{"as_of":"2026-08-09T17:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9a205fd033b2419d582f5d0dee10dd0a37c7c63ad28bcb5e2a3806893d846333","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":30,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T14:02:48.342995Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T20:30:07.231037Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2407.13278","last_updated":"2026-05-04T08:07:42Z","snapshot_observed_at":"2026-07-06T18:48:20.902620Z","submitted_at":"2024-07-18T08:31:55Z","title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-23T23:03:45.096751Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2407.13278"},"observation_digest":"sha256:67deb8e089b07b3acb67e1a4733c17cabd472d732dc260e28a88cfb5e359c52c","observation_id":"f37eb6df-cda2-4e00-911f-bceea0b3811c","resolution":{"observed_at":"2026-05-23T23:05:51.467777Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2410.04047","last_updated":"2026-04-10T06:16:18Z","snapshot_observed_at":"2026-07-06T19:28:18.775189Z","submitted_at":"2024-10-05T06:04:19Z","title":"TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis","version":6},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-23T19:45:39.130509Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2410.04047"},"observation_digest":"sha256:572e6b0ef7945d6a5d15c1110a35c713709a3ccc6fd146233849a63f5700d79a","observation_id":"fb4a7b4a-08ae-4b91-92d2-b5abd470b8f2","resolution":{"observed_at":"2026-05-23T19:45:47.124040Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-09T14:02:48.342995Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01922","last_updated":"2025-02-04T01:42:45Z","snapshot_observed_at":"2026-08-09T16:55:25.942997Z","submitted_at":"2025-02-04T01:42:45Z","title":"LAST SToP For Modeling Asynchronous Time Series","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-09T14:02:48.342995Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2502.01922"},"observation_digest":"sha256:61130c04ebe405e8506b2c5c92b69696680ca5b5de2fd9065983de66f7fe4100","observation_id":"71149463-66eb-4dbc-ba12-759e4e49ff40","resolution":{"observed_at":"2026-08-09T14:02:48.342995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-08T18:22:22.885319Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05699","last_updated":"2025-02-08T21:39:07Z","snapshot_observed_at":"2026-08-08T18:15:57.032263Z","submitted_at":"2025-02-08T21:39:07Z","title":"Context information can be more important than reasoning for time series forecasting with a large language model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T18:22:22.885319Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2502.05699"},"observation_digest":"sha256:6d9afc78031d1c63c44e14c4d7c5cfb585d32bbfb1f04a8ca09b4cb7bd79016b","observation_id":"bd92e843-f503-420c-9272-e100657fb887","resolution":{"observed_at":"2026-08-08T18:22:22.885319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-08T18:22:10.277176Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05701","last_updated":"2025-02-08T21:42:14Z","snapshot_observed_at":"2026-08-09T13:30:33.268431Z","submitted_at":"2025-02-08T21:42:14Z","title":"TOKON: TOKenization-Optimized Normalization for time series analysis with a large language model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T18:22:10.277176Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2502.05701"},"observation_digest":"sha256:8554fc0dd32a5b9316c634ff0638a719f3b600fcc2e83b76e23f4a9bb3fb53d4","observation_id":"8fc3e7cf-fdaf-48cb-8bdc-2362a821a5d3","resolution":{"observed_at":"2026-08-08T18:22:10.277176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-07T15:26:54.471141Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11040","last_updated":"2025-05-21T04:45:11Z","snapshot_observed_at":"2026-08-07T22:34:12.973486Z","submitted_at":"2025-05-21T04:45:11Z","title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T15:26:54.471141Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2506.11040"},"observation_digest":"sha256:f90afbd681543e1c841858fc4b26354b9a56b380c340a40fe1e363f62c9754fc","observation_id":"7a02a285-51cc-4b15-a390-cf430340fcc4","resolution":{"observed_at":"2026-08-07T15:26:54.471141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-07T10:28:42.267258Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12074","last_updated":"2025-06-05T17:08:27Z","snapshot_observed_at":"2026-08-09T13:20:13.760561Z","submitted_at":"2025-06-05T17:08:27Z","title":"Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T10:28:42.267258Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2506.12074"},"observation_digest":"sha256:653bae838d253f7e96d706ee272a511821dab807d4e15f6f582d648337c460b5","observation_id":"dca585c9-0c93-45da-89b6-ae6114c79f4a","resolution":{"observed_at":"2026-08-07T10:28:42.267258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T18:01:26.539715Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-09T13:26:46.443867Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:26.539715Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:21b247e56f8fd300879d4e70ecd9b86226e838bbbb04af3f6cd1478922be4825","observation_id":"6a9f81e4-710a-4225-88e1-6fb4db85126c","resolution":{"observed_at":"2026-08-06T18:01:26.539715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T17:43:36.297956Z","title":"arXiv preprint arXiv:2310.04948","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.10098","last_updated":"2025-07-14T09:33:40Z","snapshot_observed_at":"2026-08-07T22:33:46.119199Z","submitted_at":"2025-07-14T09:33:40Z","title":"Fusing Large Language Models with Temporal Transformers for Time Series Forecasting","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T17:43:36.297956Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.10098"},"observation_digest":"sha256:c01653c618dfdf606061f124dc8380f56b8cca2c902bbda248cdce50e2365461","observation_id":"5c2df10b-0997-45c6-a123-fb8e6477820e","resolution":{"observed_at":"2026-08-06T17:43:36.297956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T17:46:42.029777Z","title":"O.; Pfister, T.; Zheng, Y.; Ye, W.; and Liu, Y","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11558","last_updated":"2025-07-14T08:33:34Z","snapshot_observed_at":"2026-08-08T21:51:35.549543Z","submitted_at":"2025-07-14T08:33:34Z","title":"Reprogramming Vision Foundation Models for Spatio-Temporal Forecasting","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T17:46:42.029777Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.11558"},"observation_digest":"sha256:6233fc02c1d45c93adaff67645690f948b3c0a3414ce978711d5371211635c78","observation_id":"c88630ce-e66e-4718-be14-4e2ecd7e5649","resolution":{"observed_at":"2026-08-06T17:46:42.029777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T15:04:11.137189Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17016","last_updated":"2025-07-22T21:03:13Z","snapshot_observed_at":"2026-08-08T04:38:16.441695Z","submitted_at":"2025-07-22T21:03:13Z","title":"Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:04:11.137189Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.17016"},"observation_digest":"sha256:a70ffa29a00b58541a6237e0755c5935dd34071f80011a1ad71bc26336704f40","observation_id":"1c1804c7-6a27-4281-b914-3a2ca9111ab9","resolution":{"observed_at":"2026-08-06T15:04:11.137189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T14:52:30.402593Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-07T22:30:34.067188Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.402593Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:7a3fd8a5cb1b19879d72727f9243ad3a5138976c7fa7d2064a3a59ff331f08b4","observation_id":"0a1e5efe-ee16-46f1-b5b1-ae1ef6b27901","resolution":{"observed_at":"2026-08-06T14:52:30.402593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T12:09:39.411328Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22053","last_updated":"2025-07-29T17:56:38Z","snapshot_observed_at":"2026-08-09T01:29:10.169026Z","submitted_at":"2025-07-29T17:56:38Z","title":"Foundation Models for Demand Forecasting via Dual-Strategy Ensembling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:09:39.411328Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.22053"},"observation_digest":"sha256:f0a3d80de5a4ab1b452ae2fd3cf6b13fadcd8c95fa228b726f33e283278ff3cc","observation_id":"1ea95ebc-01cc-42aa-8e9d-c9c5f53f4c41","resolution":{"observed_at":"2026-08-06T12:09:39.411328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-05T15:10:28.304185Z","title":"O.; Pfister, T.; Zheng, Y.; Ye, W.; and Liu, Y","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20437","last_updated":"2025-08-28T05:27:45Z","snapshot_observed_at":"2026-08-08T13:59:36.660861Z","submitted_at":"2025-08-28T05:27:45Z","title":"On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-05T15:10:28.304185Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2508.20437"},"observation_digest":"sha256:341762a8cb8268d67216efde605785f23ba3397ce1681fe8bac0e9fb128d0dda","observation_id":"94b5f497-58bb-4c6d-9d0d-ed106294dbb6","resolution":{"observed_at":"2026-08-05T15:10:28.304185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-05T13:29:00.791648Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00622","last_updated":"2025-08-30T22:31:55Z","snapshot_observed_at":"2026-08-08T02:31:50.485848Z","submitted_at":"2025-08-30T22:31:55Z","title":"BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T13:29:00.791648Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2509.00622"},"observation_digest":"sha256:26361b130d9c261da656ba8eb1dd45fc9433a68bf2a1e9958e49857d77642da3","observation_id":"1645107e-eabe-4401-b9f8-e7fe40ce0e16","resolution":{"observed_at":"2026-08-05T13:29:00.791648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2509.15105","last_updated":"2026-05-22T12:07:12Z","snapshot_observed_at":"2026-08-04T04:40:44.681486Z","submitted_at":"2025-09-18T16:11:31Z","title":"Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-25T08:22:24.238459Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2509.15105"},"observation_digest":"sha256:ae08761302df411d7812acfae329052c3af5656515c5383360a31d12b3c44559","observation_id":"a4d736a0-0452-486d-a5aa-85c5f31d376f","resolution":{"observed_at":"2026-05-25T08:25:34.155340Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-04T12:38:56.312366Z","title":"Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.03117","last_updated":"2026-06-26T03:58:11Z","snapshot_observed_at":"2026-08-07T05:08:17.345661Z","submitted_at":"2025-10-03T15:43:56Z","title":"Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-04T12:38:56.312366Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2510.03117"},"observation_digest":"sha256:57c25906d121e77f940bebe390654f3d6ade3ae8e136d2603f5b78fecafd85b9","observation_id":"11f089b3-86bc-4e12-8093-03ad70f608d8","resolution":{"observed_at":"2026-08-04T12:38:56.312366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2510.23090","last_updated":"2026-05-21T14:06:12Z","snapshot_observed_at":"2026-07-06T22:34:08.878520Z","submitted_at":"2025-10-27T07:51:54Z","title":"MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-22T12:34:54.599171Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2510.23090"},"observation_digest":"sha256:12cf302ea4c5a2d242a1908d10c38d5c76c6b228799abe476b12d1661e14966b","observation_id":"7cb01dd6-19f5-4597-9867-9583270727ba","resolution":{"observed_at":"2026-05-22T12:36:32.511386Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2511.08947","last_updated":"2026-04-10T02:18:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-11-12T03:48:05Z","title":"AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting","version":5},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-17T23:07:55.891663Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2511.08947"},"observation_digest":"sha256:c0da949c6342b60a7305e0879a9c1007d3558d27d9683ea6e33d5be18d66e53c","observation_id":"4b2a3109-09d0-4f7a-af06-a22400cdc804","resolution":{"observed_at":"2026-05-17T23:10:26.178196Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2604.04475","last_updated":"2026-04-06T06:57:08Z","snapshot_observed_at":"2026-07-06T22:53:29.118925Z","submitted_at":"2026-04-06T06:57:08Z","title":"Discrete Prototypical Memories for Federated Time Series Foundation Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T18:59:18.819953Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2604.04475"},"observation_digest":"sha256:f1ae3138f09da67b813b112773d1ec3d7b77c78d9ff6aa55b3e52ceb1e661e67","observation_id":"16a61787-7cee-4c6c-8b88-df90f1660120","resolution":{"observed_at":"2026-05-10T23:35:52.028972Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2604.10291","last_updated":"2026-04-11T17:15:26Z","snapshot_observed_at":"2026-07-06T22:58:56.307161Z","submitted_at":"2026-04-11T17:15:26Z","title":"TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T15:25:02.732205Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2604.10291"},"observation_digest":"sha256:807a7031bf89813121a3ca0dbea801fbaa7de0dfbceda28e7bed43bf2a3bdf0e","observation_id":"c6f35693-62b5-468c-9965-13efab01ad94","resolution":{"observed_at":"2026-05-11T10:36:04.345350Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:62c1579190ca4cd945765e19a58493b68d6020516fb2eed302809cd6fc00195c","observation_id":"f5906bd9-77a5-4dfb-81a5-6de4cc45eed0","resolution":{"observed_at":"2026-05-11T20:36:09.707554Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2605.13711","last_updated":"2026-05-13T15:58:42Z","snapshot_observed_at":"2026-08-02T09:24:52.057644Z","submitted_at":"2026-05-13T15:58:42Z","title":"MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-14T20:16:47.340541Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2605.13711"},"observation_digest":"sha256:972be3b5c49ca4e1f9584b1747e9838f3e29d83c644334c2f6ee27a1cf04c06a","observation_id":"ba5c7cc4-386d-4f40-be46-a72ebd7dab36","resolution":{"observed_at":"2026-05-14T20:19:27.819666Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2606.06285","last_updated":"2026-06-04T15:25:03Z","snapshot_observed_at":"2026-08-07T19:25:16.434950Z","submitted_at":"2026-06-04T15:25:03Z","title":"TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T01:50:24.426751Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2606.06285"},"observation_digest":"sha256:4ec28dc3247eba84291798959a1d7ee9719c805be5593bb0d6eb46ff9e42f97f","observation_id":"3c785689-a48d-46af-bd12-85eeca7e563d","resolution":{"observed_at":"2026-07-02T12:46:57.002897Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2606.25923","last_updated":"2026-06-24T15:02:24Z","snapshot_observed_at":"2026-08-06T19:49:20.363749Z","submitted_at":"2026-06-24T15:02:24Z","title":"$\\text{DT}^2$: Decision-Targeted Digital Twins","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-06-25T20:08:13.039445Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2606.25923"},"observation_digest":"sha256:9aac6f4557f6a18e33547839ccd44c99067df5d257a4aa0f6a71a2996ecd7d1e","observation_id":"234a9197-0779-4544-b390-d3e072665460","resolution":{"observed_at":"2026-07-04T20:30:07.232671Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2607.01918","last_updated":"2026-07-02T09:16:51Z","snapshot_observed_at":"2026-07-07T00:07:23.664493Z","submitted_at":"2026-07-02T09:16:51Z","title":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-07-03T17:34:37.552706Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2607.01918"},"observation_digest":"sha256:75b74a3c262a84878745399b253694ced731e7e5527fc214b35296c0c57415cc","observation_id":"2abf6c67-add8-4ce4-986b-aa6e9ab09d16","resolution":{"observed_at":"2026-07-03T17:38:43.411552Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-13T05:38:27.357469Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08940","last_updated":"2026-07-18T17:05:31Z","snapshot_observed_at":"2026-08-07T04:58:42.085463Z","submitted_at":"2026-07-09T21:09:05Z","title":"TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T05:38:27.357469Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2607.08940"},"observation_digest":"sha256:4e4e86a8a0c57627ec718d84cf909aa71a73bd916d8db639e5959b7d390d877c","observation_id":"678a0003-5bf1-4545-b755-ff2a71d59e80","resolution":{"observed_at":"2026-07-13T05:38:27.357469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-02T07:51:11.920174Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08940","last_updated":"2026-07-18T17:05:31Z","snapshot_observed_at":"2026-08-07T04:58:42.085463Z","submitted_at":"2026-07-09T21:09:05Z","title":"TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T07:51:11.920174Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2607.08940"},"observation_digest":"sha256:6ec9450861f9669ae89f9d2c0492f979ce84556197871e756445bdb4286a0681","observation_id":"bf7f08f0-f680-4a02-b9ac-cf8563d664c9","resolution":{"observed_at":"2026-08-02T07:51:11.920174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-01T05:16:38.160646Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22299","last_updated":"2026-07-24T13:44:36Z","snapshot_observed_at":"2026-08-07T23:59:44.602960Z","submitted_at":"2026-07-24T13:44:36Z","title":"Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T05:16:38.160646Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2607.22299"},"observation_digest":"sha256:159bc297d7190feb82dc743bfc6371b9bc718ef62875ff30a7bcc327a7b16682","observation_id":"59b67d14-94d3-4e78-9a4e-8cb9d0a79f3a","resolution":{"observed_at":"2026-08-01T05:16:38.160646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-01T01:06:47.359737Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25947","last_updated":"2026-07-28T16:33:41Z","snapshot_observed_at":"2026-08-08T15:17:48.199939Z","submitted_at":"2026-07-28T16:33:41Z","title":"A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T01:06:47.359737Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2607.25947"},"observation_digest":"sha256:8c807dd4c1c208a0709fe33030a3ca915f22fd08b351511b4cb059fc00da9c25","observation_id":"08c1f456-385a-4784-bc24-f87f0f068ec8","resolution":{"observed_at":"2026-08-01T01:06:47.359737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2310.04948/citation-record","integrity":"/paper/2310.04948/integrity","json":"/paper/2310.04948/citation-record.json","paper":"/paper/2310.04948"},"outbound":[],"paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T22:29:55.641913Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2310.04948."}