{"as_of":"2026-08-15T02:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dcb8b1f5fdcc537b034af2d9a3737003459f912aafbce0fede747d857da4469d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:21:23.318211Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T20:40:07.764274Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.13867","last_updated":"2025-01-08T14:08:11Z","snapshot_observed_at":"2026-08-14T20:40:28.517737Z","submitted_at":"2024-05-22T17:48:17Z","title":"Scaling-laws for Large Time-series Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13867","snapshot_observed_at":"2026-08-11T19:21:23.318211Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06936","last_updated":"2024-12-09T19:25:29Z","snapshot_observed_at":"2026-08-12T15:44:28.859611Z","submitted_at":"2024-12-09T19:25:29Z","title":"Creating a Cooperative AI Policymaking Platform through Open Source Collaboration","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T19:21:23.318211Z"},"links":{"cited_paper":"/paper/2405.13867","citing_paper":"/paper/2412.06936"},"observation_digest":"sha256:fb9e9fd44d8eec08a743d713a015bfdaab1041247e4367b9c29f9c1511c7fcf1","observation_id":"9e855d5c-251c-4e1f-bcd4-a5f9c664d79d","resolution":{"observed_at":"2026-08-11T19:21:23.318211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13867","last_updated":"2025-01-08T14:08:11Z","snapshot_observed_at":"2026-08-14T20:40:28.517737Z","submitted_at":"2024-05-22T17:48:17Z","title":"Scaling-laws for Large Time-series Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13867","snapshot_observed_at":"2026-08-08T17:06:03.018426Z","title":"Scaling-laws for large time-series models.arXiv preprint arXiv:2405.13867, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06037","last_updated":"2025-09-10T16:22:20Z","snapshot_observed_at":"2026-08-13T19:48:38.889646Z","submitted_at":"2025-02-09T21:21:55Z","title":"Investigating Compositional Reasoning in Time Series Foundation Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T17:06:03.018426Z"},"links":{"cited_paper":"/paper/2405.13867","citing_paper":"/paper/2502.06037"},"observation_digest":"sha256:8a3985b1debbbb9d9a0e385df65200abdb8b40d3f8e11c0de586458f4ccba2a5","observation_id":"d5982cbc-c621-4a23-ade6-42c220f77682","resolution":{"observed_at":"2026-08-08T17:06:03.018426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13867","last_updated":"2025-01-08T14:08:11Z","snapshot_observed_at":"2026-08-14T20:40:28.517737Z","submitted_at":"2024-05-22T17:48:17Z","title":"Scaling-laws for Large Time-series Models","version":2},"cited_work":{"arxiv_id":"2405.13867","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.13867","snapshot_observed_at":"2026-07-04T20:40:07.764274Z","title":null,"venue":null,"work_id":"2e01ce7a-38b1-4fdc-9bf5-674ecb5b58a7","year":2024},"citing_paper":{"arxiv_id":"2605.07546","last_updated":"2026-05-08T10:21:09Z","snapshot_observed_at":"2026-08-11T14:36:58.229948Z","submitted_at":"2026-05-08T10:21:09Z","title":"On the Invariance and Generality of Neural Scaling Laws","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-11T02:34:14.087140Z"},"links":{"cited_paper":"/paper/2405.13867","citing_paper":"/paper/2605.07546"},"observation_digest":"sha256:ecb77869307775043f634cdefe5fb6157a974eaa16251d917cad780eb15bc86d","observation_id":"a7284959-9895-417f-bf9a-fb4d18ad47ee","resolution":{"observed_at":"2026-05-11T03:15:55.992563Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13867","last_updated":"2025-01-08T14:08:11Z","snapshot_observed_at":"2026-08-14T20:40:28.517737Z","submitted_at":"2024-05-22T17:48:17Z","title":"Scaling-laws for Large Time-series Models","version":2},"cited_work":{"arxiv_id":"2405.13867","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.13867","snapshot_observed_at":"2026-07-04T20:40:07.764274Z","title":null,"venue":null,"work_id":"2e01ce7a-38b1-4fdc-9bf5-674ecb5b58a7","year":2024},"citing_paper":{"arxiv_id":"2606.25986","last_updated":"2026-06-24T15:54:09Z","snapshot_observed_at":"2026-08-14T07:38:46.743208Z","submitted_at":"2026-06-24T15:54:09Z","title":"The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-25T19:56:18.285143Z"},"links":{"cited_paper":"/paper/2405.13867","citing_paper":"/paper/2606.25986"},"observation_digest":"sha256:0caf299f5719afceba4a0ab9944c8032f82050f83ae73767d4f7af71f1f988d8","observation_id":"104f8075-73ab-495d-a476-d2e71887fbd3","resolution":{"observed_at":"2026-07-04T20:40:07.765908Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13867","last_updated":"2025-01-08T14:08:11Z","snapshot_observed_at":"2026-08-14T20:40:28.517737Z","submitted_at":"2024-05-22T17:48:17Z","title":"Scaling-laws for Large Time-series Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13867","snapshot_observed_at":"2026-07-11T11:28:48.400513Z","title":"Scaling-laws for large time-series models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.04919","last_updated":"2026-07-06T10:48:11Z","snapshot_observed_at":"2026-08-12T17:13:00.656665Z","submitted_at":"2026-07-06T10:48:11Z","title":"When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-11T11:28:48.400513Z"},"links":{"cited_paper":"/paper/2405.13867","citing_paper":"/paper/2607.04919"},"observation_digest":"sha256:fb2c109150ebc87b019d2702d3c67a60991cd34f6afe45a848a2bd8ddd14b7a2","observation_id":"de221bc5-c4d0-48e4-8644-e7fbc2b01d24","resolution":{"observed_at":"2026-07-11T11:28:48.400513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13867","last_updated":"2025-01-08T14:08:11Z","snapshot_observed_at":"2026-08-14T20:40:28.517737Z","submitted_at":"2024-05-22T17:48:17Z","title":"Scaling-laws for Large Time-series Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13867","snapshot_observed_at":"2026-08-02T09:50:23.904462Z","title":"Scaling-laws for large time-series models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16251","last_updated":"2026-06-27T02:56:46Z","snapshot_observed_at":"2026-08-06T15:16:34.197063Z","submitted_at":"2026-06-27T02:56:46Z","title":"Learning Spatio-Temporal Foundation Models from Pure Synthetic Data","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-02T09:50:23.904462Z"},"links":{"cited_paper":"/paper/2405.13867","citing_paper":"/paper/2607.16251"},"observation_digest":"sha256:43f3edb3c64eb192b8c5e299bcafe55e2ffe24d5f397d78e5eea3d8c960a4ca7","observation_id":"3266b451-762d-4163-9a63-bcab426e6722","resolution":{"observed_at":"2026-08-02T09:50:23.904462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2405.13867/citation-record","integrity":"/paper/2405.13867/integrity","json":"/paper/2405.13867/citation-record.json","paper":"/paper/2405.13867"},"outbound":[],"paper":{"arxiv_id":"2405.13867","last_updated":"2025-01-08T14:08:11Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T20:40:28.517737Z","submitted_at":"2024-05-22T17:48:17Z","title":"Scaling-laws for Large Time-series Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.13867."}