{"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/bound","entry":"bound","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":0,"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":0,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":6},"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":"2302.11665","paper":"/paper/alpaserve-statistical-multiplexing-with-model","title":"AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving","date":"2023-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alpa-projects/alpa","path":"alpa/mesh_profiling.py","file_url":"https://github.com/alpa-projects/alpa/blob/HEAD/alpa/mesh_profiling.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":"2ad6f0eafb1d0278","mcp_get_code":{"code_sha256":"2ad6f0eafb1d0278"}},{"arxiv_id":"2003.00973","paper":"/paper/differential-privacy-at-risk-bridging","title":"Differential Privacy at Risk: Bridging Randomness and Privacy Budget","date":"2020-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashishdandekar/Privacy-at-risk","path":"formulae.py","file_url":"https://github.com/ashishdandekar/Privacy-at-risk/blob/HEAD/formulae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"e204aded3269579c","mcp_get_code":{"code_sha256":"e204aded3269579c"}},{"arxiv_id":"2003.00973","paper":"/paper/differential-privacy-at-risk-bridging","title":"Differential Privacy at Risk: Bridging Randomness and Privacy Budget","date":"2020-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashishdandekar/Privacy-at-risk","path":"composition.py","file_url":"https://github.com/ashishdandekar/Privacy-at-risk/blob/HEAD/composition.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"40742f74ebb94401","mcp_get_code":{"code_sha256":"40742f74ebb94401"}},{"arxiv_id":"2002.06836","paper":"/paper/control-frequency-adaptation-via-action","title":"Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning","date":"2020-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albertometelli/pfqi","path":"trlib/environments/acrobot_multitask.py","file_url":"https://github.com/albertometelli/pfqi/blob/HEAD/trlib/environments/acrobot_multitask.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dea6e2e4ceb554b6","mcp_get_code":{"code_sha256":"dea6e2e4ceb554b6"}},{"arxiv_id":"1810.09103","paper":"/paper/actor-expert-a-framework-for-using-action","title":"Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement","date":"2018-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samuelfneumann/greedyac","path":"env/Acrobot.py","file_url":"https://github.com/samuelfneumann/greedyac/blob/HEAD/env/Acrobot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9491f5abebf0343a","mcp_get_code":{"code_sha256":"9491f5abebf0343a"}},{"arxiv_id":"1509.01644","paper":"/paper/reinforcement-learning-with-parameterized","title":"Reinforcement Learning with Parameterized Actions","date":"2015-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cycraig/gym-goal","path":"gym_goal/envs/util.py","file_url":"https://github.com/cycraig/gym-goal/blob/HEAD/gym_goal/envs/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5cade6842144e12c","mcp_get_code":{"code_sha256":"5cade6842144e12c"}}]}