":"nunchaku/utils.py","file_url":"https://github.com/mit-han-lab/nunchaku/blob/HEAD/nunchaku/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":"15c37ce4033a4043","mcp_get_code":{"code_sha256":"15c37ce4033a4043"}},{"arxiv_id":"2402.14154","paper":"/paper/mm-soc-benchmarking-multimodal-large-language","title":"MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"claws-lab/mmsoc","path":"mmsoc/utils/data_utils.py","file_url":"https://github.com/claws-lab/mmsoc/blob/HEAD/mmsoc/utils/data_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1f67e155cfe5cae9","mcp_get_code":{"code_sha256":"1f67e155cfe5cae9"}},{"arxiv_id":"2310.13336","paper":"/paper/flair-a-country-scale-land-cover-semantic","title":"FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery","date":"2023-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ignf/flair-2-ai-challenge","path":"src/utils_dataset.py","file_url":"https://github.com/ignf/flair-2-ai-challenge/blob/HEAD/src/utils_dataset.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":"314a47dca812f4f4","mcp_get_code":{"code_sha256":"314a47dca812f4f4"}},{"arxiv_id":"2305.13277","paper":"/paper/u-tilise-a-sequence-to-sequence-model-for","title":"U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prs-eth/u-tilise","path":"lib/data_utils.py","file_url":"https://github.com/prs-eth/u-tilise/blob/HEAD/lib/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"50951d269094499f","mcp_get_code":{"code_sha256":"50951d269094499f"}},{"arxiv_id":"2305.12191","paper":"/paper/pointwise-mutual-information-based-metric-and","title":"Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs","date":"2023-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ynandwan/pmi-faith","path":"faithful-decode/models/modeling_nce.py","file_url":"https://github.com/ynandwan/pmi-faith/blob/HEAD/faithful-decode/models/modeling_nce.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":"2dec641c76d04d78","mcp_get_code":{"code_sha256":"2dec641c76d04d78"}},{"arxiv_id":"2305.10028","paper":"/paper/pyramid-diffusion-models-for-low-light-image","title":"Pyramid Diffusion Models For Low-light Image Enhancement","date":"2023-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"limuloo/PyDIff","path":"PyDiff/pydiff/archs/ddpm_arch.py","file_url":"https://github.com/limuloo/PyDIff/blob/HEAD/PyDiff/pydiff/archs/ddpm_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"65a5c76a5c2ce5a0","mcp_get_code":{"code_sha256":"65a5c76a5c2ce5a0"}},{"arxiv_id":"2304.09116","paper":"/paper/naturalspeech-2-latent-diffusion-models-are","title":"NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers","date":"2023-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/naturalspeech2-pytorch","path":"naturalspeech2_pytorch/aligner.py","file_url":"https://github.com/lucidrains/naturalspeech2-pytorch/blob/HEAD/naturalspeech2_pytorch/aligner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6289707dcc4058f","mcp_get_code":{"code_sha256":"c6289707dcc4058f"}},{"arxiv_id":"2301.04944","paper":"/paper/vits-for-sits-vision-transformers-for","title":"ViTs for SITS: Vision Transformers for Satellite Image Time Series","date":"2023-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VSainteuf/pastis-benchmark","path":"code/collate.py","file_url":"https://github.com/VSainteuf/pastis-benchmark/blob/HEAD/code/collate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"314a47dca812f4f4","mcp_get_code":{"code_sha256":"314a47dca812f4f4"}},{"arxiv_id":"2210.03501","paper":"/paper/towards-multi-modal-sarcasm-detection-via","title":"Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement","date":"2022-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"less-and-less-bugs/hkemodel","path":"utils/data_utils.py","file_url":"https://github.com/less-and-less-bugs/hkemodel/blob/HEAD/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e688f22521702da","mcp_get_code":{"code_sha256":"0e688f22521702da"}},{"arxiv_id":"2207.11517","paper":"/paper/contrastive-monotonic-pixel-level-modulation","title":"Contrastive Monotonic Pixel-Level Modulation","date":"2022-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lukun199/MonoPix","path":"models/networks.py","file_url":"https://github.com/lukun199/MonoPix/blob/HEAD/models/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9457774086c9e2b5","mcp_get_code":{"code_sha256":"9457774086c9e2b5"}},{"arxiv_id":"2204.10757","paper":"/paper/faithdial-a-faithful-benchmark-for","title":"FaithDial: A Faithful Benchmark for Information-Seeking Dialogue","date":"2022-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcgill-nlp/faithdial","path":"models/modeling_nce.py","file_url":"https://github.com/mcgill-nlp/faithdial/blob/HEAD/models/modeling_nce.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dec641c76d04d78","mcp_get_code":{"code_sha256":"2dec641c76d04d78"}},{"arxiv_id":"2204.08397","paper":"/paper/fast-and-memory-efficient-network-towards","title":"Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution","date":"2022-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nju-jet/fmen","path":"train_fmen.py","file_url":"https://github.com/nju-jet/fmen/blob/HEAD/train_fmen.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"36de767510d9fb56","mcp_get_code":{"code_sha256":"36de767510d9fb56"}},{"arxiv_id":"2108.06805","paper":"/paper/ssh-a-self-supervised-framework-for-image","title":"SSH: A Self-Supervised Framework for Image Harmonization","date":"2021-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-group/ssharmonization","path":"unet_tile_se_norm.py","file_url":"https://github.com/vita-group/ssharmonization/blob/HEAD/unet_tile_se_norm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2261fafa69f3393c","mcp_get_code":{"code_sha256":"2261fafa69f3393c"}},{"arxiv_id":"2107.06099","paper":"/paper/drug-target-interaction-prediction-with-graph","title":"Drug-Target Interaction Prediction with Graph Attention networks","date":"2021-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Haiyang-W/DTI-GRAPH","path":"dataloader.py","file_url":"https://github.com/Haiyang-W/DTI-GRAPH/blob/HEAD/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f0c1caa239cb57d","mcp_get_code":{"code_sha256":"9f0c1caa239cb57d"}},{"arxiv_id":"2010.09990","paper":"/paper/the-open-catalyst-2020-oc20-dataset-and","title":"The Open Catalyst 2020 (OC20) Dataset and Community Challenges","date":"2020-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gasteigerjo/ocp","path":"ocpmodels/common/gp_utils.py","file_url":"https://github.com/gasteigerjo/ocp/blob/HEAD/ocpmodels/common/gp_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"db111ae38a618c0c","mcp_get_code":{"code_sha256":"db111ae38a618c0c"}},{"arxiv_id":"2009.01439","paper":"/paper/dexterous-robotic-grasping-with-object","title":"Learning Dexterous Grasping with Object-Centric Visual Affordances","date":"2020-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"priyankamandikal/graff","path":"a2c_ppo_acktr/tensor_utils.py","file_url":"https://github.com/priyankamandikal/graff/blob/HEAD/a2c_ppo_acktr/tensor_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a0fa6632ba91e5df","mcp_get_code":{"code_sha256":"a0fa6632ba91e5df"}},{"arxiv_id":"2007.04921","paper":"/paper/graph-neural-network-based-coarse-grained","title":"Graph Neural Network Based Coarse-Grained Mapping Prediction","date":"2020-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rochesterxugroup/DSGPM","path":"dataset/collate.py","file_url":"https://github.com/rochesterxugroup/DSGPM/blob/HEAD/dataset/collate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4235cc0d67195ab3","mcp_get_code":{"code_sha256":"4235cc0d67195ab3"}},{"arxiv_id":"1906.00391","paper":"/paper/190600391","title":"Sequential Scenario-Specific Meta Learner for Online Recommendation","date":"2019-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THUDM/ScenarioMeta","path":"src/utils.py","file_url":"https://github.com/THUDM/ScenarioMeta/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec1263e33cee1529","mcp_get_code":{"code_sha256":"ec1263e33cee1529"}},{"arxiv_id":"1812.04960","paper":"/paper/object-centric-auto-encoders-and-dummy","title":"Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video","date":"2018-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fjchange/object_centric_VAD","path":"object_detection/utils/shape_utils.py","file_url":"https://github.com/fjchange/object_centric_VAD/blob/HEAD/object_detection/utils/shape_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c67344dbf665e91","mcp_get_code":{"code_sha256":"7c67344dbf665e91"}},{"arxiv_id":"1604.07143","paper":"/paper/neural-random-forests","title":"Neural Random Forests","date":"2016-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dhuynh95/cryptotree","path":"cryptotree/tree.py","file_url":"https://github.com/dhuynh95/cryptotree/blob/HEAD/cryptotree/tree.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":"254afcaedc5de2a9","mcp_get_code":{"code_sha256":"254afcaedc5de2a9"}},{"arxiv_id":"1603.08983","paper":"/paper/adaptive-computation-time-for-recurrent","title":"Adaptive Computation Time for Recurrent Neural Networks","date":"2016-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ceyzaguirre4/adaptive_computation","path":"act/utils.py","file_url":"https://github.com/ceyzaguirre4/adaptive_computation/blob/HEAD/act/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ba0c3a66ce30058","mcp_get_code":{"code_sha256":"4ba0c3a66ce30058"}}]}