{"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/management/papers/ran/1","list_of":"/task/management","task":"Management","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":119,"counts":{"archive_papers_tagged":8144,"with_a_code_link":1214,"where_syntology_ran_a_sample":119,"not_listed_spam_title":0,"listed":8144,"listed_where_code_ran":119,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":103,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":103,"listed_every_run_a_failure_of_syntologys_instrument":16,"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/management/papers/ran/1","prev":null,"next":"/task/management/papers/ran/2","papers":[{"url":"/paper/reinforcement-learning-optimization-for-large","slug":"reinforcement-learning-optimization-for-large","title":"Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library","date":"2025-06-06","arxiv_id":"2506.06122","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/reinforcement-learning-optimization-for-large#ran","syntology_url":"https://syntology.ai/paper/2506.06122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.06122"}},"official":{"repos":["alibaba/roll"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/time-travel-is-cheating-going-live-with","slug":"time-travel-is-cheating-going-live-with","title":"Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking","date":"2025-05-16","arxiv_id":"2505.11065","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/time-travel-is-cheating-going-live-with#ran","syntology_url":"https://syntology.ai/paper/2505.11065","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.11065"}},"official":{"repos":["hkustdial/deepfund"],"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/sotopia-s4-a-user-friendly-system-for","slug":"sotopia-s4-a-user-friendly-system-for","title":"SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation","date":"2025-04-19","arxiv_id":"2504.16122","repositories_listed":0,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/sotopia-s4-a-user-friendly-system-for#ran","syntology_url":"https://syntology.ai/paper/2504.16122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.16122"}},"official":null}},{"url":"/paper/a-survey-on-personalized-alignment-the","slug":"a-survey-on-personalized-alignment-the","title":"A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications","date":"2025-03-21","arxiv_id":"2503.17003","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/a-survey-on-personalized-alignment-the#ran","syntology_url":"https://syntology.ai/paper/2503.17003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.17003"}},"official":null}},{"url":"/paper/foundation-models-for-spatio-temporal-data","slug":"foundation-models-for-spatio-temporal-data","title":"Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey","date":"2025-03-12","arxiv_id":"2503.13502","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":10,"n_pointer_only":6,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 2 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/foundation-models-for-spatio-temporal-data#ran","syntology_url":"https://syntology.ai/paper/2503.13502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13502"}},"official":null}},{"url":"/paper/learning-conformal-abstention-policies-for","slug":"learning-conformal-abstention-policies-for","title":"Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models","date":"2025-02-08","arxiv_id":"2502.06884","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/learning-conformal-abstention-policies-for#ran","syntology_url":"https://syntology.ai/paper/2502.06884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.06884"}},"official":{"repos":["sinatayebati/vlm-uncertainty"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/medrax-medical-reasoning-agent-for-chest-x","slug":"medrax-medical-reasoning-agent-for-chest-x","title":"MedRAX: Medical Reasoning Agent for Chest X-ray","date":"2025-02-04","arxiv_id":"2502.02673","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/medrax-medical-reasoning-agent-for-chest-x#ran","syntology_url":"https://syntology.ai/paper/2502.02673","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.02673"}},"official":{"repos":["bowang-lab/medrax"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hygen-efficient-llm-serving-via-elastic","slug":"hygen-efficient-llm-serving-via-elastic","title":"HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location","date":"2025-01-15","arxiv_id":"2501.14808","repositories_listed":0,"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/hygen-efficient-llm-serving-via-elastic#ran","syntology_url":"https://syntology.ai/paper/2501.14808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.14808"}},"official":null}},{"url":"/paper/tradingagents-multi-agents-llm-financial","slug":"tradingagents-multi-agents-llm-financial","title":"TradingAgents: Multi-Agents LLM Financial Trading Framework","date":"2024-12-28","arxiv_id":"2412.20138","repositories_listed":1,"syntology":{"n":14,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":9,"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) · 9 unverified","sample_list":"/paper/tradingagents-multi-agents-llm-financial#ran","syntology_url":"https://syntology.ai/paper/2412.20138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.20138"}},"official":{"repos":["tauricresearch/tradingagents"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-large-scale-traffic-forecasting","slug":"efficient-large-scale-traffic-forecasting","title":"Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective","date":"2024-12-13","arxiv_id":"2412.09972","repositories_listed":3,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-large-scale-traffic-forecasting#ran","syntology_url":"https://syntology.ai/paper/2412.09972","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09972"}},"official":{"repos":["lmissher/patchstg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/explainable-machine-learning-for-neoplasms","slug":"explainable-machine-learning-for-neoplasms","title":"Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study","date":"2024-12-10","arxiv_id":"2412.07737","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/explainable-machine-learning-for-neoplasms#ran","syntology_url":"https://syntology.ai/paper/2412.07737","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07737"}},"official":{"repos":["ai4healthuol/cardiodiag"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/networkgym-reinforcement-learning","slug":"networkgym-reinforcement-learning","title":"NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation","date":"2024-10-30","arxiv_id":"2411.04138","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":2,"n_ran_checked":12,"n_instrument":3,"n_unverified":3,"n_honours":4,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"15 ran (of which 2 constructed an object rather than computing a result; 12 with no instrument failure: 4 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/networkgym-reinforcement-learning#ran","syntology_url":"https://syntology.ai/paper/2411.04138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.04138"}},"official":{"repos":["hmomin/networkgym"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/regional-ocean-forecasting-with-hierarchical","slug":"regional-ocean-forecasting-with-hierarchical","title":"Regional Ocean Forecasting with Hierarchical Graph Neural Networks","date":"2024-10-15","arxiv_id":"2410.11807","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/regional-ocean-forecasting-with-hierarchical#ran","syntology_url":"https://syntology.ai/paper/2410.11807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11807"}},"official":{"repos":["deinal/seacast"],"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/stable-hadamard-memory-revitalizing-memory","slug":"stable-hadamard-memory-revitalizing-memory","title":"Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning","date":"2024-10-14","arxiv_id":"2410.10132","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/stable-hadamard-memory-revitalizing-memory#ran","syntology_url":"https://syntology.ai/paper/2410.10132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10132"}},"official":null}},{"url":"/paper/layerkv-optimizing-large-language-model","slug":"layerkv-optimizing-large-language-model","title":"LayerKV: Optimizing Large Language Model Serving with Layer-wise KV Cache Management","date":"2024-10-01","arxiv_id":"2410.00428","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"9 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; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/layerkv-optimizing-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2410.00428","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.00428"}},"official":null}},{"url":"/paper/moss-enabling-code-driven-evolution-and","slug":"moss-enabling-code-driven-evolution-and","title":"MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents","date":"2024-09-24","arxiv_id":"2409.16120","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/moss-enabling-code-driven-evolution-and#ran","syntology_url":"https://syntology.ai/paper/2409.16120","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.16120"}},"official":{"repos":["ghost-in-moss/ghostos"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/from-commands-to-prompts-llm-based-semantic","slug":"from-commands-to-prompts-llm-based-semantic","title":"From Commands to Prompts: LLM-based Semantic File System for AIOS","date":"2024-09-23","arxiv_id":"2410.11843","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/from-commands-to-prompts-llm-based-semantic#ran","syntology_url":"https://syntology.ai/paper/2410.11843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11843"}},"official":{"repos":["agiresearch/aios-lsfs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-adapted-large-language-model-facilitates","slug":"an-adapted-large-language-model-facilitates","title":"Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management","date":"2024-09-20","arxiv_id":"2409.13191","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/an-adapted-large-language-model-facilitates#ran","syntology_url":"https://syntology.ai/paper/2409.13191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.13191"}},"official":{"repos":["waltonfuture/Diabetica"],"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/cognitive-kernel-an-open-source-agent-system","slug":"cognitive-kernel-an-open-source-agent-system","title":"Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots","date":"2024-09-16","arxiv_id":"2409.10277","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/cognitive-kernel-an-open-source-agent-system#ran","syntology_url":"https://syntology.ai/paper/2409.10277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.10277"}},"official":{"repos":["tencent/cogkernel"],"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/oneedit-a-neural-symbolic-collaboratively","slug":"oneedit-a-neural-symbolic-collaboratively","title":"OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System","date":"2024-09-09","arxiv_id":"2409.07497","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/oneedit-a-neural-symbolic-collaboratively#ran","syntology_url":"https://syntology.ai/paper/2409.07497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07497"}},"official":{"repos":["zjunlp/oneedit"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adapmoe-adaptive-sensitivity-based-expert","slug":"adapmoe-adaptive-sensitivity-based-expert","title":"AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference","date":"2024-08-19","arxiv_id":"2408.10284","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/adapmoe-adaptive-sensitivity-based-expert#ran","syntology_url":"https://syntology.ai/paper/2408.10284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.10284"}},"official":{"repos":["pku-sec-lab/adapmoe"],"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","unlocated"]}}},{"url":"/paper/hiagent-hierarchical-working-memory","slug":"hiagent-hierarchical-working-memory","title":"HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model","date":"2024-08-18","arxiv_id":"2408.09559","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hiagent-hierarchical-working-memory#ran","syntology_url":"https://syntology.ai/paper/2408.09559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.09559"}},"official":{"repos":["hiagent2024/hiagent"],"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/sustaindc-benchmarking-for-sustainable-data","slug":"sustaindc-benchmarking-for-sustainable-data","title":"SustainDC: Benchmarking for Sustainable Data Center Control","date":"2024-08-14","arxiv_id":"2408.07841","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/sustaindc-benchmarking-for-sustainable-data#ran","syntology_url":"https://syntology.ai/paper/2408.07841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.07841"}},"official":{"repos":["hewlettpackard/dc-rl"],"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/towards-assessing-data-replication-in-music","slug":"towards-assessing-data-replication-in-music","title":"Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio","date":"2024-07-19","arxiv_id":"2407.14364","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/towards-assessing-data-replication-in-music#ran","syntology_url":"https://syntology.ai/paper/2407.14364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.14364"}},"official":{"repos":["roserbatlleroca/mira"],"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/congo-compressive-online-gradient","slug":"congo-compressive-online-gradient","title":"CONGO: Compressive Online Gradient Optimization","date":"2024-07-08","arxiv_id":"2407.06325","repositories_listed":0,"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/congo-compressive-online-gradient#ran","syntology_url":"https://syntology.ai/paper/2407.06325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06325"}},"official":null}},{"url":"/paper/infinigen-efficient-generative-inference-of","slug":"infinigen-efficient-generative-inference-of","title":"InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management","date":"2024-06-28","arxiv_id":"2406.19707","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"14 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/infinigen-efficient-generative-inference-of#ran","syntology_url":"https://syntology.ai/paper/2406.19707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.19707"}},"official":null}},{"url":"/paper/seed-accelerating-reasoning-tree-construction","slug":"seed-accelerating-reasoning-tree-construction","title":"SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding","date":"2024-06-26","arxiv_id":"2406.18200","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":11,"phrase":"7 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/seed-accelerating-reasoning-tree-construction#ran","syntology_url":"https://syntology.ai/paper/2406.18200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18200"}},"official":{"repos":["Linking-ai/SEED"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/do-multimodal-foundation-models-understand","slug":"do-multimodal-foundation-models-understand","title":"WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks","date":"2024-06-19","arxiv_id":"2406.13264","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":12,"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) · 1 unverified","sample_list":"/paper/do-multimodal-foundation-models-understand#ran","syntology_url":"https://syntology.ai/paper/2406.13264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13264"}},"official":{"repos":["hazyresearch/wonderbread"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/icu-sepsis-a-benchmark-mdp-built-from-real","slug":"icu-sepsis-a-benchmark-mdp-built-from-real","title":"ICU-Sepsis: A Benchmark MDP Built from Real Medical Data","date":"2024-06-09","arxiv_id":"2406.05646","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/icu-sepsis-a-benchmark-mdp-built-from-real#ran","syntology_url":"https://syntology.ai/paper/2406.05646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05646"}},"official":{"repos":["icu-sepsis/icu-sepsis"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/villageragent-a-graph-based-multi-agent","slug":"villageragent-a-graph-based-multi-agent","title":"VillagerAgent: A Graph-Based Multi-Agent Framework for Coordinating Complex Task Dependencies in Minecraft","date":"2024-06-09","arxiv_id":"2406.05720","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/villageragent-a-graph-based-multi-agent#ran","syntology_url":"https://syntology.ai/paper/2406.05720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05720"}},"official":{"repos":["cnsdqd-dyb/villageragent"],"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/rotation-and-permutation-for-advanced-outlier","slug":"rotation-and-permutation-for-advanced-outlier","title":"DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs","date":"2024-06-03","arxiv_id":"2406.01721","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"10 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rotation-and-permutation-for-advanced-outlier#ran","syntology_url":"https://syntology.ai/paper/2406.01721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01721"}},"official":{"repos":["hsu1023/duquant"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/streamflow-prediction-with-uncertainty","slug":"streamflow-prediction-with-uncertainty","title":"Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach","date":"2024-05-31","arxiv_id":"2406.00133","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/streamflow-prediction-with-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2406.00133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00133"}},"official":{"repos":["aminegha/streampred"],"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/self-guiding-exploration-for-combinatorial","slug":"self-guiding-exploration-for-combinatorial","title":"Self-Guiding Exploration for Combinatorial Problems","date":"2024-05-28","arxiv_id":"2405.17950","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"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) · 3 unverified","sample_list":"/paper/self-guiding-exploration-for-combinatorial#ran","syntology_url":"https://syntology.ai/paper/2405.17950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17950"}},"official":{"repos":["zangir/llm-for-cp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-sustainable-urban-mobility","slug":"enhancing-sustainable-urban-mobility","title":"Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach","date":"2024-05-26","arxiv_id":"2405.17507","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":2,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":10,"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) · 5 unverified","sample_list":"/paper/enhancing-sustainable-urban-mobility#ran","syntology_url":"https://syntology.ai/paper/2405.17507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17507"}},"official":{"repos":["cy07gn/teltomob"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/econlogicqa-a-question-answering-benchmark","slug":"econlogicqa-a-question-answering-benchmark","title":"EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning","date":"2024-05-13","arxiv_id":"2405.07938","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"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) · 3 unverified","sample_list":"/paper/econlogicqa-a-question-answering-benchmark#ran","syntology_url":"https://syntology.ai/paper/2405.07938","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07938"}},"official":{"repos":["yinzhu-quan/lm-evaluation-harness"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/earthmatch-iterative-coregistration-for-fine","slug":"earthmatch-iterative-coregistration-for-fine","title":"EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography","date":"2024-05-08","arxiv_id":"2405.05422","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/earthmatch-iterative-coregistration-for-fine#ran","syntology_url":"https://syntology.ai/paper/2405.05422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05422"}},"official":{"repos":["gmberton/EarthMatch"],"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/vattention-dynamic-memory-management-for","slug":"vattention-dynamic-memory-management-for","title":"vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention","date":"2024-05-07","arxiv_id":"2405.04437","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/vattention-dynamic-memory-management-for#ran","syntology_url":"https://syntology.ai/paper/2405.04437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04437"}},"official":{"repos":["microsoft/vattention"],"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/retinexmamba-retinex-based-mamba-for-low","slug":"retinexmamba-retinex-based-mamba-for-low","title":"Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement","date":"2024-05-06","arxiv_id":"2405.03349","repositories_listed":1,"syntology":{"n":21,"n_ran":20,"n_constructed":0,"n_ran_checked":15,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":13,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/retinexmamba-retinex-based-mamba-for-low#ran","syntology_url":"https://syntology.ai/paper/2405.03349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.03349"}},"official":{"repos":["YhuoyuH/RetinexMamba"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/automating-the-enterprise-with-foundation","slug":"automating-the-enterprise-with-foundation","title":"Automating the Enterprise with Foundation Models","date":"2024-05-03","arxiv_id":"2405.03710","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/automating-the-enterprise-with-foundation#ran","syntology_url":"https://syntology.ai/paper/2405.03710","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.03710"}},"official":{"repos":["hazyresearch/eclair-agents"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-routing-for-enhanced-performance-of","slug":"semantic-routing-for-enhanced-performance-of","title":"Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration","date":"2024-04-24","arxiv_id":"2404.15869","repositories_listed":2,"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":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) · 1 unverified","sample_list":"/paper/semantic-routing-for-enhanced-performance-of#ran","syntology_url":"https://syntology.ai/paper/2404.15869","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.15869"}},"official":null}},{"url":"/paper/efficient-interactive-llm-serving-with-proxy","slug":"efficient-interactive-llm-serving-with-proxy","title":"Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction","date":"2024-04-12","arxiv_id":"2404.08509","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/efficient-interactive-llm-serving-with-proxy#ran","syntology_url":"https://syntology.ai/paper/2404.08509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.08509"}},"official":{"repos":["james-qiuhaoran/llm-serving-with-proxy-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/squeezeattention-2d-management-of-kv-cache-in","slug":"squeezeattention-2d-management-of-kv-cache-in","title":"SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget","date":"2024-04-07","arxiv_id":"2404.04793","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"8 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/squeezeattention-2d-management-of-kv-cache-in#ran","syntology_url":"https://syntology.ai/paper/2404.04793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04793"}},"official":{"repos":["hetailang/squeezeattention"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/longitudinal-targeted-minimum-loss-based","slug":"longitudinal-targeted-minimum-loss-based","title":"Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer","date":"2024-04-05","arxiv_id":"2404.04399","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/longitudinal-targeted-minimum-loss-based#ran","syntology_url":"https://syntology.ai/paper/2404.04399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04399"}},"official":null}},{"url":"/paper/contrastive-balancing-representation-learning","slug":"contrastive-balancing-representation-learning","title":"Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation","date":"2024-03-21","arxiv_id":"2403.14232","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":4,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/contrastive-balancing-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2403.14232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14232"}},"official":{"repos":["euzmin/Contrastive-Balancing-Representation-Network-CRNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/stateflow-enhancing-llm-task-solving-through","slug":"stateflow-enhancing-llm-task-solving-through","title":"StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows","date":"2024-03-17","arxiv_id":"2403.11322","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"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) · 0 unverified","sample_list":"/paper/stateflow-enhancing-llm-task-solving-through#ran","syntology_url":"https://syntology.ai/paper/2403.11322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11322"}},"official":{"repos":["kevin666aa/stateflow","yiranwu0/stateflow"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unist-a-prompt-empowered-universal-model-for","slug":"unist-a-prompt-empowered-universal-model-for","title":"UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction","date":"2024-02-19","arxiv_id":"2402.11838","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"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) · 5 unverified","sample_list":"/paper/unist-a-prompt-empowered-universal-model-for#ran","syntology_url":"https://syntology.ai/paper/2402.11838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11838"}},"official":{"repos":["tsinghua-fib-lab/unist"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/self-consistent-conformal-prediction","slug":"self-consistent-conformal-prediction","title":"Self-Calibrating Conformal Prediction","date":"2024-02-11","arxiv_id":"2402.07307","repositories_listed":1,"syntology":{"n":25,"n_ran":23,"n_constructed":4,"n_ran_checked":9,"n_instrument":14,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"23 ran (of which 4 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 14 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-consistent-conformal-prediction#ran","syntology_url":"https://syntology.ai/paper/2402.07307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07307"}},"official":{"repos":["larsvanderlaan/selfcalibratingconformal"],"state":"official (archive's flag): 23 ran","n_ran":23,"n_constructed":4,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamical-survival-analysis-with-controlled","slug":"dynamical-survival-analysis-with-controlled","title":"Dynamical Survival Analysis with Controlled Latent States","date":"2024-01-30","arxiv_id":"2401.17077","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":13,"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) · 5 unverified","sample_list":"/paper/dynamical-survival-analysis-with-controlled#ran","syntology_url":"https://syntology.ai/paper/2401.17077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17077"}},"official":{"repos":["linusbleistein/signature_survival"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/ai-driven-platform-for-systematic","slug":"ai-driven-platform-for-systematic","title":"ShennongAlpha: an AI-driven sharing and collaboration platform for intelligent curation, acquisition, and translation of natural medicinal material knowledge","date":"2023-12-27","arxiv_id":"2401.00020","repositories_listed":3,"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":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) · 0 unverified","sample_list":"/paper/ai-driven-platform-for-systematic#ran","syntology_url":"https://syntology.ai/paper/2401.00020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.00020"}},"official":{"repos":["shennong-program/pycgs","shennong-program/shennongname","shennong-program/mlmd"],"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/large-language-models-as-traffic-signal","slug":"large-language-models-as-traffic-signal","title":"LLMLight: Large Language Models as Traffic Signal Control Agents","date":"2023-12-26","arxiv_id":"2312.16044","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/large-language-models-as-traffic-signal#ran","syntology_url":"https://syntology.ai/paper/2312.16044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16044"}},"official":{"repos":["usail-hkust/llmtscs"],"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/reinforcement-learning-for-wildfire","slug":"reinforcement-learning-for-wildfire","title":"Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments","date":"2023-11-27","arxiv_id":"2311.15925","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":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) · 2 unverified","sample_list":"/paper/reinforcement-learning-for-wildfire#ran","syntology_url":"https://syntology.ai/paper/2311.15925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.15925"}},"official":{"repos":["mitrefireline/simfire"],"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/one-size-fits-all-for-semantic-shifts","slug":"one-size-fits-all-for-semantic-shifts","title":"One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning","date":"2023-11-18","arxiv_id":"2311.12048","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":1,"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/one-size-fits-all-for-semantic-shifts#ran","syntology_url":"https://syntology.ai/paper/2311.12048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.12048"}},"official":{"repos":["kaist-dmlab/adapromptcl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/technical-report-large-language-models-can","slug":"technical-report-large-language-models-can","title":"Large Language Models can Strategically Deceive their Users when Put Under Pressure","date":"2023-11-09","arxiv_id":"2311.07590","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/technical-report-large-language-models-can#ran","syntology_url":"https://syntology.ai/paper/2311.07590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07590"}},"official":{"repos":["apolloresearch/insider-trading"],"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/sbcformer-lightweight-network-capable-of-full","slug":"sbcformer-lightweight-network-capable-of-full","title":"SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers","date":"2023-11-07","arxiv_id":"2311.03747","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"5 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sbcformer-lightweight-network-capable-of-full#ran","syntology_url":"https://syntology.ai/paper/2311.03747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03747"}},"official":{"repos":["xyonglu/sbcformer"],"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":["official"]}}},{"url":"/paper/agent-specific-effects","slug":"agent-specific-effects","title":"Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs","date":"2023-10-17","arxiv_id":"2310.11334","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"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 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) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/agent-specific-effects#ran","syntology_url":"https://syntology.ai/paper/2310.11334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.11334"}},"official":{"repos":["stelios30/agent-specific-effects"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/nemo-guardrails-a-toolkit-for-controllable","slug":"nemo-guardrails-a-toolkit-for-controllable","title":"NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails","date":"2023-10-16","arxiv_id":"2310.10501","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/nemo-guardrails-a-toolkit-for-controllable#ran","syntology_url":"https://syntology.ai/paper/2310.10501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10501"}},"official":{"repos":["nvidia/nemo-guardrails"],"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/put-your-money-where-your-mouth-is-evaluating","slug":"put-your-money-where-your-mouth-is-evaluating","title":"Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena","date":"2023-10-09","arxiv_id":"2310.05746","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/put-your-money-where-your-mouth-is-evaluating#ran","syntology_url":"https://syntology.ai/paper/2310.05746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05746"}},"official":{"repos":["jiangjiechen/auction-arena"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/trafficgpt-viewing-processing-and-interacting","slug":"trafficgpt-viewing-processing-and-interacting","title":"TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models","date":"2023-09-13","arxiv_id":"2309.06719","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"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 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) · 1 unverified","sample_list":"/paper/trafficgpt-viewing-processing-and-interacting#ran","syntology_url":"https://syntology.ai/paper/2309.06719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.06719"}},"official":{"repos":["lijlansg/trafficgpt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/2309-06180","slug":"2309-06180","title":"Efficient Memory Management for Large Language Model Serving with PagedAttention","date":"2023-09-12","arxiv_id":"2309.06180","repositories_listed":7,"syntology":{"n":36,"n_ran":28,"n_constructed":0,"n_ran_checked":22,"n_instrument":6,"n_unverified":8,"n_honours":3,"n_violates":1,"n_no_contract":18,"n_pointer_only":11,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 3 honoured, 1 violated, 18 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/2309-06180#ran","syntology_url":"https://syntology.ai/paper/2309.06180","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.06180"}},"official":{"repos":["vllm-project/vllm"],"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/diffcharge-generating-ev-charging-scenarios","slug":"diffcharge-generating-ev-charging-scenarios","title":"DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model","date":"2023-08-18","arxiv_id":"2308.09857","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"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) · 2 unverified","sample_list":"/paper/diffcharge-generating-ev-charging-scenarios#ran","syntology_url":"https://syntology.ai/paper/2308.09857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09857"}},"official":{"repos":["lsy-cython/diffcharge"],"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/offline-multi-agent-reinforcement-learning-1","slug":"offline-multi-agent-reinforcement-learning-1","title":"Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization","date":"2023-07-21","arxiv_id":"2307.11620","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"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) · 2 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/offline-multi-agent-reinforcement-learning-1#ran","syntology_url":"https://syntology.ai/paper/2307.11620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11620"}},"official":{"repos":["zhengyinan-air/omiga"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/analyzing-dataset-annotation-quality","slug":"analyzing-dataset-annotation-quality","title":"Analyzing Dataset Annotation Quality Management in the Wild","date":"2023-07-16","arxiv_id":"2307.08153","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":0,"n_no_contract":1,"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, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/analyzing-dataset-annotation-quality#ran","syntology_url":"https://syntology.ai/paper/2307.08153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08153"}},"official":{"repos":["ukplab/arxiv2023-qanno","ukplab/qanno"],"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/in-context-autoencoder-for-context","slug":"in-context-autoencoder-for-context","title":"In-context Autoencoder for Context Compression in a Large Language Model","date":"2023-07-13","arxiv_id":"2307.06945","repositories_listed":2,"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":0,"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/in-context-autoencoder-for-context#ran","syntology_url":"https://syntology.ai/paper/2307.06945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06945"}},"official":{"repos":["getao/icae"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rl4co-an-extensive-reinforcement-learning-for","slug":"rl4co-an-extensive-reinforcement-learning-for","title":"RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark","date":"2023-06-29","arxiv_id":"2306.17100","repositories_listed":3,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/rl4co-an-extensive-reinforcement-learning-for#ran","syntology_url":"https://syntology.ai/paper/2306.17100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.17100"}},"official":{"repos":["ai4co/rl4co","pytorch/rl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/a-versatile-multi-agent-reinforcement","slug":"a-versatile-multi-agent-reinforcement","title":"A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management","date":"2023-06-13","arxiv_id":"2306.07542","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/a-versatile-multi-agent-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2306.07542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07542"}},"official":{"repos":["victoryxl/replenishmentenv"],"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/generating-with-confidence-uncertainty","slug":"generating-with-confidence-uncertainty","title":"Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models","date":"2023-05-30","arxiv_id":"2305.19187","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/generating-with-confidence-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2305.19187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19187"}},"official":{"repos":["zlin7/uq-nlg"],"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/uncertainty-quantification-in-machine","slug":"uncertainty-quantification-in-machine","title":"Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial","date":"2023-05-07","arxiv_id":"2305.04933","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/uncertainty-quantification-in-machine#ran","syntology_url":"https://syntology.ai/paper/2305.04933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04933"}},"official":{"repos":["vnemani14/uq_ml_review"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-conditional-hidden-markov-model-for","slug":"causal-conditional-hidden-markov-model-for","title":"Causal conditional hidden Markov model for multimodal traffic prediction","date":"2023-01-19","arxiv_id":"2301.08249","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":8,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"11 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/causal-conditional-hidden-markov-model-for#ran","syntology_url":"https://syntology.ai/paper/2301.08249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.08249"}},"official":{"repos":["eternityzy/cchmm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":8,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rmm-reinforced-memory-management-for-class-1","slug":"rmm-reinforced-memory-management-for-class-1","title":"RMM: Reinforced Memory Management for Class-Incremental Learning","date":"2023-01-14","arxiv_id":"2301.05792","repositories_listed":4,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/rmm-reinforced-memory-management-for-class-1#ran","syntology_url":"https://syntology.ai/paper/2301.05792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.05792"}},"official":{"repos":["gitlab.mpi-klsb.mpg.de/yaoyaoliu/rmm"],"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/flair-1-semantic-segmentation-and-domain","slug":"flair-1-semantic-segmentation-and-domain","title":"FLAIR #1: semantic segmentation and domain adaptation dataset","date":"2022-11-23","arxiv_id":"2211.12979","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/flair-1-semantic-segmentation-and-domain#ran","syntology_url":"https://syntology.ai/paper/2211.12979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12979"}},"official":{"repos":["IGNF/FLAIR-1-AI-Challenge"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-trajectory-representation","slug":"self-supervised-trajectory-representation","title":"Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics","date":"2022-11-17","arxiv_id":"2211.09510","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":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/self-supervised-trajectory-representation#ran","syntology_url":"https://syntology.ai/paper/2211.09510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.09510"}},"official":{"repos":["aptx1231/start"],"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/conditionally-risk-averse-contextual-bandits","slug":"conditionally-risk-averse-contextual-bandits","title":"Conditionally Risk-Averse Contextual Bandits","date":"2022-10-24","arxiv_id":"2210.13573","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/conditionally-risk-averse-contextual-bandits#ran","syntology_url":"https://syntology.ai/paper/2210.13573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13573"}},"official":{"repos":["zwd-ms/risk_averse_cb"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-bias-correction-for-improved","slug":"adaptive-bias-correction-for-improved","title":"Adaptive Bias Correction for Improved Subseasonal Forecasting","date":"2022-09-21","arxiv_id":"2209.10666","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/adaptive-bias-correction-for-improved#ran","syntology_url":"https://syntology.ai/paper/2209.10666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.10666"}},"official":{"repos":["microsoft/subseasonal_toolkit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cardinality-regularized-hawkes-granger-model-1","slug":"cardinality-regularized-hawkes-granger-model-1","title":"Cardinality-Regularized Hawkes-Granger Model","date":"2022-08-23","arxiv_id":"2208.10671","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/cardinality-regularized-hawkes-granger-model-1#ran","syntology_url":"https://syntology.ai/paper/2208.10671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.10671"}},"official":{"repos":["Idesan/HawkesGranger"],"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/improving-computed-tomography-ct","slug":"improving-computed-tomography-ct","title":"Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction","date":"2022-08-23","arxiv_id":"2208.10937","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":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) · 0 unverified","sample_list":"/paper/improving-computed-tomography-ct#ran","syntology_url":"https://syntology.ai/paper/2208.10937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.10937"}},"official":{"repos":["esizikova/medsynth_public"],"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/get-it-in-writing-formal-contracts-mitigate","slug":"get-it-in-writing-formal-contracts-mitigate","title":"Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL","date":"2022-08-22","arxiv_id":"2208.10469","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/get-it-in-writing-formal-contracts-mitigate#ran","syntology_url":"https://syntology.ai/paper/2208.10469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.10469"}},"official":{"repos":["algorithmic-alignment-lab/contracts"],"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/learning-distributed-and-fair-policies-for","slug":"learning-distributed-and-fair-policies-for","title":"Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game","date":"2022-06-03","arxiv_id":"2206.01451","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":0,"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-distributed-and-fair-policies-for#ran","syntology_url":"https://syntology.ai/paper/2206.01451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01451"}},"official":{"repos":["zhiyuanyaoj/marllb"],"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/using-constraint-programming-and-graph","slug":"using-constraint-programming-and-graph","title":"Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies","date":"2022-05-02","arxiv_id":"2205.01240","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/using-constraint-programming-and-graph#ran","syntology_url":"https://syntology.ai/paper/2205.01240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01240"}},"official":{"repos":["mikhail247/iamax"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/table-based-fact-verification-with-self-1","slug":"table-based-fact-verification-with-self-1","title":"Table-based Fact Verification with Self-adaptive Mixture of Experts","date":"2022-04-19","arxiv_id":"2204.08753","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/table-based-fact-verification-with-self-1#ran","syntology_url":"https://syntology.ai/paper/2204.08753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08753"}},"official":{"repos":["thumlp/samoe"],"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/swinunet3d-a-hierarchical-architecture-for","slug":"swinunet3d-a-hierarchical-architecture-for","title":"SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers","date":"2022-01-17","arxiv_id":"2201.06390","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":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) · 0 unverified","sample_list":"/paper/swinunet3d-a-hierarchical-architecture-for#ran","syntology_url":"https://syntology.ai/paper/2201.06390","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.06390"}},"official":{"repos":["bojesomo/Traffic4Cast2021-SwinUNet3D"],"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/machine-learning-enabling-high-throughput-and","slug":"machine-learning-enabling-high-throughput-and","title":"Machine learning enabling high-throughput and remote operations at large-scale user facilities","date":"2022-01-09","arxiv_id":"2201.03550","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":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/machine-learning-enabling-high-throughput-and#ran","syntology_url":"https://syntology.ai/paper/2201.03550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03550"}},"official":{"repos":["bnl/pub-ml_examples"],"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/next-day-wildfire-spread-a-machine-learning","slug":"next-day-wildfire-spread-a-machine-learning","title":"Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data","date":"2021-12-04","arxiv_id":"2112.02447","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"10 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; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/next-day-wildfire-spread-a-machine-learning#ran","syntology_url":"https://syntology.ai/paper/2112.02447","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02447"}},"official":null}},{"url":"/paper/simpletrack-understanding-and-rethinking-3d","slug":"simpletrack-understanding-and-rethinking-3d","title":"SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking","date":"2021-11-18","arxiv_id":"2111.09621","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"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 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) · 1 unverified","sample_list":"/paper/simpletrack-understanding-and-rethinking-3d#ran","syntology_url":"https://syntology.ai/paper/2111.09621","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09621"}},"official":{"repos":["tusimple/simpletrack"],"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/skillful-twelve-hour-precipitation-forecasts","slug":"skillful-twelve-hour-precipitation-forecasts","title":"Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks","date":"2021-11-14","arxiv_id":"2111.07470","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":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/skillful-twelve-hour-precipitation-forecasts#ran","syntology_url":"https://syntology.ai/paper/2111.07470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.07470"}},"official":null}},{"url":"/paper/unsupervised-change-detection-of-extreme","slug":"unsupervised-change-detection-of-extreme","title":"Unsupervised Change Detection of Extreme Events Using ML On-Board","date":"2021-11-04","arxiv_id":"2111.02995","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":7,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 7 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; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/unsupervised-change-detection-of-extreme#ran","syntology_url":"https://syntology.ai/paper/2111.02995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.02995"}},"official":{"repos":["spaceml-org/RaVAEn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/proximal-reinforcement-learning-efficient-off","slug":"proximal-reinforcement-learning-efficient-off","title":"Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes","date":"2021-10-28","arxiv_id":"2110.15332","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/proximal-reinforcement-learning-efficient-off#ran","syntology_url":"https://syntology.ai/paper/2110.15332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15332"}},"official":{"repos":["causalml/proximalrl"],"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/meta-learning-on-a-sequence-of-imbalanced","slug":"meta-learning-on-a-sequence-of-imbalanced","title":"Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness","date":"2021-09-29","arxiv_id":"2109.14120","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/meta-learning-on-a-sequence-of-imbalanced#ran","syntology_url":"https://syntology.ai/paper/2109.14120","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.14120"}},"official":{"repos":["joey-wang123/imbalancemeta"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/an-empirical-study-of-graph-contrastive","slug":"an-empirical-study-of-graph-contrastive","title":"An Empirical Study of Graph Contrastive Learning","date":"2021-09-02","arxiv_id":"2109.01116","repositories_listed":3,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"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; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/an-empirical-study-of-graph-contrastive#ran","syntology_url":"https://syntology.ai/paper/2109.01116","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.01116"}},"official":{"repos":["GraphCL/PyGCL"],"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":["listed","official"]}}},{"url":"/paper/dynamic-graph-convolutional-recurrent-network","slug":"dynamic-graph-convolutional-recurrent-network","title":"Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution","date":"2021-04-30","arxiv_id":"2104.14917","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":1,"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: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-graph-convolutional-recurrent-network#ran","syntology_url":"https://syntology.ai/paper/2104.14917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14917"}},"official":{"repos":["tsinghua-fib-lab/Traffic-Benchmark"],"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/pytorch-geometric-temporal-spatiotemporal","slug":"pytorch-geometric-temporal-spatiotemporal","title":"PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models","date":"2021-04-15","arxiv_id":"2104.07788","repositories_listed":5,"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/pytorch-geometric-temporal-spatiotemporal#ran","syntology_url":"https://syntology.ai/paper/2104.07788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07788"}},"official":{"repos":["benedekrozemberczki/pytorch_geometric_temporal"],"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/annotating-columns-with-pre-trained-language","slug":"annotating-columns-with-pre-trained-language","title":"Annotating Columns with Pre-trained Language Models","date":"2021-04-05","arxiv_id":"2104.01785","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/annotating-columns-with-pre-trained-language#ran","syntology_url":"https://syntology.ai/paper/2104.01785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.01785"}},"official":{"repos":["megagonlabs/doduo"],"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/unifying-cardiovascular-modelling-with-deep","slug":"unifying-cardiovascular-modelling-with-deep","title":"Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment","date":"2021-01-21","arxiv_id":"2101.08477","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/unifying-cardiovascular-modelling-with-deep#ran","syntology_url":"https://syntology.ai/paper/2101.08477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.08477"}},"official":{"repos":["thxsxth/POMDP_RLSepsis"],"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/knowledge-preserving-incremental-social-event","slug":"knowledge-preserving-incremental-social-event","title":"Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs","date":"2021-01-21","arxiv_id":"2101.08747","repositories_listed":2,"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/knowledge-preserving-incremental-social-event#ran","syntology_url":"https://syntology.ai/paper/2101.08747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.08747"}},"official":{"repos":["RingBDStack/KPGNN"],"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/forestnet-classifying-drivers-of","slug":"forestnet-classifying-drivers-of","title":"ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery","date":"2020-11-11","arxiv_id":"2011.05479","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":4,"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/forestnet-classifying-drivers-of#ran","syntology_url":"https://syntology.ai/paper/2011.05479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.05479"}},"official":null}},{"url":"/paper/leveraging-activity-recognition-to-enable","slug":"leveraging-activity-recognition-to-enable","title":"Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data","date":"2020-11-03","arxiv_id":"2011.01776","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/leveraging-activity-recognition-to-enable#ran","syntology_url":"https://syntology.ai/paper/2011.01776","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01776"}},"official":{"repos":["Mvrjustid/IMWUT-Hierarchical-HAR-PBD"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/cs2-net-deep-learning-segmentation-of","slug":"cs2-net-deep-learning-segmentation-of","title":"CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging","date":"2020-10-15","arxiv_id":"2010.07486","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":0,"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/cs2-net-deep-learning-segmentation-of#ran","syntology_url":"https://syntology.ai/paper/2010.07486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07486"}},"official":{"repos":["iMED-Lab/CS-Net"],"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/a-game-theoretic-analysis-of-networked-system","slug":"a-game-theoretic-analysis-of-networked-system","title":"A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning","date":"2020-10-15","arxiv_id":"2010.07777","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-game-theoretic-analysis-of-networked-system#ran","syntology_url":"https://syntology.ai/paper/2010.07777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07777"}},"official":{"repos":["instadeepai/EGTA-NMARL"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fc-gaga-fully-connected-gated-graph","slug":"fc-gaga-fully-connected-gated-graph","title":"FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting","date":"2020-07-30","arxiv_id":"2007.15531","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/fc-gaga-fully-connected-gated-graph#ran","syntology_url":"https://syntology.ai/paper/2007.15531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15531"}},"official":{"repos":["boreshkinai/fc-gaga"],"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/multi-agent-routing-value-iteration-network","slug":"multi-agent-routing-value-iteration-network","title":"Multi-Agent Routing Value Iteration Network","date":"2020-07-09","arxiv_id":"2007.05096","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"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) · 3 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/multi-agent-routing-value-iteration-network#ran","syntology_url":"https://syntology.ai/paper/2007.05096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05096"}},"official":{"repos":["uber/MARVIN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/predicting-length-of-stay-in-the-intensive","slug":"predicting-length-of-stay-in-the-intensive","title":"Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks","date":"2020-06-29","arxiv_id":"2006.16109","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 2 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) · 2 unverified","sample_list":"/paper/predicting-length-of-stay-in-the-intensive#ran","syntology_url":"https://syntology.ai/paper/2006.16109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16109"}},"official":{"repos":["EmmaRocheteau/eICU-LoS-prediction"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]}}}],"record_sha256":"e14c7f4a1cf235cbbec0b770005c9bb964d6ba4a5dfa8030cf707dc79b0057ec","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}