{"as_of":"2026-08-17T18:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7fb49ff3fa0107fed1cb7eb549110f7852234476e35058955f234ff9bcea6736","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T05:01:32.397410Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:40:58.659411Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T11:40:59.236173Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"cited_work":{"arxiv_id":"2502.03383","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.03383","snapshot_observed_at":"2026-08-07T11:40:59.236173Z","title":"Transformers and Their Roles as Time Series Foundation Models","venue":"cs.LG","work_id":"73a733b3-6b61-41f1-b04c-e63e494086e6","year":2025},"citing_paper":{"arxiv_id":"2506.01919","last_updated":"2025-06-02T17:39:31Z","snapshot_observed_at":"2026-08-16T02:05:22.144481Z","submitted_at":"2025-06-02T17:39:31Z","title":"Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T11:40:58.659411Z"},"links":{"cited_paper":"/paper/2502.03383","citing_paper":"/paper/2506.01919"},"observation_digest":"sha256:0b6c61bdb3e6c1a762540c557b501c8cdb1ef9c1bbd3e57ecad6a72d8c6efb4e","observation_id":"731d3c29-9af1-406b-9439-20e3501f0d56","resolution":{"observed_at":"2026-08-07T11:40:59.244921Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.03383","snapshot_observed_at":"2026-08-04T06:03:34.999731Z","title":"arXiv preprint arXiv:2502.03383 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.18662","last_updated":"2026-08-07T15:11:05Z","snapshot_observed_at":"2026-08-16T04:10:54.128342Z","submitted_at":"2026-02-20T23:47:55Z","title":"Large Causal Models for Temporal Causal Discovery","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T06:03:34.999731Z"},"links":{"cited_paper":"/paper/2502.03383","citing_paper":"/paper/2602.18662"},"observation_digest":"sha256:fc9c0c0f6d775a86624da35c1e0d2cf02bac91b3cc67aed6c837e3fb191e94d0","observation_id":"3bbd881e-cfe7-41b4-8457-cae039aa925c","resolution":{"observed_at":"2026-08-04T06:03:34.999731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.03383/citation-record","integrity":"/paper/2502.03383/integrity","json":"/paper/2502.03383/citation-record.json","paper":"/paper/2502.03383"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.02592","last_updated":"2024-05-22T11:49:59Z","snapshot_observed_at":"2026-08-16T14:21:38.958196Z","submitted_at":"2024-02-04T20:00:45Z","title":"Unified Training of Universal Time Series Forecasting Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02592","snapshot_observed_at":"2026-08-09T05:01:32.276930Z","title":"Unified training of universal time series forecasting transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.276930Z"},"links":{"cited_paper":"/paper/2402.02592","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:6e4d12392d0fac420d1101f896ffa186dc76ceb0588a5ca226b5efc89f00e875","observation_id":"1eef3191-2bf8-4744-9622-0111f367d607","resolution":{"observed_at":"2026-08-09T05:01:32.276930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07815","last_updated":"2024-11-04T17:42:45Z","snapshot_observed_at":"2026-08-16T12:24:20.200568Z","submitted_at":"2024-03-12T16:53:54Z","title":"Chronos: Learning the Language of Time Series","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07815","snapshot_observed_at":"2026-08-09T05:01:32.280949Z","title":"Chronos: Learning the language of time series","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.280949Z"},"links":{"cited_paper":"/paper/2403.07815","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:5dd07511df39a9185a3d361a2d10118866a063997d748ec2530d698a5765e5b3","observation_id":"7a8d239e-118b-4c72-9bd5-3010306a5e27","resolution":{"observed_at":"2026-08-09T05:01:32.280949Z","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":"2026-08-09T05:01:32.284455Z","title":"Foundation models for time series analysis: A tutorial and survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.284455Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:7bc9f5bfd6d71ff1243134b5c539e64e00bb5196534ef412323a053a5fc2e032","observation_id":"cb7a835d-2efa-4a34-99ee-0267e0c79525","resolution":{"observed_at":"2026-08-09T05:01:32.284455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10688","last_updated":"2024-04-17T18:24:45Z","snapshot_observed_at":"2026-08-16T13:07:39.813820Z","submitted_at":"2023-10-14T17:01:37Z","title":"A decoder-only foundation model for time-series forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10688","snapshot_observed_at":"2026-08-09T05:01:32.287913Z","title":"A decoder-only foundation model for time-series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.287913Z"},"links":{"cited_paper":"/paper/2310.10688","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:54c65391c1debb0ca0c771a1de945863b5ac48ce18bf8d805b2cdcd982d2f7af","observation_id":"7b85127f-55fd-4b81-ac26-7167ae792cfb","resolution":{"observed_at":"2026-08-09T05:01:32.287913Z","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":"2026-08-09T05:01:32.736644Z","title":"Lag-llama: Towards foundation models for time series forecasting","venue":null,"work_id":"9ba9d467-04dd-4fbe-b176-63f0427d5d1d","year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.292062Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:236530355bb617e17e0c74bda4c1dc07e34d21272d4c61d6dfa5a486f6376cd6","observation_id":"d8291d35-c29f-41f5-aa6f-e9cdde5d763f","resolution":{"observed_at":"2026-08-09T05:01:32.739716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.295249Z","title":"Time series analysis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.295249Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:65c0300835901eb3cda2a730130dc9f3cb5a9a6caeb48ad02d2137108c5933c9","observation_id":"fb71b063-c214-4cba-be9d-987b5a63932e","resolution":{"observed_at":"2026-08-09T05:01:32.295249Z","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":"2026-08-09T05:01:32.722901Z","title":"Time series techniques for economists","venue":null,"work_id":"617bf7b2-4358-4e5a-b8fe-9bcc69101c2f","year":1990},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.298610Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:5ee6d33a99a3424264bd96db8e78b161588fb9f6c4d8def255821518d0e393fa","observation_id":"e9319865-8c1e-4d17-8b7c-3596a1ac1380","resolution":{"observed_at":"2026-08-09T05:01:32.725992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.713139Z","title":null,"venue":null,"work_id":"a806ce85-659c-4334-ab61-5e624af298cd","year":1968},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.301712Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:490d50ffe63d3ab444a5944147e7c6c6bfd039fe3360844e4c3e571d0bd10794","observation_id":"198ca922-16e5-4ea6-b777-fbadaef05ebd","resolution":{"observed_at":"2026-08-09T05:01:32.716621Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.703504Z","title":"Completely analytical interactions: constructive descrip- tion","venue":null,"work_id":"8a59b1ad-6332-405c-980e-44a6eb61949b","year":1987},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.304505Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:a451d1f1e640516a14078e6ee8b461e2b75868e3f23356a27fb211ea11733f84","observation_id":"8821df1b-0f04-453f-a1de-609b3bb33c9c","resolution":{"observed_at":"2026-08-09T05:01:32.706606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.694267Z","title":"Transformers as statisticians: Provable in-context learning with in-context algorithm selection","venue":null,"work_id":"3fb0e2f1-29ee-46c9-807f-fa88904ca403","year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.307331Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:3ae447ef7e253433192648bd205bd63389275c5832e2553e78eb3f869fd72194","observation_id":"249d4ec4-419f-4b8b-8785-c4e7d5463206","resolution":{"observed_at":"2026-08-09T05:01:32.697442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.685079Z","title":"Transformers learn in-context by gradient descent","venue":null,"work_id":"1227166e-a7b7-40cc-8053-d55243c38ed4","year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.310163Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:09f362d62ede61a40ecad32d9c083e874048693b3814ba8dd5d7c0d58ec398d9","observation_id":"362a0769-d062-41eb-8dae-5885b0b33780","resolution":{"observed_at":"2026-08-09T05:01:32.688183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.675970Z","title":"Transformers as algorithms: Generalization and stability in in-context learning","venue":null,"work_id":"21fb07c0-76ad-430b-b6d9-05940cd16b70","year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.313135Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:0c0278c756753e9bf747e0af53ddfa516e9d8bade9841c904162c8d97d694878","observation_id":"252e1efa-eeb4-479d-b527-3e9d13dd7958","resolution":{"observed_at":"2026-08-09T05:01:32.679220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03576","last_updated":"2023-07-07T13:09:18Z","snapshot_observed_at":"2026-08-16T15:17:57.789299Z","submitted_at":"2023-07-07T13:09:18Z","title":"One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03576","snapshot_observed_at":"2026-08-09T05:01:32.316154Z","title":"One step of gradient descent is provably the optimal in-context learner with one layer of linear self-attention","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.316154Z"},"links":{"cited_paper":"/paper/2307.03576","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:5e76fb6183a80a477ea631ce8bb1bdaeb75f5ae06fbfdaa58e137a436b9024a9","observation_id":"5eb28838-b28f-4039-b13a-9525e9d81771","resolution":{"observed_at":"2026-08-09T05:01:32.316154Z","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":"2026-08-09T05:01:32.667056Z","title":"Transformers learn to implement preconditioned gradient descent for in-context learning","venue":null,"work_id":"ba929d4c-df51-4897-bad9-46bea7837397","year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.319350Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:3d077618ea1c9226d62d6463902741c22154cda63f99d7f7adfa61943da4bac7","observation_id":"a368d547-930f-451c-9562-6e96e7e90cc7","resolution":{"observed_at":"2026-08-09T05:01:32.670321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.322081Z","title":"Trained transformers learn linear models in-context","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.322081Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:d82c85aca338f467f2776621251de9b022bfc7b18f4d78f6b9c8379a15762d72","observation_id":"37305333-afcf-4e39-adc0-5f1c0f6c460e","resolution":{"observed_at":"2026-08-09T05:01:32.322081Z","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":"2026-08-09T05:01:32.652867Z","title":"Language models are few-shot learn- ers","venue":null,"work_id":"0178bce5-fddc-4c9b-b529-c193a64dbee1","year":1901},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.324940Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:b952fe31bcc7ac4e4ba3c525aaf109101b4d1b337160da6d81f27de64cd91792","observation_id":"3a60c466-fb29-4fdb-a62e-e077ef317a1e","resolution":{"observed_at":"2026-08-09T05:01:32.656240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.643833Z","title":"What can transformers learn in- context? a case study of simple function classes","venue":null,"work_id":"e1af6056-2b2a-4398-8fa1-a7a697ba7a19","year":2022},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.327888Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:5a6cb7ba9cded2978e80a2efd0cf7f0d5ce32fd69a245e13821d80ede449ce77","observation_id":"ef7bc9fb-ac22-4961-8d05-fbe651c1842a","resolution":{"observed_at":"2026-08-09T05:01:32.647062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.330576Z","title":"Roformer: Enhanced transformer with rotary position embedding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.330576Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:7b4db0199f24b105a020238fa7468777c0082bb5d5241f33478b028928ef0799","observation_id":"c58bf8a3-d78c-41d8-88ff-4cac77adadeb","resolution":{"observed_at":"2026-08-09T05:01:32.330576Z","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":"2026-08-09T05:01:32.629785Z","title":"What learning algo- rithm is in-context learning? investigations with linear models","venue":null,"work_id":"e159641a-2e56-4442-baf1-7ba33d85beff","year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.333089Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:4253f74efa8eb97dce99a2bff8e282b4053725d9dce010cd5d5a3cc8b39bf9e8","observation_id":"95b2c350-a328-46eb-8036-9eef6af7b4f5","resolution":{"observed_at":"2026-08-09T05:01:32.633000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08566","last_updated":"2024-05-26T04:55:19Z","snapshot_observed_at":"2026-08-16T14:52:35.184770Z","submitted_at":"2023-10-12T17:55:02Z","title":"Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08566","snapshot_observed_at":"2026-08-09T05:01:32.335815Z","title":"Transformers as decision makers: Provable in-context reinforcement learning via supervised pretraining","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.335815Z"},"links":{"cited_paper":"/paper/2310.08566","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:134f082e58667f408a68185daf30d409574c97b7141ee07de8fa9fe792d7ef77","observation_id":"253d1545-1299-4b93-9269-aff5e5d781cb","resolution":{"observed_at":"2026-08-09T05:01:32.335815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01312","last_updated":"2025-01-13T03:53:34Z","snapshot_observed_at":"2026-08-13T13:00:22.824747Z","submitted_at":"2025-01-02T15:53:25Z","title":"Learning Spectral Methods by Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.01312","snapshot_observed_at":"2026-08-09T05:01:32.338962Z","title":"Learning spectral methods by transformers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.338962Z"},"links":{"cited_paper":"/paper/2501.01312","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:96215444fef02c8b296749ff021c97cffa327d68e590b7a7a6a9cbf3b5d13e96","observation_id":"1f2c86cb-6f51-4f1a-9d1e-2723d05b16e4","resolution":{"observed_at":"2026-08-09T05:01:32.338962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.08586","last_updated":"2023-10-17T00:12:20Z","snapshot_observed_at":"2026-08-16T15:00:19.684100Z","submitted_at":"2023-09-15T17:43:40Z","title":"Replacing softmax with ReLU in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.08586","snapshot_observed_at":"2026-08-09T05:01:32.342149Z","title":"Replacing softmax with relu in vision transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.342149Z"},"links":{"cited_paper":"/paper/2309.08586","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:d6044aee84a5356d50935b79ad11415d6cc30412323706a14dca27697072a7db","observation_id":"72f0c262-72e3-4bd2-b2c0-5fdc5be8147c","resolution":{"observed_at":"2026-08-09T05:01:32.342149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07012","last_updated":"2021-10-06T14:04:59Z","snapshot_observed_at":"2026-08-16T18:31:34.860902Z","submitted_at":"2021-04-14T17:52:38Z","title":"Sparse Attention with Linear Units","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07012","snapshot_observed_at":"2026-08-09T05:01:32.345079Z","title":"Sparse attention with linear units","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.345079Z"},"links":{"cited_paper":"/paper/2104.07012","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:dd0bae583ee512a4433791ae3935249fe980fa218b38cf85684233d160c8f662","observation_id":"05e285ee-c4c2-4156-9d1b-733867176421","resolution":{"observed_at":"2026-08-09T05:01:32.345079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.06461","last_updated":"2023-02-13T15:41:20Z","snapshot_observed_at":"2026-08-16T15:55:42.409032Z","submitted_at":"2023-02-13T15:41:20Z","title":"A Study on ReLU and Softmax in Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.06461","snapshot_observed_at":"2026-08-09T05:01:32.347871Z","title":"A study on relu and softmax in transformer","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.347871Z"},"links":{"cited_paper":"/paper/2302.06461","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:19e2df9710dccfda5c341cb605705ff20bf4bd0345794a96dc2595d25a513e2b","observation_id":"d5e5a6e9-e6a1-4281-bb3e-37ba8a4b3f99","resolution":{"observed_at":"2026-08-09T05:01:32.347871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-09T05:01:32.350616Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.350616Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:8208d928805b5e4f97a678302ed1bd46ad91268747f78dc9cfa50c45719d0e28","observation_id":"1d2e0051-9687-47d8-91f5-b7873abd8aa9","resolution":{"observed_at":"2026-08-09T05:01:32.350616Z","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":"2026-08-09T05:01:32.620480Z","title":"Strategies to leverage foundational model knowledge in object affordance grounding","venue":null,"work_id":"e0d7ddb9-dbb9-48cc-90c4-877beade7401","year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.352848Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:badca7836c277ff663ce661c32123432a7b5c9b1e16b9acddcb6bcdd13de96d8","observation_id":"6504a57d-5b23-40cc-b45d-3aa0ab865f89","resolution":{"observed_at":"2026-08-09T05:01:32.623818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-16T18:24:48.477165Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-09T05:01:32.354984Z","title":"Monash time series forecasting archive","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.354984Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:b0927ee2bed121c861856f969c178a00aa3b38278e6a269a6c74dd60e927ff18","observation_id":"5096176a-01ba-48ae-87f2-bf46c5710ed7","resolution":{"observed_at":"2026-08-09T05:01:32.354984Z","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":"2026-08-09T05:01:32.611333Z","title":"Gluonts: Probabilistic and neural time series modeling in python","venue":null,"work_id":"e0a4ea43-564d-4114-bfa6-5ff5d874b2e0","year":2020},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.357636Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:495c30dfb0d266cc4321708dcc4ff5b86e5f1538aa00ad1b3526670ee6f1b677","observation_id":"dbb14712-23e3-4a97-b694-2091d6378844","resolution":{"observed_at":"2026-08-09T05:01:32.614596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.602834Z","title":"Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecasting","venue":null,"work_id":"4983e30c-12fd-4dc1-89c8-fb2f2c15240d","year":2021},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.359733Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:e31640f3338ffefd4a77cedd2bdfff6c9de1ab83e06dbc272908d69e3d1064a5","observation_id":"459c0ff3-9305-49d0-96d5-28c998086949","resolution":{"observed_at":"2026-08-09T05:01:32.605513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.361902Z","title":"Modeling long-and short-term temporal patterns with deep neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.361902Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:3895524138516d3a8661476a3198eabe69f62ab57811df6cfebbcbc9e0b90c4d","observation_id":"da57364b-92c7-47bc-9797-ca4da4a845fc","resolution":{"observed_at":"2026-08-09T05:01:32.361902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06625","last_updated":"2024-03-14T11:45:57Z","snapshot_observed_at":"2026-08-14T20:15:49.960714Z","submitted_at":"2023-10-10T13:44:09Z","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06625","snapshot_observed_at":"2026-08-09T05:01:32.363948Z","title":"itrans- former: Inverted transformers are effective for time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.363948Z"},"links":{"cited_paper":"/paper/2310.06625","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:c4174559bbd3cb27c821c3a5360ef1cfb3cfc3e8db5785818675fb5613fd7803","observation_id":"284c798f-2510-4e5c-b6fe-157fd5eb67aa","resolution":{"observed_at":"2026-08-09T05:01:32.363948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-08-17T01:22:50.943392Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-09T05:01:32.366917Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.366917Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:f6685b069801d061911138832448a1eb59b29b08c2eb4328e33dd759b3e855b7","observation_id":"8039a3c6-493e-4238-bcb1-ddb8df34af41","resolution":{"observed_at":"2026-08-09T05:01:32.366917Z","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":"2026-08-09T05:01:32.589353Z","title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","venue":null,"work_id":"5a5cfd43-b8a7-4bb8-a101-2351a8baf6bf","year":2023},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.369992Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:6fd531a362c5d56e163da3461e7d63a0d7a3eabbfc581056e8541f76e3987043","observation_id":"f1ab3aa6-3d67-469d-8989-2b6c8ce5647a","resolution":{"observed_at":"2026-08-09T05:01:32.592268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14735","last_updated":"2024-08-13T15:45:37Z","snapshot_observed_at":"2026-08-16T14:16:09.696469Z","submitted_at":"2024-02-22T17:47:03Z","title":"How Transformers Learn Causal Structure with Gradient Descent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14735","snapshot_observed_at":"2026-08-09T05:01:32.372660Z","title":"How transformers learn causal structure with gradient descent","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.372660Z"},"links":{"cited_paper":"/paper/2402.14735","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:ce4078bc35b50b910ed2514b064cf2f98c6683f5d218ccc1f8cc2d511775b6f6","observation_id":"64b2ac71-9cd4-42de-b4fe-26e85ddf615e","resolution":{"observed_at":"2026-08-09T05:01:32.372660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05787","last_updated":"2024-06-05T13:44:00Z","snapshot_observed_at":"2026-08-16T14:20:10.696997Z","submitted_at":"2024-02-08T16:24:44Z","title":"How do Transformers perform In-Context Autoregressive Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05787","snapshot_observed_at":"2026-08-09T05:01:32.375927Z","title":"How do transformers perform in-context autoregressive learning? arXiv preprint arXiv:2402.05787, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.375927Z"},"links":{"cited_paper":"/paper/2402.05787","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:259942dbfbb585ae2bb02d46b1da7cb98326cbd0b92ecc8ea02f3dd51c0b04c6","observation_id":"8dc65c7c-f971-45e3-83fb-b26e0d786c69","resolution":{"observed_at":"2026-08-09T05:01:32.375927Z","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":"2026-08-09T05:01:32.378973Z","title":"High-dimensional statistics: A non-asymptotic viewpoint, volume 48","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.378973Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:96a83e12b3ab3cbc7a89e4d8577646829ef9df78440fe317ce922d963606b77b","observation_id":"65f8d99b-db7a-4532-b337-450e8a088cf2","resolution":{"observed_at":"2026-08-09T05:01:32.378973Z","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":"2026-08-09T05:01:32.575936Z","title":"Learning from weakly dependent data under dobrushin’s condition","venue":null,"work_id":"f9d4f6a9-e035-4050-a271-dca5972463ee","year":2019},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.381737Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:c6b5ecf762947be057b6d2a22ba21ee9a93007adbac1e2254168027067edebc8","observation_id":"320e9eca-ec15-49d9-94f9-0703e9e29b05","resolution":{"observed_at":"2026-08-09T05:01:32.578637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.567287Z","title":"Concentration inequalities for functions of gibbs fields with application to diffraction and random gibbs measures","venue":null,"work_id":"19091ffb-dda5-414f-81aa-4ccdfeedae58","year":2003},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.384472Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:a891db57d1291c591860c01cd28104a3c07e9129b0929d796bcb32e83e27257b","observation_id":"64dbc17b-1b68-4c49-a9af-544a45152d00","resolution":{"observed_at":"2026-08-09T05:01:32.570427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.557778Z","title":null,"venue":null,"work_id":"ed3ad954-6285-4565-acd6-166eedbfaa1f","year":null},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.387806Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:3499303f7e35fca206d1083a39d515c78ea853497d73c9d39bbd35ed0d751461","observation_id":"a92c446d-cd8e-4837-b1dc-d786e1fe5501","resolution":{"observed_at":"2026-08-09T05:01:32.561373Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.549085Z","title":null,"venue":null,"work_id":"9c4840c6-bab0-43a8-b833-d5eb0cf17911","year":null},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.391279Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:5d0e258c1bbe0eec09f2a295cd7e3dbb4c47792c8cdfe663a6c4f0dd0da1629a","observation_id":"31ff3abe-d1b0-468b-ae5e-b63dbfe2357d","resolution":{"observed_at":"2026-08-09T05:01:32.552031Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.540648Z","title":"to get P(T ) j , for j = 1, · · ·, n","venue":null,"work_id":"926f6ac1-ca20-4862-955c-d8c82fb1092d","year":null},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.394517Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:80d0352293940c568fc3bf2075ac37729e4944d2000da56ac9647c5b5b5aeca9","observation_id":"b94cdecb-c20b-422d-b6a8-797be361657c","resolution":{"observed_at":"2026-08-09T05:01:32.543540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-09T05:01:32.529291Z","title":"We assume that for each j ∈ [n], (zj,t) has marginals equal to some distribution D for t = 1, · · ·, T","venue":null,"work_id":"efc2e9e2-921f-4854-9475-f83b744dac4d","year":null},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.397410Z"},"links":{"citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:49eac7a7eb8532f7e7622441121fb87d6f6fd0c87bd9b605098e9e08e604f74e","observation_id":"386fe85a-090e-49f9-87fd-4acc5f13bce1","resolution":{"observed_at":"2026-08-09T05:01:32.534689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":18},"total_outbound_references":42},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 2 inbound Pith citation observations for arXiv:2502.03383."}