{"as_of":"2026-08-20T12:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0aed0723c2d59ca4e72ae91a591d43ac172ba8ba28f2ea4bdc4694208b4cb491","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:27:02.563965Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2507.19513/citation-record","integrity":"/paper/2507.19513/integrity","json":"/paper/2507.19513/citation-record.json","paper":"/paper/2507.19513"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:02.400425Z","title":"Intelligent traffic adaptive resource allocation for edge computing-based 5g networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.400425Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:c24ee090aa1b88e0af3dba4448dcd5ac2f95951297593cb22a1b86daa40afe45","observation_id":"5b2d5cd7-94a4-4a36-a945-f7342de1c16f","resolution":{"observed_at":"2026-08-06T16:27:02.400425Z","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":"2021.31232","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:03.886122Z","title":"Predictive uav base station deployment and service offloading with distributed edge learning,","venue":null,"work_id":"2080425f-7e19-4816-bd1b-6693a3e097b9","year":2021},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.404586Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:b79a43b1be92e6053ab50086cca2fde64b3719756db924dfe5ac927786d43e30","observation_id":"ef2eb62e-9273-49cd-9867-eb9a35ed7435","resolution":{"observed_at":"2026-08-06T16:27:03.891891Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.30227","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:03.816519Z","title":"Millimeter-wave base station deployment using the scenario sampling approach,","venue":null,"work_id":"8da08dd8-74b0-4ab8-a93b-1b2e94632108","year":2020},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.408825Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:c7804cb844bd499b1eebb55f2498a086e58689b695eb9d054337835cad186d4f","observation_id":"aaf1cd8d-9893-4ecc-94f8-66efdea9cbe0","resolution":{"observed_at":"2026-08-06T16:27:03.822327Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.32712","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:03.738147Z","title":"Joint base station and irs deployment for enhancing network coverage: A graph-based modeling and optimization approach,","venue":null,"work_id":"59920dbc-3099-4994-823b-3204c37aa061","year":2023},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.412980Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:0bef5279e13907072e816af0fab430b6dd327556410418caec3f9d2ca41a0db4","observation_id":"2cd3e6d5-d118-4fde-9149-dacb9fdc9aad","resolution":{"observed_at":"2026-08-06T16:27:03.744888Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:04.105453Z","title":null,"venue":null,"work_id":"426f4a1a-2533-416d-bcec-d293157f06e7","year":1976},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.417639Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:efd2f558dabb1fffcebfcded62f17ebd4629d6987564e13ef0c85fafc447a0a6","observation_id":"e4dd9e45-e2a9-42ff-9cd2-ee6a75773a6a","resolution":{"observed_at":"2026-08-06T16:27:04.108677Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:02.422104Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.422104Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:ac85eab2292d78fbae2354197fbb9074acad162ed47b0c37fb24e23103d07331","observation_id":"ca04f255-b819-4c00-811f-068254881cfd","resolution":{"observed_at":"2026-08-06T16:27:02.422104Z","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":"2017.27799","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:03.667174Z","title":"Lstm fully convolutional networks for time series classification,","venue":null,"work_id":"e2e66d4a-b78b-4175-b3f6-bf55c6708a71","year":2018},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.426655Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:9199d9b8d6bfbfc2df49420867b878bcc979d628b1307a6b74278140faea5fca","observation_id":"1b7ae050-c792-451e-a0ed-56ef5421b1b3","resolution":{"observed_at":"2026-08-06T16:27:03.672809Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:04.095118Z","title":"Multivariate lstm-fcns for time series classification,","venue":null,"work_id":"06266f90-acf7-4866-ae5d-c2eec3bcd98e","year":null},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.430720Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:58c09a752a63bef6fbc98500a2f6c8640e6525324b1a687669e2dde283b65a03","observation_id":"92850d55-ee7a-4a6e-9011-6b0d918ad90d","resolution":{"observed_at":"2026-08-06T16:27:04.098473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:02.438217Z","title":"ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.438217Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:3d0f0b608b64f1640b1f766cd7bf46f7f5de4b5f36ee4164f36f5bd32769eb01","observation_id":"7f3628d1-12f8-4c9b-9046-6f36436b98e8","resolution":{"observed_at":"2026-08-06T16:27:02.438217Z","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":"2026-08-06T16:27:02.441434Z","title":"Minirocket: A very fast (almost) deterministic transform for time series classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.441434Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:0ff1e0f15fcc398f61cb4d718899569a13b6e9dff8769cd48fdd07edc1d11fd0","observation_id":"168f5380-e02d-4208-b54e-7dbe099e64a7","resolution":{"observed_at":"2026-08-06T16:27:02.441434Z","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":"6419.2007","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:03.505003Z","title":"Temporal aggregation of univariate and multivariate time series models: A survey,","venue":null,"work_id":"fdf868b8-6704-4a86-b1dc-563b1d8dfca7","year":2008},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.444749Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:e5addfee60177ecf142baa0053a0de85649da82b65c5cb22fa0fa5ad3a48853b","observation_id":"1e8123ab-231a-4c6f-93d7-be737b4da031","resolution":{"observed_at":"2026-08-06T16:27:03.510712Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:02.448637Z","title":"The great multivariate time series classification bake off: A review and experimental evaluation of recent algorithmic advances,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.448637Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:6b4a244b5545c942998d3aa4ea58f284f8481dfad1783e72c5751e7bc1bb9edc","observation_id":"469e2083-c6da-466a-a5f7-8756fe3ed564","resolution":{"observed_at":"2026-08-06T16:27:02.448637Z","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":"2026-08-06T16:27:02.451808Z","title":"Transformers in time series: a survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.451808Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:6a28e21f8110a1bc6d0d0494b43736fd4ab4125376d77656a475a34b89e010da","observation_id":"02573eb2-be34-4a97-945d-b40a015ba54a","resolution":{"observed_at":"2026-08-06T16:27:02.451808Z","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":"2026-08-06T16:27:04.084909Z","title":"Convolutional lstm network: a machine learning approach for precipitation nowcasting,","venue":null,"work_id":"a4318c85-d9ed-449d-b174-8655bda76402","year":2015},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.455175Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:4f608e091eab3c49878ce136a07631caa0bfccffb9d40bd4bcc7e42cd78e9889","observation_id":"9629865c-a69c-4189-9c39-da9c51507fc0","resolution":{"observed_at":"2026-08-06T16:27:04.088627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"9582.32096","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:03.413708Z","title":"Long-term mobile traffic forecasting using deep spatio-temporal neural networks,","venue":null,"work_id":"1d6d3950-3d75-420c-bb84-4e414a47127d","year":2018},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.458545Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:55c559a83922844737d6e03361795d133152f2b3d31320f6ed803c533fb63a9c","observation_id":"7e7b69b4-dfba-438f-907d-2ef5007049d5","resolution":{"observed_at":"2026-08-06T16:27:03.419718Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:02.461960Z","title":"Connecting the dots: Multivariate time series forecasting with graph neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.461960Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:9663b0337b61eb4b498da49abd83c1fb30e776e48760e8c37a8e5e0ba7e0b1d3","observation_id":"e5ee839d-6400-4cea-b600-6310c52fd5e5","resolution":{"observed_at":"2026-08-06T16:27:02.461960Z","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":"2026-08-06T16:27:04.074377Z","title":"Long-range transformers for dynamic spatiotemporal forecasting,","venue":null,"work_id":"a6fd9403-e482-46ad-8678-ff3150a4e893","year":2021},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.465582Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:3626b6ca242728da694b9199d8809d37c8e958bf1a0284c1d30cfbfe1602985d","observation_id":"e61db50e-b585-4c41-9df3-8a82c9a70ef9","resolution":{"observed_at":"2026-08-06T16:27:04.078016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04517","last_updated":"2024-12-06T15:42:07Z","snapshot_observed_at":"2026-08-16T13:54:34.627474Z","submitted_at":"2024-05-07T17:50:21Z","title":"xLSTM: Extended Long Short-Term Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04517","snapshot_observed_at":"2026-08-06T16:27:02.468908Z","title":"xlstm: Extended long short-term memory,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.468908Z"},"links":{"cited_paper":"/paper/2405.04517","citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:0b87778e3d676cc59494aa4901634f44bd7a07c0db3c61e53712325e5ade79d0","observation_id":"98e68108-0d0f-4893-bbf5-53bec8e9b732","resolution":{"observed_at":"2026-08-06T16:27:02.468908Z","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":"2026-08-06T16:27:02.472556Z","title":"Short-term traffic forecasting: Where we are and where we’re going,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.472556Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:a0a3e682d22666f09fd73517c9ae2f66935bd8033a9967373eb5d778f453131b","observation_id":"2125a0f2-08ab-40c0-ab37-076e40faab07","resolution":{"observed_at":"2026-08-06T16:27:02.472556Z","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":"2026-08-06T16:27:04.063469Z","title":"Base station mobile traffic prediction based on arima and lstm model,","venue":null,"work_id":"d81dd487-c12d-4942-92da-661671fff75c","year":2022},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.476247Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:726e571f40a5e030d34566c601fec6ef3c879114f605a33ab7915dacb0c989d6","observation_id":"155c8a82-a143-4549-988f-175c60830f47","resolution":{"observed_at":"2026-08-06T16:27:04.066740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:02.479688Z","title":"A survey on deep learning for cellular traffic prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.479688Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:280f8df39799a726edbc9eb175c8f6e976193849c85043f9c1043e4c14a43bf7","observation_id":"9ef039d8-2d7d-494c-989d-169725bc3d1f","resolution":{"observed_at":"2026-08-06T16:27:02.479688Z","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":"2026-08-06T16:27:04.052151Z","title":"Deep spatio-temporal adaptive 3d convolutional neural networks for traffic flow prediction,","venue":null,"work_id":"d5e3e11e-2da0-4895-9ec9-6fbe8504d3be","year":null},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.483239Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:ba3a4f2cec7bda2e7fcca4ec8eb45a8298a6a83d8302877e7ea838cf14c15cc8","observation_id":"11c116e4-7bda-4e88-a6be-41ca5f8f9f49","resolution":{"observed_at":"2026-08-06T16:27:04.056393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:04.040450Z","title":"Graph wavenet for deep spatial-temporal graph modeling,","venue":null,"work_id":"43bbe7a7-e3f2-44dc-a4c9-928e000bb04a","year":2019},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.490699Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:cdd6b3ac359e8bf4c5a890bfae0bbac0b3b8a2fd61bd9465a810a26e1111f14d","observation_id":"81a0a8dc-b600-48db-af29-ff45ca8413ff","resolution":{"observed_at":"2026-08-06T16:27:04.044780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00385","last_updated":"2024-10-15T05:44:29Z","snapshot_observed_at":"2026-08-16T13:13:53.075664Z","submitted_at":"2024-10-01T04:15:48Z","title":"STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.00385","snapshot_observed_at":"2026-08-06T16:27:02.494301Z","title":"Stgformer: Efficient spatiotemporal graph transformer for traffic forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.494301Z"},"links":{"cited_paper":"/paper/2410.00385","citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:c0fad7a25cd03a8715b8e15c091d322811193605567ab80e77d7f66a762274a8","observation_id":"457001d5-b2ee-4a5a-b617-19b4e38baf07","resolution":{"observed_at":"2026-08-06T16:27:02.494301Z","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":"2026-08-06T16:27:02.499098Z","title":"Citywide mobile traffic forecasting using spatial-temporal downsampling transformer neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.499098Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:1820dd3ec0ff4d7ab1f264af95ae33af940398c34fcf3aa8be37d6718e4ce449","observation_id":"47151731-0c07-43e7-9e34-3c0f220aad94","resolution":{"observed_at":"2026-08-06T16:27:02.499098Z","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":"2026-08-06T16:27:02.502575Z","title":"Adaptive multi-receptive field spatial-temporal graph convolutional network for traffic forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.502575Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:ead062991b50e129fe233bb297f19cf1f344af0b8a09b49ff7dc93f0362065b5","observation_id":"82ef8c1c-ab52-49e0-a557-ba0287af2977","resolution":{"observed_at":"2026-08-06T16:27:02.502575Z","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":"2026-08-06T16:27:04.028596Z","title":"Joint spatial and temporal classi- fication of mobile traffic demands,","venue":null,"work_id":"b0e28f72-41d0-4dc9-897b-b99852a9c1de","year":2017},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.506351Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:d212d1d102998f108d1931853e1ea5efacc3d2573f81681b7d80ad2d302303d3","observation_id":"1a9c9efe-d825-4c35-a0b9-59f9a58ba368","resolution":{"observed_at":"2026-08-06T16:27:04.032029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:02.510444Z","title":"Understanding mobile traffic patterns of large scale cellular towers in urban environment,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.510444Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:e1fae856a6f75ca2b43671124c52b4e4fa1f7bc993587d1107ad2cb06c1e5f77","observation_id":"9407f73b-c902-4fc7-95ae-e322f50f4f91","resolution":{"observed_at":"2026-08-06T16:27:02.510444Z","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":"2026-08-06T16:27:04.015179Z","title":"The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice,","venue":null,"work_id":"42a7375c-124b-487b-ad56-5ca5b0e5418d","year":2014},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.514158Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:761126eb086bacdff2da06fcc81011ab8faa85d57591e22fdb57aad8be3ed593","observation_id":"54773d83-1935-4a32-a41b-39107be37055","resolution":{"observed_at":"2026-08-06T16:27:04.019715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:04.003409Z","title":"Context- based interpretable spatio-temporal graph convolutional network for human motion forecasting,","venue":null,"work_id":"ab10edb0-f7ad-4966-8424-379cad3b399c","year":2024},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.519076Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:c8d319038acdcf4b9f9f13ecc0aafe42564ef9f1dc5e8eea23813d893ca28aa4","observation_id":"79e487d6-d704-45a1-b6b0-7612092328b2","resolution":{"observed_at":"2026-08-06T16:27:04.007132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.10577","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:02.977157Z","title":"Improving precipitation nowcasting using a three-dimensional convolutional neural network model from multi parameter phased array weather radar observations,","venue":null,"work_id":"954bc536-1469-4638-b9ac-0802d6d551f6","year":2021},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.522600Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:b26662173ff1f7df868f5362c7b11e10fd6a2fd3524498aced67132c932a5604","observation_id":"942dc1ed-fbf0-4d0e-9cd0-1d4b7ed02053","resolution":{"observed_at":"2026-08-06T16:27:02.984197Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.10978","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:27:02.903827Z","title":"Graph dual-stream convolutional attention fusion for precipitation nowcasting,","venue":null,"work_id":"3ddba704-dbed-4749-bc57-726de7f211f3","year":2025},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.526573Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:ca1b00f7e4771b1a7dd223c5058c7eeea5276dd83e8e53b82ae7c9e1151c61bd","observation_id":"d3a13b21-fe4f-4200-90bc-22c80b129f58","resolution":{"observed_at":"2026-08-06T16:27:02.912861Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:03.991693Z","title":"Residual networks behave like ensembles of relatively shallow networks,","venue":null,"work_id":"ad6e1b29-4f53-460b-af0d-1384eee9a053","year":2016},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.530621Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:75407381c5fc46ab0caa8fce687708aa4685f811314f58ed4aaaa4139aa6bf85","observation_id":"ab2aaad8-0c01-4ef0-87c5-584381ce3069","resolution":{"observed_at":"2026-08-06T16:27:03.995351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:03.980764Z","title":"Attention is all you need,","venue":null,"work_id":"eeb9ede5-c1b2-4c41-aed5-fa82cf9fd701","year":2017},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.534528Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:7560c076d9d19f97c3f0f31ff6dd2efcdebe3cfa712471311a68d830d0b9271b","observation_id":"e8183328-af93-4382-a23a-0eaf91b27a82","resolution":{"observed_at":"2026-08-06T16:27:03.984398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T16:27:02.539548Z","title":"Self-attention with relative position representations,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.539548Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:636d5168cfe8f3fe10108cb33f4cc15b9f14cc6b3c1b30f6ca92896f049252c1","observation_id":"0f29f63c-50cb-4c72-a418-3f9916c8e8bc","resolution":{"observed_at":"2026-08-06T16:27:02.539548Z","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":"2026-08-06T16:27:03.968872Z","title":"Cross-modal attention for multi- modal image registration,","venue":null,"work_id":"82d3a898-89c0-4e19-ae3c-f93f3ee27514","year":null},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.544246Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:84ca7b40f3273dccd2443bd2c96a121b62ae8ffabed14f8a7e04467f3bcce7e6","observation_id":"886fca97-7f69-4de2-863f-28ff17b68ecd","resolution":{"observed_at":"2026-08-06T16:27:03.972367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-15T04:53:45.483331Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-06T16:27:02.551869Z","title":"Layer normalization,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.551869Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:d589f4a224ab7bfef79d62526e653d8f80676abe43eb13a2de3cb7d0596c0fa8","observation_id":"d8383bf1-09cf-48a1-a41e-dd97aafc5f78","resolution":{"observed_at":"2026-08-06T16:27:02.551869Z","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":"2026-08-06T16:27:02.555585Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.555585Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:a42f29a2e921b9f291f41fdc29ed0b5663af5c48f8af96dbf52f38e21a37472e","observation_id":"93aaeff5-4a56-4773-b0e7-ec84c3eef610","resolution":{"observed_at":"2026-08-06T16:27:02.555585Z","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":"2026-08-06T16:27:02.548263Z","title":"Available: https://doi.org/10.1016/j.media.2022.102612","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.548263Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:19e9dc2ece07a16639f3cf452e955e3c2bd4967370f81ac0beb20f88f3db3600","observation_id":"d64d085a-d1c8-47d6-a27f-e0179d234ba4","resolution":{"observed_at":"2026-08-06T16:27:02.548263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-06T16:27:02.563965Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.563965Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:a4ef7714c861b1404336742d2b331ac8e4484520af73a3e094bd8ba15f29e8a7","observation_id":"9bccd892-683b-47d2-a52d-9d31e6eef5b1","resolution":{"observed_at":"2026-08-06T16:27:02.563965Z","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":"2026-08-06T16:27:02.559950Z","title":"A multi-source dataset of urban life in the city of milan and the province of trentino,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.559950Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:70f29b56af740aa8d027daf8d7aaff6e244c705ea506768748fa327f9a7ef161","observation_id":"7e1ad8bb-021c-4f14-b7b0-f1652246847d","resolution":{"observed_at":"2026-08-06T16:27:02.559950Z","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":"10.1016/j.neunet.2019.04.014","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Available: https://doi.org/10.1016/j.neunet.2019.04.014","venue":"Neural Networks","work_id":"6cd3bc3f-3c0c-4f28-b6ed-ec4ec4e11457","year":2019},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.434699Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:a6a5cb99c8c9cdb4c99fe38795c8e7d42cbebe250fcdb41e6aa037f259ccfa05","observation_id":"18f6c400-8f59-407c-85fc-7e5dc18ec63a","resolution":{"observed_at":"2026-08-06T16:27:02.703102Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3510829","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Available: https://doi.org/10.1145/3510829","venue":"ACM Transactions on Intelligent Systems and Technology","work_id":"1f385441-a68e-40be-bd70-7332a4b0f40a","year":null},"citing_paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:02.486987Z"},"links":{"citing_paper":"/paper/2507.19513"},"observation_digest":"sha256:e7c4c5cc5e9c1bad9dbfcaa552fae1c9c509dd8d94877b569ffa2c312054e531","observation_id":"dac33185-6b79-4eea-9c7f-3e3c2923d111","resolution":{"observed_at":"2026-08-06T16:27:02.639944Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.19513","last_updated":"2025-07-17T22:48:46Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T08:35:02.205491Z","submitted_at":"2025-07-17T22:48:46Z","title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":7,"parse_uncertain":0,"unresolved":21,"verified_exact":3,"verified_fuzzy":12},"total_outbound_references":43},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.19513."}