{"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/parse-log","entry":"parse_log","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":9,"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":10,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":10,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":8},"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":"2505.20840","paper":null,"title":"arXiv:2505.20840","date":null,"month_inferred_from_arxiv_id":"2025-05","title_source":null,"repo":"dooho00/agg-buffer","path":"evaluate/summarize.py","file_url":"https://github.com/dooho00/agg-buffer/blob/HEAD/evaluate/summarize.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":"501fc81334d9c0ba","mcp_get_code":{"code_sha256":"501fc81334d9c0ba"}},{"arxiv_id":"2502.17041","paper":"/paper/privaci-bench-evaluating-privacy-with","title":"PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance","date":"2025-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUST-KnowComp/PrivaCI-Bench","path":"search_content_for_answer.py","file_url":"https://github.com/HKUST-KnowComp/PrivaCI-Bench/blob/HEAD/search_content_for_answer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"58431336c8d2ad48","mcp_get_code":{"code_sha256":"58431336c8d2ad48"}},{"arxiv_id":"2408.01952","paper":"/paper/2408-01952","title":"CACE-Net: Co-guidance Attention and Contrastive Enhancement for Effective Audio-Visual Event Localization","date":"2024-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brain-cog-lab/cace-net","path":"CACE/plt_fig.py","file_url":"https://github.com/brain-cog-lab/cace-net/blob/HEAD/CACE/plt_fig.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ccf5c65f536b8a8","mcp_get_code":{"code_sha256":"8ccf5c65f536b8a8"}},{"arxiv_id":"2403.16218","paper":"/paper/coverup-coverage-guided-llm-based-test","title":"CoverUp: Effective High Coverage Test Generation for Python","date":"2024-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plasma-umass/coverup","path":"src/coverup/logreader.py","file_url":"https://github.com/plasma-umass/coverup/blob/HEAD/src/coverup/logreader.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":"f5192e3a7fcb2025","mcp_get_code":{"code_sha256":"f5192e3a7fcb2025"}},{"arxiv_id":"2004.10924","paper":"/paper/polylanenet-lane-estimation-via-deep","title":"PolyLaneNet: Lane Estimation via Deep Polynomial Regression","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucastabelini/PolyLaneNet","path":"utils/plot_log.py","file_url":"https://github.com/lucastabelini/PolyLaneNet/blob/HEAD/utils/plot_log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d1c3c14d98baf762","mcp_get_code":{"code_sha256":"d1c3c14d98baf762"}},{"arxiv_id":"2001.01920","paper":"/paper/feddane-a-federated-newton-type-method","title":"FedDANE: A Federated Newton-Type Method","date":"2020-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"litian96/FedDANE","path":"plot.py","file_url":"https://github.com/litian96/FedDANE/blob/HEAD/plot.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1dc8b48ea84cc7e2","mcp_get_code":{"code_sha256":"1dc8b48ea84cc7e2"}},{"arxiv_id":"1905.05393","paper":"/paper/190505393","title":"Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arcelien/pba","path":"pba/utils.py","file_url":"https://github.com/arcelien/pba/blob/HEAD/pba/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"363604d5223dc581","mcp_get_code":{"code_sha256":"363604d5223dc581"}},{"arxiv_id":"1812.06127","paper":"/paper/federated-optimization-for-heterogeneous","title":"Federated Optimization in Heterogeneous Networks","date":"2018-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"litian96/FedProx","path":"plot_fig2.py","file_url":"https://github.com/litian96/FedProx/blob/HEAD/plot_fig2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d95adf387c731eb","mcp_get_code":{"code_sha256":"3d95adf387c731eb"}},{"arxiv_id":"1812.06127","paper":"/paper/federated-optimization-for-heterogeneous","title":"Federated Optimization in Heterogeneous Networks","date":"2018-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"litian96/FedProx","path":"plot_final_e20.py","file_url":"https://github.com/litian96/FedProx/blob/HEAD/plot_final_e20.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5391ae7139f5e1e0","mcp_get_code":{"code_sha256":"5391ae7139f5e1e0"}},{"arxiv_id":"1805.11462","paper":"/paper/opennmt-neural-machine-translation-toolkit","title":"OpenNMT: Neural Machine Translation Toolkit","date":"2018-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deep-spin/OpenNMT-entmax","path":"read_translation_log.py","file_url":"https://github.com/deep-spin/OpenNMT-entmax/blob/HEAD/read_translation_log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f00183431131cace","mcp_get_code":{"code_sha256":"f00183431131cace"}}]}