{"as_of":"2026-08-09T22:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:327f7a744438196073c9a097a79159e42aaf5de6b088b218321f071093852a34","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T14:06:48.863915Z","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":"2026-06-30T22:05:05.671036Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.01085","last_updated":"2022-03-18T07:21:44Z","snapshot_observed_at":"2026-08-09T17:41:10.534807Z","submitted_at":"2021-06-02T11:39:25Z","title":"Online Coreset Selection for Rehearsal-based Continual Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01085","snapshot_observed_at":"2026-08-07T14:06:48.863915Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.20135","last_updated":"2025-05-28T16:33:14Z","snapshot_observed_at":"2026-08-08T23:51:54.824994Z","submitted_at":"2025-05-26T15:37:10Z","title":"Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:06:48.863915Z"},"links":{"cited_paper":"/paper/2106.01085","citing_paper":"/paper/2505.20135"},"observation_digest":"sha256:ae31d8da8d3f2937a8a47cb953b1e362a7d68358b2d05ebdbb6053e5809e7f02","observation_id":"9ce17eac-2fb6-481e-95d2-a82e85c84fb5","resolution":{"observed_at":"2026-08-07T14:06:48.863915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01085","last_updated":"2022-03-18T07:21:44Z","snapshot_observed_at":"2026-08-09T17:41:10.534807Z","submitted_at":"2021-06-02T11:39:25Z","title":"Online Coreset Selection for Rehearsal-based Continual Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01085","snapshot_observed_at":"2026-08-07T04:55:52.623988Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.09347","last_updated":"2025-06-11T02:54:29Z","snapshot_observed_at":"2026-08-09T08:18:38.254742Z","submitted_at":"2025-06-11T02:54:29Z","title":"ErrorEraser: Unlearning Data Bias for Improved Continual Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T04:55:52.623988Z"},"links":{"cited_paper":"/paper/2106.01085","citing_paper":"/paper/2506.09347"},"observation_digest":"sha256:32f58bd72b782fbe3e86fed46485cfc34e5d8f7febd32c31cbad091d71086b47","observation_id":"a3d1403a-4100-4083-8a74-f78e5ca80e52","resolution":{"observed_at":"2026-08-07T04:55:52.623988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01085","last_updated":"2022-03-18T07:21:44Z","snapshot_observed_at":"2026-08-09T17:41:10.534807Z","submitted_at":"2021-06-02T11:39:25Z","title":"Online Coreset Selection for Rehearsal-based Continual Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01085","snapshot_observed_at":"2026-08-04T20:52:17.085382Z","title":"Online core- set selection for rehearsal-based continual learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08300","last_updated":"2025-09-10T05:39:49Z","snapshot_observed_at":"2026-08-04T20:52:16.755519Z","submitted_at":"2025-09-10T05:39:49Z","title":"\\emph{FoQuS}: A Forgetting-Quality Coreset Selection Framework for Automatic Modulation Recognition","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T20:52:17.085382Z"},"links":{"cited_paper":"/paper/2106.01085","citing_paper":"/paper/2509.08300"},"observation_digest":"sha256:dd6bad9d4e093f7f5c50a60af0fd76decd86cc3a737a1daedec524a05741c954","observation_id":"4de10110-13f2-4611-8ad9-686e2bc499f8","resolution":{"observed_at":"2026-08-04T20:52:17.085382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01085","last_updated":"2022-03-18T07:21:44Z","snapshot_observed_at":"2026-08-09T17:41:10.534807Z","submitted_at":"2021-06-02T11:39:25Z","title":"Online Coreset Selection for Rehearsal-based Continual Learning","version":4},"cited_work":{"arxiv_id":"2106.01085","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01085","snapshot_observed_at":"2026-06-30T22:05:05.671036Z","title":"Online core- set selection for rehearsal-based continual learning","venue":null,"work_id":"c2861389-0e74-4cd5-b77f-93426502812f","year":2021},"citing_paper":{"arxiv_id":"2604.17396","last_updated":"2026-04-19T11:59:58Z","snapshot_observed_at":"2026-08-01T18:23:32.699442Z","submitted_at":"2026-04-19T11:59:58Z","title":"Representation-Guided Parameter-Efficient LLM Unlearning","version":1},"reference_index":138,"source":"arxiv_source","source_observed_at":"2026-05-10T06:01:46.885030Z"},"links":{"cited_paper":"/paper/2106.01085","citing_paper":"/paper/2604.17396"},"observation_digest":"sha256:a072470518786c246881e4d3b45f5b1021ada8f4966fe525fed755418706378e","observation_id":"bbd89b62-57ff-4fc8-9d30-3873a276d5e1","resolution":{"observed_at":"2026-05-10T06:06:19.199522Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2106.01085","last_updated":"2022-03-18T07:21:44Z","snapshot_observed_at":"2026-08-09T17:41:10.534807Z","submitted_at":"2021-06-02T11:39:25Z","title":"Online Coreset Selection for Rehearsal-based Continual Learning","version":4},"cited_work":{"arxiv_id":"2106.01085","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01085","snapshot_observed_at":"2026-06-30T22:05:05.671036Z","title":"Online core- set selection for rehearsal-based continual learning","venue":null,"work_id":"c2861389-0e74-4cd5-b77f-93426502812f","year":2021},"citing_paper":{"arxiv_id":"2605.17367","last_updated":"2026-05-17T10:20:57Z","snapshot_observed_at":"2026-07-31T08:53:30.655257Z","submitted_at":"2026-05-17T10:20:57Z","title":"Bridging Data Trials and Task Barriers: A Unified Framework for Sketch Biometric Identification","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-20T13:27:30.002289Z"},"links":{"cited_paper":"/paper/2106.01085","citing_paper":"/paper/2605.17367"},"observation_digest":"sha256:6f689bb4f5644d29d7f948ae361e00035f64166f299443c275d826c61006e84c","observation_id":"a996074f-41c9-48b9-8365-b3369bc015e1","resolution":{"observed_at":"2026-05-20T13:28:18.892566Z","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":"2106.01085","last_updated":"2022-03-18T07:21:44Z","snapshot_observed_at":"2026-08-09T17:41:10.534807Z","submitted_at":"2021-06-02T11:39:25Z","title":"Online Coreset Selection for Rehearsal-based Continual Learning","version":4},"cited_work":{"arxiv_id":"2106.01085","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01085","snapshot_observed_at":"2026-06-30T22:05:05.671036Z","title":"Online core- set selection for rehearsal-based continual learning","venue":null,"work_id":"c2861389-0e74-4cd5-b77f-93426502812f","year":2021},"citing_paper":{"arxiv_id":"2605.23969","last_updated":"2026-05-13T06:36:24Z","snapshot_observed_at":"2026-08-09T04:48:40.503387Z","submitted_at":"2026-05-13T06:36:24Z","title":"SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-30T22:02:30.217607Z"},"links":{"cited_paper":"/paper/2106.01085","citing_paper":"/paper/2605.23969"},"observation_digest":"sha256:0faf78f3da702cbce070581b7d30fe04a02e1a5e2defbfe25dccd37e9026fb6e","observation_id":"361a7f57-2758-4c76-a906-b988204f6e89","resolution":{"observed_at":"2026-06-30T22:05:05.673118Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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/2106.01085/citation-record","integrity":"/paper/2106.01085/integrity","json":"/paper/2106.01085/citation-record.json","paper":"/paper/2106.01085"},"outbound":[],"paper":{"arxiv_id":"2106.01085","last_updated":"2022-03-18T07:21:44Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T17:41:10.534807Z","submitted_at":"2021-06-02T11:39:25Z","title":"Online Coreset Selection for Rehearsal-based Continual Learning"},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.01085."}