{"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/relational-reasoning/papers/ran/1","list_of":"/task/relational-reasoning","task":"Relational Reasoning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,58],"of":58,"counts":{"archive_papers_tagged":483,"with_a_code_link":179,"where_syntology_ran_a_sample":58,"not_listed_spam_title":0,"listed":483,"listed_where_code_ran":58,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":52,"every_run_a_failure_of_syntologys_instrument":6,"listed_with_a_run_with_no_instrument_failure":52,"listed_every_run_a_failure_of_syntologys_instrument":6,"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/relational-reasoning/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/benchmarking-and-understanding-compositional","slug":"benchmarking-and-understanding-compositional","title":"Benchmarking and Understanding Compositional Relational Reasoning of LLMs","date":"2024-12-17","arxiv_id":"2412.12841","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/benchmarking-and-understanding-compositional#ran","syntology_url":"https://syntology.ai/paper/2412.12841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12841"}},"official":{"repos":["caiyun-ai/gar"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/disentangling-and-integrating-relational-and","slug":"disentangling-and-integrating-relational-and","title":"Disentangling and Integrating Relational and Sensory Information in Transformer Architectures","date":"2024-05-26","arxiv_id":"2405.16727","repositories_listed":2,"syntology":{"n":21,"n_ran":14,"n_constructed":9,"n_ran_checked":10,"n_instrument":4,"n_unverified":7,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":0,"phrase":"14 ran (of which 9 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/disentangling-and-integrating-relational-and#ran","syntology_url":"https://syntology.ai/paper/2405.16727","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16727"}},"official":{"repos":["awni00/dual-attention"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mlps-learn-in-context","slug":"mlps-learn-in-context","title":"MLPs Learn In-Context on Regression and Classification Tasks","date":"2024-05-24","arxiv_id":"2405.15618","repositories_listed":2,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mlps-learn-in-context#ran","syntology_url":"https://syntology.ai/paper/2405.15618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15618"}},"official":{"repos":["wtong98/mlp-icl"],"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":["listed","official"]}}},{"url":"/paper/pix2code-learning-to-compose-neural-visual","slug":"pix2code-learning-to-compose-neural-visual","title":"Pix2Code: Learning to Compose Neural Visual Concepts as Programs","date":"2024-02-13","arxiv_id":"2402.08280","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/pix2code-learning-to-compose-neural-visual#ran","syntology_url":"https://syntology.ai/paper/2402.08280","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08280"}},"official":{"repos":["ml-research/pix2code"],"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/moltc-towards-molecular-relational-modeling","slug":"moltc-towards-molecular-relational-modeling","title":"MolTC: Towards Molecular Relational Modeling In Language Models","date":"2024-02-06","arxiv_id":"2402.03781","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/moltc-towards-molecular-relational-modeling#ran","syntology_url":"https://syntology.ai/paper/2402.03781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03781"}},"official":{"repos":["MangoKiller/MolTC"],"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/earthvqa-towards-queryable-earth-via","slug":"earthvqa-towards-queryable-earth-via","title":"EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering","date":"2023-12-19","arxiv_id":"2312.12222","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"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) · 0 unverified","sample_list":"/paper/earthvqa-towards-queryable-earth-via#ran","syntology_url":"https://syntology.ai/paper/2312.12222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12222"}},"official":{"repos":["Junjue-Wang/EarthVQA"],"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":["official"]}}},{"url":"/paper/zero-shot-relational-learning-on-temporal","slug":"zero-shot-relational-learning-on-temporal","title":"zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models","date":"2023-11-15","arxiv_id":"2311.10112","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":11,"phrase":"11 ran (of which 0 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/zero-shot-relational-learning-on-temporal#ran","syntology_url":"https://syntology.ai/paper/2311.10112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.10112"}},"official":{"repos":["zifengding/zrllm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/when-can-transformers-reason-with-abstract","slug":"when-can-transformers-reason-with-abstract","title":"When can transformers reason with abstract symbols?","date":"2023-10-15","arxiv_id":"2310.09753","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"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, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/when-can-transformers-reason-with-abstract#ran","syntology_url":"https://syntology.ai/paper/2310.09753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09753"}},"official":{"repos":["eboix/relational-reasoning"],"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/large-language-models-can-learn-rules","slug":"large-language-models-can-learn-rules","title":"Large Language Models can Learn Rules","date":"2023-10-10","arxiv_id":"2310.07064","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/large-language-models-can-learn-rules#ran","syntology_url":"https://syntology.ai/paper/2310.07064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07064"}},"official":{"repos":["google-deepmind/llms_can_learn_rules"],"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/redundancy-free-self-supervised-relational","slug":"redundancy-free-self-supervised-relational","title":"Redundancy-Free Self-Supervised Relational Learning for Graph Clustering","date":"2023-09-09","arxiv_id":"2309.04694","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":2,"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/redundancy-free-self-supervised-relational#ran","syntology_url":"https://syntology.ai/paper/2309.04694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.04694"}},"official":{"repos":["yisiyu95/r2fgc"],"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/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/shift-robust-molecular-relational-learning","slug":"shift-robust-molecular-relational-learning","title":"Shift-Robust Molecular Relational Learning with Causal Substructure","date":"2023-05-29","arxiv_id":"2305.18451","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/shift-robust-molecular-relational-learning#ran","syntology_url":"https://syntology.ai/paper/2305.18451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18451"}},"official":{"repos":["namkyeong/cmrl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/in-context-analogical-reasoning-with-pre","slug":"in-context-analogical-reasoning-with-pre","title":"In-Context Analogical Reasoning with Pre-Trained Language Models","date":"2023-05-28","arxiv_id":"2305.17626","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/in-context-analogical-reasoning-with-pre#ran","syntology_url":"https://syntology.ai/paper/2305.17626","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17626"}},"official":{"repos":["hxiaoyang/lm-raven"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/conditional-graph-information-bottleneck-for","slug":"conditional-graph-information-bottleneck-for","title":"Conditional Graph Information Bottleneck for Molecular Relational Learning","date":"2023-04-29","arxiv_id":"2305.01520","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/conditional-graph-information-bottleneck-for#ran","syntology_url":"https://syntology.ai/paper/2305.01520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.01520"}},"official":{"repos":["namkyeong/cgib"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/relational-context-learning-for-human-object","slug":"relational-context-learning-for-human-object","title":"Relational Context Learning for Human-Object Interaction Detection","date":"2023-04-11","arxiv_id":"2304.04997","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":4,"n_ran_checked":7,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":12,"phrase":"10 ran (of which 4 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/relational-context-learning-for-human-object#ran","syntology_url":"https://syntology.ai/paper/2304.04997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04997"}},"official":{"repos":["OreoChocolate/MUREN"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":4,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/abstractors-transformer-modules-for-symbolic","slug":"abstractors-transformer-modules-for-symbolic","title":"Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers","date":"2023-04-01","arxiv_id":"2304.00195","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":5,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":12,"phrase":"9 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/abstractors-transformer-modules-for-symbolic#ran","syntology_url":"https://syntology.ai/paper/2304.00195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.00195"}},"official":{"repos":["awni00/abstractor"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/your-diffusion-model-is-secretly-a-zero-shot","slug":"your-diffusion-model-is-secretly-a-zero-shot","title":"Your Diffusion Model is Secretly a Zero-Shot Classifier","date":"2023-03-28","arxiv_id":"2303.16203","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/your-diffusion-model-is-secretly-a-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2303.16203","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16203"}},"official":{"repos":["diffusion-classifier/diffusion-classifier"],"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":["listed","official"]}}},{"url":"/paper/breakpoint-transformers-for-modeling-and","slug":"breakpoint-transformers-for-modeling-and","title":"Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs","date":"2022-11-15","arxiv_id":"2211.07950","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/breakpoint-transformers-for-modeling-and#ran","syntology_url":"https://syntology.ai/paper/2211.07950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07950"}},"official":{"repos":["allenai/situation_modeling"],"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":["found_in_text"]}}},{"url":"/paper/metric-guided-distillation-distilling","slug":"metric-guided-distillation-distilling","title":"Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning","date":"2022-10-21","arxiv_id":"2210.11708","repositories_listed":0,"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/metric-guided-distillation-distilling#ran","syntology_url":"https://syntology.ai/paper/2210.11708","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11708"}},"official":null}},{"url":"/paper/few-shot-relational-reasoning-via-connection","slug":"few-shot-relational-reasoning-via-connection","title":"Few-shot Relational Reasoning via Connection Subgraph Pretraining","date":"2022-10-13","arxiv_id":"2210.06722","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":0,"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/few-shot-relational-reasoning-via-connection#ran","syntology_url":"https://syntology.ai/paper/2210.06722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06722"}},"official":{"repos":["snap-stanford/csr"],"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/cross-modal-causal-relational-reasoning-for","slug":"cross-modal-causal-relational-reasoning-for","title":"Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering","date":"2022-07-26","arxiv_id":"2207.12647","repositories_listed":2,"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/cross-modal-causal-relational-reasoning-for#ran","syntology_url":"https://syntology.ai/paper/2207.12647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12647"}},"official":{"repos":["hcplab-sysu/cmcir","yangliu9208/cmcir"],"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/r5-rule-discovery-with-reinforced-and-1","slug":"r5-rule-discovery-with-reinforced-and-1","title":"R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning","date":"2022-05-13","arxiv_id":"2205.06454","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/r5-rule-discovery-with-reinforced-and-1#ran","syntology_url":"https://syntology.ai/paper/2205.06454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.06454"}},"official":{"repos":["sluxsr/r5_graph_reasoning"],"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/groupnet-multiscale-hypergraph-neural","slug":"groupnet-multiscale-hypergraph-neural","title":"GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning","date":"2022-04-19","arxiv_id":"2204.08770","repositories_listed":1,"syntology":{"n":19,"n_ran":12,"n_constructed":6,"n_ran_checked":7,"n_instrument":5,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"12 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 5 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/groupnet-multiscale-hypergraph-neural#ran","syntology_url":"https://syntology.ai/paper/2204.08770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08770"}},"official":{"repos":["mediabrain-sjtu/groupnet"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-reason-deductively-math-word","slug":"learning-to-reason-deductively-math-word","title":"Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction","date":"2022-03-19","arxiv_id":"2203.10316","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-to-reason-deductively-math-word#ran","syntology_url":"https://syntology.ai/paper/2203.10316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10316"}},"official":{"repos":["allanj/deductive-mwp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/global-reasoned-multi-task-learning-model-for","slug":"global-reasoned-multi-task-learning-model-for","title":"Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding","date":"2022-01-28","arxiv_id":"2201.11957","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/global-reasoned-multi-task-learning-model-for#ran","syntology_url":"https://syntology.ai/paper/2201.11957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.11957"}},"official":{"repos":["lalithjets/global-reasoned-multi-task-model"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/representing-prior-knowledge-using-randomly","slug":"representing-prior-knowledge-using-randomly","title":"Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection","date":"2021-11-20","arxiv_id":"2111.10686","repositories_listed":2,"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/representing-prior-knowledge-using-randomly#ran","syntology_url":"https://syntology.ai/paper/2111.10686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.10686"}},"official":{"repos":["jyhong0304/visual-relationship-detection-rwfn","pavliclab/aaai2022-clear2022-visual_relationship_detection-rwfn"],"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/topological-relational-learning-on-graphs","slug":"topological-relational-learning-on-graphs","title":"Topological Relational Learning on Graphs","date":"2021-10-29","arxiv_id":"2110.15529","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/topological-relational-learning-on-graphs#ran","syntology_url":"https://syntology.ai/paper/2110.15529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15529"}},"official":{"repos":["tri-gnn/tri-gnn"],"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/prototypical-representation-learning-for-1","slug":"prototypical-representation-learning-for-1","title":"Prototypical Representation Learning for Relation Extraction","date":"2021-03-22","arxiv_id":"2103.11647","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/prototypical-representation-learning-for-1#ran","syntology_url":"https://syntology.ai/paper/2103.11647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.11647"}},"official":{"repos":["Alibaba-NLP/ProtoRE"],"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/automatic-generation-of-contrast-sets-from","slug":"automatic-generation-of-contrast-sets-from","title":"Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA","date":"2021-03-17","arxiv_id":"2103.09591","repositories_listed":2,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/automatic-generation-of-contrast-sets-from#ran","syntology_url":"https://syntology.ai/paper/2103.09591","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.09591"}},"official":{"repos":["yonatanbitton/AutoGenOfContrastSetsFromSceneGraphs"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/erica-improving-entity-and-relation","slug":"erica-improving-entity-and-relation","title":"ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning","date":"2020-12-30","arxiv_id":"2012.15022","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/erica-improving-entity-and-relation#ran","syntology_url":"https://syntology.ai/paper/2012.15022","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.15022"}},"official":{"repos":["thunlp/ERICA"],"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","unlocated"]}}},{"url":"/paper/scale-localized-abstract-reasoning","slug":"scale-localized-abstract-reasoning","title":"Scale-Localized Abstract Reasoning","date":"2020-09-20","arxiv_id":"2009.09405","repositories_listed":2,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/scale-localized-abstract-reasoning#ran","syntology_url":"https://syntology.ai/paper/2009.09405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09405"}},"official":{"repos":["yanivbenny/MRNet","yanivbenny/RAVEN_FAIR"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-from-protein-structure-with","slug":"learning-from-protein-structure-with","title":"Learning from Protein Structure with Geometric Vector Perceptrons","date":"2020-09-03","arxiv_id":"2009.01411","repositories_listed":3,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"5 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-from-protein-structure-with#ran","syntology_url":"https://syntology.ai/paper/2009.01411","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.01411"}},"official":{"repos":["drorlab/gvp-pytorch"],"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":["listed","named_in_paper"]}}},{"url":"/paper/uncertainty-based-traffic-accident","slug":"uncertainty-based-traffic-accident","title":"Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning","date":"2020-08-01","arxiv_id":"2008.00334","repositories_listed":2,"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/uncertainty-based-traffic-accident#ran","syntology_url":"https://syntology.ai/paper/2008.00334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.00334"}},"official":{"repos":["Cogito2012/CarCrashDataset","Cogito2012/UString"],"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/distributed-memory-based-self-supervised","slug":"distributed-memory-based-self-supervised","title":"Distributed Associative Memory Network with Memory Refreshing Loss","date":"2020-07-21","arxiv_id":"2007.10637","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/distributed-memory-based-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2007.10637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10637"}},"official":{"repos":["taewonpark/DAM"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-based-social-relation-reasoning","slug":"graph-based-social-relation-reasoning","title":"Graph-Based Social Relation Reasoning","date":"2020-07-15","arxiv_id":"2007.07453","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/graph-based-social-relation-reasoning#ran","syntology_url":"https://syntology.ai/paper/2007.07453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.07453"}},"official":{"repos":["Li-Wanhua/GR2N"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-graph-neural-networks-with-lifted","slug":"beyond-graph-neural-networks-with-lifted","title":"Beyond Graph Neural Networks with Lifted Relational Neural Networks","date":"2020-07-13","arxiv_id":"2007.06286","repositories_listed":2,"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":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) · 0 unverified","sample_list":"/paper/beyond-graph-neural-networks-with-lifted#ran","syntology_url":"https://syntology.ai/paper/2007.06286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.06286"}},"official":{"repos":["GustikS/NeuraLogic","GustikS/GNNwLRNNs"],"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/learning-reasoning-strategies-in-end-to-end","slug":"learning-reasoning-strategies-in-end-to-end","title":"Learning Reasoning Strategies in End-to-End Differentiable Proving","date":"2020-07-13","arxiv_id":"2007.06477","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"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 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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/learning-reasoning-strategies-in-end-to-end#ran","syntology_url":"https://syntology.ai/paper/2007.06477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.06477"}},"official":{"repos":["uclnlp/ctp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/explaining-local-global-and-higher-order","slug":"explaining-local-global-and-higher-order","title":"Explaining Local, Global, And Higher-Order Interactions In Deep Learning","date":"2020-06-12","arxiv_id":"2006.08601","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/explaining-local-global-and-higher-order#ran","syntology_url":"https://syntology.ai/paper/2006.08601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08601"}},"official":{"repos":["slerman12/ExplainingInteractions"],"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/self-supervised-relational-reasoning-for","slug":"self-supervised-relational-reasoning-for","title":"Self-Supervised Relational Reasoning for Representation Learning","date":"2020-06-10","arxiv_id":"2006.05849","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/self-supervised-relational-reasoning-for#ran","syntology_url":"https://syntology.ai/paper/2006.05849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05849"}},"official":{"repos":["mpatacchiola/self-supervised-relational-reasoning"],"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/reasoning-with-latent-structure-refinement","slug":"reasoning-with-latent-structure-refinement","title":"Reasoning with Latent Structure Refinement for Document-Level Relation Extraction","date":"2020-05-13","arxiv_id":"2005.06312","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":1,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 1 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","sample_list":"/paper/reasoning-with-latent-structure-refinement#ran","syntology_url":"https://syntology.ai/paper/2005.06312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.06312"}},"official":{"repos":["nanguoshun/LSR"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-relational-reasoning-graph-network-for","slug":"deep-relational-reasoning-graph-network-for","title":"Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection","date":"2020-03-17","arxiv_id":"2003.07493","repositories_listed":2,"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/deep-relational-reasoning-graph-network-for#ran","syntology_url":"https://syntology.ai/paper/2003.07493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.07493"}},"official":{"repos":["GXYM/DRRG"],"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/self-assttentive-associative-memory","slug":"self-assttentive-associative-memory","title":"Self-Attentive Associative Memory","date":"2020-02-10","arxiv_id":"2002.03519","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 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) · 0 unverified","sample_list":"/paper/self-assttentive-associative-memory#ran","syntology_url":"https://syntology.ai/paper/2002.03519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.03519"}},"official":{"repos":["thaihungle/SAM"],"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/generative-adversarial-zero-shot-relational","slug":"generative-adversarial-zero-shot-relational","title":"Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs","date":"2020-01-08","arxiv_id":"2001.02332","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/generative-adversarial-zero-shot-relational#ran","syntology_url":"https://syntology.ai/paper/2001.02332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.02332"}},"official":{"repos":["Panda0406/Zero-shot-knowledge-graph-relational-learning"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/inductive-relation-prediction-on-knowledge","slug":"inductive-relation-prediction-on-knowledge","title":"Inductive Relation Prediction by Subgraph Reasoning","date":"2019-11-16","arxiv_id":"1911.06962","repositories_listed":10,"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/inductive-relation-prediction-on-knowledge#ran","syntology_url":"https://syntology.ai/paper/1911.06962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06962"}},"official":{"repos":["kkteru/grail"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/commongen-a-constrained-text-generation","slug":"commongen-a-constrained-text-generation","title":"CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning","date":"2019-11-09","arxiv_id":"1911.03705","repositories_listed":3,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/commongen-a-constrained-text-generation#ran","syntology_url":"https://syntology.ai/paper/1911.03705","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03705"}},"official":null}},{"url":"/paper/interaction-relational-network-for-mutual","slug":"interaction-relational-network-for-mutual","title":"Interaction Relational Network for Mutual Action Recognition","date":"2019-10-11","arxiv_id":"1910.04963","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/interaction-relational-network-for-mutual#ran","syntology_url":"https://syntology.ai/paper/1910.04963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.04963"}},"official":{"repos":["mauriciolp/inter-rel-net"],"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/clutrr-a-diagnostic-benchmark-for-inductive","slug":"clutrr-a-diagnostic-benchmark-for-inductive","title":"CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text","date":"2019-08-16","arxiv_id":"1908.06177","repositories_listed":5,"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/clutrr-a-diagnostic-benchmark-for-inductive#ran","syntology_url":"https://syntology.ai/paper/1908.06177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06177"}},"official":{"repos":["facebookresearch/clutrr"],"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/gmnn-graph-markov-neural-networks","slug":"gmnn-graph-markov-neural-networks","title":"GMNN: Graph Markov Neural Networks","date":"2019-05-15","arxiv_id":"1905.06214","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/gmnn-graph-markov-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1905.06214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.06214"}},"official":null}},{"url":"/paper/language-conditioned-graph-networks-for","slug":"language-conditioned-graph-networks-for","title":"Language-Conditioned Graph Networks for Relational Reasoning","date":"2019-05-10","arxiv_id":"1905.04405","repositories_listed":1,"syntology":{"n":10,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/language-conditioned-graph-networks-for#ran","syntology_url":"https://syntology.ai/paper/1905.04405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.04405"}},"official":null}},{"url":"/paper/neural-logic-machines-1","slug":"neural-logic-machines-1","title":"Neural Logic Machines","date":"2019-04-26","arxiv_id":"1904.11694","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/neural-logic-machines-1#ran","syntology_url":"https://syntology.ai/paper/1904.11694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.11694"}},"official":null}},{"url":"/paper/object-oriented-dynamics-learning-through","slug":"object-oriented-dynamics-learning-through","title":"Object-Oriented Dynamics Learning through Multi-Level Abstraction","date":"2019-04-16","arxiv_id":"1904.07482","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/object-oriented-dynamics-learning-through#ran","syntology_url":"https://syntology.ai/paper/1904.07482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.07482"}},"official":null}},{"url":"/paper/graph-based-global-reasoning-networks","slug":"graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","arxiv_id":"1811.12814","repositories_listed":9,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":10,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":7,"phrase":"15 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; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-based-global-reasoning-networks#ran","syntology_url":"https://syntology.ai/paper/1811.12814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12814"}},"official":{"repos":["facebookresearch/GloRe"],"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":["listed","official"]}}},{"url":"/paper/relational-recurrent-neural-networks","slug":"relational-recurrent-neural-networks","title":"Relational recurrent neural networks","date":"2018-06-05","arxiv_id":"1806.01822","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/relational-recurrent-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1806.01822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.01822"}},"official":null}},{"url":"/paper/relational-deep-reinforcement-learning","slug":"relational-deep-reinforcement-learning","title":"Relational Deep Reinforcement Learning","date":"2018-06-05","arxiv_id":"1806.01830","repositories_listed":7,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"5 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/relational-deep-reinforcement-learning#ran","syntology_url":"https://syntology.ai/paper/1806.01830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.01830"}},"official":null}},{"url":"/paper/relational-inductive-biases-deep-learning-and","slug":"relational-inductive-biases-deep-learning-and","title":"Relational inductive biases, deep learning, and graph networks","date":"2018-06-04","arxiv_id":"1806.01261","repositories_listed":31,"syntology":{"n":51,"n_ran":39,"n_constructed":5,"n_ran_checked":39,"n_instrument":0,"n_unverified":12,"n_honours":4,"n_violates":0,"n_no_contract":35,"n_pointer_only":15,"phrase":"39 ran (of which 5 constructed an object rather than computing a result; 39 with no instrument failure: 4 honoured, 0 violated, 35 with no contract checked; 0 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/relational-inductive-biases-deep-learning-and#ran","syntology_url":"https://syntology.ai/paper/1806.01261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.01261"}},"official":{"repos":["deepmind/graph_nets"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/temporal-relational-reasoning-in-videos","slug":"temporal-relational-reasoning-in-videos","title":"Temporal Relational Reasoning in Videos","date":"2017-11-22","arxiv_id":"1711.08496","repositories_listed":5,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/temporal-relational-reasoning-in-videos#ran","syntology_url":"https://syntology.ai/paper/1711.08496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.08496"}},"official":null}},{"url":"/paper/a-simple-neural-network-module-for-relational","slug":"a-simple-neural-network-module-for-relational","title":"A simple neural network module for relational reasoning","date":"2017-06-05","arxiv_id":"1706.01427","repositories_listed":20,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/a-simple-neural-network-module-for-relational#ran","syntology_url":"https://syntology.ai/paper/1706.01427","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.01427"}},"official":null}},{"url":"/paper/holographic-embeddings-of-knowledge-graphs","slug":"holographic-embeddings-of-knowledge-graphs","title":"Holographic Embeddings of Knowledge Graphs","date":"2015-10-16","arxiv_id":"1510.04935","repositories_listed":4,"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":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) · 0 unverified","sample_list":"/paper/holographic-embeddings-of-knowledge-graphs#ran","syntology_url":"https://syntology.ai/paper/1510.04935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1510.04935"}},"official":null}}],"record_sha256":"6fcded072ba878e4cc27b47decf48f0d2791daeb2754dc14d06e6483b575c99a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}