{"as_of":"2026-08-07T16:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:609b419e246b5a3ddca26be60378d73e42b7eb58960a2d4ccb85d63885e7344c","coverage":[{"denominator":7,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T17:51:45.676999Z","state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2508.15612/citation-record","integrity":"/paper/2508.15612/integrity","json":"/paper/2508.15612/citation-record.json","paper":"/paper/2508.15612"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T17:51:46.178639Z","title":"In International conference on machine learn- ing, pages 3987–3995","venue":null,"work_id":"1e16968e-6c71-46db-8d7d-f40c1ad80c19","year":2013},"citing_paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T17:51:45.523623Z"},"links":{"citing_paper":"/paper/2508.15612"},"observation_digest":"sha256:d9558eeca716169eb599df18b1ca359d034563351fb134a240e480a9aa9b6ee1","observation_id":"e2e2eef2-a56e-411d-ad40-a59cfbb4714a","resolution":{"observed_at":"2026-08-05T17:51:46.263230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T17:51:46.036984Z","title":"republican","venue":null,"work_id":"f8853b65-8bc8-40e7-817f-475454cc3201","year":2017},"citing_paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T17:51:45.598790Z"},"links":{"citing_paper":"/paper/2508.15612"},"observation_digest":"sha256:9a7ce683dccb07535af5f27cd1372c06b4c590628df32429e4b718f374660bdb","observation_id":"e037f424-d20f-4a35-9754-26a483245775","resolution":{"observed_at":"2026-08-05T17:51:46.112524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T17:51:45.911299Z","title":"politics,","venue":null,"work_id":"7d7ef46f-a39b-417f-aa5c-7737a9f3c669","year":1997},"citing_paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T17:51:45.676999Z"},"links":{"citing_paper":"/paper/2508.15612"},"observation_digest":"sha256:5cd41fc18093611ed130fa5ffc799a5e3b7d53c990729963b522957f1919aa1b","observation_id":"38675730-279d-4cd7-8de8-d6014a3f6ac0","resolution":{"observed_at":"2026-08-05T17:51:45.969940Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2411.05930","last_updated":"2024-11-21T16:06:05Z","snapshot_observed_at":"2026-07-06T19:47:42.919421Z","submitted_at":"2024-11-08T19:31:19Z","title":"BERTrend: Neural Topic Modeling for Emerging Trends Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05930","snapshot_observed_at":"2026-08-05T17:51:45.272864Z","title":"Journal of machine Learning research, 3(Jan):993–1022","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model","version":1},"reference_index":2003,"source":"pdf_text","source_observed_at":"2026-08-05T17:51:45.272864Z"},"links":{"cited_paper":"/paper/2411.05930","citing_paper":"/paper/2508.15612"},"observation_digest":"sha256:2abe555f3441ed50814e8de24e2f56c8f27aacf7abaac9b4a8276d10e093e343","observation_id":"e186ea0e-ec67-4ed8-b224-0db77fee0850","resolution":{"observed_at":"2026-08-05T17:51:45.272864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.10616","last_updated":"2026-05-12T03:07:01Z","snapshot_observed_at":"2026-07-06T11:50:13.578611Z","submitted_at":"2021-09-22T09:31:50Z","title":"Enriching and Controlling Global Semantics for Text Summarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.10616","snapshot_observed_at":"2026-08-05T17:51:45.352156Z","title":"In Proceedings of the 14th Conference of the Euro- pean Chapter of the Association for Computational Linguistics, pages 530–539","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-05T17:51:45.352156Z"},"links":{"cited_paper":"/paper/2109.10616","citing_paper":"/paper/2508.15612"},"observation_digest":"sha256:1ffae8ddb41eda8e2932536a9e647960ea33d95e836cf76384891fa85280e441","observation_id":"d175ae3c-7e53-4305-8972-5979ce180880","resolution":{"observed_at":"2026-08-05T17:51:45.352156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.03974","last_updated":"2021-06-17T11:06:11Z","snapshot_observed_at":"2026-07-06T09:10:57.234689Z","submitted_at":"2020-04-08T12:37:51Z","title":"Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence","version":2},"cited_work":{"arxiv_id":"2004.03974","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.03974","snapshot_observed_at":"2026-08-05T17:51:45.782104Z","title":"Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence","venue":"cs.CL","work_id":"86861def-45f3-4cd3-9819-2feeef5fc5e9","year":2020},"citing_paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-05T17:51:45.229914Z"},"links":{"cited_paper":"/paper/2004.03974","citing_paper":"/paper/2508.15612"},"observation_digest":"sha256:ff72cacf508eff04e711792011608caf23b53c1ac25e4b5fd1ac2e41e276a41a","observation_id":"c8fd08f7-8b55-4a49-b179-62e2c4f8c7a7","resolution":{"observed_at":"2026-08-05T17:51:45.826214Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1206.3298","last_updated":"2015-05-16T22:57:04Z","snapshot_observed_at":"2026-07-06T02:50:00.009891Z","submitted_at":"2012-06-13T15:56:33Z","title":"Continuous Time Dynamic Topic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1206.3298","snapshot_observed_at":"2026-08-05T17:51:45.450631Z","title":"In Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, volume 180 of Proceedings of Machine Learning Research, pages 1950–1959","venue":null,"work_id":null,"year":1950},"citing_paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-05T17:51:45.450631Z"},"links":{"cited_paper":"/paper/1206.3298","citing_paper":"/paper/2508.15612"},"observation_digest":"sha256:7d4a030b5636ba1c3fb49ec6dda11ad313cac5acc956e242d4d896e685e425af","observation_id":"b18155c0-9de4-4cf0-b7e8-d3ca1628ac6e","resolution":{"observed_at":"2026-08-05T17:51:45.450631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.15612","last_updated":"2025-08-21T14:36:53Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T06:20:37.946722Z","submitted_at":"2025-08-21T14:36:53Z","title":"Continual Neural Topic Model"},"reference_resolution":{"displayed":7,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":7},"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 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2508.15612."}