{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/listops/papers/ran/1","list_of":"/task/listops","task":"ListOps","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,11],"of":11,"counts":{"archive_papers_tagged":22,"with_a_code_link":17,"where_syntology_ran_a_sample":11,"not_listed_spam_title":0,"listed":22,"listed_where_code_ran":11,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":10,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":10,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/listops/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/sequence-modeling-with-multiresolution","slug":"sequence-modeling-with-multiresolution","title":"Sequence Modeling with Multiresolution Convolutional Memory","date":"2023-05-02","arxiv_id":"2305.01638","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/sequence-modeling-with-multiresolution#ran","syntology_url":"https://syntology.ai/paper/2305.01638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.01638"}},"official":{"repos":["thjashin/multires-conv"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/mega-moving-average-equipped-gated-attention","slug":"mega-moving-average-equipped-gated-attention","title":"Mega: Moving Average Equipped Gated Attention","date":"2022-09-21","arxiv_id":"2209.10655","repositories_listed":7,"syntology":{"n":13,"n_ran":12,"n_constructed":6,"n_ran_checked":8,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"12 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mega-moving-average-equipped-gated-attention#ran","syntology_url":"https://syntology.ai/paper/2209.10655","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.10655"}},"official":{"repos":["facebookresearch/mega"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/simplified-state-space-layers-for-sequence","slug":"simplified-state-space-layers-for-sequence","title":"Simplified State Space Layers for Sequence Modeling","date":"2022-08-09","arxiv_id":"2208.04933","repositories_listed":6,"syntology":{"n":19,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":6,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/simplified-state-space-layers-for-sequence#ran","syntology_url":"https://syntology.ai/paper/2208.04933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.04933"}},"official":{"repos":["lindermanlab/S5"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/training-discrete-deep-generative-models-via","slug":"training-discrete-deep-generative-models-via","title":"Training Discrete Deep Generative Models via Gapped Straight-Through Estimator","date":"2022-06-15","arxiv_id":"2206.07235","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/training-discrete-deep-generative-models-via#ran","syntology_url":"https://syntology.ai/paper/2206.07235","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07235"}},"official":{"repos":["chijames/gst"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-token-normalization-improves-vision-1","slug":"dynamic-token-normalization-improves-vision-1","title":"Dynamic Token Normalization Improves Vision Transformers","date":"2021-12-05","arxiv_id":"2112.02624","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/dynamic-token-normalization-improves-vision-1#ran","syntology_url":"https://syntology.ai/paper/2112.02624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02624"}},"official":{"repos":["wqshao126/dtn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficiently-modeling-long-sequences-with-1","slug":"efficiently-modeling-long-sequences-with-1","title":"Efficiently Modeling Long Sequences with Structured State Spaces","date":"2021-10-31","arxiv_id":"2111.00396","repositories_listed":8,"syntology":{"n":55,"n_ran":37,"n_constructed":8,"n_ran_checked":21,"n_instrument":16,"n_unverified":18,"n_honours":2,"n_violates":1,"n_no_contract":18,"n_pointer_only":3,"phrase":"37 ran (of which 8 constructed an object rather than computing a result; 21 with no instrument failure: 2 honoured, 1 violated, 18 with no contract checked; 16 where Syntology's instrument failed) · 18 unverified","sample_list":"/paper/efficiently-modeling-long-sequences-with-1#ran","syntology_url":"https://syntology.ai/paper/2111.00396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.00396"}},"official":{"repos":["state-spaces/s4"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["community","listed","official"]}}},{"url":"/paper/the-neural-data-router-adaptive-control-flow","slug":"the-neural-data-router-adaptive-control-flow","title":"The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization","date":"2021-10-14","arxiv_id":"2110.07732","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":5,"n_ran_checked":6,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/the-neural-data-router-adaptive-control-flow#ran","syntology_url":"https://syntology.ai/paper/2110.07732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07732"}},"official":{"repos":["robertcsordas/ndr"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/going-beyond-linear-transformers-with","slug":"going-beyond-linear-transformers-with","title":"Going Beyond Linear Transformers with Recurrent Fast Weight Programmers","date":"2021-06-11","arxiv_id":"2106.06295","repositories_listed":5,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/going-beyond-linear-transformers-with#ran","syntology_url":"https://syntology.ai/paper/2106.06295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06295"}},"official":{"repos":["IDSIA/recurrent-fwp"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/modeling-hierarchical-structures-with","slug":"modeling-hierarchical-structures-with","title":"Modeling Hierarchical Structures with Continuous Recursive Neural Networks","date":"2021-06-10","arxiv_id":"2106.06038","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/modeling-hierarchical-structures-with#ran","syntology_url":"https://syntology.ai/paper/2106.06038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06038"}},"official":{"repos":["JRC1995/Continuous-RvNN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/long-range-arena-a-benchmark-for-efficient-1","slug":"long-range-arena-a-benchmark-for-efficient-1","title":"Long Range Arena: A Benchmark for Efficient Transformers","date":"2020-11-08","arxiv_id":"2011.04006","repositories_listed":5,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/long-range-arena-a-benchmark-for-efficient-1#ran","syntology_url":"https://syntology.ai/paper/2011.04006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.04006"}},"official":{"repos":["google-research/long-range-arena"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/ordered-memory","slug":"ordered-memory","title":"Ordered Memory","date":"2019-10-29","arxiv_id":"1910.13466","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ordered-memory#ran","syntology_url":"https://syntology.ai/paper/1910.13466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13466"}},"official":{"repos":["yikangshen/Ordered-Memory"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"a4b6a497bbb16ab79731ceb632054cc6e059bed8a67dd31de1ef1ade3039f7a2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}