{"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/factorial","entry":"factorial","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":2,"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":2,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":3},"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":"2409.02606","paper":null,"title":"arXiv:2409.02606","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":null,"repo":"princetonlips/neural_fdm","path":"src/neural_fdm/generators/bezier.py","file_url":"https://github.com/princetonlips/neural_fdm/blob/HEAD/src/neural_fdm/generators/bezier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b9e2c3a69d0028fa","mcp_get_code":{"code_sha256":"b9e2c3a69d0028fa"}},{"arxiv_id":"2402.02686","paper":"/paper/multi-region-markovian-gaussian-process-an","title":"Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions","date":"2024-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weihanlikk/mrm-gp","path":"utils.py","file_url":"https://github.com/weihanlikk/mrm-gp/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f2867b206304f4ae","mcp_get_code":{"code_sha256":"f2867b206304f4ae"}},{"arxiv_id":"2309.03882","paper":"/paper/on-large-language-models-selection-bias-in","title":"Large Language Models Are Not Robust Multiple Choice Selectors","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chujiezheng/llm-mcq-bias","path":"code/debias_utils.py","file_url":"https://github.com/chujiezheng/llm-mcq-bias/blob/HEAD/code/debias_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0b23663382f28f16","mcp_get_code":{"code_sha256":"0b23663382f28f16"}},{"arxiv_id":"2304.12217","paper":"/paper/impact-oriented-contextual-scholar-profiling","title":"Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs","date":"2023-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visdata/geneticflow","path":"Alphabetically_Ordered_Paper_Detection/alphabetically_ordered_paper_detection_algorithm.py","file_url":"https://github.com/visdata/geneticflow/blob/HEAD/Alphabetically_Ordered_Paper_Detection/alphabetically_ordered_paper_detection_algorithm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5c8511205a777da","mcp_get_code":{"code_sha256":"f5c8511205a777da"}},{"arxiv_id":"2111.01721","paper":"/paper/bayes-newton-methods-for-approximate-bayesian","title":"Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees","date":"2021-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AaltoML/BayesNewton","path":"bayesnewton/kernels.py","file_url":"https://github.com/AaltoML/BayesNewton/blob/HEAD/bayesnewton/kernels.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":"286d7367835461f3","mcp_get_code":{"code_sha256":"286d7367835461f3"}}]}