{"as_of":"2026-08-15T09:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ea0e24d02321d72df6398bdee5c3e05fd553f348e911628fec500114ad78bbb1","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:16:22.385727Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2506.21570/citation-record","integrity":"/paper/2506.21570/integrity","json":"/paper/2506.21570/citation-record.json","paper":"/paper/2506.21570"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.07815","last_updated":"2024-11-04T17:42:45Z","snapshot_observed_at":"2026-08-14T11:34:28.022352Z","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":"2026-08-07T04:16:20.706920Z","title":"F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., Shchur, O., Rangapuram, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:20.706920Z"},"links":{"cited_paper":"/paper/2403.07815","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:e674ba8afa5bbcd4ca0f9e69e986011bce07f5f375f7a4f1d79c22e2473e867a","observation_id":"06598154-b1a0-4f5f-bc14-977e4c35f3fe","resolution":{"observed_at":"2026-08-07T04:16:20.706920Z","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":"2026-08-07T04:16:23.398276Z","title":null,"venue":null,"work_id":"359fd932-2a41-411b-926e-bdfc5049ae8f","year":2024},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:20.806216Z"},"links":{"citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:09c125ced7cf6bd8bdb959b1c286faf816492d25a7e61cfcc18a22f2fc7d772a","observation_id":"ab2a4ad7-06e5-4814-b20d-cbcdb60f91bb","resolution":{"observed_at":"2026-08-07T04:16:23.466494Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-07T04:16:20.885318Z","title":"A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:20.885318Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:62a4a170ed4fc5bc9f21de84d31f0d8487f104bcd2ac17a3e253bc7631604f90","observation_id":"28eea64a-ad28-4eed-b4cf-58543c2f0543","resolution":{"observed_at":"2026-08-07T04:16:20.885318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11416","last_updated":"2022-12-06T21:39:48Z","snapshot_observed_at":"2026-07-06T14:08:18.855958Z","submitted_at":"2022-10-20T16:58:32Z","title":"Scaling Instruction-Finetuned Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.11416","snapshot_observed_at":"2026-08-07T04:16:20.994738Z","title":"W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:20.994738Z"},"links":{"cited_paper":"/paper/2210.11416","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:49d4fd47aa479d89c02a564489934ccce6c7d63d02de86df2179e2bd99d7ba3b","observation_id":"cc812f25-e325-457b-a8db-bbfe74b790e0","resolution":{"observed_at":"2026-08-07T04:16:20.994738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07820","last_updated":"2024-08-12T00:43:56Z","snapshot_observed_at":"2026-08-15T03:35:58.968026Z","submitted_at":"2023-10-11T19:01:28Z","title":"Large Language Models Are Zero-Shot Time Series Forecasters","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07820","snapshot_observed_at":"2026-08-07T04:16:21.107088Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.107088Z"},"links":{"cited_paper":"/paper/2310.07820","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:c8d73a824b3dd8cce8e5c6b7c735e1ae7f4b8e0fae0a62ad1b76da998f26da9b","observation_id":"49559f20-620e-4951-ab56-3f4fedd22644","resolution":{"observed_at":"2026-08-07T04:16:21.107088Z","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":"2026-08-07T04:16:23.278586Z","title":"Scaling laws for transfer, 2021","venue":null,"work_id":"509d20e9-e99a-4feb-ae0f-73e96e5a0114","year":2021},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.201638Z"},"links":{"citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:a2baa0f7a39cf76622831a72d3025be9977d157cca22f27622fbc206791e1317","observation_id":"11fcacc2-1117-4cfe-815b-da7c50f91cd5","resolution":{"observed_at":"2026-08-07T04:16:23.323915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T04:16:23.148560Z","title":"Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q","venue":null,"work_id":"a5e8a24c-7416-487b-9e48-a556fedcea5e","year":2023},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.286220Z"},"links":{"citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:d14e5f9751154c3c34e1faf685d667e55aa632059b8fafc7608a27e5fb4258a9","observation_id":"205753f0-3259-47ba-a391-437f34578e81","resolution":{"observed_at":"2026-08-07T04:16:23.202493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-07T04:16:21.360580Z","title":"J., Brown, T","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.360580Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:e76892f9fffa49b2879a370bd7243ed4e4755c3ee52082e8e3601b926f236314","observation_id":"daa43046-084e-4e25-993b-abeea03f6ee8","resolution":{"observed_at":"2026-08-07T04:16:21.360580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04355","last_updated":"2025-09-10T16:11:35Z","snapshot_observed_at":"2026-08-13T04:24:50.510314Z","submitted_at":"2024-02-06T19:39:26Z","title":"PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04355","snapshot_observed_at":"2026-08-07T04:16:21.458075Z","title":"Pqmass: Probabilistic assessment of the quality of generative models using probability mass estimation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.458075Z"},"links":{"cited_paper":"/paper/2402.04355","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:bd6f54dd7c725b44ffffeec119f2fb191f412231c0b083f3de65d3eed64d6661","observation_id":"38287725-ba8e-4cd7-8c31-d82deb66ca6b","resolution":{"observed_at":"2026-08-07T04:16:21.458075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.05247","last_updated":"2021-06-30T17:34:46Z","snapshot_observed_at":"2026-08-06T05:47:18.065659Z","submitted_at":"2021-03-09T06:39:56Z","title":"Pretrained Transformers as Universal Computation Engines","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.05247","snapshot_observed_at":"2026-08-07T04:16:21.575185Z","title":"Pretrained transformers as universal computation engines, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.575185Z"},"links":{"cited_paper":"/paper/2103.05247","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:8a6afc616e0b4daa7a53719ad4106de881480bd6942d60a7d431d0c78de862ad","observation_id":"1d57a6a4-cc7d-4532-9c06-d8c3d9dfcc5d","resolution":{"observed_at":"2026-08-07T04:16:21.575185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04884","last_updated":"2021-03-25T07:39:38Z","snapshot_observed_at":"2026-08-13T22:29:09.811327Z","submitted_at":"2020-06-08T19:06:24Z","title":"On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04884","snapshot_observed_at":"2026-08-07T04:16:21.672189Z","title":"On the stability of fine-tuning bert: Misconceptions, explanations, and strong baselines, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.672189Z"},"links":{"cited_paper":"/paper/2006.04884","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:6117cb8bfb3983447b7d9c5ed7d5558c12c4898f380eb1b8c129b888be119d0a","observation_id":"33762d6f-5081-40f4-9daa-680859041d19","resolution":{"observed_at":"2026-08-07T04:16:21.672189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.10683","last_updated":"2023-09-19T15:14:48Z","snapshot_observed_at":"2026-08-14T16:16:21.567225Z","submitted_at":"2019-10-23T17:37:36Z","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.10683","snapshot_observed_at":"2026-08-07T04:16:21.754616Z","title":null,"venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.754616Z"},"links":{"cited_paper":"/paper/1910.10683","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:559554ff31ac030c4aebf1b52f1983270479c9e1d67e10a612e7fc9dd7580151","observation_id":"6c98152a-aeac-4fda-9790-76263581624c","resolution":{"observed_at":"2026-08-07T04:16:21.754616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08278","last_updated":"2024-02-08T05:49:04Z","snapshot_observed_at":"2026-08-14T18:24:11.697211Z","submitted_at":"2023-10-12T12:29:32Z","title":"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08278","snapshot_observed_at":"2026-08-07T04:16:21.840048Z","title":"R., Ghonia, H., Bhagwatkar, R., Khorasani, A., Bayazi, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.840048Z"},"links":{"cited_paper":"/paper/2310.08278","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:67bf1f5eb437ae8f68c0397ff02d44c6db417f418d8891b1ded9d7f56e79e32e","observation_id":"758ed7b2-724a-4f94-bba0-bb97d7f5df63","resolution":{"observed_at":"2026-08-07T04:16:21.840048Z","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":"2026-08-07T04:16:22.977265Z","title":null,"venue":null,"work_id":"5deedc4d-ec8f-4cda-ad08-1caac1bb2ffc","year":2024},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:21.922236Z"},"links":{"citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:394cfbae66a986ed7c6936e67a91b302803a0a6875c2e8732b4ce90ca298d5dc","observation_id":"fb853581-8106-46f5-9a92-9a8679e85f04","resolution":{"observed_at":"2026-08-07T04:16:23.052226Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.10686","last_updated":"2022-01-30T16:42:46Z","snapshot_observed_at":"2026-08-14T18:21:00.075690Z","submitted_at":"2021-09-22T12:29:15Z","title":"Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.10686","snapshot_observed_at":"2026-08-07T04:16:22.020141Z","title":"W., Narang, S., Yogatama, D., Vaswani, A., and Metzler, D","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:22.020141Z"},"links":{"cited_paper":"/paper/2109.10686","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:8012f8b9811d2ef302efa637aa0e830e733a67186838cf388a2b0c53831d0d3a","observation_id":"7f3d18c2-6685-4b5f-a35a-2ac7f7ab6ca8","resolution":{"observed_at":"2026-08-07T04:16:22.020141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01652","last_updated":"2022-02-08T20:26:45Z","snapshot_observed_at":"2026-08-14T06:11:14.515796Z","submitted_at":"2021-09-03T17:55:52Z","title":"Finetuned Language Models Are Zero-Shot Learners","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01652","snapshot_observed_at":"2026-08-07T04:16:22.109407Z","title":"Y., Guu, K., Yu, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:22.109407Z"},"links":{"cited_paper":"/paper/2109.01652","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:efdbd6a5f0ad4f3829765637fea0a91723bfe5011b7f24d9b0b6795ddb0806f0","observation_id":"a695f355-aef3-49e9-8426-859e58cd1c5a","resolution":{"observed_at":"2026-08-07T04:16:22.109407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18959","last_updated":"2025-06-05T17:25:49Z","snapshot_observed_at":"2026-08-12T22:15:40.774172Z","submitted_at":"2024-10-24T17:56:08Z","title":"Context is Key: A Benchmark for Forecasting with Essential Textual Information","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18959","snapshot_observed_at":"2026-08-07T04:16:22.191093Z","title":"R., Ashok, A., Marcotte, \\'E ., Zantedeschi, V., Subramanian, J., Riachi, R., Requeima, J., Lacoste, A., Rish, I., Chapados, N., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:22.191093Z"},"links":{"cited_paper":"/paper/2410.18959","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:72cfa93d2761754fe438417b099a608f16fd259ffc326c47a2183b3d391b60fc","observation_id":"eacd3d10-922f-4e55-9c8f-205ebed68ed0","resolution":{"observed_at":"2026-08-07T04:16:22.191093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02592","last_updated":"2024-05-22T11:49:59Z","snapshot_observed_at":"2026-08-13T04:27:14.899922Z","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":"2026-08-07T04:16:22.281869Z","title":"Unified training of universal time series forecasting transformers, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:22.281869Z"},"links":{"cited_paper":"/paper/2402.02592","citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:e7a2ae21f714aa5491bc91030389f4ae205f59a6045ebfd2b01797a3f789f102","observation_id":"5ee91e22-2d09-4819-a54d-1c35bd1af9d7","resolution":{"observed_at":"2026-08-07T04:16:22.281869Z","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":"2026-08-07T04:16:22.815190Z","title":"One fits all:power general time series analysis by pretrained lm, 2023","venue":null,"work_id":"cc5b68b1-eaca-493f-b3b5-6a2a0089d1e9","year":2023},"citing_paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T04:16:22.385727Z"},"links":{"citing_paper":"/paper/2506.21570"},"observation_digest":"sha256:1787939db50be059982daaa77399cd1eceefb7a57f6028f15e0647bf02ce1269","observation_id":"fed88b2e-631f-4349-b2b0-5d41dda4b59c","resolution":{"observed_at":"2026-08-07T04:16:22.877329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.21570","last_updated":"2025-06-12T18:39:38Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T05:30:44.583618Z","submitted_at":"2025-06-12T18:39:38Z","title":"Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":3},"total_outbound_references":19},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2506.21570."}