{"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/get-rot","entry":"get_rot","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":8,"n_papers_ran":2,"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":4,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":2},"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":"2309.03467","paper":"/paper/autoregressive-omni-aware-outpainting-for","title":"Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuqiangLu/AOG-NET-360","path":"models/modules.py","file_url":"https://github.com/zhuqiangLu/AOG-NET-360/blob/HEAD/models/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"472bbea9bdb24a81","mcp_get_code":{"code_sha256":"472bbea9bdb24a81"}},{"arxiv_id":"2304.00670","paper":"/paper/crn-camera-radar-net-for-accurate-robust","title":"CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception","date":"2023-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"youngskkim/CRN","path":"datasets/nusc_det_dataset.py","file_url":"https://github.com/youngskkim/CRN/blob/HEAD/datasets/nusc_det_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"85baeab3da252238","mcp_get_code":{"code_sha256":"85baeab3da252238"}},{"arxiv_id":"2211.12501","paper":"/paper/aedet-azimuth-invariant-multi-view-3d-object","title":"AeDet: Azimuth-invariant Multi-view 3D Object Detection","date":"2022-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fcjian/AeDet","path":"dataset/nusc_mv_det_dataset.py","file_url":"https://github.com/fcjian/AeDet/blob/HEAD/dataset/nusc_mv_det_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dd096050170984b","mcp_get_code":{"code_sha256":"2dd096050170984b"}},{"arxiv_id":"2211.09445","paper":"/paper/aimotive-dataset-a-multimodal-dataset-for","title":"aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimotive/mm_training","path":"dataset/nusc_mv_det_dataset.py","file_url":"https://github.com/aimotive/mm_training/blob/HEAD/dataset/nusc_mv_det_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dd096050170984b","mcp_get_code":{"code_sha256":"2dd096050170984b"}},{"arxiv_id":"2209.10248","paper":"/paper/bevstereo-enhancing-depth-estimation-in-multi","title":"BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo","date":"2022-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZRandomize/MatrixVT","path":"bevdepth/datasets/nusc_det_dataset.py","file_url":"https://github.com/ZRandomize/MatrixVT/blob/HEAD/bevdepth/datasets/nusc_det_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dd096050170984b","mcp_get_code":{"code_sha256":"2dd096050170984b"}},{"arxiv_id":"2010.09350","paper":"/paper/the-efficacy-of-neural-planning-metrics-a","title":"The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes","date":"2020-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nv-tlabs/planning-centric-metrics","path":"planning_centric_metrics/planning_kl.py","file_url":"https://github.com/nv-tlabs/planning-centric-metrics/blob/HEAD/planning_centric_metrics/planning_kl.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"619154382cb66a93","mcp_get_code":{"code_sha256":"619154382cb66a93"}},{"arxiv_id":"aaai_28449","paper":null,"title":"arXiv:aaai_28449","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cskkxjk/Vampire","path":"src/datasets/nusc_det_seg_dataset.py","file_url":"https://github.com/cskkxjk/Vampire/blob/HEAD/src/datasets/nusc_det_seg_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dd096050170984b","mcp_get_code":{"code_sha256":"2dd096050170984b"}},{"arxiv_id":"aaai_25233","paper":null,"title":"arXiv:aaai_25233","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Megvii-BaseDetection/BEVDepth","path":"bevdepth/datasets/nusc_det_dataset.py","file_url":"https://github.com/Megvii-BaseDetection/BEVDepth/blob/HEAD/bevdepth/datasets/nusc_det_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dd096050170984b","mcp_get_code":{"code_sha256":"2dd096050170984b"}}]}