{"as_of":"2026-08-05T03:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:02e9689eef2f2fd21bd9d03afbcec3770b52ff46045a243f61b9af6cb0ef46f0","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T13:03:25.595089Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.06361/citation-record","integrity":"/paper/2605.06361/integrity","json":"/paper/2605.06361/citation-record.json","paper":"/paper/2605.06361"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.07815","last_updated":"2024-11-04T17:42:45Z","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:53:54Z","title":"Chronos: Learning the Language of Time Series","version":3},"cited_work":{"arxiv_id":"2403.07815","doi":"10.48550/arxiv.2403.07815","metadata_source":"pith","pith_arxiv_id":"2403.07815","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Chronos: Learning the Language of Time Series","venue":"cs.LG","work_id":"d8b9a3a4-4dd9-4544-8c39-96b0be0b7af0","year":2024},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"cited_paper":"/paper/2403.07815","citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:602ab5bdf9b54a9a486a43d3b1567a695357df2861c962e6c51221cfea1e98d1","observation_id":"eb2f5cbf-ca57-4638-8439-477833384f1f","resolution":{"observed_at":"2026-05-13T08:27:23.855695Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-12T09:20:17.505608+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T09:20:17.505608+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Synthetic data generation for time series imputation: Comparing the foundation model chronos with established methods","venue":null,"work_id":"eb2de8ba-badc-456d-8e56-b7a579cb7234","year":2025},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:9e53f9fec9560e13453f86399e28b82e6b741ca3640265131d0319c4c74249c4","observation_id":"6a0ac44d-c8fa-4f59-9aa9-d1d521df9869","resolution":{"observed_at":"2026-05-26T12:47:49.549200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.11954","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unicast: A unified multimodal prompting framework for time series forecasting","venue":null,"work_id":"ce80b0bb-4987-4759-bcaf-eced3f1fd901","year":2025},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:42c38e37bad78f6c329010a3904134184b967dfdb49480509666b97316c19f1a","observation_id":"4d4f9f8d-ce0d-49de-97ca-d9cf70421b1c","resolution":{"observed_at":"2026-05-11T19:01:16.471489Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"A more realistic evaluation of cross-frequency transfer learning and foundation forecasting models","venue":null,"work_id":"a7f19a3b-7ac8-4143-90c5-6327adca945c","year":null},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:d443d149acc9393db193828cae4c31f51c558c59a6d78b153a9ec0e76d4c1209","observation_id":"c00e90bf-4bb9-42de-bcc6-94592ff5307a","resolution":{"observed_at":"2026-05-26T12:47:49.569527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.19465","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2509.19465","venue":null,"work_id":"cb0e5f3f-b3e4-4566-aacf-b82e46af9848","year":null},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:8f68b07e466c00ebbbfb9960dff07285d92d8e0ade034bf4aa2aaf8c8387d900","observation_id":"a35023b6-f2f1-4bf3-ba73-748fa34e6107","resolution":{"observed_at":"2026-05-11T19:01:16.478337Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Forecasting heart rate variability using wearable sensor data","venue":null,"work_id":"ae14a803-6555-483b-8f71-f9ae7887f355","year":2025},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:8852d890df836136b4721a6fd59d1f6a4af3cf2f4e127420483b2bb67fccdcb5","observation_id":"84479c8e-387a-45a4-8be6-a277f72121e3","resolution":{"observed_at":"2026-05-26T12:47:49.560228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Neuro-symbolic fusion of wi-fi sensing data for passive radar with inter-modal knowledge transfer","venue":null,"work_id":"7d2e0103-2bae-4fcf-8a65-b6d64421a93f","year":2024},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:0c6ced846c9e15a5a49f0e8c573857be36900cb5519243b0ab7967eca3ba04d8","observation_id":"42cb633b-bdb5-4305-bd83-d6f1733448f8","resolution":{"observed_at":"2026-05-26T12:47:49.583176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Preliminary insights into resource-constrained neuro- symbolic causal complex event processing","venue":null,"work_id":"10d18023-87d7-4181-83c3-49203d65adef","year":2025},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:2781fbfba4486877061cb88ead1cdee27b9c94a4e36ddba6e2258482604fd48c","observation_id":"7487fbf1-e95a-48a8-a7a8-a62d8ae4e5e1","resolution":{"observed_at":"2026-05-26T12:47:49.538870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2510.15821","last_updated":"2025-10-17T17:00:53Z","snapshot_observed_at":"2026-07-06T22:32:57.286856Z","submitted_at":"2025-10-17T17:00:53Z","title":"Chronos-2: From Univariate to Universal Forecasting","version":1},"cited_work":{"arxiv_id":"2510.15821","doi":"10.48550/arxiv.2510.15821","metadata_source":"pith","pith_arxiv_id":"2510.15821","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Chronos-2: From Univariate to Universal Forecasting","venue":"cs.LG","work_id":"c00ac266-1edf-4787-a2b3-23692973b1b8","year":2025},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"cited_paper":"/paper/2510.15821","citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:b79ddf33432c4a64daa68188c2e6ce8c1021f581b593214cf0154d9bb0b2197b","observation_id":"0878f7f8-7f21-4d31-b52a-24b0c1601f7b","resolution":{"observed_at":"2026-05-15T01:20:36.981071Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Chronos forecasting","venue":null,"work_id":"024c7ad2-f833-4fd0-854e-9c6c27779ff9","year":2024},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:221d8259920a7ddad00b5e06361b5171383d92f83d73918d1f7bc94435290eed","observation_id":"017a8202-7dab-4611-8374-bef403ab6e22","resolution":{"observed_at":"2026-05-26T12:47:49.542034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Sequential learning of neural networks for prequential MDL","venue":null,"work_id":"4361f311-0660-4fb1-8245-38182b920d27","year":2023},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:54da36501d62debd3a181b683d16abc20b3babe1eba6a46419717033473f4f3f","observation_id":"a0893153-2f96-4bba-8cfd-f7c3308fbed6","resolution":{"observed_at":"2026-05-26T12:47:49.572164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Information-theoretic probing with minimum description length","venue":null,"work_id":"d3d75a73-da2b-4995-abf4-04671158e1cc","year":2020},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:1d542dbeddba6e905f22ad297c16d8f7c8de42e477ca22dfb845cdcf322a28f3","observation_id":"11c8cac8-be84-455f-b06c-f90dbfe60afa","resolution":{"observed_at":"2026-05-26T12:47:49.544985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Leace: perfect linear concept erasure in closed form","venue":null,"work_id":"2d11956d-2b32-4170-aaaf-202a35b2b2b0","year":2023},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:7015b2b474b88040ae5decd2eb2433974f4cd241f38622ad385f94a907a2c8bb","observation_id":"5e01b396-1481-410b-8c55-dd5c369a5fe1","resolution":{"observed_at":"2026-05-26T12:47:49.555248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":"2b8867a1-6a61-4029-9428-88efd5abda42","year":2020},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:fde6f569dbdb05255a46526c1b9c63929e2d362e938a749cc8e37572cd001d39","observation_id":"da4dfc75-7517-4096-8549-33fd29ec9077","resolution":{"observed_at":"2026-05-26T12:47:49.579895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":"deee2b58-35eb-4cfa-958f-431cc7da6a83","year":2020},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:81e3adf48b4dc5fed535158edb79fce3b11c60eecc1fba1d014adefae3dd13ab","observation_id":"a08db45c-a8a9-48e3-b06b-d6c8f2bc5202","resolution":{"observed_at":"2026-05-26T12:47:49.552186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Designing and interpreting probes with control tasks","venue":null,"work_id":"fe9cf0a5-a66f-4abe-858d-de415c986a77","year":2019},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:8c0852bc5bc135453df3c7ffc0245b0c44835c61bc55e5e0da413bbd29689fe5","observation_id":"ee5bf066-d010-4091-a33b-b0cffa36e560","resolution":{"observed_at":"2026-05-26T12:47:49.563603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.08884","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spectral predictability as a fast reliability indicator for time series forecasting model selection","venue":null,"work_id":"22b688a6-b1c3-4e00-a8cb-58074cd688d6","year":2025},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:6134f9412a8b6947a77553749358f6d04a1d68065d5f2703489e597db1013076","observation_id":"8f74325a-7583-4a6c-a073-22b2bb57283a","resolution":{"observed_at":"2026-05-11T19:01:16.456336Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Performance analysis of lossless type of compression algorithm in compression data","venue":null,"work_id":"3b17bbdc-d293-4f55-ab79-36c3757f896a","year":2022},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:d2bf35d1f000373923bcd9b977ccf38a13b93437ebff5260bb5047150b168518","observation_id":"86e809e0-8058-4c66-9e65-9160d5f9a966","resolution":{"observed_at":"2026-05-26T12:47:49.577231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Concept erasure: LEACE implementation","venue":null,"work_id":"0e731abf-ec9d-4299-a846-7d99a4c6ec3d","year":2023},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:d6c4d5d4e9fd87a6ec65d28e4e2671ae4423b2f4c487f2053bc58d76e457dabd","observation_id":"33ed3036-4363-4084-a900-7b4b73d6a60a","resolution":{"observed_at":"2026-05-26T12:47:49.566362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Pytorch: An imperative style, high- performance deep learning library","venue":null,"work_id":"d66b06db-df5e-40d2-a1d4-8d50d279b615","year":2019},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:3947d4228a53a4bba6d50ac51e092819b8a5ccacbb9b24a6243554bb52345915","observation_id":"25aece92-2a88-4b21-9c69-04f410f9fdc6","resolution":{"observed_at":"2026-05-26T12:47:49.574550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Akiba, S","venue":null,"work_id":"1baf3ddf-e02a-4f1d-bcb5-d371be1ff29b","year":2019},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:164f1374d659624099a758db77c18e957e9fd1d42e32204e42def3f2e2ada41c","observation_id":"1df60ede-18a9-4681-8ac4-04e88d80e9c5","resolution":{"observed_at":"2026-05-08T21:34:13.171595Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15771","last_updated":"2023-07-28T19:13:26Z","snapshot_observed_at":"2026-07-06T16:00:00.278754Z","submitted_at":"2023-07-28T19:13:26Z","title":"The Hydra Effect: Emergent Self-repair in Language Model Computations","version":1},"cited_work":{"arxiv_id":"2307.15771","doi":"10.48550/arxiv.2307.15771","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.15771","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2307.15771 , year=","venue":"arXiv (Cornell University)","work_id":"a5d4c873-682e-44c0-8d78-6e2bbe3f89de","year":2023},"citing_paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-08T13:03:25.595089Z"},"links":{"cited_paper":"/paper/2307.15771","citing_paper":"/paper/2605.06361"},"observation_digest":"sha256:8361eafefe12195f4373fdb852252a0c15e0b6c8123b8b90699b160ea4051ef9","observation_id":"5da02b9d-b5d0-4510-a7ec-e0438b658c6a","resolution":{"observed_at":"2026-05-11T19:01:16.449650Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.06361","last_updated":"2026-05-07T14:37:54Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:37:54Z","title":"Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":0,"verified_exact":5,"verified_fuzzy":15},"total_outbound_references":22},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2605.06361."}