{"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/cdf","entry":"cdf","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":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":4,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":4,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"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":"2501.09740","paper":"/paper/regulation-of-algorithmic-collusion-refined","title":"Regulation of Algorithmic Collusion, Refined: Testing Pessimistic Calibrated Regret","date":null,"month_inferred_from_arxiv_id":"2025-01","title_source":"archive","repo":"wangchang327/collusion-cslaw25","path":"config.py","file_url":"https://github.com/wangchang327/collusion-cslaw25/blob/HEAD/config.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":"1714e755f0ecbce7","mcp_get_code":{"code_sha256":"1714e755f0ecbce7"}},{"arxiv_id":"2401.02413","paper":"/paper/simulation-based-inference-with-quantile","title":"Simulation-Based Inference with Quantile Regression","date":"2024-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"h3jia/nqe","path":"nqe/qnet.py","file_url":"https://github.com/h3jia/nqe/blob/HEAD/nqe/qnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"46ab70b65a6d269a","mcp_get_code":{"code_sha256":"46ab70b65a6d269a"}},{"arxiv_id":"2305.00934","paper":"/paper/variational-inference-for-bayesian-neural","title":"Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty","date":"2023-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"e477c45bcdf7c37e","mcp_get_code":{"code_sha256":"e477c45bcdf7c37e"}},{"arxiv_id":"1903.07594","paper":"/paper/combining-model-and-parameter-uncertainty-in","title":"Combining Model and Parameter Uncertainty in Bayesian Neural Networks","date":"2019-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aliaksah/variational-inference-for-bayesian-neural-networks-under-model-and-parameter-uncertainty","path":"FMNIST/BNN-GP-CMF-FMNIST.py","file_url":"https://github.com/aliaksah/variational-inference-for-bayesian-neural-networks-under-model-and-parameter-uncertainty/blob/HEAD/FMNIST/BNN-GP-CMF-FMNIST.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e477c45bcdf7c37e","mcp_get_code":{"code_sha256":"e477c45bcdf7c37e"}},{"arxiv_id":"1305.0215","paper":"/paper/powerlaw-a-python-package-for-analysis-of","title":"Powerlaw: a Python package for analysis of heavy-tailed distributions","date":null,"month_inferred_from_arxiv_id":"2013-05","title_source":"archive","repo":"jeffalstott/powerlaw","path":"powerlaw/statistics.py","file_url":"https://github.com/jeffalstott/powerlaw/blob/HEAD/powerlaw/statistics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d45632b465f9418","mcp_get_code":{"code_sha256":"8d45632b465f9418"}}]}