{"as_of":"2026-08-18T15:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b27baf4bab73e62c701123b15fa9f14bbc5fd25c22e628a5b6ce544096142651","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":"2026-08-18T06:34:40.430872+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":"2026-08-16T11:41:52.878541Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T16:42:43.121218Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.02460","last_updated":"2025-05-31T12:13:46Z","snapshot_observed_at":"2026-08-18T13:04:12.419488Z","submitted_at":"2025-01-05T07:03:14Z","title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.02460","snapshot_observed_at":"2026-08-16T11:41:52.878541Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.14917","last_updated":"2025-04-21T07:35:24Z","snapshot_observed_at":"2026-08-18T13:06:18.133674Z","submitted_at":"2025-04-21T07:35:24Z","title":"POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applications","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-16T11:41:52.878541Z"},"links":{"cited_paper":"/paper/2501.02460","citing_paper":"/paper/2504.14917"},"observation_digest":"sha256:fd71afadad713cfa426bdf9b04f2fd9729b1f53cd9dc28bce403e30d445caa6e","observation_id":"0c98647c-cbd7-46ca-b150-0a20222fab6c","resolution":{"observed_at":"2026-08-16T11:41:52.878541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.02460","last_updated":"2025-05-31T12:13:46Z","snapshot_observed_at":"2026-08-18T13:04:12.419488Z","submitted_at":"2025-01-05T07:03:14Z","title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","version":3},"cited_work":{"arxiv_id":"2501.02460","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.02460","snapshot_observed_at":"2026-08-06T16:42:43.121218Z","title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","venue":"cs.CL","work_id":"6bc39437-3778-4235-8356-0de9a93b36db","year":2025},"citing_paper":{"arxiv_id":"2507.12774","last_updated":"2025-07-17T04:31:55Z","snapshot_observed_at":"2026-08-14T01:04:48.483410Z","submitted_at":"2025-07-17T04:31:55Z","title":"A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T16:42:22.955677Z"},"links":{"cited_paper":"/paper/2501.02460","citing_paper":"/paper/2507.12774"},"observation_digest":"sha256:9a8a79928e4c640b3834fc0d31486d7cfb7402f6e2a662ea53f41404df11bb6b","observation_id":"cad576ed-a261-4e72-9545-3c08f6bdcff9","resolution":{"observed_at":"2026-08-06T16:42:43.124696Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.02460","last_updated":"2025-05-31T12:13:46Z","snapshot_observed_at":"2026-08-18T13:04:12.419488Z","submitted_at":"2025-01-05T07:03:14Z","title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.02460","snapshot_observed_at":"2026-08-15T17:54:29.137189Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.20059","last_updated":"2025-07-26T20:57:24Z","snapshot_observed_at":"2026-08-17T19:08:50.793711Z","submitted_at":"2025-07-26T20:57:24Z","title":"RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T17:54:29.137189Z"},"links":{"cited_paper":"/paper/2501.02460","citing_paper":"/paper/2507.20059"},"observation_digest":"sha256:970d0e17ea766dd3685e1698808b6767c541e4bff63dd7c5f96e9ef4296a39de","observation_id":"f4191ccf-a891-4289-8ebd-a5be5dd8f057","resolution":{"observed_at":"2026-08-15T17:54:29.137189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2501.02460/citation-record","integrity":"/paper/2501.02460/integrity","json":"/paper/2501.02460/citation-record.json","paper":"/paper/2501.02460"},"outbound":[],"paper":{"arxiv_id":"2501.02460","last_updated":"2025-05-31T12:13:46Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-18T13:04:12.419488Z","submitted_at":"2025-01-05T07:03:14Z","title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2501.02460."}