{"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/graph-partitioning/papers/ran/1","list_of":"/task/graph-partitioning","task":"graph partitioning","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":208,"with_a_code_link":69,"where_syntology_ran_a_sample":11,"not_listed_spam_title":0,"listed":208,"listed_where_code_ran":11,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":11,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":11,"listed_every_run_a_failure_of_syntologys_instrument":0,"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/graph-partitioning/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/distmlip-a-distributed-inference-platform-for","slug":"distmlip-a-distributed-inference-platform-for","title":"DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials","date":"2025-05-28","arxiv_id":"2506.02023","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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","sample_list":"/paper/distmlip-a-distributed-inference-platform-for#ran","syntology_url":"https://syntology.ai/paper/2506.02023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.02023"}},"official":{"repos":["AegisIK/DistMLIP"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-key-point-analysis-with-pairwise","slug":"exploring-key-point-analysis-with-pairwise","title":"Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning","date":"2024-04-17","arxiv_id":"2404.11384","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":1,"n_instrument":7,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":13,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 7 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/exploring-key-point-analysis-with-pairwise#ran","syntology_url":"https://syntology.ai/paper/2404.11384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11384"}},"official":{"repos":["alibaba-nlp/key-point-analysis"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/cuvler-enhanced-unsupervised-object","slug":"cuvler-enhanced-unsupervised-object","title":"CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers","date":"2024-03-12","arxiv_id":"2403.07700","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cuvler-enhanced-unsupervised-object#ran","syntology_url":"https://syntology.ai/paper/2403.07700","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07700"}},"official":{"repos":["shahaf-arica/cuvler"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-ladling-shockingly-simple-parallel-gnn","slug":"graph-ladling-shockingly-simple-parallel-gnn","title":"Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication","date":"2023-06-18","arxiv_id":"2306.10466","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-ladling-shockingly-simple-parallel-gnn#ran","syntology_url":"https://syntology.ai/paper/2306.10466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10466"}},"official":{"repos":["vita-group/graph_ladling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-solve-combinatorial-graph","slug":"learning-to-solve-combinatorial-graph","title":"Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration","date":"2022-05-27","arxiv_id":"2205.14105","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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) · 2 unverified","sample_list":"/paper/learning-to-solve-combinatorial-graph#ran","syntology_url":"https://syntology.ai/paper/2205.14105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14105"}},"official":{"repos":["tomdbar/ecord"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-spectral-methods-a-surprisingly-strong","slug":"deep-spectral-methods-a-surprisingly-strong","title":"Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization","date":"2022-05-16","arxiv_id":"2205.07839","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/deep-spectral-methods-a-surprisingly-strong#ran","syntology_url":"https://syntology.ai/paper/2205.07839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07839"}},"official":{"repos":["lukemelas/deep-spectral-segmentation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/learnable-graph-matching-incorporating-graph","slug":"learnable-graph-matching-incorporating-graph","title":"Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking","date":"2021-03-30","arxiv_id":"2103.16178","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":1,"n_ran_checked":3,"n_instrument":7,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":12,"phrase":"10 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learnable-graph-matching-incorporating-graph#ran","syntology_url":"https://syntology.ai/paper/2103.16178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16178"}},"official":{"repos":["jiaweihe1996/GMTracker"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/graph-neural-network-based-coarse-grained","slug":"graph-neural-network-based-coarse-grained","title":"Graph Neural Network Based Coarse-Grained Mapping Prediction","date":"2020-06-24","arxiv_id":"2007.04921","repositories_listed":2,"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/graph-neural-network-based-coarse-grained#ran","syntology_url":"https://syntology.ai/paper/2007.04921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04921"}},"official":{"repos":["rochesterxugroup/DSGPM","rochesterxugroup/HAM_dataset"],"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"]}}},{"url":"/paper/a-spectral-approach-to-unsupervised-object","slug":"a-spectral-approach-to-unsupervised-object","title":"A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time","date":"2019-07-05","arxiv_id":"1907.02731","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-spectral-approach-to-unsupervised-object#ran","syntology_url":"https://syntology.ai/paper/1907.02731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02731"}},"official":{"repos":["bit-ml/sfseg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/scalable-gromov-wasserstein-learning-for","slug":"scalable-gromov-wasserstein-learning-for","title":"Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching","date":"2019-05-18","arxiv_id":"1905.07645","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scalable-gromov-wasserstein-learning-for#ran","syntology_url":"https://syntology.ai/paper/1905.07645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.07645"}},"official":{"repos":["HongtengXu/s-gwl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-sublinear-time-indexing-for-nearest","slug":"learning-sublinear-time-indexing-for-nearest","title":"Learning Space Partitions for Nearest Neighbor Search","date":"2019-01-24","arxiv_id":"1901.08544","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-sublinear-time-indexing-for-nearest#ran","syntology_url":"https://syntology.ai/paper/1901.08544","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08544"}},"official":null}}],"record_sha256":"d837a20439525dbda5b68c9d7d312391d22afb2d49ba0a65f3b20a222aef4398","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}