{"as_of":"2026-08-07T16:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c5d6f30c9307d21bb83989a85d206857f49ce97ab179243a32bbb8b021782cc3","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:09:21.384318Z","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":"2026-05-24T05:13:57.205372Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08518","last_updated":"2023-12-16T06:50:09Z","snapshot_observed_at":"2026-07-06T15:03:38.819154Z","submitted_at":"2023-03-15T10:53:49Z","title":"UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation","version":4},"cited_work":{"arxiv_id":"2303.08518","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.08518","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Uprise: Universal prompt retrieval for improving zero-shot evaluation","venue":null,"work_id":"d19f7028-ce03-4dcc-a4f3-6c335d6afc84","year":2023},"citing_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T05:10:25.171044Z"},"links":{"cited_paper":"/paper/2303.08518","citing_paper":"/paper/2312.10997"},"observation_digest":"sha256:1294c5a238cb619d0fd33b567d7317551f6e7494218f07ead01e9fcbf390cd8d","observation_id":"1b2d6bc1-39d7-4e4a-acb7-80c8ba10a402","resolution":{"observed_at":"2026-05-24T05:13:57.208668Z","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":"2303.08518","last_updated":"2023-12-16T06:50:09Z","snapshot_observed_at":"2026-07-06T15:03:38.819154Z","submitted_at":"2023-03-15T10:53:49Z","title":"UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08518","snapshot_observed_at":"2026-08-07T14:09:21.384318Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19966","last_updated":"2025-05-26T13:26:56Z","snapshot_observed_at":"2026-08-07T14:00:23.814326Z","submitted_at":"2025-05-26T13:26:56Z","title":"Learning to Select In-Context Demonstration Preferred by Large Language Model","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T14:09:21.384318Z"},"links":{"cited_paper":"/paper/2303.08518","citing_paper":"/paper/2505.19966"},"observation_digest":"sha256:6439ed86318767a9e4efbc0af79ec2463e3d3be08e406aab0d86e557c93221bc","observation_id":"a8d708c7-942b-40ee-ae3e-c18ab61dfaf4","resolution":{"observed_at":"2026-08-07T14:09:21.384318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08518","last_updated":"2023-12-16T06:50:09Z","snapshot_observed_at":"2026-07-06T15:03:38.819154Z","submitted_at":"2023-03-15T10:53:49Z","title":"UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08518","snapshot_observed_at":"2026-08-07T11:17:39.040515Z","title":"Uprise: Universal prompt retrieval for improving zero-shot evaluation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.03100","last_updated":"2025-06-09T10:35:22Z","snapshot_observed_at":"2026-08-07T11:06:27.053756Z","submitted_at":"2025-06-03T17:31:53Z","title":"Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T11:17:39.040515Z"},"links":{"cited_paper":"/paper/2303.08518","citing_paper":"/paper/2506.03100"},"observation_digest":"sha256:42490d3a5a3bbff659ccda0436856135f402b4159dd5aaea1ae28fb549d98250","observation_id":"0c486fa1-47fe-41f7-b8d4-bcf6a3a86513","resolution":{"observed_at":"2026-08-07T11:17:39.040515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08518","last_updated":"2023-12-16T06:50:09Z","snapshot_observed_at":"2026-07-06T15:03:38.819154Z","submitted_at":"2023-03-15T10:53:49Z","title":"UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation","version":4},"cited_work":{"arxiv_id":"2303.08518","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.08518","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Uprise: Universal prompt retrieval for improving zero-shot evaluation","venue":null,"work_id":"d19f7028-ce03-4dcc-a4f3-6c335d6afc84","year":2023},"citing_paper":{"arxiv_id":"2506.20670","last_updated":"2025-06-25T17:59:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-25T17:59:42Z","title":"MMSearch-R1: Incentivizing LMMs to Search","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-16T15:27:04.228144Z"},"links":{"cited_paper":"/paper/2303.08518","citing_paper":"/paper/2506.20670"},"observation_digest":"sha256:b0b394db12e21c8b72a2d68a3e49316f23f84091cbacc6958d9106c6f0fa0101","observation_id":"fbc4531d-c318-4367-899c-a4fa4613917c","resolution":{"observed_at":"2026-05-16T15:27:04.337424Z","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":"2303.08518","last_updated":"2023-12-16T06:50:09Z","snapshot_observed_at":"2026-07-06T15:03:38.819154Z","submitted_at":"2023-03-15T10:53:49Z","title":"UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08518","snapshot_observed_at":"2026-08-06T20:03:44.974947Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05288","last_updated":"2025-07-05T09:52:21Z","snapshot_observed_at":"2026-08-06T19:55:51.769745Z","submitted_at":"2025-07-05T09:52:21Z","title":"A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T20:03:44.974947Z"},"links":{"cited_paper":"/paper/2303.08518","citing_paper":"/paper/2507.05288"},"observation_digest":"sha256:e0a1215153cd0a6418c3d0c85d35fff8564dc8193ac284f650854d30c75e68c1","observation_id":"45bb491b-8ec1-45b8-b655-4f781d3a52db","resolution":{"observed_at":"2026-08-06T20:03:44.974947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2303.08518/citation-record","integrity":"/paper/2303.08518/integrity","json":"/paper/2303.08518/citation-record.json","paper":"/paper/2303.08518"},"outbound":[],"paper":{"arxiv_id":"2303.08518","last_updated":"2023-12-16T06:50:09Z","latest_version":4,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T15:03:38.819154Z","submitted_at":"2023-03-15T10:53:49Z","title":"UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation"},"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 5 inbound Pith citation observations for arXiv:2303.08518."}