{"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/build-from-cfg","entry":"build_from_cfg","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":7,"n_papers_ran":1,"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":1,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":6},"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":"2503.16429","paper":"/paper/sonata-self-supervised-learning-of-reliable","title":"Sonata: Self-Supervised Learning of Reliable Point Representations","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/sonata","path":"sonata/registry.py","file_url":"https://github.com/facebookresearch/sonata/blob/HEAD/sonata/registry.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":"5ded1e61a05c5a3b","mcp_get_code":{"code_sha256":"5ded1e61a05c5a3b"}},{"arxiv_id":"2503.08363","paper":"/paper/parametric-point-cloud-completion-for","title":"Parametric Point Cloud Completion for Polygonal Surface Reconstruction","date":"2025-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"parametric-completion/paco","path":"utils/registry.py","file_url":"https://github.com/parametric-completion/paco/blob/HEAD/utils/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"458c1e96997138e0","mcp_get_code":{"code_sha256":"458c1e96997138e0"}},{"arxiv_id":"2503.06800","paper":"/paper/videophy-2-a-challenging-action-centric","title":"VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation","date":"2025-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hritikbansal/videophy","path":"VIDEOPHY2/data_utils/registry.py","file_url":"https://github.com/Hritikbansal/videophy/blob/HEAD/VIDEOPHY2/data_utils/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df4a72cfc8859ecd","mcp_get_code":{"code_sha256":"df4a72cfc8859ecd"}},{"arxiv_id":"2410.00263","paper":"/paper/procedure-aware-surgical-video-language","title":"Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation","date":"2024-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CAMMA-public/SurgVLP","path":"surgvlp/mmcv.py","file_url":"https://github.com/CAMMA-public/SurgVLP/blob/HEAD/surgvlp/mmcv.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6c6f8816d16c800","mcp_get_code":{"code_sha256":"b6c6f8816d16c800"}},{"arxiv_id":"2401.14729","paper":"/paper/sketch-and-refine-towards-fast-and-accurate","title":"Sketch and Refine: Towards Fast and Accurate Lane Detection","date":"2024-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"passerer/SRLane","path":"srlane/registry.py","file_url":"https://github.com/passerer/SRLane/blob/HEAD/srlane/registry.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"eb6c9e7e0571124f","mcp_get_code":{"code_sha256":"eb6c9e7e0571124f"}},{"arxiv_id":"2110.10538","paper":"/paper/anisotropic-separable-set-abstraction-for","title":"ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning","date":"2021-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guochengqian/assanet","path":"models/encoder/assanet_encoder.py","file_url":"https://github.com/guochengqian/assanet/blob/HEAD/models/encoder/assanet_encoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"17095c6950af969e","mcp_get_code":{"code_sha256":"17095c6950af969e"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hukkelas/DSFD-Pytorch-Inference","path":"face_detection/registry.py","file_url":"https://github.com/hukkelas/DSFD-Pytorch-Inference/blob/HEAD/face_detection/registry.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":"8241f65f228cbc9d","mcp_get_code":{"code_sha256":"8241f65f228cbc9d"}}]}