{"as_of":"2026-08-10T02:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6daf0ce4c147655599a93ce763e31b645545497e3aef6ce72ad39496c1253746","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T23:12:11.283075Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T23:14:01.243262Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.04787","last_updated":"2025-05-09T05:22:21Z","snapshot_observed_at":"2026-08-07T15:49:00.720233Z","submitted_at":"2025-05-07T20:29:31Z","title":"Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay","version":2},"cited_work":{"arxiv_id":"2505.04787","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.04787","snapshot_observed_at":"2026-06-29T23:14:01.243262Z","title":"Mandalika, H","venue":null,"work_id":"148f82c5-8972-4b00-af00-21469e7347ff","year":2025},"citing_paper":{"arxiv_id":"2604.16207","last_updated":"2026-04-25T13:46:00Z","snapshot_observed_at":"2026-07-06T23:03:34.795570Z","submitted_at":"2026-04-17T16:17:12Z","title":"AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T08:53:13.455361Z"},"links":{"cited_paper":"/paper/2505.04787","citing_paper":"/paper/2604.16207"},"observation_digest":"sha256:6e3fae6fb3d69d9e4953fa3d0a0ae090505552d5500a0d00a3b14663b65a67e0","observation_id":"ff639f10-ff7b-4449-bcfc-f4e295399ec9","resolution":{"observed_at":"2026-05-10T08:58:12.957873Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.04787","last_updated":"2025-05-09T05:22:21Z","snapshot_observed_at":"2026-08-07T15:49:00.720233Z","submitted_at":"2025-05-07T20:29:31Z","title":"Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay","version":2},"cited_work":{"arxiv_id":"2505.04787","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.04787","snapshot_observed_at":"2026-06-29T23:14:01.243262Z","title":"Mandalika, H","venue":null,"work_id":"148f82c5-8972-4b00-af00-21469e7347ff","year":2025},"citing_paper":{"arxiv_id":"2605.25708","last_updated":"2026-05-25T11:09:48Z","snapshot_observed_at":"2026-08-03T09:57:52.313247Z","submitted_at":"2026-05-25T11:09:48Z","title":"CMAP: Cross-Modal Adaptive Prompting for Multi-Domain Task-Incremental Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T23:12:11.283075Z"},"links":{"cited_paper":"/paper/2505.04787","citing_paper":"/paper/2605.25708"},"observation_digest":"sha256:1a7d3ac43b35af23f66eee1bcb7d3578cf61fa29b0e4b218e3bb0ceb42d25499","observation_id":"52d706d3-4c3e-4a4f-86ac-5ff8457770bf","resolution":{"observed_at":"2026-06-29T23:14:01.244995Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.04787/citation-record","integrity":"/paper/2505.04787/integrity","json":"/paper/2505.04787/citation-record.json","paper":"/paper/2505.04787"},"outbound":[],"paper":{"arxiv_id":"2505.04787","last_updated":"2025-05-09T05:22:21Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T15:49:00.720233Z","submitted_at":"2025-05-07T20:29:31Z","title":"Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2505.04787."}