{"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/speed","entry":"speed","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":2,"n_samples_ran":0,"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":0,"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":"2404.18911","paper":"/paper/kangaroo-lossless-self-speculative-decoding","title":"Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting","date":"2024-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Equationliu/Kangaroo","path":"evaluation/speed.py","file_url":"https://github.com/Equationliu/Kangaroo/blob/HEAD/evaluation/speed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aac7165992097239","mcp_get_code":{"code_sha256":"aac7165992097239"}},{"arxiv_id":"2110.00284","paper":"/paper/learning-reward-functions-from-scale-feedback","title":"Learning Reward Functions from Scale Feedback","date":"2021-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanford-iliad/reward-learning-scale-feedback","path":"feature.py","file_url":"https://github.com/stanford-iliad/reward-learning-scale-feedback/blob/HEAD/feature.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbfd88d6d34e2002","mcp_get_code":{"code_sha256":"bbfd88d6d34e2002"}},{"arxiv_id":"1910.04365","paper":"/paper/asking-easy-questions-a-user-friendly","title":"Asking Easy Questions: A User-Friendly Approach to Active Reward Learning","date":"2019-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Stanford-ILIAD/easy-active-learning","path":"feature.py","file_url":"https://github.com/Stanford-ILIAD/easy-active-learning/blob/HEAD/feature.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbfd88d6d34e2002","mcp_get_code":{"code_sha256":"bbfd88d6d34e2002"}},{"arxiv_id":"1906.07975","paper":"/paper/batch-active-learning-using-determinantal","title":"Batch Active Learning Using Determinantal Point Processes","date":"2019-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Stanford-ILIAD/DPP-Batch-Active-Learning","path":"reward_learning/feature.py","file_url":"https://github.com/Stanford-ILIAD/DPP-Batch-Active-Learning/blob/HEAD/reward_learning/feature.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbfd88d6d34e2002","mcp_get_code":{"code_sha256":"bbfd88d6d34e2002"}},{"arxiv_id":"1810.04303","paper":"/paper/batch-active-preference-based-learning-of","title":"Batch Active Preference-Based Learning of Reward Functions","date":"2018-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Stanford-ILIAD/batch-active-preference-based-learning","path":"feature.py","file_url":"https://github.com/Stanford-ILIAD/batch-active-preference-based-learning/blob/HEAD/feature.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbfd88d6d34e2002","mcp_get_code":{"code_sha256":"bbfd88d6d34e2002"}}]}