{"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-schedule-fn","entry":"get_schedule_fn","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":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":4,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"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":"2501.13767","paper":"/paper/an-efficient-diffusion-based-non","title":"An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem","date":"2025-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deitsp/deitsp","path":"utils/lr_schedulers.py","file_url":"https://github.com/deitsp/deitsp/blob/HEAD/utils/lr_schedulers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b04bf96acaa8e607","mcp_get_code":{"code_sha256":"b04bf96acaa8e607"}},{"arxiv_id":"2410.02942","paper":"/paper/symmetricdiffusers-learning-discrete","title":"SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DSL-Lab/SymmetricDiffusers","path":"lr_schedulers.py","file_url":"https://github.com/DSL-Lab/SymmetricDiffusers/blob/HEAD/lr_schedulers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"529d8aee3aacc498","mcp_get_code":{"code_sha256":"529d8aee3aacc498"}},{"arxiv_id":"2302.08224","paper":"/paper/difusco-graph-based-diffusion-solvers-for-1","title":"DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization","date":"2023-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edward-sun/difusco","path":"difusco/utils/lr_schedulers.py","file_url":"https://github.com/edward-sun/difusco/blob/HEAD/difusco/utils/lr_schedulers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b04bf96acaa8e607","mcp_get_code":{"code_sha256":"b04bf96acaa8e607"}},{"arxiv_id":"2103.07607","paper":"/paper/solving-compositional-reinforcement-learning-1","title":"Solving Compositional Reinforcement Learning Problems via Task Reduction","date":"2021-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IrisLi17/self-imitation-via-reduction","path":"baselines/ppo_sir/ppo_sir.py","file_url":"https://github.com/IrisLi17/self-imitation-via-reduction/blob/HEAD/baselines/ppo_sir/ppo_sir.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e9a0454914deaf73","mcp_get_code":{"code_sha256":"e9a0454914deaf73"}},{"arxiv_id":"2007.12401","paper":"/paper/predictive-information-accelerates-learning","title":"Predictive Information Accelerates Learning in RL","date":"2020-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/pisac","path":"pisac/schedule_utils.py","file_url":"https://github.com/google-research/pisac/blob/HEAD/pisac/schedule_utils.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":"b8a75ea16d98165d","mcp_get_code":{"code_sha256":"b8a75ea16d98165d"}}]}