{"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/denormalize","entry":"denormalize","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":64,"n_papers_ran":30,"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":51,"n_samples_ran":20,"n_samples_fingerprinted":12,"n_places":65,"n_places_pointer_only":18,"by_status":{"ran_honours":4,"ran_violates":2,"ran_draft_wrong":4,"ran_fixture":3,"ran":7,"unverified":31},"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":"2605.26702","paper":"/paper/arxiv-2605-26702","title":"Rotation-Invariant Spherical Watermarking via Third-Order SO(3) Representation Coupling","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"JerCCC7/TRIAD","path":"triad/model.py","file_url":"https://github.com/JerCCC7/TRIAD/blob/HEAD/triad/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d094cdb029a3669","mcp_get_code":{"code_sha256":"2d094cdb029a3669"}},{"arxiv_id":"2605.25194","paper":"/paper/arxiv-2605-25194","title":"Localize and Neutralize: Gradient-Guided Token Suppression Against Visual Prompt Injection Attack","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"fish883/GTM-Defense","path":"attack/qwen_attack.py","file_url":"https://github.com/fish883/GTM-Defense/blob/HEAD/attack/qwen_attack.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"950e8a45a86aee7b","mcp_get_code":{"code_sha256":"950e8a45a86aee7b"}},{"arxiv_id":"2601.08608","paper":"/paper/arxiv-2601-08608","title":"SfMamba: Efficient Source-Free Domain Adaptation via Selective Scan Modeling","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"chenxi52/SfMamba","path":"models/sfMamba.py","file_url":"https://github.com/chenxi52/SfMamba/blob/HEAD/models/sfMamba.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7896a351a26cc5c1","mcp_get_code":{"code_sha256":"7896a351a26cc5c1"}},{"arxiv_id":"2504.12739","paper":"/paper/mask-image-watermarking","title":"Mask Image Watermarking","date":"2025-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hurunyi/maskwm","path":"inference.py","file_url":"https://github.com/hurunyi/maskwm/blob/HEAD/inference.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d094cdb029a3669","mcp_get_code":{"code_sha256":"2d094cdb029a3669"}},{"arxiv_id":"2504.01955","paper":"/paper/scene-centric-unsupervised-panoptic","title":"Scene-Centric Unsupervised Panoptic Segmentation","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/cups","path":"cups/utils.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/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":"60c827a00b5a152a","mcp_get_code":{"code_sha256":"60c827a00b5a152a"}},{"arxiv_id":"2502.17237","paper":"/paper/megaloc-one-retrieval-to-place-them-all","title":"MegaLoc: One Retrieval to Place Them All","date":"2025-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gmberton/VPR-methods-evaluation","path":"vpr_models/utils.py","file_url":"https://github.com/gmberton/VPR-methods-evaluation/blob/HEAD/vpr_models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10c34609af1437f3","mcp_get_code":{"code_sha256":"10c34609af1437f3"}},{"arxiv_id":"2411.19946","paper":"/paper/delt-a-simple-diversity-driven-earlylate","title":"DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation","date":"2024-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vila-lab/delt","path":"recover/utils.py","file_url":"https://github.com/vila-lab/delt/blob/HEAD/recover/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"70d1e8cd2526f3ae","mcp_get_code":{"code_sha256":"70d1e8cd2526f3ae"}},{"arxiv_id":"2411.01578","paper":"/paper/integrating-graph-neural-networks-and-many","title":"Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces","date":"2024-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lin-group-at-umass/fbgnn-mbe","path":"src/train_energy_staged.py","file_url":"https://github.com/lin-group-at-umass/fbgnn-mbe/blob/HEAD/src/train_energy_staged.py","status":"ran_honours","verification_level":2,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600affad5e62864a","mcp_get_code":{"code_sha256":"600affad5e62864a"}},{"arxiv_id":"2409.07961","paper":"/paper/estimating-atmospheric-variables-from-digital","title":"Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models","date":"2024-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tammyling/typhoon-forecasting","path":"magnitude.py","file_url":"https://github.com/tammyling/typhoon-forecasting/blob/HEAD/magnitude.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b778ce8816352373","mcp_get_code":{"code_sha256":"b778ce8816352373"}},{"arxiv_id":"2408.16769","paper":"/paper/promptsmooth-certifying-robustness-of-medical","title":"PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nhussein/promptsmooth","path":"certify_promptsmooth_plip.py","file_url":"https://github.com/nhussein/promptsmooth/blob/HEAD/certify_promptsmooth_plip.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"59c2ebaf239e6558","mcp_get_code":{"code_sha256":"59c2ebaf239e6558"}},{"arxiv_id":"2406.10580","paper":"/paper/imdl-benco-a-comprehensive-benchmark-and","title":"IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization","date":"2024-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scu-zjz/IMDLBenCo","path":"IMDLBenCo/datasets/utils.py","file_url":"https://github.com/scu-zjz/IMDLBenCo/blob/HEAD/IMDLBenCo/datasets/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"CC-BY-4.0","inline_ok":false,"code_sha256_prefix":"1d5da97bb67bcbb1","mcp_get_code":{"code_sha256":"1d5da97bb67bcbb1"}},{"arxiv_id":"2405.15757","paper":"/paper/looking-backward-streaming-video-to-video","title":"Looking Backward: Streaming Video-to-Video Translation with Feature Banks","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jeff-LiangF/streamv2v","path":"src/streamv2v/image_utils.py","file_url":"https://github.com/Jeff-LiangF/streamv2v/blob/HEAD/src/streamv2v/image_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5dd5ccc011afb9c3","mcp_get_code":{"code_sha256":"5dd5ccc011afb9c3"}},{"arxiv_id":"2404.12501","paper":"/paper/spidepth-strengthened-pose-information-for","title":"SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation","date":"2024-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lavreniuk/SPIdepth","path":"finetune/evaluate_metric_depth.py","file_url":"https://github.com/Lavreniuk/SPIdepth/blob/HEAD/finetune/evaluate_metric_depth.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89177c685e07c0df","mcp_get_code":{"code_sha256":"89177c685e07c0df"}},{"arxiv_id":"2403.07187","paper":"/paper/ups-towards-foundation-models-for-pde-solving","title":"UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjunhongshen/UnifiedPDESolvers","path":"utils.py","file_url":"https://github.com/sjunhongshen/UnifiedPDESolvers/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ffb8f206fe96f08a","mcp_get_code":{"code_sha256":"ffb8f206fe96f08a"}},{"arxiv_id":"2312.12491","paper":"/paper/streamdiffusion-a-pipeline-level-solution-for","title":"StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation","date":"2023-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cumulo-autumn/streamdiffusion","path":"src/streamdiffusion/image_utils.py","file_url":"https://github.com/cumulo-autumn/streamdiffusion/blob/HEAD/src/streamdiffusion/image_utils.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":"5dd5ccc011afb9c3","mcp_get_code":{"code_sha256":"5dd5ccc011afb9c3"}},{"arxiv_id":"2312.04552","paper":"/paper/generating-illustrated-instructions","title":"Generating Illustrated Instructions","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sachit-menon/generating-illustrated-instructions-reproduction","path":"utils.py","file_url":"https://github.com/sachit-menon/generating-illustrated-instructions-reproduction/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"191a0905a11888a1","mcp_get_code":{"code_sha256":"191a0905a11888a1"}},{"arxiv_id":"2312.02249","paper":"/paper/recursive-visual-programming","title":"Recursive Visual Programming","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"para-lost/rvp","path":"utils.py","file_url":"https://github.com/para-lost/rvp/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"191a0905a11888a1","mcp_get_code":{"code_sha256":"191a0905a11888a1"}},{"arxiv_id":"2310.19512","paper":"/paper/videocrafter1-open-diffusion-models-for-high","title":"VideoCrafter1: Open Diffusion Models for High-Quality Video Generation","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"invictus717/interactivevideo","path":"models/streamdiffusion/image_utils.py","file_url":"https://github.com/invictus717/interactivevideo/blob/HEAD/models/streamdiffusion/image_utils.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":"5dd5ccc011afb9c3","mcp_get_code":{"code_sha256":"5dd5ccc011afb9c3"}},{"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":"shariqfarooq123/AdaBins","path":"utils.py","file_url":"https://github.com/shariqfarooq123/AdaBins/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"55b19362aa7a3a3d","mcp_get_code":{"code_sha256":"55b19362aa7a3a3d"}},{"arxiv_id":"2307.14863","paper":"/paper/iml-vit-image-manipulation-localization-by","title":"IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunnyhaze/iml-vit","path":"utils/datasets.py","file_url":"https://github.com/sunnyhaze/iml-vit/blob/HEAD/utils/datasets.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d5da97bb67bcbb1","mcp_get_code":{"code_sha256":"1d5da97bb67bcbb1"}},{"arxiv_id":"2306.17253","paper":"/paper/towards-zero-shot-scale-aware-monocular-depth","title":"Towards Zero-Shot Scale-Aware Monocular Depth Estimation","date":"2023-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cake-lab/Mobile-AR-Depth-Estimation","path":"models/ZoeDepth/zoedepth/models/zoedepth/zoedepth_v1.py","file_url":"https://github.com/cake-lab/Mobile-AR-Depth-Estimation/blob/HEAD/models/ZoeDepth/zoedepth/models/zoedepth/zoedepth_v1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"0a23d935ca395f43","mcp_get_code":{"code_sha256":"0a23d935ca395f43"}},{"arxiv_id":"2306.13092","paper":"/paper/squeeze-recover-and-relabel-dataset","title":"Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective","date":"2023-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shaoshitong/EDC","path":"Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.py","file_url":"https://github.com/shaoshitong/EDC/blob/HEAD/Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6cec6dc0ac87904","mcp_get_code":{"code_sha256":"b6cec6dc0ac87904"}},{"arxiv_id":"2306.07282","paper":"/paper/waffling-around-for-performance-visual","title":"Waffling around for Performance: Visual Classification with Random Words and Broad Concepts","date":"2023-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ExplainableML/WaffleCLIP","path":"waffle_tools.py","file_url":"https://github.com/ExplainableML/WaffleCLIP/blob/HEAD/waffle_tools.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7496518009557dce","mcp_get_code":{"code_sha256":"7496518009557dce"}},{"arxiv_id":"2306.06354","paper":"/paper/eventclip-adapting-clip-for-event-based","title":"EventCLIP: Adapting CLIP for Event-based Object Recognition","date":"2023-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wuziyi616/EventCLIP","path":"method.py","file_url":"https://github.com/Wuziyi616/EventCLIP/blob/HEAD/method.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8f7ff725c27a34c8","mcp_get_code":{"code_sha256":"8f7ff725c27a34c8"}},{"arxiv_id":"2303.17959","paper":"/paper/diffusion-action-segmentation","title":"Diffusion Action Segmentation","date":"2023-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"finspire13/diffact","path":"model.py","file_url":"https://github.com/finspire13/diffact/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a26089b06616104c","mcp_get_code":{"code_sha256":"a26089b06616104c"}},{"arxiv_id":"2210.07183","paper":"/paper/visual-classification-via-description-from","title":"Visual Classification via Description from Large Language Models","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainableml/waffleclip","path":"waffle_tools.py","file_url":"https://github.com/explainableml/waffleclip/blob/HEAD/waffle_tools.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7496518009557dce","mcp_get_code":{"code_sha256":"7496518009557dce"}},{"arxiv_id":"2208.10762","paper":"/paper/depth-map-decomposition-for-monocular-depth","title":"Depth Map Decomposition for Monocular Depth Estimation","date":"2022-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jyjunmcl/Depth-Map-Decomposition","path":"utils.py","file_url":"https://github.com/jyjunmcl/Depth-Map-Decomposition/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55b19362aa7a3a3d","mcp_get_code":{"code_sha256":"55b19362aa7a3a3d"}},{"arxiv_id":"2205.10203","paper":"/paper/learning-to-count-anything-reference-less","title":"Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision","date":"2022-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"activevisionlab/learningtocountanything","path":"utils/data_utils.py","file_url":"https://github.com/activevisionlab/learningtocountanything/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":"ec3db0f866e1aa94","mcp_get_code":{"code_sha256":"ec3db0f866e1aa94"}},{"arxiv_id":"2204.13713","paper":"/paper/learning-cosmology-and-clustering-with-cosmic","title":"Learning cosmology and clustering with cosmic graphs","date":"2022-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PabloVD/CosmoGraphNet","path":"Source/plotting.py","file_url":"https://github.com/PabloVD/CosmoGraphNet/blob/HEAD/Source/plotting.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"92d24907643e22fd","mcp_get_code":{"code_sha256":"92d24907643e22fd"}},{"arxiv_id":"2204.03646","paper":"/paper/finediving-a-fine-grained-dataset-for","title":"FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment","date":"2022-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xujinglin/finediving","path":"utils/misc.py","file_url":"https://github.com/xujinglin/finediving/blob/HEAD/utils/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"40bb8a7eee8f9c75","mcp_get_code":{"code_sha256":"40bb8a7eee8f9c75"}},{"arxiv_id":"2203.09301","paper":"/paper/one-shot-adaptation-of-gan-in-just-one-clip","title":"One-Shot Adaptation of GAN in Just One CLIP","date":"2022-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anon96652/oneshotclip","path":"metrics/utils.py","file_url":"https://github.com/anon96652/oneshotclip/blob/HEAD/metrics/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f63545a8e689c49","mcp_get_code":{"code_sha256":"4f63545a8e689c49"}},{"arxiv_id":"2110.04176","paper":"/paper/lightweight-convolutional-neural-networks-by","title":"PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions","date":"2021-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ispamm/hi2i","path":"core/utils.py","file_url":"https://github.com/ispamm/hi2i/blob/HEAD/core/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f63545a8e689c49","mcp_get_code":{"code_sha256":"4f63545a8e689c49"}},{"arxiv_id":"2106.05042","paper":"/paper/polynomial-magic-hermite-polynomials-for","title":"Hermite Polynomial Features for Private Data Generation","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"parklabml/dp-hp","path":"dp_mehp/aux.py","file_url":"https://github.com/parklabml/dp-hp/blob/HEAD/dp_mehp/aux.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d24c6690a4970ba","mcp_get_code":{"code_sha256":"9d24c6690a4970ba"}},{"arxiv_id":"2105.09452","paper":"/paper/minimum-delay-adaptation-in-non-stationary","title":"Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection","date":"2021-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LucasAlegre/mbcd","path":"mbcd/utils/dataset.py","file_url":"https://github.com/LucasAlegre/mbcd/blob/HEAD/mbcd/utils/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6847f28a35bbacb4","mcp_get_code":{"code_sha256":"6847f28a35bbacb4"}},{"arxiv_id":"2103.13253","paper":"/paper/learning-versatile-neural-architectures-by","title":"Learning Versatile Neural Architectures by Propagating Network Codes","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/NCP","path":"tools/ncp.py","file_url":"https://github.com/dingmyu/NCP/blob/HEAD/tools/ncp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"01cd9fa7bdf70ca2","mcp_get_code":{"code_sha256":"01cd9fa7bdf70ca2"}},{"arxiv_id":"2103.12057","paper":"/paper/an-experimental-review-on-deep-learning","title":"An Experimental Review on Deep Learning Architectures for Time Series Forecasting","date":"2021-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pedrolarben/TimeSeriesForecasting-DeepLearning","path":"experiments/preprocessing.py","file_url":"https://github.com/pedrolarben/TimeSeriesForecasting-DeepLearning/blob/HEAD/experiments/preprocessing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84c57a912acc0144","mcp_get_code":{"code_sha256":"84c57a912acc0144"}},{"arxiv_id":"2010.13303","paper":"/paper/trajectory-wise-multiple-choice-learning-for","title":"Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning","date":"2020-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"younggyoseo/trajectory_mcl","path":"tmcl/dynamics/tmcl.py","file_url":"https://github.com/younggyoseo/trajectory_mcl/blob/HEAD/tmcl/dynamics/tmcl.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"60a2ef2b4904edd8","mcp_get_code":{"code_sha256":"60a2ef2b4904edd8"}},{"arxiv_id":"2008.00188","paper":"/paper/augmented-skeleton-based-contrastive-action","title":"Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition","date":"2020-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mikexu007/AS_CAL","path":"feeders/sbu_feeder.py","file_url":"https://github.com/Mikexu007/AS_CAL/blob/HEAD/feeders/sbu_feeder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc89adec7492521a","mcp_get_code":{"code_sha256":"dc89adec7492521a"}},{"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":"utils.py","file_url":"https://github.com/milmor/LadaGAN-pytorch/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"05e9b8206464518b","mcp_get_code":{"code_sha256":"05e9b8206464518b"}},{"arxiv_id":"2006.07815","paper":"/paper/optimistic-distributionally-robust-policy","title":"Optimistic Distributionally Robust Policy Optimization","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kadysongbb/dr-trpo","path":"continuous_control/GAC/helpers.py","file_url":"https://github.com/kadysongbb/dr-trpo/blob/HEAD/continuous_control/GAC/helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00277beee4c6e0a9","mcp_get_code":{"code_sha256":"00277beee4c6e0a9"}},{"arxiv_id":"2005.06800","paper":"/paper/context-aware-dynamics-model-for","title":"Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning","date":"2020-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"younggyoseo/CaDM","path":"cadm/dynamics/mlp_cadm_ensemble_cem_dynamics.py","file_url":"https://github.com/younggyoseo/CaDM/blob/HEAD/cadm/dynamics/mlp_cadm_ensemble_cem_dynamics.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"60a2ef2b4904edd8","mcp_get_code":{"code_sha256":"60a2ef2b4904edd8"}},{"arxiv_id":"2004.03267","paper":"/paper/guided-dialog-policy-learning-without","title":"Guided Dialog Policy Learning without Adversarial Learning in the Loop","date":"2020-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"360840f3f05824c7","mcp_get_code":{"code_sha256":"360840f3f05824c7"}},{"arxiv_id":"1908.09186","paper":"/paper/efficient-learning-on-point-clouds-with-basis","title":"Efficient Learning on Point Clouds with Basis Point Sets","date":"2019-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sergeyprokudin/bps","path":"bps/bps.py","file_url":"https://github.com/sergeyprokudin/bps/blob/HEAD/bps/bps.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT-0","inline_ok":false,"code_sha256_prefix":"5a6f9a48f129ba89","mcp_get_code":{"code_sha256":"5a6f9a48f129ba89"}},{"arxiv_id":"1904.08637","paper":"/paper/convlab-multi-domain-end-to-end-dialog-system","title":"ConvLab: Multi-Domain End-to-End Dialog System Platform","date":"2019-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ConvLab/ConvLab","path":"convlab/modules/e2e/multiwoz/Mem2Seq/Mem2Seq.py","file_url":"https://github.com/ConvLab/ConvLab/blob/HEAD/convlab/modules/e2e/multiwoz/Mem2Seq/Mem2Seq.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":"360840f3f05824c7","mcp_get_code":{"code_sha256":"360840f3f05824c7"}},{"arxiv_id":"1901.09184","paper":"/paper/action-robust-reinforcement-learning-and","title":"Action Robust Reinforcement Learning and Applications in Continuous Control","date":"2019-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icml2019-anonymous-author/Action-Robust-Reinforcement-Learning","path":"ddpg.py","file_url":"https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning/blob/HEAD/ddpg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00277beee4c6e0a9","mcp_get_code":{"code_sha256":"00277beee4c6e0a9"}},{"arxiv_id":"1810.11545","paper":"/paper/efficiently-combining-human-demonstrations","title":"Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"viniciusguigo/complete_col","path":"ddpg_col.py","file_url":"https://github.com/viniciusguigo/complete_col/blob/HEAD/ddpg_col.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2cb95d0b3539063b","mcp_get_code":{"code_sha256":"2cb95d0b3539063b"}},{"arxiv_id":"1806.07851","paper":"/paper/sim-to-real-reinforcement-learning-for","title":"Sim-to-Real Reinforcement Learning for Deformable Object Manipulation","date":"2018-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JanMatas/Rainbow_ddpg","path":"rainbow_ddpg/ddpg.py","file_url":"https://github.com/JanMatas/Rainbow_ddpg/blob/HEAD/rainbow_ddpg/ddpg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00277beee4c6e0a9","mcp_get_code":{"code_sha256":"00277beee4c6e0a9"}},{"arxiv_id":"1805.09501","paper":"/paper/autoaugment-learning-augmentation-policies","title":"AutoAugment: Learning Augmentation Policies from Data","date":"2018-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YaCpotato/deepaugmentFix","path":"deepaugment/augmenter.py","file_url":"https://github.com/YaCpotato/deepaugmentFix/blob/HEAD/deepaugment/augmenter.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"92ae1c2343aa2f75","mcp_get_code":{"code_sha256":"92ae1c2343aa2f75"}},{"arxiv_id":"1802.10062","paper":"/paper/csrnet-dilated-convolutional-neural-networks","title":"CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes","date":"2018-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CommissarMa/CSRNet-pytorch","path":"utils.py","file_url":"https://github.com/CommissarMa/CSRNet-pytorch/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c0c074850363dcfc","mcp_get_code":{"code_sha256":"c0c074850363dcfc"}},{"arxiv_id":"1802.08797","paper":"/paper/residual-dense-network-for-image-super","title":"Residual Dense Network for Image Super-Resolution","date":"2018-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjn870/rdn-pytorch","path":"utils.py","file_url":"https://github.com/yjn870/rdn-pytorch/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1147a2dbb2f252eb","mcp_get_code":{"code_sha256":"1147a2dbb2f252eb"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ethanleet/CapsNet","path":"tools.py","file_url":"https://github.com/ethanleet/CapsNet/blob/HEAD/tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c5b8a89cd91d2f6","mcp_get_code":{"code_sha256":"5c5b8a89cd91d2f6"}},{"arxiv_id":"1710.04026","paper":"/paper/ffdnet-toward-a-fast-and-flexible-solution","title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","date":"2017-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SamirMitha/Denoising","path":"FFDNet-Keras/utils.py","file_url":"https://github.com/SamirMitha/Denoising/blob/HEAD/FFDNet-Keras/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9826ce88e0ace708","mcp_get_code":{"code_sha256":"9826ce88e0ace708"}},{"arxiv_id":"1708.02596","paper":"/paper/neural-network-dynamics-for-model-based-deep","title":"Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning","date":"2017-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mehdimashayekhi/Some-RL-Implementation","path":"Model_based_RL/dynamics.py","file_url":"https://github.com/mehdimashayekhi/Some-RL-Implementation/blob/HEAD/Model_based_RL/dynamics.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdbe81deead4099d","mcp_get_code":{"code_sha256":"bdbe81deead4099d"}},{"arxiv_id":"1612.07828","paper":"/paper/learning-from-simulated-and-unsupervised","title":"Learning from Simulated and Unsupervised Images through Adversarial Training","date":"2016-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carpedm20/simulated-unsupervised-tensorflow","path":"layers.py","file_url":"https://github.com/carpedm20/simulated-unsupervised-tensorflow/blob/HEAD/layers.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":"78fe835ab7648084","mcp_get_code":{"code_sha256":"78fe835ab7648084"}},{"arxiv_id":"1609.04802","paper":"/paper/photo-realistic-single-image-super-resolution","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","date":"2016-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anujtyagi2802/SRGAN","path":"test_new.py","file_url":"https://github.com/anujtyagi2802/SRGAN/blob/HEAD/test_new.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8a74ff00c12f2f24","mcp_get_code":{"code_sha256":"8a74ff00c12f2f24"}},{"arxiv_id":"1608.04644","paper":"/paper/towards-evaluating-the-robustness-of-neural","title":"Towards Evaluating the Robustness of Neural Networks","date":"2016-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PerryXDeng/adversarial_mnist_attacks","path":"data_preparation.py","file_url":"https://github.com/PerryXDeng/adversarial_mnist_attacks/blob/HEAD/data_preparation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6c0b9095663ceff6","mcp_get_code":{"code_sha256":"6c0b9095663ceff6"}},{"arxiv_id":"1606.00968","paper":"/paper/smooth-imitation-learning-for-online-sequence","title":"Smooth Imitation Learning for Online Sequence Prediction","date":"2016-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucianacendon/simile","path":"Lib/utils.py","file_url":"https://github.com/lucianacendon/simile/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":"8099bd273824efbe","mcp_get_code":{"code_sha256":"8099bd273824efbe"}},{"arxiv_id":"1508.06576","paper":"/paper/a-neural-algorithm-of-artistic-style","title":"A Neural Algorithm of Artistic Style","date":"2015-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kautenja/a-neural-algorithm-of-artistic-style","path":"src/normalize.py","file_url":"https://github.com/Kautenja/a-neural-algorithm-of-artistic-style/blob/HEAD/src/normalize.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"51810239167bbb4c","mcp_get_code":{"code_sha256":"51810239167bbb4c"}},{"arxiv_id":"1406.6247","paper":"/paper/recurrent-models-of-visual-attention","title":"Recurrent Models of Visual Attention","date":"2014-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MiuGod0126/RAM-Paddle","path":"utils.py","file_url":"https://github.com/MiuGod0126/RAM-Paddle/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aba2291398c33f9e","mcp_get_code":{"code_sha256":"aba2291398c33f9e"}},{"arxiv_id":"ijcai2023_0487","paper":null,"title":"arXiv:ijcai2023_0487","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"nabenabe0928/meta-learn-tpe","path":"optimizers/meta_learn_bo/utils.py","file_url":"https://github.com/nabenabe0928/meta-learn-tpe/blob/HEAD/optimizers/meta_learn_bo/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":"e83ca4ec027be65d","mcp_get_code":{"code_sha256":"e83ca4ec027be65d"}},{"arxiv_id":"aaai_28383","paper":null,"title":"arXiv:aaai_28383","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hisfog/SfMNeXt-Impl","path":"finetune/evaluate_metric_depth.py","file_url":"https://github.com/hisfog/SfMNeXt-Impl/blob/HEAD/finetune/evaluate_metric_depth.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89177c685e07c0df","mcp_get_code":{"code_sha256":"89177c685e07c0df"}},{"arxiv_id":"Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper","paper":null,"title":"arXiv:Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KU-CVLAB/LANIT","path":"core/utils.py","file_url":"https://github.com/KU-CVLAB/LANIT/blob/HEAD/core/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f63545a8e689c49","mcp_get_code":{"code_sha256":"4f63545a8e689c49"}},{"arxiv_id":"Ashesh_uSplit_Image_Decomposition_for_Fluorescence_Microscopy_ICCV_2023_paper","paper":null,"title":"arXiv:Ashesh_uSplit_Image_Decomposition_for_Fluorescence_Microscopy_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"juglab/uSplit","path":"usplit/core/data_utils.py","file_url":"https://github.com/juglab/uSplit/blob/HEAD/usplit/core/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":"9782043408ace1b2","mcp_get_code":{"code_sha256":"9782043408ace1b2"}},{"arxiv_id":"2025.findings-emnlp.1095","paper":null,"title":"arXiv:2025.findings-emnlp.1095","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"AngelAlita/AsD","path":"AsD.py","file_url":"https://github.com/AngelAlita/AsD/blob/HEAD/AsD.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"61c1b4f78c423ba5","mcp_get_code":{"code_sha256":"61c1b4f78c423ba5"}},{"arxiv_id":"2025.findings-emnlp.1095","paper":null,"title":"arXiv:2025.findings-emnlp.1095","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"AngelAlita/AsD","path":"adv_image.py","file_url":"https://github.com/AngelAlita/AsD/blob/HEAD/adv_image.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f5cc74d866882a0","mcp_get_code":{"code_sha256":"4f5cc74d866882a0"}}]}