{"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/grid","entry":"grid","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":20,"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":17,"n_samples_ran":8,"n_samples_fingerprinted":0,"n_places":21,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":7,"unverified":9},"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":"2606.19802","paper":"/paper/arxiv-2606-19802","title":"Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"nzilberstein/Flow-map-denoisers","path":"run_baselines.py","file_url":"https://github.com/nzilberstein/Flow-map-denoisers/blob/HEAD/run_baselines.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"76cfa754c0ba6a8a","mcp_get_code":{"code_sha256":"76cfa754c0ba6a8a"}},{"arxiv_id":"2605.20257","paper":"/paper/arxiv-2605-20257","title":"Instance Discrimination for Link Prediction","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"valentincuzin/GCL-Link-Prediction","path":"other_methods/MPLP-main/graph_generation.py","file_url":"https://github.com/valentincuzin/GCL-Link-Prediction/blob/HEAD/other_methods/MPLP-main/graph_generation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d854e401e59d083","mcp_get_code":{"code_sha256":"6d854e401e59d083"}},{"arxiv_id":"2602.08913","paper":"/paper/arxiv-2602-08913","title":"GEMSS: A Variational Method for Discovering Multiple Sparse Solutions in Classification and Regression Problems","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"kat-er-ina/gemss_testing","path":"algorithm_comparison/diagnostics/tuned_compare.py","file_url":"https://github.com/kat-er-ina/gemss_testing/blob/HEAD/algorithm_comparison/diagnostics/tuned_compare.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"717e32343d81591f","mcp_get_code":{"code_sha256":"717e32343d81591f"}},{"arxiv_id":"2410.13749","paper":"/paper/supervised-kernel-thinning","title":"Supervised Kernel Thinning","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ag2435/npr","path":"npr/util_sample.py","file_url":"https://github.com/ag2435/npr/blob/HEAD/npr/util_sample.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"aeda13fd13f0720b","mcp_get_code":{"code_sha256":"aeda13fd13f0720b"}},{"arxiv_id":"2405.03188","paper":"/paper/hyperbolic-geometric-latent-diffusion-model","title":"Hyperbolic Geometric Latent Diffusion Model for Graph Generation","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ringbdstack/hypdiff","path":"Synthatic_graph_generator.py","file_url":"https://github.com/ringbdstack/hypdiff/blob/HEAD/Synthatic_graph_generator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5f8e5c848498bc23","mcp_get_code":{"code_sha256":"5f8e5c848498bc23"}},{"arxiv_id":"2401.05907","paper":"/paper/efficient-image-deblurring-networks-based-on","title":"Efficient Image Deblurring Networks based on Diffusion Models","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bnm6900030/swintormer","path":"ldm/models/autoencoder.py","file_url":"https://github.com/bnm6900030/swintormer/blob/HEAD/ldm/models/autoencoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fb932e0777ff7f87","mcp_get_code":{"code_sha256":"fb932e0777ff7f87"}},{"arxiv_id":"2309.01617","paper":"/paper/devil-decoding-vision-features-into-language","title":"DeViL: Decoding Vision features into Language","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ExplainableML/DeViL","path":"src/evaluation.py","file_url":"https://github.com/ExplainableML/DeViL/blob/HEAD/src/evaluation.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":"6c584e98426ef212","mcp_get_code":{"code_sha256":"6c584e98426ef212"}},{"arxiv_id":"2309.00976","paper":"/paper/pure-message-passing-can-estimate-common","title":"Pure Message Passing Can Estimate Common Neighbor for Link Prediction","date":"2023-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Barcavin/efficient-node-labelling","path":"graph_generation.py","file_url":"https://github.com/Barcavin/efficient-node-labelling/blob/HEAD/graph_generation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d854e401e59d083","mcp_get_code":{"code_sha256":"6d854e401e59d083"}},{"arxiv_id":"2308.11239","paper":"/paper/locate-self-supervised-object-discovery-via","title":"LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"silky1708/locate","path":"dist.py","file_url":"https://github.com/silky1708/locate/blob/HEAD/dist.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"819b7f223154f50c","mcp_get_code":{"code_sha256":"819b7f223154f50c"}},{"arxiv_id":"2307.13372","paper":"/paper/submodular-reinforcement-learning","title":"Submodular Reinforcement Learning","date":"2023-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manish-pra/non-additive-rl","path":"utils/environment.py","file_url":"https://github.com/manish-pra/non-additive-rl/blob/HEAD/utils/environment.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46361cb3c9786ea9","mcp_get_code":{"code_sha256":"46361cb3c9786ea9"}},{"arxiv_id":"2110.03753","paper":"/paper/from-stars-to-subgraphs-uplifting-any-gnn","title":"From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness","date":"2021-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GNNAsKernel/GNNAsKernel","path":"core/data_utils/graph_generation.py","file_url":"https://github.com/GNNAsKernel/GNNAsKernel/blob/HEAD/core/data_utils/graph_generation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5547f9317a2e9d3e","mcp_get_code":{"code_sha256":"5547f9317a2e9d3e"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dataflowr/notebooks","path":"graphs/lib/grid_graph.py","file_url":"https://github.com/dataflowr/notebooks/blob/HEAD/graphs/lib/grid_graph.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":"e0f51f35088884b5","mcp_get_code":{"code_sha256":"e0f51f35088884b5"}},{"arxiv_id":"2006.09535","paper":"/paper/multipole-graph-neural-operator-for","title":"Multipole Graph Neural Operator for Parametric Partial Differential Equations","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wumming/graph-pde","path":"graph-neural-operator/utilities.py","file_url":"https://github.com/wumming/graph-pde/blob/HEAD/graph-neural-operator/utilities.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e433b681c7d291e","mcp_get_code":{"code_sha256":"7e433b681c7d291e"}},{"arxiv_id":"2003.05653","paper":"/paper/towards-high-fidelity-3d-face-reconstruction","title":"Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks","date":"2020-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yiyuan1991/3D-Face-GCNs","path":"lib/graph.py","file_url":"https://github.com/yiyuan1991/3D-Face-GCNs/blob/HEAD/lib/graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00b211ed8d329331","mcp_get_code":{"code_sha256":"00b211ed8d329331"}},{"arxiv_id":"2003.04774","paper":"/paper/entmoot-a-framework-for-optimization-over","title":"ENTMOOT: A Framework for Optimization over Ensemble Tree Models","date":"2020-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cog-imperial/entmoot","path":"entmoot/utils.py","file_url":"https://github.com/cog-imperial/entmoot/blob/HEAD/entmoot/utils.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":"f01eb438bc424617","mcp_get_code":{"code_sha256":"f01eb438bc424617"}},{"arxiv_id":"1912.04958","paper":"/paper/analyzing-and-improving-the-image-quality-of","title":"Analyzing and Improving the Image Quality of StyleGAN","date":"2019-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simplerick/stylegan2_pytorch","path":"misc.py","file_url":"https://github.com/simplerick/stylegan2_pytorch/blob/HEAD/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fa6245afd0bd3943","mcp_get_code":{"code_sha256":"fa6245afd0bd3943"}},{"arxiv_id":"1703.02161","paper":"/paper/distance-metric-learning-using-graph","title":"Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks","date":"2017-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sk1712/gcn_metric_learning","path":"lib/graph.py","file_url":"https://github.com/sk1712/gcn_metric_learning/blob/HEAD/lib/graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00b211ed8d329331","mcp_get_code":{"code_sha256":"00b211ed8d329331"}},{"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":"andymogul/gcrnn_revised","path":"graph.py","file_url":"https://github.com/andymogul/gcrnn_revised/blob/HEAD/graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00b211ed8d329331","mcp_get_code":{"code_sha256":"00b211ed8d329331"}},{"arxiv_id":"1604.08140","paper":"/paper/how-should-spin-weighted-spherical-functions","title":"How should spin-weighted spherical functions be defined?","date":null,"month_inferred_from_arxiv_id":"2016-04","title_source":"archive","repo":"moble/spherical","path":"spherical/modes/utilities.py","file_url":"https://github.com/moble/spherical/blob/HEAD/spherical/modes/utilities.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd39c20114eb94e2","mcp_get_code":{"code_sha256":"bd39c20114eb94e2"}},{"arxiv_id":"1604.08140","paper":"/paper/how-should-spin-weighted-spherical-functions","title":"How should spin-weighted spherical functions be defined?","date":null,"month_inferred_from_arxiv_id":"2016-04","title_source":"archive","repo":"moble/spherical_functions","path":"spherical_functions/SWSH_modes/utilities.py","file_url":"https://github.com/moble/spherical_functions/blob/HEAD/spherical_functions/SWSH_modes/utilities.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1adba7e0f60adafc","mcp_get_code":{"code_sha256":"1adba7e0f60adafc"}},{"arxiv_id":"1506.05163","paper":"/paper/deep-convolutional-networks-on-graph","title":"Deep Convolutional Networks on Graph-Structured Data","date":"2015-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zdcuob/cnn_graph-","path":"lib/graph.py","file_url":"https://github.com/zdcuob/cnn_graph-/blob/HEAD/lib/graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00b211ed8d329331","mcp_get_code":{"code_sha256":"00b211ed8d329331"}}]}