{"as_of":"2026-08-17T19:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a7e1d53d9ed2e7ad1f469190e2a7792d700cbc2b16f52615fc20b02ac81bbd6e","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:27:31.758257Z","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":"2026-08-15T22:04:24.976579Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.04032","last_updated":"2025-04-05T02:55:44Z","snapshot_observed_at":"2026-08-16T12:43:48.091567Z","submitted_at":"2025-04-05T02:55:44Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04032","snapshot_observed_at":"2026-08-16T11:27:31.758257Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.15491","last_updated":"2025-04-21T23:49:10Z","snapshot_observed_at":"2026-08-17T19:13:43.022077Z","submitted_at":"2025-04-21T23:49:10Z","title":"Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T11:27:31.758257Z"},"links":{"cited_paper":"/paper/2504.04032","citing_paper":"/paper/2504.15491"},"observation_digest":"sha256:48609e4f7614684778826b38341cdcb23b639605a47d6ffa2bc38006f3ca804f","observation_id":"51b3d4e7-3aa1-410c-8065-f1d812a2cb03","resolution":{"observed_at":"2026-08-16T11:27:31.758257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04032","last_updated":"2025-04-05T02:55:44Z","snapshot_observed_at":"2026-08-16T12:43:48.091567Z","submitted_at":"2025-04-05T02:55:44Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04032","snapshot_observed_at":"2026-08-16T05:52:08.828648Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19583","last_updated":"2025-06-01T22:38:52Z","snapshot_observed_at":"2026-08-16T05:46:42.784920Z","submitted_at":"2025-04-28T08:42:35Z","title":"Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:08.828648Z"},"links":{"cited_paper":"/paper/2504.04032","citing_paper":"/paper/2504.19583"},"observation_digest":"sha256:afd5a650ceb79d20943c2521db414ff68996345b19cde7ef0b65b231a8aef1b4","observation_id":"28102bfe-3154-4a5e-aeb1-22e194aa43fc","resolution":{"observed_at":"2026-08-16T05:52:08.828648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04032","last_updated":"2025-04-05T02:55:44Z","snapshot_observed_at":"2026-08-16T12:43:48.091567Z","submitted_at":"2025-04-05T02:55:44Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04032","snapshot_observed_at":"2026-08-16T04:49:08.737405Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.00299","last_updated":"2025-05-01T04:42:48Z","snapshot_observed_at":"2026-08-16T04:43:46.292478Z","submitted_at":"2025-05-01T04:42:48Z","title":"Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:08.737405Z"},"links":{"cited_paper":"/paper/2504.04032","citing_paper":"/paper/2505.00299"},"observation_digest":"sha256:189a14f1dc5cb3b757ad780ac6ab11783f3795717283030d070b35edd528ff36","observation_id":"48c79814-7820-457d-a61e-466a2748f6d8","resolution":{"observed_at":"2026-08-16T04:49:08.737405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04032","last_updated":"2025-04-05T02:55:44Z","snapshot_observed_at":"2026-08-16T12:43:48.091567Z","submitted_at":"2025-04-05T02:55:44Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04032","snapshot_observed_at":"2026-08-15T22:55:36.749538Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.05989","last_updated":"2025-05-09T12:18:34Z","snapshot_observed_at":"2026-08-15T22:48:44.004202Z","submitted_at":"2025-05-09T12:18:34Z","title":"Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T22:55:36.749538Z"},"links":{"cited_paper":"/paper/2504.04032","citing_paper":"/paper/2505.05989"},"observation_digest":"sha256:b4c4abedd21c4c1eca138d91f3341dcaf0515b138ca740f42259cbedb8c26a3a","observation_id":"8795c170-4b4f-438c-a261-b57d1dda8f6b","resolution":{"observed_at":"2026-08-15T22:55:36.749538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04032","last_updated":"2025-04-05T02:55:44Z","snapshot_observed_at":"2026-08-16T12:43:48.091567Z","submitted_at":"2025-04-05T02:55:44Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04032","snapshot_observed_at":"2026-08-15T22:50:04.430239Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.06145","last_updated":"2025-05-09T15:54:08Z","snapshot_observed_at":"2026-08-15T22:45:06.781302Z","submitted_at":"2025-05-09T15:54:08Z","title":"Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T22:50:04.430239Z"},"links":{"cited_paper":"/paper/2504.04032","citing_paper":"/paper/2505.06145"},"observation_digest":"sha256:7958093329f23e558871ea3c2b35d191ce1757508de6ef7f6fb06e1514fe0c35","observation_id":"af8e2e52-f3dc-4401-9d45-ef1dfd85a38b","resolution":{"observed_at":"2026-08-15T22:50:04.430239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04032","last_updated":"2025-04-05T02:55:44Z","snapshot_observed_at":"2026-08-16T12:43:48.091567Z","submitted_at":"2025-04-05T02:55:44Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","version":1},"cited_work":{"arxiv_id":"2504.04032","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.04032","snapshot_observed_at":"2026-08-15T22:04:24.976579Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining","venue":"cs.LG","work_id":"5ead6015-ca57-4e79-82dc-401df156354f","year":2025},"citing_paper":{"arxiv_id":"2505.08220","last_updated":"2025-05-19T02:18:49Z","snapshot_observed_at":"2026-08-16T13:49:05.945939Z","submitted_at":"2025-05-13T04:32:21Z","title":"Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:24.761836Z"},"links":{"cited_paper":"/paper/2504.04032","citing_paper":"/paper/2505.08220"},"observation_digest":"sha256:30fc675c354015735a0336c3409c9293f31ecb0c2b48a8c3ef50f4826ab35596","observation_id":"36173cac-d91e-4667-b7f0-769aea266f37","resolution":{"observed_at":"2026-08-15T22:04:24.987101Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.04032/citation-record","integrity":"/paper/2504.04032/integrity","json":"/paper/2504.04032/citation-record.json","paper":"/paper/2504.04032"},"outbound":[],"paper":{"arxiv_id":"2504.04032","last_updated":"2025-04-05T02:55:44Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T12:43:48.091567Z","submitted_at":"2025-04-05T02:55:44Z","title":"Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2504.04032."}