{"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/get-positive-expectation","entry":"get_positive_expectation","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-25T09:33:49+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":5,"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":6,"n_samples_ran":6,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":3,"ran":3,"unverified":0},"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":"2406.14929","paper":"/paper/efficient-graph-similarity-computation-with","title":"Efficient Graph Similarity Computation with Alignment Regularization","date":"2024-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhuow/eric","path":"model/GSC.py","file_url":"https://github.com/jhuow/eric/blob/HEAD/model/GSC.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"677840a48213de47","mcp_get_code":{"code_sha256":"677840a48213de47"}},{"arxiv_id":"2406.14929","paper":"/paper/efficient-graph-similarity-computation-with","title":"Efficient Graph Similarity Computation with Alignment Regularization","date":"2024-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JhuoW/ERIC","path":"utils/gan_losses.py","file_url":"https://github.com/JhuoW/ERIC/blob/HEAD/utils/gan_losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"565c2a174cca323f","mcp_get_code":{"code_sha256":"565c2a174cca323f"}},{"arxiv_id":"2402.16302","paper":"/paper/graph-diffusion-policy-optimization","title":"Graph Diffusion Policy Optimization","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/gdpo","path":"model/infomax.py","file_url":"https://github.com/sail-sg/gdpo/blob/HEAD/model/infomax.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a17d699ffe7e149f","mcp_get_code":{"code_sha256":"a17d699ffe7e149f"}},{"arxiv_id":"2401.17580","paper":"/paper/graph-contrastive-learning-with-cohesive","title":"Graph Contrastive Learning with Cohesive Subgraph Awareness","date":"2024-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wuyucheng2002/ctaug","path":"CTAug/model.py","file_url":"https://github.com/wuyucheng2002/ctaug/blob/HEAD/CTAug/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f35d8f7e5f4d9c27","mcp_get_code":{"code_sha256":"f35d8f7e5f4d9c27"}},{"arxiv_id":"2203.08553","paper":"/paper/pmic-improving-multi-agent-reinforcement-1","title":"PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yeshenpy/pmic","path":"algorithms/mpe_new_maxminMADDPG.py","file_url":"https://github.com/yeshenpy/pmic/blob/HEAD/algorithms/mpe_new_maxminMADDPG.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0edef8661a209cf5","mcp_get_code":{"code_sha256":"0edef8661a209cf5"}},{"arxiv_id":"2105.06247","paper":"/paper/video-corpus-moment-retrieval-with","title":"Video Corpus Moment Retrieval with Contrastive Learning","date":"2021-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IsaacChanghau/ReLoCLNet","path":"method_tvr/contrastive.py","file_url":"https://github.com/IsaacChanghau/ReLoCLNet/blob/HEAD/method_tvr/contrastive.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d71a913407877e0a","mcp_get_code":{"code_sha256":"d71a913407877e0a"}}]}