{"as_of":"2026-08-08T09:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a28e1d70a162d008b62786425ff0b18b10bc2d4d51208589d42e806fa905b08","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T12:08:54.192441Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2607.19659/citation-record","integrity":"/paper/2607.19659/integrity","json":"/paper/2607.19659/citation-record.json","paper":"/paper/2607.19659"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T12:08:49.890834Z","title":"Proceedings of the 41st International Conference on Machine Learning , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:49.890834Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:11c803eeaba6098cb92b6085c16e44034a7c0fd44935e1540d59f922a7cbb3e2","observation_id":"d009f65d-b309-4c4f-bc48-0b051d77ce72","resolution":{"observed_at":"2026-08-01T12:08:49.890834Z","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":"2026-08-01T12:08:49.957931Z","title":"Proceedings of the IEEE , volume=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:49.957931Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:3fd4a26590c7ba0e7c08d6cefaf8efd59f7ea67cdf6bb5c282792059ac57adfd","observation_id":"1963c9f2-b9cc-4e8f-9f2a-12920a1edfbd","resolution":{"observed_at":"2026-08-01T12:08:49.957931Z","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":"2026-08-01T12:08:50.074785Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:50.074785Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:9e3504a49de97659ec2bcc9e8a909949f1798e5313f84f38d9a568f7c5dcdc28","observation_id":"45a6bd53-bbc4-4b99-acea-1f2a1d4ef2bb","resolution":{"observed_at":"2026-08-01T12:08:50.074785Z","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":"2026-08-07T22:30:30.596133Z","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":"2026-08-01T12:08:50.147491Z","title":"arXiv preprint arXiv:2310.08278 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:50.147491Z"},"links":{"cited_paper":"/paper/2310.08278","citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:6e9554ab9e926dd9c895107fb1550c2eef8e8d4c04e315890e0408ca476ed461","observation_id":"428693c5-929a-419b-a51b-5649b2fa2fb1","resolution":{"observed_at":"2026-08-01T12:08:50.147491Z","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":"2026-08-01T12:08:50.276361Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:50.276361Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:609f10c1a4c22a95d73ee8a8a4fb619be7602e5e01e810ff8b6b21d242173421","observation_id":"3e370c1e-8870-4893-9c32-c1d953d7b842","resolution":{"observed_at":"2026-08-01T12:08:50.276361Z","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":"2026-08-01T12:08:50.399794Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:50.399794Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:6a67437cb1b8225b55542ade3f5737a07343bcc50948a002dc6d9d21ab72e993","observation_id":"6e0ae222-2559-4641-ada3-ef20475ab5b6","resolution":{"observed_at":"2026-08-01T12:08:50.399794Z","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":"2026-08-01T12:08:50.621064Z","title":"Proceedings of the AAAI conference on artificial intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:50.621064Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:21dbb63134405e82e1ac7e632e8a2856579adbbf3cc5b61621f0561f7e5a0f24","observation_id":"2b36bb34-9776-4a97-ac46-f48b570133c9","resolution":{"observed_at":"2026-08-01T12:08:50.621064Z","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":"2026-08-01T12:08:50.812386Z","title":"and Bergmeir, Christoph , journal=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:50.812386Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:d00a82f9fbd4c66eec175a18829006b1f40a2448cb2b186c14409cd05f6b0c21","observation_id":"25c29446-bb5c-44b8-8086-024ccc7dd17b","resolution":{"observed_at":"2026-08-01T12:08:50.812386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12920","last_updated":"2025-07-30T17:23:56Z","snapshot_observed_at":"2026-08-07T18:08:20.714874Z","submitted_at":"2025-02-18T15:01:02Z","title":"Lightweight Online Adaption for Time Series Foundation Model Forecasts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12920","snapshot_observed_at":"2026-08-01T12:08:50.998382Z","title":"arXiv preprint arXiv:2502.12920 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:50.998382Z"},"links":{"cited_paper":"/paper/2502.12920","citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:b6de1409d4bce69f3f4334e2a085d32377c5a26be7f5decb6fb62e8e1a00f77f","observation_id":"193134db-efe8-4af4-94da-96ca72fcda04","resolution":{"observed_at":"2026-08-01T12:08:50.998382Z","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":"2026-08-01T12:08:51.151712Z","title":"Handbook of Statistics , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:51.151712Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:585535dc7ffe709279b2f6f9428d55a838fadf229ea96ea63a29eca1f86ab30d","observation_id":"ff94ebbb-ecf8-46dc-ac12-4c15f3297f65","resolution":{"observed_at":"2026-08-01T12:08:51.151712Z","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":"2026-08-01T12:08:51.265940Z","title":"Swarm and Evolutionary Computation , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:51.265940Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:419942479bf347e05538717b39f44e4b1ad80ab97ee86013aa9d0dfd2d03ac47","observation_id":"4e175739-4e62-42bb-8007-91c456db75c6","resolution":{"observed_at":"2026-08-01T12:08:51.265940Z","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":"2026-08-01T12:08:51.523142Z","title":"2004 , publisher=","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:51.523142Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:07acf03d494f0e2802656d2aa7e7cf21ae1fb5bc220103071d34e1b43787fd75","observation_id":"f2b0d55c-8eb2-4102-a10d-187e23dc2ae9","resolution":{"observed_at":"2026-08-01T12:08:51.523142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-01T12:08:51.744671Z","title":"arXiv preprint arXiv:2408.03314 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:51.744671Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:cf5aa9630bd829f4050f70fd994633d013e1294ef781078dfa9762194abcbdf4","observation_id":"cabdfde2-80a8-48d2-a370-06472aadb813","resolution":{"observed_at":"2026-08-01T12:08:51.744671Z","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":"2026-08-01T12:08:51.900081Z","title":"International Journal of forecasting , volume=","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:51.900081Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:0e1145803d9addc83707fc2b6f4acf359a92cbda916afef245d0920016ecb9ba","observation_id":"f51eab7f-4c5c-42aa-be1c-179d8ccb369b","resolution":{"observed_at":"2026-08-01T12:08:51.900081Z","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":"2026-08-01T12:08:52.016426Z","title":"International journal of forecasting , volume=","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:52.016426Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:a2e41293d21941067143ec08cab382dd672828e6fc8a86dac2e8cfb87035586d","observation_id":"ee6ccd09-b0b3-4c56-a272-29fc09591e55","resolution":{"observed_at":"2026-08-01T12:08:52.016426Z","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":"2026-08-01T12:08:52.167921Z","title":"IEEE Transactions on Intelligent Transportation Systems , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:52.167921Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:b3e055ebb52ddea5e8d131c5df600c2af8c2c9e1fd880d96a1fd9cc885ae7ea3","observation_id":"47b43783-ecee-4437-9841-f5885619effe","resolution":{"observed_at":"2026-08-01T12:08:52.167921Z","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":"2026-08-01T12:08:52.359690Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:52.359690Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:a1f0d9912395d4c1c744cf9724fa249e49412d6eb467f6e82b8e3178806a2c0c","observation_id":"6d205d91-fc8f-44f1-b57e-44d75b6d9f69","resolution":{"observed_at":"2026-08-01T12:08:52.359690Z","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":"2026-08-01T12:08:52.513632Z","title":"The Fourteenth International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:52.513632Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:2bb9fcc265e65278116e78dfdf2cba16c1afae23b8ea27db3f343a5a8402a7ec","observation_id":"9b2b8cd5-8e2b-4b0f-98ad-02c4e08c8d5a","resolution":{"observed_at":"2026-08-01T12:08:52.513632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.09043","last_updated":"2020-09-18T19:51:30Z","snapshot_observed_at":"2026-08-05T08:02:40.727007Z","submitted_at":"2020-09-18T19:51:30Z","title":"Cross-Entropy Method Variants for Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.09043","snapshot_observed_at":"2026-08-01T12:08:52.658893Z","title":"arXiv preprint arXiv:2009.09043 , year=","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:52.658893Z"},"links":{"cited_paper":"/paper/2009.09043","citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:9e1b7f1f67ae3bd9c625084ba31f46f6c4c8d65265c676c256526b1dc1992c93","observation_id":"ba530d55-8ff3-4db1-a3d8-a2e26ddfe090","resolution":{"observed_at":"2026-08-01T12:08:52.658893Z","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":"2026-08-01T12:08:52.755717Z","title":"and Carpov, Dmitri and Chapados, Nicolas and Bengio, Yoshua , booktitle=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:52.755717Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:3b63e1c230731e99fe65a5f1e787bf00fb8fc252fb4fe639c7cb610dec4e02a4","observation_id":"14e674eb-644b-4d11-8673-9b01837fb32a","resolution":{"observed_at":"2026-08-01T12:08:52.755717Z","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":"2026-08-01T12:08:52.859055Z","title":"Forty-first International Conference on Machine Learning , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:52.859055Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:eef17369eeae27f5f33de59f49f97a8861988fde1e0b47e7fc52d99cb661505e","observation_id":"a91a78d7-76aa-438c-9b92-6cd8cfdd9f6e","resolution":{"observed_at":"2026-08-01T12:08:52.859055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.15821","snapshot_observed_at":"2026-08-01T12:08:53.025898Z","title":"arXiv preprint arXiv:2510.15821 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:53.025898Z"},"links":{"cited_paper":"/paper/2510.15821","citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:81b726e1c09f2879a5c78eb03ee9935a2c77147ff36b5accf12050dfffbcce22","observation_id":"981378c5-40d0-4ecb-a26a-8267dc7792f1","resolution":{"observed_at":"2026-08-01T12:08:53.025898Z","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":"2026-08-01T12:08:53.101599Z","title":"Transactions on Machine Learning Research , volume=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:53.101599Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:f8c90828eac1173351227c4713f0faa98b6e84837186a8ebf5eee481e3a8f27e","observation_id":"1629393c-4877-4197-a0df-fe9fdc1cc5b2","resolution":{"observed_at":"2026-08-01T12:08:53.101599Z","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":"2026-08-01T12:08:53.235351Z","title":"NeurIPS Workshop on Time Series in the Age of Large Models , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:53.235351Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:c90696015d414ccb17d8004c84c55d398f24fb9fa016ca3a089849b55531dac5","observation_id":"954109f1-ce6c-477c-8f73-0d3a71418a5c","resolution":{"observed_at":"2026-08-01T12:08:53.235351Z","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":"2026-08-01T12:08:53.392990Z","title":"Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang , title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:53.392990Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:a38ccf92df2aefa466e28e3efbad914de404ead87f19389e0ac3722d08e38ae0","observation_id":"891f97af-eb6e-4849-b0d7-e5c6ac9f42f3","resolution":{"observed_at":"2026-08-01T12:08:53.392990Z","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":"2026-08-01T12:08:53.614792Z","title":"2025 IEEE International Conference on Data Mining (ICDM) , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:53.614792Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:9696f54333e633810ca67b4fe50b81fe2a32f8e9e8d43a83c084923bf00cac1c","observation_id":"a905b1db-62f4-4133-a78e-a954c3398cb2","resolution":{"observed_at":"2026-08-01T12:08:53.614792Z","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":"2026-08-01T12:08:53.774843Z","title":"International Conference on Machine Learning , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:53.774843Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:510a8fd6cb2108e8c03bcee0ef8e35e80dd5ffcda4a7f3635c2a0c1b5aa1d034","observation_id":"870bf5f8-2daf-4297-b035-606958cde052","resolution":{"observed_at":"2026-08-01T12:08:53.774843Z","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":"2026-08-01T12:08:53.990722Z","title":"arXiv preprint arXiv:2511.11698 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:53.990722Z"},"links":{"citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:b8a2cc0513273f2141fd72628c2b659f0a3ae808a46cb6ffee51606b8920b310","observation_id":"155d5562-cfbd-4f32-95ad-882963aed605","resolution":{"observed_at":"2026-08-01T12:08:53.990722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00816","last_updated":"2026-05-09T07:11:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-02T14:52:50Z","title":"Sundial: A Family of Highly Capable Time Series Foundation Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00816","snapshot_observed_at":"2026-08-01T12:08:54.121766Z","title":"Proceedings of the 42nd International Conference on Machine Learning (ICML) , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:54.121766Z"},"links":{"cited_paper":"/paper/2502.00816","citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:6173fd12ec2533aa0431de3849edaa1bb7493fb23dc03e44134c63553d87efbf","observation_id":"8ef63c67-df9b-4c97-81f2-59efcdb852eb","resolution":{"observed_at":"2026-08-01T12:08:54.121766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.12147","last_updated":"2026-06-03T06:00:21Z","snapshot_observed_at":"2026-08-08T08:36:01.804671Z","submitted_at":"2026-02-12T16:31:01Z","title":"It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.12147","snapshot_observed_at":"2026-08-01T12:08:54.192441Z","title":"arXiv preprint arXiv:2602.12147 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T12:08:54.192441Z"},"links":{"cited_paper":"/paper/2602.12147","citing_paper":"/paper/2607.19659"},"observation_digest":"sha256:b20eb7acdb2315492a4dbd9c4d251345798b642cf0c749cef05a11af57300a6c","observation_id":"ec44f3a8-dcf9-4e4a-9d73-b3e69636c10f","resolution":{"observed_at":"2026-08-01T12:08:54.192441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.19659","last_updated":"2026-07-22T01:49:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T20:21:04.821439Z","submitted_at":"2026-07-22T01:49:23Z","title":"Expert-Guided Forecast Editing for Time-Series Foundation Models"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":30},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.19659."}