{"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/generate-grid","entry":"generate_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":14,"n_papers_ran":3,"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":12,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":14,"n_places_pointer_only":4,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"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":"2606.02073","paper":"/paper/arxiv-2606-02073","title":"Planar Symmetric Pattern Generation","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"GLAD-RUC/Sym2D","path":"lattice/lattice_base.py","file_url":"https://github.com/GLAD-RUC/Sym2D/blob/HEAD/lattice/lattice_base.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a297f21214f415c6","mcp_get_code":{"code_sha256":"a297f21214f415c6"}},{"arxiv_id":"2405.19985","paper":"/paper/targeted-sequential-indirect-experiment","title":"Targeted Sequential Indirect Experiment Design","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EAiler/targeted-iv-experiments","path":"src/run_strategy.py","file_url":"https://github.com/EAiler/targeted-iv-experiments/blob/HEAD/src/run_strategy.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6ea84890b9e49100","mcp_get_code":{"code_sha256":"6ea84890b9e49100"}},{"arxiv_id":"2310.03249","paper":"/paper/can-large-language-models-be-good-path","title":"Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mohamedaghzal/llms-as-path-planners","path":"gpt-4-path-planning/src/generate_samples.py","file_url":"https://github.com/mohamedaghzal/llms-as-path-planners/blob/HEAD/gpt-4-path-planning/src/generate_samples.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89b81366df015ab8","mcp_get_code":{"code_sha256":"89b81366df015ab8"}},{"arxiv_id":"2305.03462","paper":"/paper/general-neural-gauge-fields","title":"General Neural Gauge Fields","date":"2023-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fnzhan/Neural-Gauge-Fields","path":"InfoInv/utils.py","file_url":"https://github.com/fnzhan/Neural-Gauge-Fields/blob/HEAD/InfoInv/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"039915ff93d91a14","mcp_get_code":{"code_sha256":"039915ff93d91a14"}},{"arxiv_id":"2208.01893","paper":"/paper/flow-annealed-importance-sampling-bootstrap","title":"Flow Annealed Importance Sampling Bootstrap","date":"2022-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimat-lab/coarse-graining-al","path":"main/active_learning/active_learning_managers.py","file_url":"https://github.com/aimat-lab/coarse-graining-al/blob/HEAD/main/active_learning/active_learning_managers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2327b3b50e622283","mcp_get_code":{"code_sha256":"2327b3b50e622283"}},{"arxiv_id":"2207.02202","paper":"/paper/cobevt-cooperative-bird-s-eye-view-semantic","title":"CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers","date":"2022-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"derrickxunu/cobevt","path":"nuscenes/cross_view_transformer/model/encoder.py","file_url":"https://github.com/derrickxunu/cobevt/blob/HEAD/nuscenes/cross_view_transformer/model/encoder.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":"09eca71083cd884e","mcp_get_code":{"code_sha256":"09eca71083cd884e"}},{"arxiv_id":"2206.04584","paper":"/paper/efficient-and-robust-2d-to-bev-representation","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","date":"2022-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hustvl/gkt","path":"segmentation/cross_view_transformer/model/encoder.py","file_url":"https://github.com/hustvl/gkt/blob/HEAD/segmentation/cross_view_transformer/model/encoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09eca71083cd884e","mcp_get_code":{"code_sha256":"09eca71083cd884e"}},{"arxiv_id":"2206.03830","paper":"/paper/generative-myocardial-motion-tracking-via","title":"Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior","date":"2022-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cq615/bigm-motion-tracking","path":"utils.py","file_url":"https://github.com/cq615/bigm-motion-tracking/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":"1f1da787c0501742","mcp_get_code":{"code_sha256":"1f1da787c0501742"}},{"arxiv_id":"2108.12250","paper":"/paper/a-comparison-of-approaches-to-improve-worst","title":"A comparison of approaches to improve worst-case predictive model performance over patient subpopulations","date":"2021-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"som-shahlab/subpopulation_robustness","path":"group_robustness_fairness/omop/create_grid_baselines.py","file_url":"https://github.com/som-shahlab/subpopulation_robustness/blob/HEAD/group_robustness_fairness/omop/create_grid_baselines.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b6571ff03b12b40","mcp_get_code":{"code_sha256":"7b6571ff03b12b40"}},{"arxiv_id":"2106.12673","paper":"/paper/conditional-deformable-image-registration","title":"Conditional Deformable Image Registration with Convolutional Neural Network","date":"2021-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cwmok/Conditional_LapIRN","path":"Code/Functions.py","file_url":"https://github.com/cwmok/Conditional_LapIRN/blob/HEAD/Code/Functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"526f7aabe47bb29e","mcp_get_code":{"code_sha256":"526f7aabe47bb29e"}},{"arxiv_id":"2009.11508","paper":"/paper/improving-query-efficiency-of-black-box-1","title":"Improving Query Efficiency of Black-box Adversarial Attack","date":"2020-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sandy-Zeng/NPAttack","path":"NPAttack_IMAGENET.py","file_url":"https://github.com/Sandy-Zeng/NPAttack/blob/HEAD/NPAttack_IMAGENET.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"705a6ab5b983b4ee","mcp_get_code":{"code_sha256":"705a6ab5b983b4ee"}},{"arxiv_id":"2004.12585","paper":"/paper/a-batch-normalized-inference-network-keeps","title":"A Batch Normalized Inference Network Keeps the KL Vanishing Away","date":"2020-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valdersoul/bn-vae","path":"modules/utils.py","file_url":"https://github.com/valdersoul/bn-vae/blob/HEAD/modules/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"69f9f574272f9c32","mcp_get_code":{"code_sha256":"69f9f574272f9c32"}},{"arxiv_id":"1901.05534","paper":"/paper/lagging-inference-networks-and-posterior","title":"Lagging Inference Networks and Posterior Collapse in Variational Autoencoders","date":"2019-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jxhe/vae-lagging-encoder","path":"modules/utils.py","file_url":"https://github.com/jxhe/vae-lagging-encoder/blob/HEAD/modules/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"69f9f574272f9c32","mcp_get_code":{"code_sha256":"69f9f574272f9c32"}},{"arxiv_id":"1807.01622","paper":"/paper/neural-processes","title":"Neural Processes","date":"2018-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jusonn/Neural-Process","path":"np_mnist.py","file_url":"https://github.com/jusonn/Neural-Process/blob/HEAD/np_mnist.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d51da53a48566f61","mcp_get_code":{"code_sha256":"d51da53a48566f61"}}]}