{"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/gather-nd","entry":"gather_nd","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":6,"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":7,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":1,"ran":0,"unverified":4},"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":"2603.07454","paper":"/paper/arxiv-2603-07454","title":"SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognition","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"m-saeid/SLNet","path":"model_slnet_t/encoder.py","file_url":"https://github.com/m-saeid/SLNet/blob/HEAD/model_slnet_t/encoder.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"669c4efc9ce03507","mcp_get_code":{"code_sha256":"669c4efc9ce03507"}},{"arxiv_id":"2304.06116","paper":"/paper/autoshot-a-short-video-dataset-and-state-of-1","title":"AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection","date":"2023-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wentaozhu/AutoShot","path":"supernet_flattransf_3_8_8_8_13_12_0_16_60.py","file_url":"https://github.com/wentaozhu/AutoShot/blob/HEAD/supernet_flattransf_3_8_8_8_13_12_0_16_60.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36fdeb9e221db861","mcp_get_code":{"code_sha256":"36fdeb9e221db861"}},{"arxiv_id":"2111.07668","paper":"/paper/fast-axiomatic-attribution-for-neural","title":"Fast Axiomatic Attribution for Neural Networks","date":"2021-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/fast-axiomatic-attribution","path":"imagenet/utils/expected_gradients.py","file_url":"https://github.com/visinf/fast-axiomatic-attribution/blob/HEAD/imagenet/utils/expected_gradients.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c07c10d6dabb86be","mcp_get_code":{"code_sha256":"c07c10d6dabb86be"}},{"arxiv_id":"2106.04779","paper":"/paper/point-cloud-upsampling-via-disentangled","title":"Point Cloud Upsampling via Disentangled Refinement","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryanphilly/DIS-PU-pytorch","path":"dispu/generator.py","file_url":"https://github.com/ryanphilly/DIS-PU-pytorch/blob/HEAD/dispu/generator.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":"cca0f5613a67743b","mcp_get_code":{"code_sha256":"cca0f5613a67743b"}},{"arxiv_id":"1906.10670","paper":"/paper/learning-explainable-models-using-attribution","title":"Improving performance of deep learning models with axiomatic attribution priors and expected gradients","date":"2019-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"suinleelab/attributionpriors","path":"attributionpriors/pytorch_ops.py","file_url":"https://github.com/suinleelab/attributionpriors/blob/HEAD/attributionpriors/pytorch_ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f017ff3e185b740","mcp_get_code":{"code_sha256":"1f017ff3e185b740"}},{"arxiv_id":"aaai_25161","paper":null,"title":"arXiv:aaai_25161","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"THU-LYJ-Lab/DarkFeat","path":"darkfeat.py","file_url":"https://github.com/THU-LYJ-Lab/DarkFeat/blob/HEAD/darkfeat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"502520365b2084c9","mcp_get_code":{"code_sha256":"502520365b2084c9"}},{"arxiv_id":"aaai_25161","paper":null,"title":"arXiv:aaai_25161","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"THU-LYJ-Lab/DarkFeat","path":"nets/geom.py","file_url":"https://github.com/THU-LYJ-Lab/DarkFeat/blob/HEAD/nets/geom.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0424d60f39f241f4","mcp_get_code":{"code_sha256":"0424d60f39f241f4"}}]}