{"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-generation/papers/ran/1","list_of":"/task/graph-generation","task":"Graph Generation","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":2,"rows_per_page":100,"rows":[1,100],"of":111,"counts":{"archive_papers_tagged":712,"with_a_code_link":326,"where_syntology_ran_a_sample":111,"not_listed_spam_title":0,"listed":712,"listed_where_code_ran":111,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":98,"every_run_a_failure_of_syntologys_instrument":13,"listed_with_a_run_with_no_instrument_failure":98,"listed_every_run_a_failure_of_syntologys_instrument":13,"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-generation/papers/ran/1","prev":null,"next":"/task/graph-generation/papers/ran/2","papers":[{"url":"/paper/discosg-towards-discourse-level-text-scene","slug":"discosg-towards-discourse-level-text-scene","title":"DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement","date":"2025-06-18","arxiv_id":"2506.15583","repositories_listed":2,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":16,"n_pointer_only":19,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 1 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discosg-towards-discourse-level-text-scene#ran","syntology_url":"https://syntology.ai/paper/2506.15583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.15583"}},"official":{"repos":["shaoqlin/discosg"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/directed-graph-grammars-for-sequence-based","slug":"directed-graph-grammars-for-sequence-based","title":"Directed Graph Grammars for Sequence-based Learning","date":"2025-05-29","arxiv_id":"2505.22949","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 3 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) · 2 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/directed-graph-grammars-for-sequence-based#ran","syntology_url":"https://syntology.ai/paper/2505.22949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.22949"}},"official":{"repos":["shiningsunnyday/induction"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/gcal-adapting-graph-models-to-evolving-domain","slug":"gcal-adapting-graph-models-to-evolving-domain","title":"GCAL: Adapting Graph Models to Evolving Domain Shifts","date":"2025-05-22","arxiv_id":"2505.16860","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":1,"n_ran_checked":4,"n_instrument":3,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":10,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/gcal-adapting-graph-models-to-evolving-domain#ran","syntology_url":"https://syntology.ai/paper/2505.16860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.16860"}},"official":{"repos":["joe817/gcal"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/critical-iterative-denoising-a-discrete","slug":"critical-iterative-denoising-a-discrete","title":"Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs","date":"2025-03-27","arxiv_id":"2503.21592","repositories_listed":0,"syntology":{"n":10,"n_ran":9,"n_constructed":2,"n_ran_checked":6,"n_instrument":3,"n_unverified":1,"n_honours":3,"n_violates":1,"n_no_contract":2,"n_pointer_only":10,"phrase":"9 ran (of which 2 constructed an object rather than computing a result; 6 with no instrument failure: 3 honoured, 1 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/critical-iterative-denoising-a-discrete#ran","syntology_url":"https://syntology.ai/paper/2503.21592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.21592"}},"official":null}},{"url":"/paper/weakly-supervised-video-scene-graph","slug":"weakly-supervised-video-scene-graph","title":"Weakly Supervised Video Scene Graph Generation via Natural Language Supervision","date":"2025-02-21","arxiv_id":"2502.15370","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weakly-supervised-video-scene-graph#ran","syntology_url":"https://syntology.ai/paper/2502.15370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.15370"}},"official":{"repos":["rlqja1107/NL-VSGG"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fragfm-efficient-fragment-based-molecular","slug":"fragfm-efficient-fragment-based-molecular","title":"FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching","date":"2025-02-19","arxiv_id":"2502.15805","repositories_listed":0,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/fragfm-efficient-fragment-based-molecular#ran","syntology_url":"https://syntology.ai/paper/2502.15805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.15805"}},"official":null}},{"url":"/paper/towards-fast-graph-generation-via","slug":"towards-fast-graph-generation-via","title":"Towards Fast Graph Generation via Autoregressive Noisy Filtration Modeling","date":"2025-02-04","arxiv_id":"2502.02415","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-fast-graph-generation-via#ran","syntology_url":"https://syntology.ai/paper/2502.02415","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.02415"}},"official":{"repos":["borgwardtlab/anfm"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ra-sgg-retrieval-augmented-scene-graph","slug":"ra-sgg-retrieval-augmented-scene-graph","title":"RA-SGG: Retrieval-Augmented Scene Graph Generation Framework via Multi-Prototype Learning","date":"2024-12-17","arxiv_id":"2412.12788","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":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ra-sgg-retrieval-augmented-scene-graph#ran","syntology_url":"https://syntology.ai/paper/2412.12788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12788"}},"official":{"repos":["KanghoonYoon/torch-rasgg"],"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/provision-programmatically-scaling-vision","slug":"provision-programmatically-scaling-vision","title":"ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models","date":"2024-12-09","arxiv_id":"2412.07012","repositories_listed":1,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/provision-programmatically-scaling-vision#ran","syntology_url":"https://syntology.ai/paper/2412.07012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07012"}},"official":{"repos":["jieyuz2/provision"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/layerdag-a-layerwise-autoregressive-diffusion","slug":"layerdag-a-layerwise-autoregressive-diffusion","title":"LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation","date":"2024-11-04","arxiv_id":"2411.02322","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/layerdag-a-layerwise-autoregressive-diffusion#ran","syntology_url":"https://syntology.ai/paper/2411.02322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.02322"}},"official":{"repos":["graph-com/layerdag"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-twigs-with-loop-guidance-for","slug":"diffusion-twigs-with-loop-guidance-for","title":"Diffusion Twigs with Loop Guidance for Conditional Graph Generation","date":"2024-10-31","arxiv_id":"2410.24012","repositories_listed":1,"syntology":{"n":21,"n_ran":16,"n_constructed":0,"n_ran_checked":11,"n_instrument":5,"n_unverified":5,"n_honours":1,"n_violates":1,"n_no_contract":9,"n_pointer_only":1,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/diffusion-twigs-with-loop-guidance-for#ran","syntology_url":"https://syntology.ai/paper/2410.24012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.24012"}},"official":{"repos":["aalto-quml/diffusion_twigs"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/scene-graph-generation-with-role-playing","slug":"scene-graph-generation-with-role-playing","title":"Scene Graph Generation with Role-Playing Large Language Models","date":"2024-10-20","arxiv_id":"2410.15364","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/scene-graph-generation-with-role-playing#ran","syntology_url":"https://syntology.ai/paper/2410.15364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15364"}},"official":{"repos":["guikunchen/sdsgg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gesubnet-gene-interaction-inference-for","slug":"gesubnet-gene-interaction-inference-for","title":"GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation","date":"2024-10-17","arxiv_id":"2410.13178","repositories_listed":0,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/gesubnet-gene-interaction-inference-for#ran","syntology_url":"https://syntology.ai/paper/2410.13178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13178"}},"official":null}},{"url":"/paper/multimodal-large-language-models-for-inverse","slug":"multimodal-large-language-models-for-inverse","title":"Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning","date":"2024-10-05","arxiv_id":"2410.04223","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multimodal-large-language-models-for-inverse#ran","syntology_url":"https://syntology.ai/paper/2410.04223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04223"}},"official":{"repos":["liugangcode/Llamole"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/defog-discrete-flow-matching-for-graph","slug":"defog-discrete-flow-matching-for-graph","title":"DeFoG: Discrete Flow Matching for Graph Generation","date":"2024-10-05","arxiv_id":"2410.04263","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/defog-discrete-flow-matching-for-graph#ran","syntology_url":"https://syntology.ai/paper/2410.04263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04263"}},"official":{"repos":["manuelmlmadeira/DeFoG"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/hygene-a-diffusion-based-hypergraph","slug":"hygene-a-diffusion-based-hypergraph","title":"HYGENE: A Diffusion-based Hypergraph Generation Method","date":"2024-08-29","arxiv_id":"2408.16457","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":0,"n_no_contract":4,"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, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hygene-a-diffusion-based-hypergraph#ran","syntology_url":"https://syntology.ai/paper/2408.16457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.16457"}},"official":{"repos":["DorianGailhard/HYGENE"],"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/semantic-diversity-aware-prototype-based","slug":"semantic-diversity-aware-prototype-based","title":"Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation","date":"2024-07-22","arxiv_id":"2407.15396","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/semantic-diversity-aware-prototype-based#ran","syntology_url":"https://syntology.ai/paper/2407.15396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15396"}},"official":{"repos":["jeonjaehyeong/dpl"],"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/generative-modelling-of-structurally","slug":"generative-modelling-of-structurally","title":"Generative Modelling of Structurally Constrained Graphs","date":"2024-06-25","arxiv_id":"2406.17341","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generative-modelling-of-structurally#ran","syntology_url":"https://syntology.ai/paper/2406.17341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17341"}},"official":{"repos":["manuelmlmadeira/ConStruct"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/skysensegpt-a-fine-grained-instruction-tuning","slug":"skysensegpt-a-fine-grained-instruction-tuning","title":"SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding","date":"2024-06-14","arxiv_id":"2406.10100","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/skysensegpt-a-fine-grained-instruction-tuning#ran","syntology_url":"https://syntology.ai/paper/2406.10100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10100"}},"official":{"repos":["luo-z13/skysensegpt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-predicate-and-triplet-learning-for","slug":"leveraging-predicate-and-triplet-learning-for","title":"Leveraging Predicate and Triplet Learning for Scene Graph Generation","date":"2024-06-04","arxiv_id":"2406.02038","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":3,"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/leveraging-predicate-and-triplet-learning-for#ran","syntology_url":"https://syntology.ai/paper/2406.02038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02038"}},"official":{"repos":["jkli1998/drm"],"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/oed-towards-one-stage-end-to-end-dynamic","slug":"oed-towards-one-stage-end-to-end-dynamic","title":"OED: Towards One-stage End-to-End Dynamic Scene Graph Generation","date":"2024-05-27","arxiv_id":"2405.16925","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":0,"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/oed-towards-one-stage-end-to-end-dynamic#ran","syntology_url":"https://syntology.ai/paper/2405.16925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16925"}},"official":{"repos":["guanw-pku/oed"],"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/alignment-is-key-for-applying-diffusion","slug":"alignment-is-key-for-applying-diffusion","title":"Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models","date":"2024-05-27","arxiv_id":"2405.17656","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":2,"n_ran_checked":8,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":16,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/alignment-is-key-for-applying-diffusion#ran","syntology_url":"https://syntology.ai/paper/2405.17656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17656"}},"official":{"repos":["aalto-quml/diffalign"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/discrete-state-continuous-time-diffusion-for","slug":"discrete-state-continuous-time-diffusion-for","title":"Discrete-state Continuous-time Diffusion for Graph Generation","date":"2024-05-19","arxiv_id":"2405.11416","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discrete-state-continuous-time-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2405.11416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11416"}},"official":{"repos":["pricexu/disco"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/4d-panoptic-scene-graph-generation-1","slug":"4d-panoptic-scene-graph-generation-1","title":"4D Panoptic Scene Graph Generation","date":"2024-05-16","arxiv_id":"2405.10305","repositories_listed":3,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":10,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/4d-panoptic-scene-graph-generation-1#ran","syntology_url":"https://syntology.ai/paper/2405.10305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.10305"}},"official":{"repos":["jingkang50/psg4d","Jingkang50/OpenPSG","jingkang50/openpvsg"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperbolic-geometric-latent-diffusion-model","slug":"hyperbolic-geometric-latent-diffusion-model","title":"Hyperbolic Geometric Latent Diffusion Model for Graph Generation","date":"2024-05-06","arxiv_id":"2405.03188","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/hyperbolic-geometric-latent-diffusion-model#ran","syntology_url":"https://syntology.ai/paper/2405.03188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.03188"}},"official":{"repos":["ringbdstack/hypdiff"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/a-review-and-efficient-implementation-of","slug":"a-review-and-efficient-implementation-of","title":"A Review and Efficient Implementation of Scene Graph Generation Metrics","date":"2024-04-15","arxiv_id":"2404.09616","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-review-and-efficient-implementation-of#ran","syntology_url":"https://syntology.ai/paper/2404.09616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.09616"}},"official":{"repos":["lorjul/sgbench"],"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/egtr-extracting-graph-from-transformer-for","slug":"egtr-extracting-graph-from-transformer-for","title":"EGTR: Extracting Graph from Transformer for Scene Graph Generation","date":"2024-04-02","arxiv_id":"2404.02072","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":12,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":4,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/egtr-extracting-graph-from-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2404.02072","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02072"}},"official":{"repos":["naver-ai/egtr"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/from-pixels-to-graphs-open-vocabulary-scene","slug":"from-pixels-to-graphs-open-vocabulary-scene","title":"From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models","date":"2024-04-01","arxiv_id":"2404.00906","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/from-pixels-to-graphs-open-vocabulary-scene#ran","syntology_url":"https://syntology.ai/paper/2404.00906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00906"}},"official":{"repos":["shtuplus/pix2grp_cvpr2024"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/set-aligning-framework-for-auto-regressive","slug":"set-aligning-framework-for-auto-regressive","title":"Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation","date":"2024-04-01","arxiv_id":"2404.01532","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":1,"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/set-aligning-framework-for-auto-regressive#ran","syntology_url":"https://syntology.ai/paper/2404.01532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01532"}},"official":{"repos":["xingwei-warwick/set-aligning-event-temporal-graph-generation"],"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/graphinstruct-empowering-large-language","slug":"graphinstruct-empowering-large-language","title":"GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability","date":"2024-03-07","arxiv_id":"2403.04483","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graphinstruct-empowering-large-language#ran","syntology_url":"https://syntology.ai/paper/2403.04483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.04483"}},"official":{"repos":["cgcl-codes/graphinstruct"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-diffusion-policy-optimization","slug":"graph-diffusion-policy-optimization","title":"Graph Diffusion Policy Optimization","date":"2024-02-26","arxiv_id":"2402.16302","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/graph-diffusion-policy-optimization#ran","syntology_url":"https://syntology.ai/paper/2402.16302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16302"}},"official":{"repos":["sail-sg/gdpo"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["community","official"]}}},{"url":"/paper/a-graph-is-worth-k-words-euclideanizing-graph","slug":"a-graph-is-worth-k-words-euclideanizing-graph","title":"A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer","date":"2024-02-04","arxiv_id":"2402.02464","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-graph-is-worth-k-words-euclideanizing-graph#ran","syntology_url":"https://syntology.ai/paper/2402.02464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02464"}},"official":{"repos":["A4Bio/GraphsGPT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-self-training-framework-for-fine","slug":"adaptive-self-training-framework-for-fine","title":"Adaptive Self-training Framework for Fine-grained Scene Graph Generation","date":"2024-01-18","arxiv_id":"2401.09786","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-self-training-framework-for-fine#ran","syntology_url":"https://syntology.ai/paper/2401.09786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09786"}},"official":{"repos":["rlqja1107/torch-st-sgg"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-and-scalable-representation-for","slug":"a-simple-and-scalable-representation-for","title":"A Simple and Scalable Representation for Graph Generation","date":"2023-12-04","arxiv_id":"2312.02230","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"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) · 3 unverified","sample_list":"/paper/a-simple-and-scalable-representation-for#ran","syntology_url":"https://syntology.ai/paper/2312.02230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02230"}},"official":{"repos":["yunhuijang/geel"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/panoptic-video-scene-graph-generation-1","slug":"panoptic-video-scene-graph-generation-1","title":"Panoptic Video Scene Graph Generation","date":"2023-11-28","arxiv_id":"2311.17058","repositories_listed":3,"syntology":{"n":11,"n_ran":11,"n_constructed":2,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":3,"phrase":"11 ran (of which 2 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/panoptic-video-scene-graph-generation-1#ran","syntology_url":"https://syntology.ai/paper/2311.17058","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17058"}},"official":{"repos":["jingkang50/openpvsg","lilydaytoy/openpvsg","lilydaytoy/pvsgannotation"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":2,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/autokg-efficient-automated-knowledge-graph","slug":"autokg-efficient-automated-knowledge-graph","title":"AutoKG: Efficient Automated Knowledge Graph Generation for Language Models","date":"2023-11-22","arxiv_id":"2311.14740","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/autokg-efficient-automated-knowledge-graph#ran","syntology_url":"https://syntology.ai/paper/2311.14740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14740"}},"official":{"repos":["wispcarey/autokg"],"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/sparse-training-of-discrete-diffusion-models","slug":"sparse-training-of-discrete-diffusion-models","title":"Sparse Training of Discrete Diffusion Models for Graph Generation","date":"2023-11-03","arxiv_id":"2311.02142","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/sparse-training-of-discrete-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2311.02142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.02142"}},"official":{"repos":["qym7/sparsediff"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/graphmaker-can-diffusion-models-generate","slug":"graphmaker-can-diffusion-models-generate","title":"GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?","date":"2023-10-20","arxiv_id":"2310.13833","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/graphmaker-can-diffusion-models-generate#ran","syntology_url":"https://syntology.ai/paper/2310.13833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13833"}},"official":{"repos":["graph-com/graphmaker"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/less-is-more-toward-zero-shot-local-scene","slug":"less-is-more-toward-zero-shot-local-scene","title":"Less is More: Toward Zero-Shot Local Scene Graph Generation via Foundation Models","date":"2023-10-02","arxiv_id":"2310.01356","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/less-is-more-toward-zero-shot-local-scene#ran","syntology_url":"https://syntology.ai/paper/2310.01356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01356"}},"official":null}},{"url":"/paper/adaptive-visual-scene-understanding","slug":"adaptive-visual-scene-understanding","title":"Adaptive Visual Scene Understanding: Incremental Scene Graph Generation","date":"2023-10-02","arxiv_id":"2310.01636","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/adaptive-visual-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/2310.01636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01636"}},"official":{"repos":["zhanglab-deepneurocoglab/csegg"],"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":["official"]}}},{"url":"/paper/node-aligned-graph-to-graph-generation-for","slug":"node-aligned-graph-to-graph-generation-for","title":"Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis","date":"2023-09-27","arxiv_id":"2309.15798","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/node-aligned-graph-to-graph-generation-for#ran","syntology_url":"https://syntology.ai/paper/2309.15798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.15798"}},"official":{"repos":["dptech-corp/nag2g"],"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/haystack-a-panoptic-scene-graph-dataset-to","slug":"haystack-a-panoptic-scene-graph-dataset-to","title":"Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes","date":"2023-09-05","arxiv_id":"2309.02286","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/haystack-a-panoptic-scene-graph-dataset-to#ran","syntology_url":"https://syntology.ai/paper/2309.02286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.02286"}},"official":{"repos":["lorjul/haystack"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rlipv2-fast-scaling-of-relational-language","slug":"rlipv2-fast-scaling-of-relational-language","title":"RLIPv2: Fast Scaling of Relational Language-Image Pre-training","date":"2023-08-18","arxiv_id":"2308.09351","repositories_listed":3,"syntology":{"n":30,"n_ran":22,"n_constructed":5,"n_ran_checked":18,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":0,"phrase":"22 ran (of which 5 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/rlipv2-fast-scaling-of-relational-language#ran","syntology_url":"https://syntology.ai/paper/2308.09351","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09351"}},"official":{"repos":["jacobyuan7/rlipv2"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/vision-relation-transformer-for-unbiased","slug":"vision-relation-transformer-for-unbiased","title":"Vision Relation Transformer for Unbiased Scene Graph Generation","date":"2023-08-18","arxiv_id":"2308.09472","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vision-relation-transformer-for-unbiased#ran","syntology_url":"https://syntology.ai/paper/2308.09472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09472"}},"official":{"repos":["visinf/veto"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/compositional-feature-augmentation-for","slug":"compositional-feature-augmentation-for","title":"Compositional Feature Augmentation for Unbiased Scene Graph Generation","date":"2023-08-13","arxiv_id":"2308.06712","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":3,"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/compositional-feature-augmentation-for#ran","syntology_url":"https://syntology.ai/paper/2308.06712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06712"}},"official":{"repos":["hkust-longgroup/cfa"],"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/swingnn-rethinking-permutation-invariance-in","slug":"swingnn-rethinking-permutation-invariance-in","title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation","date":"2023-07-04","arxiv_id":"2307.01646","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"8 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; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/swingnn-rethinking-permutation-invariance-in#ran","syntology_url":"https://syntology.ai/paper/2307.01646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01646"}},"official":{"repos":["qiyan98/swingnn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/hyperbolic-graph-diffusion-model-for-molecule","slug":"hyperbolic-graph-diffusion-model-for-molecule","title":"Hyperbolic Graph Diffusion Model","date":"2023-06-13","arxiv_id":"2306.07618","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hyperbolic-graph-diffusion-model-for-molecule#ran","syntology_url":"https://syntology.ai/paper/2306.07618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07618"}},"official":{"repos":["lf-wen/hgdm"],"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/optimized-crystallographic-graph-generation","slug":"optimized-crystallographic-graph-generation","title":"Optimized Crystallographic Graph Generation for Material Science","date":"2023-06-07","arxiv_id":"2307.05380","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimized-crystallographic-graph-generation#ran","syntology_url":"https://syntology.ai/paper/2307.05380","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.05380"}},"official":{"repos":["aklipf/mat-graph"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-graph-generation-with-k-2-trees","slug":"hierarchical-graph-generation-with-k-2-trees","title":"Graph Generation with $K^2$-trees","date":"2023-05-30","arxiv_id":"2305.19125","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/hierarchical-graph-generation-with-k-2-trees#ran","syntology_url":"https://syntology.ai/paper/2305.19125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19125"}},"official":{"repos":["yunhuijang/hggt"],"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/higen-hierarchical-graph-generative-networks","slug":"higen-hierarchical-graph-generative-networks","title":"HiGen: Hierarchical Graph Generative Networks","date":"2023-05-30","arxiv_id":"2305.19337","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/higen-hierarchical-graph-generative-networks#ran","syntology_url":"https://syntology.ai/paper/2305.19337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19337"}},"official":{"repos":["karami-m/higen_main"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-joint-2d-3d-diffusion-models-for","slug":"learning-joint-2d-3d-diffusion-models-for","title":"Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation","date":"2023-05-21","arxiv_id":"2305.12347","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-joint-2d-3d-diffusion-models-for#ran","syntology_url":"https://syntology.ai/paper/2305.12347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12347"}},"official":{"repos":["graph-0/jodo"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/molhf-a-hierarchical-normalizing-flow-for","slug":"molhf-a-hierarchical-normalizing-flow-for","title":"MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation","date":"2023-05-15","arxiv_id":"2305.08457","repositories_listed":1,"syntology":{"n":23,"n_ran":14,"n_constructed":7,"n_ran_checked":12,"n_instrument":2,"n_unverified":9,"n_honours":1,"n_violates":1,"n_no_contract":10,"n_pointer_only":0,"phrase":"14 ran (of which 7 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 1 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/molhf-a-hierarchical-normalizing-flow-for#ran","syntology_url":"https://syntology.ai/paper/2305.08457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.08457"}},"official":{"repos":["violet-sto/molhf"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":7,"n_ran_no_instrument_failure":12,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-and-degree-guided-graph-generation","slug":"efficient-and-degree-guided-graph-generation","title":"Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling","date":"2023-05-06","arxiv_id":"2305.04111","repositories_listed":1,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":11,"n_pointer_only":4,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-and-degree-guided-graph-generation#ran","syntology_url":"https://syntology.ai/paper/2305.04111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04111"}},"official":{"repos":["tufts-ml/graph-generation-edge"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-equivariant-generative-framework-for","slug":"an-equivariant-generative-framework-for","title":"An Equivariant Generative Framework for Molecular Graph-Structure Co-Design","date":"2023-04-12","arxiv_id":"2304.12436","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/an-equivariant-generative-framework-for#ran","syntology_url":"https://syntology.ai/paper/2304.12436","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.12436"}},"official":{"repos":["zaixizhang/molcode"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/scene-graph-generation-from-hierarchical","slug":"scene-graph-generation-from-hierarchical","title":"Hierarchical Relationships: A New Perspective to Enhance Scene Graph Generation","date":"2023-03-13","arxiv_id":"2303.06842","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/scene-graph-generation-from-hierarchical#ran","syntology_url":"https://syntology.ai/paper/2303.06842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06842"}},"official":{"repos":["bowen-upenn/scene_graph_commonsense"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/prototype-based-embedding-network-for-scene","slug":"prototype-based-embedding-network-for-scene","title":"Prototype-based Embedding Network for Scene Graph Generation","date":"2023-03-13","arxiv_id":"2303.07096","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":3,"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/prototype-based-embedding-network-for-scene#ran","syntology_url":"https://syntology.ai/paper/2303.07096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.07096"}},"official":{"repos":["vl-group/penet"],"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/geometry-complete-diffusion-for-3d-molecule","slug":"geometry-complete-diffusion-for-3d-molecule","title":"Geometry-Complete Diffusion for 3D Molecule Generation and Optimization","date":"2023-02-08","arxiv_id":"2302.04313","repositories_listed":3,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/geometry-complete-diffusion-for-3d-molecule#ran","syntology_url":"https://syntology.ai/paper/2302.04313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04313"}},"official":{"repos":["bioinfomachinelearning/bio-diffusion","bioinfomachinelearning/gcdm-sbdd"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-generation-with-destination-driven","slug":"graph-generation-with-destination-driven","title":"Graph Generation with Diffusion Mixture","date":"2023-02-07","arxiv_id":"2302.03596","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/graph-generation-with-destination-driven#ran","syntology_url":"https://syntology.ai/paper/2302.03596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.03596"}},"official":{"repos":["harryjo97/drum","harryjo97/grum"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-graph-generation-by-restricting","slug":"improving-graph-generation-by-restricting","title":"Improving Graph Generation by Restricting Graph Bandwidth","date":"2023-01-25","arxiv_id":"2301.10857","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-graph-generation-by-restricting#ran","syntology_url":"https://syntology.ai/paper/2301.10857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10857"}},"official":{"repos":["genentech/bandwidth-graph-generation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/promptcal-contrastive-affinity-learning-via","slug":"promptcal-contrastive-affinity-learning-via","title":"PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery","date":"2022-12-11","arxiv_id":"2212.05590","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/promptcal-contrastive-affinity-learning-via#ran","syntology_url":"https://syntology.ai/paper/2212.05590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.05590"}},"official":{"repos":["sheng-eatamath/promptcal"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/graphgdp-generative-diffusion-processes-for","slug":"graphgdp-generative-diffusion-processes-for","title":"GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation","date":"2022-12-04","arxiv_id":"2212.01842","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graphgdp-generative-diffusion-processes-for#ran","syntology_url":"https://syntology.ai/paper/2212.01842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.01842"}},"official":{"repos":["graph-0/graphgdp"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rntrajrec-road-network-enhanced-trajectory","slug":"rntrajrec-road-network-enhanced-trajectory","title":"RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer","date":"2022-11-23","arxiv_id":"2211.13234","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rntrajrec-road-network-enhanced-trajectory#ran","syntology_url":"https://syntology.ai/paper/2211.13234","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13234"}},"official":{"repos":["chenyuqi990215/rntrajrec"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-graphon-neural-representation","slug":"implicit-graphon-neural-representation","title":"Implicit Graphon Neural Representation","date":"2022-11-07","arxiv_id":"2211.03329","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":7,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/implicit-graphon-neural-representation#ran","syntology_url":"https://syntology.ai/paper/2211.03329","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.03329"}},"official":{"repos":["mishne-lab/ignr"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/regularized-graph-structure-learning-with","slug":"regularized-graph-structure-learning-with","title":"Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting","date":"2022-10-12","arxiv_id":"2210.06126","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":3,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/regularized-graph-structure-learning-with#ran","syntology_url":"https://syntology.ai/paper/2210.06126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06126"}},"official":{"repos":["alipay/rgsl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gradient-guided-importance-sampling-for-1","slug":"gradient-guided-importance-sampling-for-1","title":"Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models","date":"2022-10-11","arxiv_id":"2210.05782","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":10,"phrase":"6 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; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/gradient-guided-importance-sampling-for-1#ran","syntology_url":"https://syntology.ai/paper/2210.05782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05782"}},"official":{"repos":["divelab/rmwggis"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/digress-discrete-denoising-diffusion-for","slug":"digress-discrete-denoising-diffusion-for","title":"DiGress: Discrete Denoising diffusion for graph generation","date":"2022-09-29","arxiv_id":"2209.14734","repositories_listed":3,"syntology":{"n":25,"n_ran":15,"n_constructed":10,"n_ran_checked":10,"n_instrument":5,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"15 ran (of which 10 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/digress-discrete-denoising-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2209.14734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.14734"}},"official":{"repos":["cvignac/digress"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["listed"]}}},{"url":"/paper/the-devil-is-in-the-labels-noisy-label-1","slug":"the-devil-is-in-the-labels-noisy-label-1","title":"The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation","date":"2022-06-07","arxiv_id":"2206.03014","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/the-devil-is-in-the-labels-noisy-label-1#ran","syntology_url":"https://syntology.ai/paper/2206.03014","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.03014"}},"official":{"repos":["muktilin/nice"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-domain-generalization-with-drift","slug":"temporal-domain-generalization-with-drift","title":"Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks","date":"2022-05-21","arxiv_id":"2205.10664","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":0,"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/temporal-domain-generalization-with-drift#ran","syntology_url":"https://syntology.ai/paper/2205.10664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10664"}},"official":{"repos":["baithebest/drain"],"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/ru-net-regularized-unrolling-network-for","slug":"ru-net-regularized-unrolling-network-for","title":"RU-Net: Regularized Unrolling Network for Scene Graph Generation","date":"2022-05-03","arxiv_id":"2205.01297","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ru-net-regularized-unrolling-network-for#ran","syntology_url":"https://syntology.ai/paper/2205.01297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01297"}},"official":{"repos":["siml3/ru-net"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hl-net-heterophily-learning-network-for-scene","slug":"hl-net-heterophily-learning-network-for-scene","title":"HL-Net: Heterophily Learning Network for Scene Graph Generation","date":"2022-05-03","arxiv_id":"2205.01316","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":1,"n_ran_checked":2,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/hl-net-heterophily-learning-network-for-scene#ran","syntology_url":"https://syntology.ai/paper/2205.01316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01316"}},"official":{"repos":["siml3/hl-net"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/fine-grained-predicates-learning-for-scene","slug":"fine-grained-predicates-learning-for-scene","title":"Fine-Grained Predicates Learning for Scene Graph Generation","date":"2022-04-06","arxiv_id":"2204.02597","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":3,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":10,"phrase":"9 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fine-grained-predicates-learning-for-scene#ran","syntology_url":"https://syntology.ai/paper/2204.02597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02597"}},"official":{"repos":["xinyulyu/fgpl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/spectre-spectral-conditioning-helps-to","slug":"spectre-spectral-conditioning-helps-to","title":"SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators","date":"2022-04-04","arxiv_id":"2204.01613","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spectre-spectral-conditioning-helps-to#ran","syntology_url":"https://syntology.ai/paper/2204.01613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01613"}},"official":{"repos":["karolismart/spectre"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/stacked-hybrid-attention-and-group","slug":"stacked-hybrid-attention-and-group","title":"Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation","date":"2022-03-18","arxiv_id":"2203.09811","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stacked-hybrid-attention-and-group#ran","syntology_url":"https://syntology.ai/paper/2203.09811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09811"}},"official":{"repos":["dongxingning/sha-gcl-for-sgg"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/score-based-generative-modeling-of-graphs-via","slug":"score-based-generative-modeling-of-graphs-via","title":"Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations","date":"2022-02-05","arxiv_id":"2202.02514","repositories_listed":2,"syntology":{"n":26,"n_ran":14,"n_constructed":3,"n_ran_checked":7,"n_instrument":7,"n_unverified":12,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":13,"phrase":"14 ran (of which 3 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 7 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/score-based-generative-modeling-of-graphs-via#ran","syntology_url":"https://syntology.ai/paper/2202.02514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.02514"}},"official":{"repos":["harryjo97/gdss"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/resistance-training-using-prior-bias-toward","slug":"resistance-training-using-prior-bias-toward","title":"Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation","date":"2022-01-18","arxiv_id":"2201.06794","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":3,"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/resistance-training-using-prior-bias-toward#ran","syntology_url":"https://syntology.ai/paper/2201.06794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.06794"}},"official":{"repos":["chch1999/rtpb"],"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/sgtr-end-to-end-scene-graph-generation-with","slug":"sgtr-end-to-end-scene-graph-generation-with","title":"SGTR: End-to-end Scene Graph Generation with Transformer","date":"2021-12-24","arxiv_id":"2112.12970","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sgtr-end-to-end-scene-graph-generation-with#ran","syntology_url":"https://syntology.ai/paper/2112.12970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.12970"}},"official":{"repos":["scarecrow0/sgtr"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/top-n-equivariant-set-and-graph-generation","slug":"top-n-equivariant-set-and-graph-generation","title":"Top-N: Equivariant set and graph generation without exchangeability","date":"2021-10-05","arxiv_id":"2110.02096","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/top-n-equivariant-set-and-graph-generation#ran","syntology_url":"https://syntology.ai/paper/2110.02096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.02096"}},"official":{"repos":["cvignac/top-n"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/from-general-to-specific-informative-scene","slug":"from-general-to-specific-informative-scene","title":"From General to Specific: Informative Scene Graph Generation via Balance Adjustment","date":"2021-08-30","arxiv_id":"2108.13129","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":1,"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/from-general-to-specific-informative-scene#ran","syntology_url":"https://syntology.ai/paper/2108.13129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.13129"}},"official":{"repos":["zhugekongkong/sgg-g2s"],"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/regen-reinforcement-learning-for-text-and","slug":"regen-reinforcement-learning-for-text-and","title":"ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models","date":"2021-08-27","arxiv_id":"2108.12472","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/regen-reinforcement-learning-for-text-and#ran","syntology_url":"https://syntology.ai/paper/2108.12472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.12472"}},"official":{"repos":["IBM/regen"],"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/zero-shot-scene-graph-relation-prediction","slug":"zero-shot-scene-graph-relation-prediction","title":"Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration","date":"2021-07-11","arxiv_id":"2107.05080","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/zero-shot-scene-graph-relation-prediction#ran","syntology_url":"https://syntology.ai/paper/2107.05080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.05080"}},"official":{"repos":["Wayfear/Coacher"],"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/graphpiece-efficiently-generating-high","slug":"graphpiece-efficiently-generating-high","title":"Molecule Generation by Principal Subgraph Mining and Assembling","date":"2021-06-29","arxiv_id":"2106.15098","repositories_listed":3,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":4,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/graphpiece-efficiently-generating-high#ran","syntology_url":"https://syntology.ai/paper/2106.15098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.15098"}},"official":{"repos":["kxz18/gp-vae","thunlp-mt/ps-vae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/structured-sparse-r-cnn-for-direct-scene","slug":"structured-sparse-r-cnn-for-direct-scene","title":"Structured Sparse R-CNN for Direct Scene Graph Generation","date":"2021-06-21","arxiv_id":"2106.10815","repositories_listed":4,"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/structured-sparse-r-cnn-for-direct-scene#ran","syntology_url":"https://syntology.ai/paper/2106.10815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10815"}},"official":{"repos":["mcg-nju/structured-sparse-rcnn"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/order-matters-probabilistic-modeling-of-node","slug":"order-matters-probabilistic-modeling-of-node","title":"Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation","date":"2021-06-11","arxiv_id":"2106.06189","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/order-matters-probabilistic-modeling-of-node#ran","syntology_url":"https://syntology.ai/paper/2106.06189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06189"}},"official":{"repos":["tufts-ml/graph-generation-vi"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/multiresolution-graph-variational-autoencoder","slug":"multiresolution-graph-variational-autoencoder","title":"Multiresolution Equivariant Graph Variational Autoencoder","date":"2021-06-02","arxiv_id":"2106.00967","repositories_listed":2,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/multiresolution-graph-variational-autoencoder#ran","syntology_url":"https://syntology.ai/paper/2106.00967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.00967"}},"official":{"repos":["hytruongson/mgvae"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/explagraphs-an-explanation-graph-generation","slug":"explagraphs-an-explanation-graph-generation","title":"ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning","date":"2021-04-15","arxiv_id":"2104.07644","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/explagraphs-an-explanation-graph-generation#ran","syntology_url":"https://syntology.ai/paper/2104.07644","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07644"}},"official":{"repos":["swarnaHub/ExplaGraphs"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/fully-convolutional-scene-graph-generation","slug":"fully-convolutional-scene-graph-generation","title":"Fully Convolutional Scene Graph Generation","date":"2021-03-30","arxiv_id":"2103.16083","repositories_listed":1,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fully-convolutional-scene-graph-generation#ran","syntology_url":"https://syntology.ai/paper/2103.16083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16083"}},"official":{"repos":["liuhengyue/fcsgg"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-distant-supervision-for-scene-graph","slug":"visual-distant-supervision-for-scene-graph","title":"Visual Distant Supervision for Scene Graph Generation","date":"2021-03-29","arxiv_id":"2103.15365","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"phrase":"5 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/visual-distant-supervision-for-scene-graph#ran","syntology_url":"https://syntology.ai/paper/2103.15365","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15365"}},"official":{"repos":["thunlp/visualds"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/accurate-learning-of-graph-representations-1","slug":"accurate-learning-of-graph-representations-1","title":"Accurate Learning of Graph Representations with Graph Multiset Pooling","date":"2021-02-23","arxiv_id":"2102.11533","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/accurate-learning-of-graph-representations-1#ran","syntology_url":"https://syntology.ai/paper/2102.11533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.11533"}},"official":{"repos":["JinheonBaek/GMT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-and-reasoning-with-the-graph","slug":"learning-and-reasoning-with-the-graph","title":"Learning and Reasoning with the Graph Structure Representation in Robotic Surgery","date":"2020-07-07","arxiv_id":"2007.03357","repositories_listed":2,"syntology":{"n":8,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"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) · 7 unverified","sample_list":"/paper/learning-and-reasoning-with-the-graph#ran","syntology_url":"https://syntology.ai/paper/2007.03357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03357"}},"official":{"repos":["mobarakol/Surgical_SceneGraph_Generation"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"url":"/paper/adaptive-graph-convolutional-recurrent","slug":"adaptive-graph-convolutional-recurrent","title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","date":"2020-07-06","arxiv_id":"2007.02842","repositories_listed":3,"syntology":{"n":7,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/adaptive-graph-convolutional-recurrent#ran","syntology_url":"https://syntology.ai/paper/2007.02842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02842"}},"official":{"repos":["LeiBAI/AGCRN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/gpt-gnn-generative-pre-training-of-graph","slug":"gpt-gnn-generative-pre-training-of-graph","title":"GPT-GNN: Generative Pre-Training of Graph Neural Networks","date":"2020-06-27","arxiv_id":"2006.15437","repositories_listed":2,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/gpt-gnn-generative-pre-training-of-graph#ran","syntology_url":"https://syntology.ai/paper/2006.15437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15437"}},"official":{"repos":["acbull/GPT-GNN"],"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/neuro-symbolic-visual-reasoning-disentangling","slug":"neuro-symbolic-visual-reasoning-disentangling","title":"Neuro-Symbolic Visual Reasoning: Disentangling \"Visual\" from \"Reasoning\"","date":"2020-06-20","arxiv_id":"2006.11524","repositories_listed":0,"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/neuro-symbolic-visual-reasoning-disentangling#ran","syntology_url":"https://syntology.ai/paper/2006.11524","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11524"}},"official":null}},{"url":"/paper/learning-visual-commonsense-for-robust-scene","slug":"learning-visual-commonsense-for-robust-scene","title":"Learning Visual Commonsense for Robust Scene Graph Generation","date":"2020-06-17","arxiv_id":"2006.09623","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-visual-commonsense-for-robust-scene#ran","syntology_url":"https://syntology.ai/paper/2006.09623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.09623"}},"official":null}},{"url":"/paper/moflow-an-invertible-flow-model-for","slug":"moflow-an-invertible-flow-model-for","title":"MoFlow: An Invertible Flow Model for Generating Molecular Graphs","date":"2020-06-17","arxiv_id":"2006.10137","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/moflow-an-invertible-flow-model-for#ran","syntology_url":"https://syntology.ai/paper/2006.10137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.10137"}},"official":{"repos":["calvin-zcx/moflow"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cyclegt-unsupervised-graph-to-text-and-text","slug":"cyclegt-unsupervised-graph-to-text-and-text","title":"CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training","date":"2020-06-08","arxiv_id":"2006.04702","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cyclegt-unsupervised-graph-to-text-and-text#ran","syntology_url":"https://syntology.ai/paper/2006.04702","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04702"}},"official":{"repos":["QipengGuo/CycleGT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/graph-density-aware-losses-for-novel","slug":"graph-density-aware-losses-for-novel","title":"Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation","date":"2020-05-17","arxiv_id":"2005.08230","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-density-aware-losses-for-novel#ran","syntology_url":"https://syntology.ai/paper/2005.08230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08230"}},"official":{"repos":["bknyaz/sgg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-general-theory-of-permutation-equivarant","slug":"the-general-theory-of-permutation-equivarant","title":"The general theory of permutation equivarant neural networks and higher order graph variational encoders","date":"2020-04-08","arxiv_id":"2004.03990","repositories_listed":1,"syntology":{"n":17,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/the-general-theory-of-permutation-equivarant#ran","syntology_url":"https://syntology.ai/paper/2004.03990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03990"}},"official":null}},{"url":"/paper/gps-net-graph-property-sensing-network-for","slug":"gps-net-graph-property-sensing-network-for","title":"GPS-Net: Graph Property Sensing Network for Scene Graph Generation","date":"2020-03-29","arxiv_id":"2003.12962","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gps-net-graph-property-sensing-network-for#ran","syntology_url":"https://syntology.ai/paper/2003.12962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.12962"}},"official":{"repos":["taksau/GPS-Net"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unbiased-scene-graph-generation-from-biased","slug":"unbiased-scene-graph-generation-from-biased","title":"Unbiased Scene Graph Generation from Biased Training","date":"2020-02-27","arxiv_id":"2002.11949","repositories_listed":6,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"6 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unbiased-scene-graph-generation-from-biased#ran","syntology_url":"https://syntology.ai/paper/2002.11949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.11949"}},"official":{"repos":["KaihuaTang/Scene-Graph-Benchmark.pytorch"],"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":["listed","official","unlocated"]}}},{"url":"/paper/edge-based-sequential-graph-generation-with","slug":"edge-based-sequential-graph-generation-with","title":"Edge-based sequential graph generation with recurrent neural networks","date":"2020-01-31","arxiv_id":"2002.00102","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/edge-based-sequential-graph-generation-with#ran","syntology_url":"https://syntology.ai/paper/2002.00102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.00102"}},"official":{"repos":["marcopodda/grapher"],"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"]}}}],"record_sha256":"6f5612bc514cf138593821792227f72a86e18c9537bda5b6f9e7ff9fae9e89bd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}