{"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/bfs","entry":"bfs","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":5,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"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":"2406.07897","paper":"/paper/when-do-skills-help-reinforcement-learning-a","title":"When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions","date":"2024-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uranium11010/rl-skill-theory","path":"rldiff_envs.py","file_url":"https://github.com/uranium11010/rl-skill-theory/blob/HEAD/rldiff_envs.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"66e887f6d6eb02c2","mcp_get_code":{"code_sha256":"66e887f6d6eb02c2"}},{"arxiv_id":"2404.00909","paper":"/paper/learning-by-correction-efficient-tuning-task","title":"Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shtuplus/iccc_cvpr2024","path":"iccc_data_generation.py","file_url":"https://github.com/shtuplus/iccc_cvpr2024/blob/HEAD/iccc_data_generation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"d7775e9e08c81ed2","mcp_get_code":{"code_sha256":"d7775e9e08c81ed2"}},{"arxiv_id":"2402.11628","paper":"/paper/discrete-neural-algorithmic-reasoning","title":"Discrete Neural Algorithmic Reasoning","date":"2024-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yandex-research/dnar","path":"generate_data.py","file_url":"https://github.com/yandex-research/dnar/blob/HEAD/generate_data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1b250b791459ce0e","mcp_get_code":{"code_sha256":"1b250b791459ce0e"}},{"arxiv_id":"2303.00837","paper":"/paper/predictive-flows-for-faster-ford-fulkerson","title":"Predictive Flows for Faster Ford-Fulkerson","date":null,"month_inferred_from_arxiv_id":"2023-03","title_source":"archive","repo":"wang-yuyan/warmstart-graphcut-algorithms-pulic","path":"augmentingPath.py","file_url":"https://github.com/wang-yuyan/warmstart-graphcut-algorithms-pulic/blob/HEAD/augmentingPath.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fa75a7ef81db9995","mcp_get_code":{"code_sha256":"fa75a7ef81db9995"}},{"arxiv_id":"2212.11042","paper":"/paper/hi-lassie-high-fidelity-articulated-shape-and","title":"Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble","date":"2022-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/hi-lassie","path":"main/extract_skeleton.py","file_url":"https://github.com/google/hi-lassie/blob/HEAD/main/extract_skeleton.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":"135f37701edf71c1","mcp_get_code":{"code_sha256":"135f37701edf71c1"}}]}