{"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-mat","entry":"get_rot_mat","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":6,"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":3,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":7,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":1},"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":"2609.12454","paper":"/paper/arxiv-2609-12454","title":"Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"JuzhengMiao/Spatial-FAD","path":"custom_utils.py","file_url":"https://github.com/JuzhengMiao/Spatial-FAD/blob/HEAD/custom_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0bead498d7b3068","mcp_get_code":{"code_sha256":"f0bead498d7b3068"}},{"arxiv_id":"2409.15727","paper":"/paper/lapose-laplacian-mixture-shape-modeling-for","title":"LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation","date":"2024-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lolrudy/LaPose","path":"network/PoseNet.py","file_url":"https://github.com/lolrudy/LaPose/blob/HEAD/network/PoseNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7fc242fb372d5b3b","mcp_get_code":{"code_sha256":"7fc242fb372d5b3b"}},{"arxiv_id":"2403.12570","paper":"/paper/adapting-visual-language-models-for","title":"Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mediabrain-sjtu/mvfa-ad","path":"utils.py","file_url":"https://github.com/mediabrain-sjtu/mvfa-ad/blob/HEAD/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0bead498d7b3068","mcp_get_code":{"code_sha256":"f0bead498d7b3068"}},{"arxiv_id":"2402.18813","paper":"/paper/protein-multimer-structure-prediction-via","title":"Protein Multimer Structure Prediction via Prompt Learning","date":null,"month_inferred_from_arxiv_id":"2024-02","title_source":"archive","repo":"zqgao22/PromptMSP","path":"dimer/cal_db5_rmsd_prody.py","file_url":"https://github.com/zqgao22/PromptMSP/blob/HEAD/dimer/cal_db5_rmsd_prody.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"64ef8c256996fa77","mcp_get_code":{"code_sha256":"64ef8c256996fa77"}},{"arxiv_id":"2308.15939","paper":"/paper/anovl-adapting-vision-language-models-for","title":"Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization","date":"2023-08-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hq-deng/AnoVL","path":"utils.py","file_url":"https://github.com/hq-deng/AnoVL/blob/HEAD/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0bead498d7b3068","mcp_get_code":{"code_sha256":"f0bead498d7b3068"}},{"arxiv_id":"2212.06079","paper":"/paper/robust-perception-through-equivariance","title":"Robust Perception through Equivariance","date":"2022-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-columbia/equi4rob","path":"learning/sentive_loss.py","file_url":"https://github.com/cvlab-columbia/equi4rob/blob/HEAD/learning/sentive_loss.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":"f0bead498d7b3068","mcp_get_code":{"code_sha256":"f0bead498d7b3068"}},{"arxiv_id":"2111.07786","paper":"/paper/independent-se-3-equivariant-models-for-end-1","title":"Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking","date":"2021-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"octavian-ganea/equidock_public","path":"src/inference_rigid.py","file_url":"https://github.com/octavian-ganea/equidock_public/blob/HEAD/src/inference_rigid.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"64ef8c256996fa77","mcp_get_code":{"code_sha256":"64ef8c256996fa77"}}]}