{"as_of":"2026-08-16T19:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:71257f98ac3714ebd9a9938dd978a6b6f8b54764f0102a013542a8922d8efa7c","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-15T14:58:03.612340Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2603.04873/citation-record","integrity":"/paper/2603.04873/integrity","json":"/paper/2603.04873/citation-record.json","paper":"/paper/2603.04873"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Pitch classification using variational bayesian gaussian mixture models on trackman data.Journal of Sports Sciences, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:5e0d9a12e17631d0a4de2697651eb09c59a7890d2fe3093f645c5b149e1ff3e3","observation_id":"8838e852-58cf-4a4f-9655-a6a2cc2c2395","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Clausi, and John S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:d15419090fb21e4a4f190b5bbb3db5a14e8f8eef3b0e1036d36a8eb59515d619","observation_id":"9fe98b66-7db6-46f4-821a-5dc078159546","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09063","last_updated":"2024-03-14T03:07:58Z","snapshot_observed_at":"2026-08-16T14:09:58.276722Z","submitted_at":"2024-03-14T03:07:58Z","title":"Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09063","snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Distribution and depth- aware transformers for 3d human mesh recovery.arXiv preprint arXiv:2403.09063, 4(6):7, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"cited_paper":"/paper/2403.09063","citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:d7ecb695111649dbcbede535c28aa5b07d244d4a8cbe726d279695968627255a","observation_id":"fba36100-c3a9-4801-96ac-9d8f1b49a244","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Scalable injury-risk screening in baseball pitching from broadcast video.arXiv preprint arXiv:2511.09502, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:9c48de05e3faee83ef29a90ea6bee21df9a7570ae792341c4ebffacf6bd8af3e","observation_id":"e7dae257-9744-4410-8f25-6e676b0fae93","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Baseball pitch type recognition based on broadcast videos","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:d5680c9da5eed36173631ac60be09a9d29078146053c6e2a07a134ca7dbb925d","observation_id":"555c3b34-ccb8-455e-b214-d13db2e82dd7","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Xgboost: A scalable tree boosting system","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:6532e9073e68e60cdf9afffb4c98ebbe8ade0b186668332993dce9abf554c346","observation_id":"006d8991-ddbd-46fc-9805-7509dd95670c","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:1847001b4d97edc1b230e0292625a1ef2fb92702912ffd1e681fe3db3e6b8932","observation_id":"44ee828d-d9df-4fc9-b75e-d6400d85fbaf","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Video-based pitch type classification us- ing openpose and st-gcn in baseball.Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition Workshops, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:c75f502d526294c78b4bf95fc579442cc1f768292f2395b8d523b79aa0621985","observation_id":"dc258798-bf5f-4063-9969-d91d9e7c3d76","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Statcast pitch classifications","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:c020ff4245e0ada62c89d5127d35af47b5a2089590bef0ec2e19d182c2feb4f7","observation_id":"7c4881f7-eaa2-4c90-9896-2f51e58cc846","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Applying machine learning tech- niques to baseball pitch prediction","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:60e58af64ca5f9ce2d40c787bdc9a221f69af42f5d7fb9f309b477be73fa01cc","observation_id":"431392d4-cf27-4419-aad7-4270d7011c3f","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Apply- ing machine learning techniques to baseball pitch prediction","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:f1531cba6e057f78de730f75130029700568d8056f8d0ae2cbcc03d9992ab4cd","observation_id":"97d912fa-7488-411a-b28f-09bb0b1d7a97","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12345","last_updated":"2025-01-21T18:23:42Z","snapshot_observed_at":"2026-08-15T02:13:24.349114Z","submitted_at":"2025-01-21T18:23:42Z","title":"The doubly librating Plutinos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12345","snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Pitch type classification using 2d pose estimation and st-gcn on mlb-youtube dataset.arXiv preprint arXiv:2501.12345, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"cited_paper":"/paper/2501.12345","citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:54e9ddc57d4ad47e241460b7cfdc4710bdd049f6907bd3e89cbf9b25120c4eb3","observation_id":"3360c54d-74f1-4dd6-8c7c-5b584774dbd6","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Black, David W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:b83c9b190f61cd5605be2acb55f7569225b756137c2b2fc81bc59ab285ba280d","observation_id":"b05c2091-17d8-4c4b-bd5a-72853fed8c26","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:d391ef490b2f86eaa9cd99d2a05c922f2fbae48d8ac4938b8f96c5583d3d2707","observation_id":"0d6d2e22-23f6-4cab-a3e5-42b1ed30947a","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Prediction of pitch type and location in baseball us- ing ensemble model of deep neural networks.Journal of Sports Analytics, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:2b71e64837d704ad7b0c7c4b286c4bd0c82728dff312e08e031e4c99cfa64321","observation_id":"6a7136ab-5202-421b-9717-52599d3de15c","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Prediction of pitch type and location in baseball using ensemble model of deep neural networks.Journal of Sports Analytics, 8(2):115–126, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:05d9635ad99fd3b9615960ad43fe4442dfac56ed23e3ef1417987ef873721b23","observation_id":"7fdac935-30f5-4900-8ae1-9c15f331dab0","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.08172","last_updated":"2019-06-14T05:49:22Z","snapshot_observed_at":"2026-08-15T17:22:09.610128Z","submitted_at":"2019-06-14T05:49:22Z","title":"MediaPipe: A Framework for Building Perception Pipelines","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.08172","snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Medi- apipe: A framework for building perception pipelines.arXiv preprint arXiv:1906.08172, 2019","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"cited_paper":"/paper/1906.08172","citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:1d8b5a46081af81481147bea886872a516cb28f82ec35551b7610e4b3e0fb135","observation_id":"f1638478-6476-4109-b9e4-26f5f207f43c","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Classification of fast and off-speed pitches using pelvis and trunk kinematics in youth baseball pitchers using machine learning.Journal of Science and Medicine in Sport,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:4df62e752f6e3f160719719bcbaafdeca0643cef92b054ef2d16f5fe443c9a29","observation_id":"8d6978e6-8b77-49de-887b-acb08b3a7c71","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Classification of four pitching styles in japanese baseball players.International Journal of Sports Science & Coaching, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:6ad7eba2002db283a9af287f97cba06358a8455894d6d9d3b6fe787163323099","observation_id":"201ce4dc-c930-4666-a670-90605b079484","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Automated classi- fication of baseball pitching phases using machine learning and artificial intelligence-based posture estimation.Applied Sciences, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:8824d002f73b5280d4176b01ed4351058925c973593941f2f589af5680b8e667","observation_id":"c6ca7be2-4088-4991-9372-d665236c5738","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Au- tomated classification of baseball pitching phases using ma- chine learning and artificial intelligence-based posture esti- mation.Applied Sciences, 15(22):12155, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:5d4a7233bd960a8f963c60f8ad345001a54242a41c2b78c8a8073efad457022d","observation_id":"9cd91cc4-056c-4fe3-a6be-c62cbb8df991","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1304.1756","last_updated":"2013-04-05T16:27:49Z","snapshot_observed_at":"2026-08-15T00:22:28.275166Z","submitted_at":"2013-04-05T16:27:49Z","title":"Trouble With The Curve: Improving MLB Pitch Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1304.1756","snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Trouble with the curve: Improving mlb pitch classification.arXiv preprint arXiv:1304.1756, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"cited_paper":"/paper/1304.1756","citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:15775c9b1ee06c6118de10a04b1470d11204e459879676d7a20aa46728a1fcff","observation_id":"037c6ccc-ea3b-454e-92b6-72d95e003dce","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Classifying pitch types in baseball us- ing machine learning algorithms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:b1b3b2abce911f2c9d19efc59a4f0d2ce9bf95d8daa8ee9437eed52f99467cc8","observation_id":"224b786b-60fb-4340-9aae-f152801cb672","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.03559","last_updated":"2017-04-11T22:42:53Z","snapshot_observed_at":"2026-08-14T21:07:08.718908Z","submitted_at":"2017-04-11T22:42:53Z","title":"Calculating Kolmogorov Complexity from the Transcriptome Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.03559","snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Model-based clustering for classifying professional baseball pitches.arXiv preprint arXiv:1704.03559, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"cited_paper":"/paper/1704.03559","citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:d2649201306b76b9edd1458dfa3c544111dace4bf89742a998f0360cb24eed6d","observation_id":"fb035fa2-23a4-4a06-9283-0ead0fec92e3","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Using multi-class classification methods to predict baseball pitch types.Journal of Sports Analytics,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:7e1b7445c09811455d7f3452c9d49c87c93291be55e6ca5581ac9cec836d37ad","observation_id":"1a70eece-e964-44f5-8c44-2361aff06a4f","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Hawk eye: A logi- cal innovative technology use in sports for effective decision making.Sport Science Review, 21, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:39cc3cd2735c3d11db4c6805dccde0ce8eca54f2efadec3bdf81c9f78e8c9fa3","observation_id":"15e658ee-8255-480f-9949-9f60fa3f4cf9","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Automatic pitch type recognition from baseball broadcast videos.2008 Tenth IEEE International Symposium on Multimedia, 2008","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:793ce7f600a941c32351c9372505253e5f91aba562c2391aa503d5ac51a6c1d9","observation_id":"15c65fdb-273a-4c38-927c-835053e0f2f0","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:f56e7560ad3bc228e27d3d11cd5ed50ec4caf2552a05b744fef126bdac2a9197","observation_id":"46ef8a16-ea3e-419e-b80b-fedd1ffefbe6","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Utilization of pattern recognition techniques to classify baseball pitches.Research in Sports Medicine, 24(4):348–357, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:972b091e7bb1084fe4b7e5b50cb8de983e35f05c3bb382aff9c0144e2643eca6","observation_id":"3dc029c6-728c-44ce-b835-fab0a4e1580d","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T14:58:03.612340Z","title":"Vit- pose++: Vision transformer for generic body pose estima- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(2):1212–1230, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-15T14:58:03.612340Z"},"links":{"citing_paper":"/paper/2603.04873"},"observation_digest":"sha256:befe926aeb751acb7af26905371a915ec238462df12f26f04c20fc8e7195eca0","observation_id":"c69ea435-67ab-4838-9678-0ee09051e136","resolution":{"observed_at":"2026-07-15T14:58:03.612340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.04873","last_updated":"2026-06-26T01:09:25Z","latest_version":3,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-15T22:20:32.634330Z","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":30},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2603.04873."}