{"as_of":"2026-08-07T18:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b4ed2e4c380dd21f89297bc5717df43a8b578fcaf2562a6df31bf8407101c911","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:02:25.181481Z","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":"2026-07-02T20:57:23.218534Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.06400","last_updated":"2023-11-10T21:22:22Z","snapshot_observed_at":"2026-07-06T16:45:55.261144Z","submitted_at":"2023-11-10T21:22:22Z","title":"EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.06400","snapshot_observed_at":"2026-08-06T22:02:25.181481Z","title":"& Chen, Q","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01055","last_updated":"2025-06-28T03:06:25Z","snapshot_observed_at":"2026-08-07T10:42:26.273444Z","submitted_at":"2025-06-28T03:06:25Z","title":"Prompt Mechanisms in Medical Imaging: A Comprehensive Survey","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:25.181481Z"},"links":{"cited_paper":"/paper/2311.06400","citing_paper":"/paper/2507.01055"},"observation_digest":"sha256:b49f1eed3d93275ec0eafd3a2d88f8cfa0924184a01b83d8d2fc5da8a39bf0f5","observation_id":"417b3318-3893-45b9-82e6-2221afb05710","resolution":{"observed_at":"2026-08-06T22:02:25.181481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06400","last_updated":"2023-11-10T21:22:22Z","snapshot_observed_at":"2026-07-06T16:45:55.261144Z","submitted_at":"2023-11-10T21:22:22Z","title":"EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images","version":1},"cited_work":{"arxiv_id":"2311.06400","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.06400","snapshot_observed_at":"2026-07-02T20:57:23.218534Z","title":"arXiv preprint arXiv:2311.06400 , year=","venue":null,"work_id":"c0ff1ce2-4b11-4393-bb6b-7f4ce9545d02","year":2023},"citing_paper":{"arxiv_id":"2606.07953","last_updated":"2026-06-06T03:06:10Z","snapshot_observed_at":"2026-07-31T12:59:23.300619Z","submitted_at":"2026-06-06T03:06:10Z","title":"Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-27T20:07:49.478014Z"},"links":{"cited_paper":"/paper/2311.06400","citing_paper":"/paper/2606.07953"},"observation_digest":"sha256:5b728dafaecd441ac2932791a1a8d3ec67297a6513b82b0bfbf031e36f60171f","observation_id":"86ceae1e-9898-44a5-8402-dd0903b25b12","resolution":{"observed_at":"2026-07-02T20:47:23.222561Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06400","last_updated":"2023-11-10T21:22:22Z","snapshot_observed_at":"2026-07-06T16:45:55.261144Z","submitted_at":"2023-11-10T21:22:22Z","title":"EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images","version":1},"cited_work":{"arxiv_id":"2311.06400","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.06400","snapshot_observed_at":"2026-07-02T20:57:23.218534Z","title":"arXiv preprint arXiv:2311.06400 , year=","venue":null,"work_id":"c0ff1ce2-4b11-4393-bb6b-7f4ce9545d02","year":2023},"citing_paper":{"arxiv_id":"2606.07965","last_updated":"2026-06-06T03:48:12Z","snapshot_observed_at":"2026-07-06T23:47:33.680573Z","submitted_at":"2026-06-06T03:48:12Z","title":"Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-06-27T20:03:13.515338Z"},"links":{"cited_paper":"/paper/2311.06400","citing_paper":"/paper/2606.07965"},"observation_digest":"sha256:6a82bd0cdbf24863fa1265d54727a91fbe9febef9ae0ca2441a0c7ae3e525195","observation_id":"5f661ad4-42bd-47e9-82a4-7758a4d37bd1","resolution":{"observed_at":"2026-07-02T20:57:23.219855Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2311.06400/citation-record","integrity":"/paper/2311.06400/integrity","json":"/paper/2311.06400/citation-record.json","paper":"/paper/2311.06400"},"outbound":[],"paper":{"arxiv_id":"2311.06400","last_updated":"2023-11-10T21:22:22Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T16:45:55.261144Z","submitted_at":"2023-11-10T21:22:22Z","title":"EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2311.06400."}