{"as_of":"2026-08-11T21:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4ea703c38df0c396ce350f35badd88296735b51bd3c4f4ac2125f4da4eb14fa2","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":"2026-08-11T06:34:44.6726+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":"2026-08-05T22:01:59.299433Z","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":"2026-07-04T04:39:34.278714Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.12809","last_updated":"2024-02-03T07:28:51Z","snapshot_observed_at":"2026-07-06T15:30:30.381545Z","submitted_at":"2023-05-22T08:10:43Z","title":"Relabeling Minimal Training Subset to Flip a Prediction","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12809","snapshot_observed_at":"2026-08-05T22:01:59.299433Z","title":"Relabeling minimal training subset to flip a prediction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.07713","last_updated":"2025-08-11T07:39:20Z","snapshot_observed_at":"2026-08-05T22:01:47.612025Z","submitted_at":"2025-08-11T07:39:20Z","title":"Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-05T22:01:59.299433Z"},"links":{"cited_paper":"/paper/2305.12809","citing_paper":"/paper/2508.07713"},"observation_digest":"sha256:10b08dd9fdf3ea71e934efb9623d0d8fa2289c53fa3a99cd0842e0a052d86b3b","observation_id":"d644869c-5761-4248-a17b-3c9f0c044b98","resolution":{"observed_at":"2026-08-05T22:01:59.299433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12809","last_updated":"2024-02-03T07:28:51Z","snapshot_observed_at":"2026-07-06T15:30:30.381545Z","submitted_at":"2023-05-22T08:10:43Z","title":"Relabeling Minimal Training Subset to Flip a Prediction","version":4},"cited_work":{"arxiv_id":"2305.12809","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.12809","snapshot_observed_at":"2026-07-04T04:39:34.278714Z","title":"arXiv preprint arXiv:2305.12809 , year=","venue":null,"work_id":"99f591ee-63c4-4478-962d-a4abd7c48044","year":null},"citing_paper":{"arxiv_id":"2606.19998","last_updated":"2026-06-18T09:34:22Z","snapshot_observed_at":"2026-08-06T01:11:24.313445Z","submitted_at":"2026-06-18T09:34:22Z","title":"Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory","version":1},"reference_index":284,"source":"arxiv_source","source_observed_at":"2026-06-26T16:53:53.441274Z"},"links":{"cited_paper":"/paper/2305.12809","citing_paper":"/paper/2606.19998"},"observation_digest":"sha256:6ec6bd4a20310a042dea83bad063303d4d7dd0ad89558b6fd2718e913bd61d75","observation_id":"1103d686-4562-41c7-a0eb-cd64e8fadb62","resolution":{"observed_at":"2026-07-04T04:39:34.280807Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.12809/citation-record","integrity":"/paper/2305.12809/integrity","json":"/paper/2305.12809/citation-record.json","paper":"/paper/2305.12809"},"outbound":[],"paper":{"arxiv_id":"2305.12809","last_updated":"2024-02-03T07:28:51Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T15:30:30.381545Z","submitted_at":"2023-05-22T08:10:43Z","title":"Relabeling Minimal Training Subset to Flip a Prediction"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.12809."}