{"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/recvall","entry":"recvall","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":1,"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":"2602.13700","paper":"/paper/arxiv-2602-13700","title":"Optimal Regret for Policy Optimization in Contextual Bandits","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"vowpalwabbit/vowpal_wabbit","path":"utl/active_interactor.py","file_url":"https://github.com/vowpalwabbit/vowpal_wabbit/blob/HEAD/utl/active_interactor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"46032debef5e34c8","mcp_get_code":{"code_sha256":"46032debef5e34c8"}},{"arxiv_id":"2311.06295","paper":"/paper/gradual-optimization-learning-for","title":"Gradual Optimization Learning for Conformational Energy Minimization","date":"2023-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airi-institute/golf","path":"env/dft.py","file_url":"https://github.com/airi-institute/golf/blob/HEAD/env/dft.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f33201b615cc0380","mcp_get_code":{"code_sha256":"f33201b615cc0380"}},{"arxiv_id":"2103.12352","paper":"/paper/imap-implicit-mapping-and-positioning-in-real","title":"iMAP: Implicit Mapping and Positioning in Real-Time","date":"2021-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xiaorui-Huang/DISORF","path":"server_end/packet_subscriber.py","file_url":"https://github.com/Xiaorui-Huang/DISORF/blob/HEAD/server_end/packet_subscriber.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":"d11ca86a6274fd9a","mcp_get_code":{"code_sha256":"d11ca86a6274fd9a"}},{"arxiv_id":"2003.13376","paper":"/paper/end-to-end-evaluation-of-federated-learning","title":"End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Minki-Kim95/Federated-Learning-and-Split-Learning-with-raspberry-pi","path":"federated_learning/cifar10_Resnet20/cifar10_resnet20_fd_client_rasp.py","file_url":"https://github.com/Minki-Kim95/Federated-Learning-and-Split-Learning-with-raspberry-pi/blob/HEAD/federated_learning/cifar10_Resnet20/cifar10_resnet20_fd_client_rasp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"19c7476768726041","mcp_get_code":{"code_sha256":"19c7476768726041"}},{"arxiv_id":"aaai_19877","paper":null,"title":"arXiv:aaai_19877","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"snuhcs/veca","path":"veca/utils.py","file_url":"https://github.com/snuhcs/veca/blob/HEAD/veca/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a7572e6adf4af42","mcp_get_code":{"code_sha256":"3a7572e6adf4af42"}}]}