{"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/add-argument-group","entry":"add_argument_group","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":31,"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":6,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":31,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":0,"ran":1,"unverified":1},"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":"2407.11668","paper":"/paper/learning-to-make-keypoints-sub-pixel-accurate","title":"Learning to Make Keypoints Sub-Pixel Accurate","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kimsinjeong/keypt2subpx","path":"settings.py","file_url":"https://github.com/kimsinjeong/keypt2subpx/blob/HEAD/settings.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":"8f8a9f02683a9743","mcp_get_code":{"code_sha256":"8f8a9f02683a9743"}},{"arxiv_id":"2403.03532","paper":"/paper/extend-your-own-correspondences-unsupervised","title":"Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension","date":"2024-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuquan98/eyoc","path":"config.py","file_url":"https://github.com/liuquan98/eyoc/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2312.05391","paper":"/paper/loss-functions-in-the-era-of-semantic","title":"Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook","date":"2023-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yilmazkadir/segmentation_losses","path":"config.py","file_url":"https://github.com/yilmazkadir/segmentation_losses/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2310.13892","paper":"/paper/specify-robust-causal-representation-from","title":"Specify Robust Causal Representation from Mixed Observations","date":"2023-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ymy4323460/cari","path":"codebase/method/config.py","file_url":"https://github.com/ymy4323460/cari/blob/HEAD/codebase/method/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2305.02893","paper":"/paper/apr-online-distant-point-cloud-registration","title":"APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction","date":"2023-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuQuan98/APR","path":"FCGF_APR/config.py","file_url":"https://github.com/liuQuan98/APR/blob/HEAD/FCGF_APR/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2304.01514","paper":"/paper/robust-outlier-rejection-for-3d-registration","title":"Robust Outlier Rejection for 3D Registration with Variational Bayes","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jiang-HB/VBReg","path":"config.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2203.14493","paper":"/paper/arcs-accurate-rotation-and-correspondence","title":"ARCS: Accurate Rotation and Correspondence Search","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zgojcic/3DSmoothNet","path":"core/config.py","file_url":"https://github.com/zgojcic/3DSmoothNet/blob/HEAD/core/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2201.12716","paper":"/paper/you-only-demonstrate-once-category-level","title":"You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration","date":"2022-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenbowen123/BundleTrack","path":"lf-net-release/common/argparse_utils.py","file_url":"https://github.com/wenbowen123/BundleTrack/blob/HEAD/lf-net-release/common/argparse_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":"7ac251a2c9e673b8","mcp_get_code":{"code_sha256":"7ac251a2c9e673b8"}},{"arxiv_id":"2008.05049","paper":"/paper/distantly-supervised-relation-extraction-in","title":"Distantly Supervised Relation Extraction in Federated Settings","date":"2020-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DianboWork/FedDS","path":"decentralized_main.py","file_url":"https://github.com/DianboWork/FedDS/blob/HEAD/decentralized_main.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc116b79b5825e85","mcp_get_code":{"code_sha256":"fc116b79b5825e85"}},{"arxiv_id":"2007.14628","paper":"/paper/solving-the-blind-perspective-n-point-problem","title":"Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization","date":"2020-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liumouliu/Deep_blind_PnP","path":"config.py","file_url":"https://github.com/Liumouliu/Deep_blind_PnP/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2005.00803","paper":"/paper/lagrangian-neural-style-transfer-for-fluids","title":"Lagrangian Neural Style Transfer for Fluids","date":"2020-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"byungsook/neural-flow-style","path":"config.py","file_url":"https://github.com/byungsook/neural-flow-style/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2004.11540","paper":"/paper/deep-global-registration","title":"Deep Global Registration","date":"2020-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chrischoy/FCGF","path":"config.py","file_url":"https://github.com/chrischoy/FCGF/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"2003.08723","paper":"/paper/latent-space-subdivision-stable-and","title":"Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow","date":"2020-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lij131/LatentSpaceSubdivision-base","path":"config.py","file_url":"https://github.com/lij131/LatentSpaceSubdivision-base/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1906.05226","paper":"/paper/continual-and-multi-task-architecture-search","title":"Continual and Multi-Task Architecture Search","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ramakanth-pasunuru/CAS-MAS","path":"config.py","file_url":"https://github.com/ramakanth-pasunuru/CAS-MAS/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1904.01701","paper":"/paper/3dregnet-a-deep-neural-network-for-3d-point","title":"3DRegNet: A Deep Neural Network for 3D Point Registration","date":"2019-04-02","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":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1811.06879","paper":"/paper/the-perfect-match-3d-point-cloud-matching","title":"The Perfect Match: 3D Point Cloud Matching with Smoothed Densities","date":"2018-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LONG-9621/Match_SmoothNet","path":"core/config.py","file_url":"https://github.com/LONG-9621/Match_SmoothNet/blob/HEAD/core/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1805.04980","paper":"/paper/unifying-and-merging-well-trained-deep-neural","title":"Unifying and Merging Well-trained Deep Neural Networks for Inference Stage","date":"2018-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ivclab/NeuralMerger","path":"Fine-tuning/config.py","file_url":"https://github.com/ivclab/NeuralMerger/blob/HEAD/Fine-tuning/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1805.04803","paper":"/paper/zero-shot-dialog-generation-with-cross-domain","title":"Zero-Shot Dialog Generation with Cross-Domain Latent Actions","date":"2018-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snakeztc/NeuralDialog-ZSDG","path":"simdial-zsdg.py","file_url":"https://github.com/snakeztc/NeuralDialog-ZSDG/blob/HEAD/simdial-zsdg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1804.08069","paper":"/paper/unsupervised-discrete-sentence-representation","title":"Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation","date":"2018-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShiminLei/LA-Dialog-Generation-System","path":"dailydialog-utt-skip.py","file_url":"https://github.com/ShiminLei/LA-Dialog-Generation-System/blob/HEAD/dailydialog-utt-skip.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1711.05971","paper":"/paper/learning-to-find-good-correspondences","title":"Learning to Find Good Correspondences","date":"2017-11-16","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":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1709.02023","paper":"/paper/causalgan-learning-causal-implicit-generative","title":"CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training","date":"2017-09-06","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":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"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":"config.py","file_url":"https://github.com/carpedm20/simulated-unsupervised-tensorflow/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1612.07659","paper":"/paper/structured-sequence-modeling-with-graph","title":"Structured Sequence Modeling with Graph Convolutional Recurrent Networks","date":"2016-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"youngjoo-epfl/gconvRNN","path":"config.py","file_url":"https://github.com/youngjoo-epfl/gconvRNN/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae8c11cb85308517","mcp_get_code":{"code_sha256":"ae8c11cb85308517"}},{"arxiv_id":"1611.01578","paper":"/paper/neural-architecture-search-with-reinforcement","title":"Neural Architecture Search with Reinforcement Learning","date":"2016-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carpedm20/ENAS-pytorch","path":"config.py","file_url":"https://github.com/carpedm20/ENAS-pytorch/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"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":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"1506.03134","paper":"/paper/pointer-networks","title":"Pointer Networks","date":"2015-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"devsisters/pointer-network-tensorflow","path":"config.py","file_url":"https://github.com/devsisters/pointer-network-tensorflow/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"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":"kevinzakka/recurrent-visual-attention","path":"config.py","file_url":"https://github.com/kevinzakka/recurrent-visual-attention/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b7dd47e91ac1448a","mcp_get_code":{"code_sha256":"b7dd47e91ac1448a"}},{"arxiv_id":"aaai_25456","paper":null,"title":"arXiv:aaai_25456","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SuhZhang/ConvMatch","path":"core/config.py","file_url":"https://github.com/SuhZhang/ConvMatch/blob/HEAD/core/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"aaai_19917","paper":null,"title":"arXiv:aaai_19917","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ZhiChen902/DetarNet","path":"config.py","file_url":"https://github.com/ZhiChen902/DetarNet/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"Zhong_T-Net_Effective_Permutation-Equivariant_Network_for_Two-View_Correspondence_Learning_ICCV_2021_paper","paper":null,"title":"arXiv:Zhong_T-Net_Effective_Permutation-Equivariant_Network_for_Two-View_Correspondence_Learning_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"x-gb/T-Net","path":"config.py","file_url":"https://github.com/x-gb/T-Net/blob/HEAD/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"arxiv_id":"Zhang_DeMatch_Deep_Decomposition_of_Motion_Field_for_Two-View_Correspondence_Learning_CVPR_2024_paper","paper":null,"title":"arXiv:Zhang_DeMatch_Deep_Decomposition_of_Motion_Field_for_Two-View_Correspondence_Learning_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SuhZhang/DeMatch","path":"core/config.py","file_url":"https://github.com/SuhZhang/DeMatch/blob/HEAD/core/config.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d80592fd6d47b2d","mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}}]}