{"as_of":"2026-08-17T05:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a1947277bd22256766be586f2a564cb1e4eede756aa02cb0fcdf219a066b1b75","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:43:51.890716Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"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/2412.05534/citation-record","integrity":"/paper/2412.05534/integrity","json":"/paper/2412.05534/citation-record.json","paper":"/paper/2412.05534"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.499959Z","title":"A novel architecture of parking management for smart cities,","venue":null,"work_id":"c965011e-416b-4bef-81c0-0fba54098105","year":2012},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.682407Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:6645d7c1e732b54cec486765adcd52e0df72027d63ce80436283639562e06edf","observation_id":"bac0bc57-48b7-4de4-bdb2-cb8b1fc416b3","resolution":{"observed_at":"2026-08-11T20:43:52.505385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.485888Z","title":"An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city,","venue":null,"work_id":"37cd6dce-5399-4d05-b76e-eab4126a2252","year":2021},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.687340Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:6578e758730d18a9b9b4b3091325cdf04c2e49c939ab54f98dcbdbb8d3d63412","observation_id":"c65508d0-0dde-4a7d-bba9-e894fabbcf7a","resolution":{"observed_at":"2026-08-11T20:43:52.490786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.472496Z","title":"Spatial-temporal hypergraph self-supervised learning for crime prediction,","venue":null,"work_id":"dd9bff9a-5995-4646-9dd5-c49553b9c32e","year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.691478Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:aec1532fa59a34fc8f40ae9df0912b879b93c71e48a673df5fec1939e39925ac","observation_id":"9fe2c55c-ef13-40f1-a247-de4fbb059947","resolution":{"observed_at":"2026-08-11T20:43:52.476832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.459268Z","title":"Apots: A model for adversarial prediction of traffic speed,","venue":null,"work_id":"dcb55717-8c6e-491d-ba07-b76589e4bf29","year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.696584Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:f54807880f6a526a988f170e36755b9c80173786b9a1e857951dd91b5bc3f5d9","observation_id":"b46ab596-f2fd-48b0-8eed-5d9d3cc9772c","resolution":{"observed_at":"2026-08-11T20:43:52.463806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.446922Z","title":"Roi- demand traffic prediction: A pre-train, query and fine-tune framework,","venue":null,"work_id":"0c8c2bd5-e3de-431f-8073-9cf86ba2e7ee","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.700256Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:0a21fba967796ea62b32619b60895d1d74d16bd7befca3e419d098443bd9d3fc","observation_id":"28e636ac-ed9d-4616-b629-6cb0195befc6","resolution":{"observed_at":"2026-08-11T20:43:52.451039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.01926","last_updated":"2018-02-22T19:52:51Z","snapshot_observed_at":"2026-08-14T20:49:13.849412Z","submitted_at":"2017-07-06T18:20:59Z","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.01926","snapshot_observed_at":"2026-08-11T20:43:51.705330Z","title":"Diffusion convolutional re- current neural network: Data-driven traffic forecasting,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.705330Z"},"links":{"cited_paper":"/paper/1707.01926","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:08a95ccf62f19dfed7dbdaab75d3825d82978560c6fd78e1edcc125cdd04f27a","observation_id":"d7871a61-5443-42c8-bba5-8131d99186cd","resolution":{"observed_at":"2026-08-11T20:43:51.705330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.00121","last_updated":"2019-05-31T23:53:20Z","snapshot_observed_at":"2026-08-14T16:22:43.478101Z","submitted_at":"2019-05-31T23:53:20Z","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.00121","snapshot_observed_at":"2026-08-11T20:43:51.710135Z","title":"Graph wavenet for deep spatial-temporal graph modeling,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.710135Z"},"links":{"cited_paper":"/paper/1906.00121","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:37dfad3fa42709cfb58c5dbfdcd4c4b0bf1cd558e1e53d8475dab91cf7ec4730","observation_id":"76b6ae32-dffe-44a3-879b-3377696a1cac","resolution":{"observed_at":"2026-08-11T20:43:51.710135Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.434677Z","title":"Spatial-temporal pricing for ride-sourcing platform with reinforcement learning,","venue":null,"work_id":"d86c6400-8c24-43b3-af37-97c798b96aab","year":2021},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.714205Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:6bc3bb55966402886a2fae92f50b8c4f5c914512a73dbe6d53ca2def38b066d1","observation_id":"1fad2c88-5200-491d-81c6-47cbbae638e3","resolution":{"observed_at":"2026-08-11T20:43:52.439000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:51.719068Z","title":"Deep spatio-temporal residual networks for citywide crowd flows prediction,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.719068Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:97c901b8600d1855d9c58eef750e8a80ebb4672fe120b1a756a41805363a94eb","observation_id":"f5dc0a5b-e1af-4ce5-bdeb-125f447e5084","resolution":{"observed_at":"2026-08-11T20:43:51.719068Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.411956Z","title":"Gallat: A spatiotemporal graph attention network for passenger demand prediction,","venue":null,"work_id":"49277a01-2035-441f-8143-dca1299de668","year":2021},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.724046Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:12b05855b068bd943749e251617bb5afed88207e7f80b7aee273e9445a5cd235","observation_id":"0a398b52-82f3-4b7b-83a6-86875af1b676","resolution":{"observed_at":"2026-08-11T20:43:52.417245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.04875","last_updated":"2018-07-12T07:55:09Z","snapshot_observed_at":"2026-08-14T20:33:05.918309Z","submitted_at":"2017-09-14T16:54:41Z","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.04875","snapshot_observed_at":"2026-08-11T20:43:51.728241Z","title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.728241Z"},"links":{"cited_paper":"/paper/1709.04875","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:06e6470b54ca446155fa8b224e0755d65e99ebf5566fe275da7a03d6d8199cee","observation_id":"c27110aa-e13f-4f80-b312-6eb5acdcd903","resolution":{"observed_at":"2026-08-11T20:43:51.728241Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.397314Z","title":"Attention-based spatial-temporal graph convolutional recurrent networks for traffic forecasting,","venue":null,"work_id":"dcc3c8ec-d83e-4bdb-b3d1-7a620416405e","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.733179Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:2b2b1836f361c1121c83a8a76b3d184f238841abd72aab0725bedd66a6a63cac","observation_id":"83b1098e-3816-4296-b3bc-2ecc717c12ae","resolution":{"observed_at":"2026-08-11T20:43:52.402054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:51.737145Z","title":"Adaptive graph convolutional recurrent network for traffic forecasting,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.737145Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:84351186cfcf1022a9a65bf833d1219d5c54d1b2ef7074c5b1da39f38496f77c","observation_id":"6201f5ae-1c1d-43e0-a2d7-8461a66ee537","resolution":{"observed_at":"2026-08-11T20:43:51.737145Z","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":"2026-08-11T20:43:51.741209Z","title":"Con- necting the dots: Multivariate time series forecasting with graph neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.741209Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:fc12f66f3a9a236cccb18cfef62129b83235493fba42464b47d3a84b571a8a99","observation_id":"ea2ef6f9-29e7-4677-a1c4-3fb35a4cf87b","resolution":{"observed_at":"2026-08-11T20:43:51.741209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.06861","last_updated":"2021-04-21T03:42:14Z","snapshot_observed_at":"2026-08-16T18:51:45.754972Z","submitted_at":"2021-01-18T03:36:33Z","title":"Discrete Graph Structure Learning for Forecasting Multiple Time Series","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.06861","snapshot_observed_at":"2026-08-11T20:43:51.744992Z","title":"Discrete graph structure learning for forecasting multiple time series,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.744992Z"},"links":{"cited_paper":"/paper/2101.06861","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:3c038e1dd9f4cf9c1c9fa6d658a2a286a501e36bf2e5ce0d6f1d90435d093d64","observation_id":"f0c4e906-e26a-498a-922e-d61e582271ca","resolution":{"observed_at":"2026-08-11T20:43:51.744992Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.368100Z","title":"Spatio-temporal self-supervised learning for traffic flow prediction,","venue":null,"work_id":"b2192859-6053-4d8f-a3ec-ee4ce14e5a75","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.749056Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:e87fa5383f17e638c2cbe3479290549347d71f667101f7927c8b3e3782ab72ec","observation_id":"b3521fcb-b749-4771-9d26-059976dde056","resolution":{"observed_at":"2026-08-11T20:43:52.372887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:51.752768Z","title":"Taming local effects in graph-based spatiotemporal forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.752768Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:dbc395ed0a25bb1d2a9a779aefe8d0b0c495ca0976711d4da486d0a3fea401f9","observation_id":"722ba840-125f-49f2-94b6-0ee602a3d581","resolution":{"observed_at":"2026-08-11T20:43:51.752768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T04:59:42.373592Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-11T20:43:51.756573Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.756573Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:28d3e2fb7a960d2e5537bb2303187f3f08063b1d8b4a80eeb199db638a8920a6","observation_id":"77d58918-4fc1-4c6d-8274-c7daf0b9765e","resolution":{"observed_at":"2026-08-11T20:43:51.756573Z","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":"2026-08-11T20:43:51.760943Z","title":"Convolutional neural networks on graphs with fast localized spectral filtering,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.760943Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:7f1f37e004d1fedef2194dd6505d8c3aa22bff345f2d4a3196cdd41d5ab8ce0f","observation_id":"d583daee-78bc-4967-bd53-81c9c5e8b0d3","resolution":{"observed_at":"2026-08-11T20:43:51.760943Z","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":"2026-08-11T20:43:51.764948Z","title":"Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.764948Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:73fa1b9afb0b0c2fa870ecbb3b13583d69ab12a94587f79af2ebd8c80d034447","observation_id":"f8b291e3-9d9e-4f62-aee6-f6cc4de39578","resolution":{"observed_at":"2026-08-11T20:43:51.764948Z","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":"2026-08-11T20:43:51.768591Z","title":"Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.768591Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:98af1645984c56606708e4fff26d5e8ea88a1fe9aa1e1fb7b624328cde8b6228","observation_id":"60990f95-5409-4c58-9113-6fc54f2a6205","resolution":{"observed_at":"2026-08-11T20:43:51.768591Z","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":"2026-08-11T20:43:51.772182Z","title":"Invariant models for causal transfer learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.772182Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:0634171e4bf905c2e7b9d7218ba9fd64ff5f2b8a9af7281f2ddb9429d3930a8c","observation_id":"f46165f9-c16d-4a61-aa15-f400b3a3fd0b","resolution":{"observed_at":"2026-08-11T20:43:51.772182Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.320977Z","title":"Invariant risk minimization games,","venue":null,"work_id":"e9025ca2-cc5d-4c1d-a606-5b6232a41274","year":2020},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.776058Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:1c3c9128487c5aac68f4d1b0242035ef74412e449a4be68f471ae859c822af39","observation_id":"aa44aa7c-1206-425e-bb02-f9a15f7c1640","resolution":{"observed_at":"2026-08-11T20:43:52.324695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02893","last_updated":"2020-03-27T19:07:58Z","snapshot_observed_at":"2026-08-16T03:32:37.688772Z","submitted_at":"2019-07-05T15:26:26Z","title":"Invariant Risk Minimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02893","snapshot_observed_at":"2026-08-11T20:43:51.780374Z","title":"Invariant risk minimization,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.780374Z"},"links":{"cited_paper":"/paper/1907.02893","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:d44af86bcca04dd7ef5eb25465f109f215e47bba83f4d97bf6eafbc6f5f640eb","observation_id":"6d031064-a642-493d-9ea9-6f8f62dc0459","resolution":{"observed_at":"2026-08-11T20:43:51.780374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.02466","last_updated":"2024-08-16T08:25:42Z","snapshot_observed_at":"2026-08-16T17:23:28.571307Z","submitted_at":"2022-02-05T02:31:01Z","title":"Handling Distribution Shifts on Graphs: An Invariance Perspective","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.02466","snapshot_observed_at":"2026-08-11T20:43:51.784748Z","title":"Handling distribution shifts on graphs: An invariance perspective,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.784748Z"},"links":{"cited_paper":"/paper/2202.02466","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:c8c7fecb839cd7a259e105d9147de8c12b7ccf8871bc0f8c769a2457c42b66f6","observation_id":"a277e3c9-9011-4dca-95cb-f3056c589f28","resolution":{"observed_at":"2026-08-11T20:43:51.784748Z","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":"2026-08-11T20:43:51.788789Z","title":"Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.788789Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:361e73d9b82fffc3ff7d039b7e34531125655ad793e47a6d1abcad99dfbdbf88","observation_id":"b5570db9-0127-473b-966b-adb6bef964f9","resolution":{"observed_at":"2026-08-11T20:43:51.788789Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.300290Z","title":"Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000,","venue":null,"work_id":"0252c18d-f6d5-4809-a817-9b145ad51f57","year":2000},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.792372Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:088b432e8c0ca12300efe260036a8b5808842bf603cc1f8c5ab890ed30124f6d","observation_id":"0caa6fcd-7381-42f5-a304-43c0b29cf90c","resolution":{"observed_at":"2026-08-11T20:43:52.305034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.288980Z","title":"Pearl, Causal inference in statistics: a primer","venue":null,"work_id":"721fdd76-5816-40c7-934d-b928714d20a0","year":2016},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.796664Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:fa95f10445701e156c20bca32a770a9fcec27b4f5306bdc17cd513d7a6c78316","observation_id":"7c9b95fe-9230-47e6-b9a4-491a81e1046b","resolution":{"observed_at":"2026-08-11T20:43:52.292597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.276068Z","title":"Dynamic graph neural networks under spatio-temporal distribution shift,","venue":null,"work_id":"416ed7a9-bd04-4300-b2e0-e4a3f3dc52c9","year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.800521Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:524496e23d8d5d15c3d3f9b9b60306c3cbc15d05b744e4bc9135619927f32511","observation_id":"dedb2321-4404-4a1f-a0a1-67b298d9f726","resolution":{"observed_at":"2026-08-11T20:43:52.280356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.265406Z","title":"Causality and independence enhancement for biased node classifica- tion,","venue":null,"work_id":"d11e0f48-6b05-4216-8e10-422eec134cee","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.804039Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:cf3727199e9b06bcfbd6485a2154e78d8f515ae9a7f4e1a56033bdb0cca3ef0c","observation_id":"fb72fd5f-286e-4149-84db-f5b728312a96","resolution":{"observed_at":"2026-08-11T20:43:52.268670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12872","last_updated":"2022-01-30T16:43:40Z","snapshot_observed_at":"2026-08-16T17:24:52.587581Z","submitted_at":"2022-01-30T16:43:40Z","title":"Discovering Invariant Rationales for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12872","snapshot_observed_at":"2026-08-11T20:43:51.807989Z","title":"Discov- ering invariant rationales for graph neural networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.807989Z"},"links":{"cited_paper":"/paper/2201.12872","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:0f02ff1e971742c161693a7d14e8267f63ea3a35a7faa61afc7d38ebd7a390a2","observation_id":"e28b5d15-d865-4764-9291-c6bb0e03db6b","resolution":{"observed_at":"2026-08-11T20:43:51.807989Z","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":"2026-08-11T20:43:51.811765Z","title":"Deciphering spatio-temporal graph forecasting: A causal lens and treatment,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.811765Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:80412fcf168e1be68f8a3df267e97391aea0b67fdc647ec78bd501893f49b81c","observation_id":"26607623-e3dd-4bcf-b497-34d13844f572","resolution":{"observed_at":"2026-08-11T20:43:51.811765Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.245869Z","title":"Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning,","venue":null,"work_id":"9b14954b-78e5-43b9-9317-b2cf8d256557","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.815129Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:dcfd055a4dce8cbced100ed611943c75f237584c711f8699304c9ee184e358e6","observation_id":"87255b86-885c-49e0-8227-17fa62599cea","resolution":{"observed_at":"2026-08-11T20:43:52.250357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.233014Z","title":"Long-term occupancy grid prediction using recurrent neural networks,","venue":null,"work_id":"20b66666-2a47-4396-bfc1-bceafde66e6c","year":2019},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.818733Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:a676b2e2c777b21345be20ec7cb1b8a0170c642d11b286c48fb4de72ca36352d","observation_id":"691c5f82-d361-4e51-96e3-3ec0459fde8f","resolution":{"observed_at":"2026-08-11T20:43:52.237148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.221106Z","title":"Deep learning: A generic approach for extreme condition traffic forecasting,","venue":null,"work_id":"f7ac1ee8-9c94-46d8-a7e1-caeab4ed5567","year":2017},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.822330Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:52225b053638cf5eea1c499e3604a8d7c03e9da2c3abfcee60fda1faf24be44d","observation_id":"18a0e3a2-887e-4a95-9aa9-8467cf392ed1","resolution":{"observed_at":"2026-08-11T20:43:52.225214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.208973Z","title":"Time-series extreme event forecasting with neural networks at uber,","venue":null,"work_id":"a4f9d950-b1f0-4c83-9490-05a06009be84","year":2017},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.826493Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:3f1f81935b9dee43866a15692acfa5542b249ab79284ba391cf714569e3d2f66","observation_id":"34ef6945-e78f-4752-a393-168789554f08","resolution":{"observed_at":"2026-08-11T20:43:52.213410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.197160Z","title":"Dnn-based prediction model for spatio-temporal data,","venue":null,"work_id":"bdefbe67-2c18-4581-b9b0-5942da75050c","year":2016},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.829912Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:f27dee227f5029e215809acb9b74fed935b27c31bbd757dfcd142654e37b87ea","observation_id":"4c247939-9a5e-4fb7-a056-c405558c64c8","resolution":{"observed_at":"2026-08-11T20:43:52.201082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.185854Z","title":"Spatiotemporal multi-graph convolution network for ride-hailing de- mand forecasting,","venue":null,"work_id":"aebe77a2-a2d8-49e7-94b6-29a6405695f0","year":2019},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.834058Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:6c5dcfa3b343cdd210a04792c7747d55cd728a65d7c77d6dffa9fe60c87e365e","observation_id":"472ef0ef-9084-402d-b327-e601dcd0f785","resolution":{"observed_at":"2026-08-11T20:43:52.189766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:51.838364Z","title":"Gman: A graph multi-attention network for traffic prediction,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.838364Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:ecd45c8a5a53601f153d745ee20c871b8c1c28e4c71ae3526797fd897fdf1e47","observation_id":"7dbfb3c1-6d30-4975-a1f5-4d576a393230","resolution":{"observed_at":"2026-08-11T20:43:51.838364Z","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":"2026-08-11T20:43:51.842216Z","title":"Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.842216Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:a63343a8298710d8345053cd7b4e1f6a5a377749fb79cd77faed8ab9756084e9","observation_id":"3eb12f89-e81d-47b2-b9f5-74baa1ae8186","resolution":{"observed_at":"2026-08-11T20:43:51.842216Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.160583Z","title":"Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction,","venue":null,"work_id":"182433a8-c9f0-40e9-a191-d0439e363c96","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.845918Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:ce0a89f363a9f49db45df3915a7e9065261758cac31dd95019ece4519fe45038","observation_id":"99698462-44db-4fdf-bc21-dbd120b20795","resolution":{"observed_at":"2026-08-11T20:43:52.164772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.149660Z","title":"Spatio-temporal meta-graph learning for traffic forecasting,","venue":null,"work_id":"e1f2e94e-caba-4418-a955-5aceca80a361","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.849591Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:03011d85fe68db6cbebf91fc61111425c83ae8ddf9a871a6d12b045c3ea5619e","observation_id":"36747b31-710b-483f-b2f5-d93d02b11d68","resolution":{"observed_at":"2026-08-11T20:43:52.153358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13605","last_updated":"2024-08-06T14:55:04Z","snapshot_observed_at":"2026-08-16T13:33:00.393981Z","submitted_at":"2024-07-18T15:44:23Z","title":"Physics-guided Active Sample Reweighting for Urban Flow Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.13605","snapshot_observed_at":"2026-08-11T20:43:51.853315Z","title":"Physics-guided active sample reweighting for urban flow prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.853315Z"},"links":{"cited_paper":"/paper/2407.13605","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:6deaebe5157087a6f14d9351ec7c763d91b0bd4c4ee3c92fced558bebde47948","observation_id":"8ace96c6-fb10-4813-ad69-8b170572825e","resolution":{"observed_at":"2026-08-11T20:43:51.853315Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.138637Z","title":"Stden: Towards physics- guided neural networks for traffic flow prediction,","venue":null,"work_id":"d17c3602-7eb4-4146-aa80-5c4270933803","year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.857840Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:0b07ecf275e94219b56f1f5e432736b7c8ac2f9e8c5d39ea36cbdf569c871c4e","observation_id":"9685ee2f-440a-4d8f-9075-2ee40ee82334","resolution":{"observed_at":"2026-08-11T20:43:52.142622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.01575","last_updated":"2022-05-05T08:48:46Z","snapshot_observed_at":"2026-08-16T17:23:52.740024Z","submitted_at":"2022-02-03T13:17:38Z","title":"CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.01575","snapshot_observed_at":"2026-08-11T20:43:51.861464Z","title":"Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.861464Z"},"links":{"cited_paper":"/paper/2202.01575","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:57a1409b32b2542e69ced3aca8423d5d63ca0bf6e301cee524bca527cdf16f49","observation_id":"da14a745-ffb2-4a7f-a5e4-d3c8eab49c65","resolution":{"observed_at":"2026-08-11T20:43:51.861464Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.125207Z","title":"Towards out-of- distribution sequential event prediction: A causal treatment,","venue":null,"work_id":"a41c98c3-4eb0-4ff5-bd5e-cddc3e11f251","year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.865584Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:05f3438dfdcf5220f1fa2745014c151e42ccc43aab29ccf4e53a55949aec6444","observation_id":"494bda6e-9eba-455b-8797-0aac8b4e8157","resolution":{"observed_at":"2026-08-11T20:43:52.129769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.110367Z","title":"Adarnn: Adaptive learning and forecasting of time series,","venue":null,"work_id":"4c395cdc-e95f-4b9f-8da7-c5f512e4c46e","year":2021},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.869231Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:8ba55a8f6f066d9f840bdc0ba9997f6e61e000cab06e5feb29b02b91968c6cb8","observation_id":"4b700bab-cc5c-46dc-8aaa-8c56c162fe1c","resolution":{"observed_at":"2026-08-11T20:43:52.116369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.095282Z","title":"Dish-ts: a general paradigm for alleviating distribution shift in time series forecast- ing,","venue":null,"work_id":"ba280e72-9369-4bf3-b755-5bc4dc937f76","year":2023},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.872772Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:8362503b81c5e822f85678f9d2eb0bc96f692145c5c08643b098eec7f7feb0de","observation_id":"2e77b0b4-9848-40ca-9590-749b4be70a88","resolution":{"observed_at":"2026-08-11T20:43:52.099649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.082802Z","title":"Stone: A spatio-temporal ood learning framework kills both spatial and temporal shifts,","venue":null,"work_id":"f8d52fe8-ebf8-495f-8469-783bcb1b48d8","year":2024},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.876465Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:c15240414a954ee1d0874ed5052a5c59e94fcd7937c58ef86c5b8a218ff0b3b3","observation_id":"c26c687b-e1ee-4ae5-a1be-9da738d68b54","resolution":{"observed_at":"2026-08-11T20:43:52.086735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.071804Z","title":"Msdr: Multi-step dependency relation networks for spatial temporal forecasting,","venue":null,"work_id":"bf206157-6d4b-438a-97b5-bfa66813df9e","year":2022},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.880028Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:6eda54fabde1f50465d71daef80b1258d55f10d191116ef79ff128b7ad680d8e","observation_id":"96e27142-4816-42c3-9acd-0dddeabc362f","resolution":{"observed_at":"2026-08-11T20:43:52.075228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:52.058486Z","title":"Graph out-of-distribution generalization via causal intervention,","venue":null,"work_id":"c645a490-921c-4ac3-a939-83deface1ab6","year":2024},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.883578Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:f269626b7d09d31b356875ad91aa85c46555a476427e3fbc9eefc14142440aa0","observation_id":"91e1740d-5d5d-4dc4-9ab0-c180dea9a9e5","resolution":{"observed_at":"2026-08-11T20:43:52.064101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:43:51.887304Z","title":"St-norm: Spatial and temporal normalization for multi-variate time series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.887304Z"},"links":{"citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:d1e3cf9972f3763ec3b91f692d7bf868925f3b6639d6ab2c4a983e6c1cb582e6","observation_id":"13242c91-4f6c-4959-aed7-9b27f3e6e3c8","resolution":{"observed_at":"2026-08-11T20:43:51.887304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02600","last_updated":"2024-03-05T02:27:52Z","snapshot_observed_at":"2026-08-16T22:18:32.028255Z","submitted_at":"2024-03-05T02:27:52Z","title":"TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02600","snapshot_observed_at":"2026-08-11T20:43:51.890716Z","title":"Testam: a time-enhanced spatio-temporal attention model with mixture of experts,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T20:43:51.890716Z"},"links":{"cited_paper":"/paper/2403.02600","citing_paper":"/paper/2412.05534"},"observation_digest":"sha256:eafda727080a1309501d4edf144cb810199c6f81b6d59217f6384c6c60d36980","observation_id":"22a31b17-17bc-4f55-a0a0-1fe9efd60e5a","resolution":{"observed_at":"2026-08-11T20:43:51.890716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.05534","last_updated":"2024-12-07T04:35:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T00:50:03.072821Z","submitted_at":"2024-12-07T04:35:07Z","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":29},"total_outbound_references":53},"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 17 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2412.05534."}