{"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/getdataset","entry":"GetDataset","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":5,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"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":"2601.05355","paper":"/paper/arxiv-2601-05355","title":"An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"LeyingGuan/LCPexperiments","path":"datasets/datasets.py","file_url":"https://github.com/LeyingGuan/LCPexperiments/blob/HEAD/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e1180835f968ba1","mcp_get_code":{"code_sha256":"9e1180835f968ba1"}},{"arxiv_id":"2502.18508","paper":"/paper/refine-inversion-free-backdoor-defense-via","title":"REFINE: Inversion-Free Backdoor Defense via Model Reprogramming","date":"2025-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"whitolfchen/refine","path":"refine.py","file_url":"https://github.com/whitolfchen/refine/blob/HEAD/refine.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fdba41a2fab1727d","mcp_get_code":{"code_sha256":"fdba41a2fab1727d"}},{"arxiv_id":"2306.02732","paper":"/paper/conformal-prediction-with-missing-values","title":"Conformal Prediction with Missing Values","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mzaffran/conformalpredictionmissingvalues","path":"datasets.py","file_url":"https://github.com/mzaffran/conformalpredictionmissingvalues/blob/HEAD/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"66b8b878d49f845d","mcp_get_code":{"code_sha256":"66b8b878d49f845d"}},{"arxiv_id":"2302.12238","paper":"/paper/improving-adaptive-conformal-prediction-using","title":"Improving Adaptive Conformal Prediction Using Self-Supervised Learning","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seedatnabeel/sscp","path":"src/datasets.py","file_url":"https://github.com/seedatnabeel/sscp/blob/HEAD/src/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4eceac6d8e1b7e65","mcp_get_code":{"code_sha256":"4eceac6d8e1b7e65"}},{"arxiv_id":"2105.08747","paper":"/paper/conformal-histogram-regression","title":"Conformal Prediction using Conditional Histograms","date":"2021-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"msesia/chr","path":"experiments/dataset.py","file_url":"https://github.com/msesia/chr/blob/HEAD/experiments/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"93251605a6d98dbc","mcp_get_code":{"code_sha256":"93251605a6d98dbc"}}]}