{"as_of":"2026-08-07T07:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:26b477d2bf04ff5d52de1d7a0bb8b6d896c2d111014f2f7eecde64078d5c63c6","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:52:59.146043Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":5,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1708.00065","last_updated":"2018-07-20T00:49:34Z","snapshot_observed_at":"2026-08-03T14:05:24.150114Z","submitted_at":"2017-07-31T20:36:37Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","version":4},"cited_work":{"arxiv_id":"1708.00065","doi":"10.48550/arxiv.1708.00065","metadata_source":"pith","pith_arxiv_id":"1708.00065","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","venue":"cs.LG","work_id":"d34cd9d0-3e6a-4170-87c1-63a468c76911","year":2017},"citing_paper":{"arxiv_id":"1806.07366","last_updated":"2019-12-14T02:01:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-06-19T17:50:12Z","title":"Neural Ordinary Differential Equations","version":5},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-15T13:00:58.393405Z"},"links":{"cited_paper":"/paper/1708.00065","citing_paper":"/paper/1806.07366"},"observation_digest":"sha256:e7a64a5b0e88795d656e0598561f732d67b335351b1663cccbc463fa7f721a06","observation_id":"e2cb9d14-e4e4-4e7b-bf22-42b215557f32","resolution":{"observed_at":"2026-05-15T13:00:58.427458Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00065","last_updated":"2018-07-20T00:49:34Z","snapshot_observed_at":"2026-08-03T14:05:24.150114Z","submitted_at":"2017-07-31T20:36:37Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","version":4},"cited_work":{"arxiv_id":"1708.00065","doi":"10.48550/arxiv.1708.00065","metadata_source":"pith","pith_arxiv_id":"1708.00065","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","venue":"cs.LG","work_id":"d34cd9d0-3e6a-4170-87c1-63a468c76911","year":2017},"citing_paper":{"arxiv_id":"1907.05321","last_updated":"2019-07-11T15:47:39Z","snapshot_observed_at":"2026-08-02T16:18:10.239339Z","submitted_at":"2019-07-11T15:47:39Z","title":"Time2Vec: Learning a Vector Representation of Time","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-24T23:04:21.488605Z"},"links":{"cited_paper":"/paper/1708.00065","citing_paper":"/paper/1907.05321"},"observation_digest":"sha256:9de26c060c019ab32921d2382f7c083043252cb549d11292decf705f49cc6613","observation_id":"54076ff5-d28c-412b-9981-b018e1cafe55","resolution":{"observed_at":"2026-05-24T23:05:03.137500Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00065","last_updated":"2018-07-20T00:49:34Z","snapshot_observed_at":"2026-08-03T14:05:24.150114Z","submitted_at":"2017-07-31T20:36:37Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","version":4},"cited_work":{"arxiv_id":"1708.00065","doi":"10.48550/arxiv.1708.00065","metadata_source":"pith","pith_arxiv_id":"1708.00065","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","venue":"cs.LG","work_id":"d34cd9d0-3e6a-4170-87c1-63a468c76911","year":2017},"citing_paper":{"arxiv_id":"2309.17257","last_updated":"2026-04-12T21:40:57Z","snapshot_observed_at":"2026-07-06T16:25:29.858870Z","submitted_at":"2023-09-29T14:07:56Z","title":"A Survey on Deep Learning Techniques for Action Anticipation","version":2},"reference_index":181,"source":"pdf_text","source_observed_at":"2026-05-24T06:41:15.508744Z"},"links":{"cited_paper":"/paper/1708.00065","citing_paper":"/paper/2309.17257"},"observation_digest":"sha256:7f577a3580bf9573ae36d8a4ae7e7bfe0584a87ebf824f8018adc9bf29746b97","observation_id":"9b812704-9671-44f6-b40d-cb40f4577d65","resolution":{"observed_at":"2026-05-24T06:44:02.379880Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00065","last_updated":"2018-07-20T00:49:34Z","snapshot_observed_at":"2026-08-03T14:05:24.150114Z","submitted_at":"2017-07-31T20:36:37Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00065","snapshot_observed_at":"2026-08-05T10:52:59.146043Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.03643","last_updated":"2025-09-05T12:40:38Z","snapshot_observed_at":"2026-08-05T10:52:58.405655Z","submitted_at":"2025-09-03T18:50:03Z","title":"CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:52:59.146043Z"},"links":{"cited_paper":"/paper/1708.00065","citing_paper":"/paper/2509.03643"},"observation_digest":"sha256:6d2f4ac204d20336743f844e01fc26487ee1639aed5adbb7a5afc1379dc9b9bc","observation_id":"fa350067-f464-4583-b1e9-7c26a07ba8c2","resolution":{"observed_at":"2026-08-05T10:52:59.146043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00065","last_updated":"2018-07-20T00:49:34Z","snapshot_observed_at":"2026-08-03T14:05:24.150114Z","submitted_at":"2017-07-31T20:36:37Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","version":4},"cited_work":{"arxiv_id":"1708.00065","doi":"10.48550/arxiv.1708.00065","metadata_source":"pith","pith_arxiv_id":"1708.00065","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction","venue":"cs.LG","work_id":"d34cd9d0-3e6a-4170-87c1-63a468c76911","year":2017},"citing_paper":{"arxiv_id":"2606.24985","last_updated":"2026-06-23T14:24:43Z","snapshot_observed_at":"2026-07-06T23:59:28.120338Z","submitted_at":"2026-06-23T14:24:43Z","title":"Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection","version":1},"reference_index":191,"source":"arxiv_source","source_observed_at":"2026-06-26T00:27:41.609691Z"},"links":{"cited_paper":"/paper/1708.00065","citing_paper":"/paper/2606.24985"},"observation_digest":"sha256:3d36cd9ce1e9f875a9506665cc1d694e2a6c3066d6140da24904e37054321702","observation_id":"1b958526-7a29-4b74-9697-63ac7ba470e2","resolution":{"observed_at":"2026-06-26T00:28:42.892767Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1708.00065/citation-record","integrity":"/paper/1708.00065/integrity","json":"/paper/1708.00065/citation-record.json","paper":"/paper/1708.00065"},"outbound":[],"paper":{"arxiv_id":"1708.00065","last_updated":"2018-07-20T00:49:34Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T14:05:24.150114Z","submitted_at":"2017-07-31T20:36:37Z","title":"Time-Dependent Representation for Neural Event Sequence Prediction"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1708.00065."}