{"as_of":"2026-08-07T19:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d3dea17d8387169abaeef9aa3adacf71b371d6fcdcc6d27bcf00f34c65420ad6","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:44:46.618587Z","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":"2026-05-19T14:42:37.660302Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.02469","last_updated":"2023-05-04T00:22:49Z","snapshot_observed_at":"2026-08-04T09:46:04.755475Z","submitted_at":"2023-05-04T00:22:49Z","title":"The System Model and the User Model: Exploring AI Dashboard Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02469","snapshot_observed_at":"2026-08-06T12:44:46.618587Z","title":"The system model and the user model: Exploring ai dashboard design","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T00:56:09.000858Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.618587Z"},"links":{"cited_paper":"/paper/2305.02469","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:de19633b86f9c6ff1655a3971c04fa85b72c3a87fba5a4448bd4af5fbf0c7926","observation_id":"079575da-6efd-4e19-9194-e20e2339800d","resolution":{"observed_at":"2026-08-06T12:44:46.618587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02469","last_updated":"2023-05-04T00:22:49Z","snapshot_observed_at":"2026-08-04T09:46:04.755475Z","submitted_at":"2023-05-04T00:22:49Z","title":"The System Model and the User Model: Exploring AI Dashboard Design","version":1},"cited_work":{"arxiv_id":"2305.02469","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.02469","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"14f66a36-8a4e-484e-a2fe-9386d2894bb2","year":2023},"citing_paper":{"arxiv_id":"2605.15455","last_updated":"2026-05-14T22:37:56Z","snapshot_observed_at":"2026-08-02T22:51:11.741087Z","submitted_at":"2026-05-14T22:37:56Z","title":"Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-19T14:37:45.304949Z"},"links":{"cited_paper":"/paper/2305.02469","citing_paper":"/paper/2605.15455"},"observation_digest":"sha256:7b3acaf7e0117b113027764f1e83eef346e5c72de4eab274e6b9c07206dbbaee","observation_id":"913d9bfb-e68c-4461-94cb-f87ef33c34b2","resolution":{"observed_at":"2026-05-19T14:42:37.664125Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02469","last_updated":"2023-05-04T00:22:49Z","snapshot_observed_at":"2026-08-04T09:46:04.755475Z","submitted_at":"2023-05-04T00:22:49Z","title":"The System Model and the User Model: Exploring AI Dashboard Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02469","snapshot_observed_at":"2026-07-14T15:55:25.318583Z","title":"arXiv preprint arXiv:2305.02469 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09776","last_updated":"2026-07-15T15:49:55Z","snapshot_observed_at":"2026-08-02T10:22:56.853033Z","submitted_at":"2026-07-08T03:03:38Z","title":"HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-07-14T15:55:25.318583Z"},"links":{"cited_paper":"/paper/2305.02469","citing_paper":"/paper/2607.09776"},"observation_digest":"sha256:a2053b83f322dc7ca1e86cd7d8022e2ff1713875251c2617d233cb7d67bbbc74","observation_id":"71ed08d2-0e12-4398-85c0-0ebae347bc25","resolution":{"observed_at":"2026-07-14T15:55:25.318583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02469","last_updated":"2023-05-04T00:22:49Z","snapshot_observed_at":"2026-08-04T09:46:04.755475Z","submitted_at":"2023-05-04T00:22:49Z","title":"The System Model and the User Model: Exploring AI Dashboard Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02469","snapshot_observed_at":"2026-08-02T08:13:05.335753Z","title":"arXiv preprint arXiv:2305.02469 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09776","last_updated":"2026-07-15T15:49:55Z","snapshot_observed_at":"2026-08-02T10:22:56.853033Z","submitted_at":"2026-07-08T03:03:38Z","title":"HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-02T08:13:05.335753Z"},"links":{"cited_paper":"/paper/2305.02469","citing_paper":"/paper/2607.09776"},"observation_digest":"sha256:53b0ee7a9190d74a9fdb1bffbe99f0329a43008c13338b106bcc8cc8e5715fa2","observation_id":"366007c0-3a8e-4db9-8a1e-07085679b003","resolution":{"observed_at":"2026-08-02T08:13:05.335753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2305.02469/citation-record","integrity":"/paper/2305.02469/integrity","json":"/paper/2305.02469/citation-record.json","paper":"/paper/2305.02469"},"outbound":[],"paper":{"arxiv_id":"2305.02469","last_updated":"2023-05-04T00:22:49Z","latest_version":1,"primary_category":"cs.HC","snapshot_observed_at":"2026-08-04T09:46:04.755475Z","submitted_at":"2023-05-04T00:22:49Z","title":"The System Model and the User Model: Exploring AI Dashboard Design"},"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 4 inbound Pith citation observations for arXiv:2305.02469."}