{"as_of":"2026-08-21T08:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:899b9b8db36bf01686e40b50d424545d3ea0ae2a5ff4151371bbf3d3acb34554","coverage":[{"denominator":3,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:56:46.743764Z","state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:32:31.229677Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T15:32:33.648068Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.12723","last_updated":"2025-08-01T23:01:30Z","snapshot_observed_at":"2026-08-21T05:19:46.311925Z","submitted_at":"2025-01-22T08:53:12Z","title":"Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach","version":2},"cited_work":{"arxiv_id":"2501.12723","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.12723","snapshot_observed_at":"2026-08-06T15:32:33.648068Z","title":"Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach","venue":"cs.LG","work_id":"70233ef6-5650-44f4-b9bc-19144db6e9e4","year":2025},"citing_paper":{"arxiv_id":"2507.15584","last_updated":"2026-06-18T12:09:46Z","snapshot_observed_at":"2026-08-20T14:55:09.575805Z","submitted_at":"2025-07-21T13:02:49Z","title":"We Need to Rethink Benchmarking in Anomaly Detection","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T15:32:31.229677Z"},"links":{"cited_paper":"/paper/2501.12723","citing_paper":"/paper/2507.15584"},"observation_digest":"sha256:6eb3bb9a2f9b97f93a04769e9cbebc860d3f29b0c2620405f5f511e73929523c","observation_id":"cf5010a6-b0b4-4c35-89f9-a329e8dc4833","resolution":{"observed_at":"2026-08-06T15:32:33.651539Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.12723/citation-record","integrity":"/paper/2501.12723/integrity","json":"/paper/2501.12723/citation-record.json","paper":"/paper/2501.12723"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2011.06803","last_updated":"2020-11-13T08:12:00Z","snapshot_observed_at":"2026-08-20T06:02:15.799431Z","submitted_at":"2020-11-13T08:12:00Z","title":"Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations","version":1},"cited_work":{"arxiv_id":"2011.06803","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.06803","snapshot_observed_at":"2026-08-10T16:56:46.789936Z","title":"Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations","venue":"cs.LG","work_id":"6ed7b328-b6c7-4c59-902d-0d3dbf99c4c5","year":2020},"citing_paper":{"arxiv_id":"2501.12723","last_updated":"2025-08-01T23:01:30Z","snapshot_observed_at":"2026-08-21T05:19:46.311925Z","submitted_at":"2025-01-22T08:53:12Z","title":"Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T16:56:46.736410Z"},"links":{"cited_paper":"/paper/2011.06803","citing_paper":"/paper/2501.12723"},"observation_digest":"sha256:bc71457c99d3379d7e2d351bc36df8f0fc1e568526911dbf9109c541253f6ea8","observation_id":"fb45b01d-6964-4b3f-8112-db73f15492c9","resolution":{"observed_at":"2026-08-10T16:56:46.793651Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T16:56:46.800176Z","title":"X., Gutierrez-Portela, F., Moreno Hernandez, J","venue":null,"work_id":"4123fdc5-5402-4730-afce-ebf47198a006","year":2024},"citing_paper":{"arxiv_id":"2501.12723","last_updated":"2025-08-01T23:01:30Z","snapshot_observed_at":"2026-08-21T05:19:46.311925Z","submitted_at":"2025-01-22T08:53:12Z","title":"Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T16:56:46.740562Z"},"links":{"citing_paper":"/paper/2501.12723"},"observation_digest":"sha256:7aaca483e6c8d83666b2eaab743328fe385cf34bf77b63ba14df38999cceef37","observation_id":"5bdfdb79-01ef-4223-a557-8940ffa2f3f1","resolution":{"observed_at":"2026-08-10T16:56:46.803438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.05254","last_updated":"2018-08-01T15:47:52Z","snapshot_observed_at":"2026-08-17T20:04:27.027819Z","submitted_at":"2017-09-15T15:07:29Z","title":"Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks","version":2},"cited_work":{"arxiv_id":"1709.05254","doi":null,"metadata_source":"pith","pith_arxiv_id":"1709.05254","snapshot_observed_at":"2026-08-10T16:56:46.773465Z","title":"Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks","venue":"cs.LG","work_id":"6ad3491b-5dd3-489e-8443-db3d9d752716","year":2017},"citing_paper":{"arxiv_id":"2501.12723","last_updated":"2025-08-01T23:01:30Z","snapshot_observed_at":"2026-08-21T05:19:46.311925Z","submitted_at":"2025-01-22T08:53:12Z","title":"Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach","version":2},"reference_index":450,"source":"pdf_text","source_observed_at":"2026-08-10T16:56:46.743764Z"},"links":{"cited_paper":"/paper/1709.05254","citing_paper":"/paper/2501.12723"},"observation_digest":"sha256:f5497efecb4db9210a3ac4ac6ff1fbaff9fbb108f6ea65cd9335e02ec1794cd1","observation_id":"bdbb43a8-d5a4-4d91-acda-78db830d0941","resolution":{"observed_at":"2026-08-10T16:56:46.779231Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.12723","last_updated":"2025-08-01T23:01:30Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-21T05:19:46.311925Z","submitted_at":"2025-01-22T08:53:12Z","title":"Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach"},"reference_resolution":{"displayed":3,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":1,"verified_fuzzy":1},"total_outbound_references":3},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 1 inbound Pith citation observation for arXiv:2501.12723."}