{"as_of":"2026-08-10T10:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a216bbaa7abe4072f1abea18b5f9460db124b510e21abc62c36a5c01043e9ff","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T16:19:07.357028Z","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":"2026-05-22T13:54:53.072448Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.18443","last_updated":"2024-10-04T03:25:34Z","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T05:40:08Z","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18443","snapshot_observed_at":"2026-08-08T16:19:07.357028Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06252","last_updated":"2025-04-08T10:32:20Z","snapshot_observed_at":"2026-08-10T06:59:04.265529Z","submitted_at":"2025-02-10T08:33:47Z","title":"CliniQ: A Multi-faceted Benchmark for Electronic Health Record Retrieval with Semantic Match Assessment","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-08T16:19:07.357028Z"},"links":{"cited_paper":"/paper/2404.18443","citing_paper":"/paper/2502.06252"},"observation_digest":"sha256:632e7c77fffaf99de9b4597da17d4a6fea1baf1a75a9f1768a458c598838c16e","observation_id":"1f91c4f3-1ac9-4036-a000-3af911776af9","resolution":{"observed_at":"2026-08-08T16:19:07.357028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18443","last_updated":"2024-10-04T03:25:34Z","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T05:40:08Z","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","version":2},"cited_work":{"arxiv_id":"2404.18443","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.18443","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May D Wang, Joyce C Ho, Chao Zhang, and Carl Yang","venue":null,"work_id":"c1e2149a-925b-49d4-99ac-a970d1770b3c","year":2024},"citing_paper":{"arxiv_id":"2505.14558","last_updated":"2026-04-03T16:04:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-20T16:15:30Z","title":"R2MED: A Benchmark for Reasoning-Driven Medical Retrieval","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-22T13:52:15.835379Z"},"links":{"cited_paper":"/paper/2404.18443","citing_paper":"/paper/2505.14558"},"observation_digest":"sha256:e66ec8c951de1c2741ad00e245ef0228bbca9ea1a15f80ed792e40a48d1335bd","observation_id":"035af879-247c-4fbb-aa6f-767496230781","resolution":{"observed_at":"2026-05-22T13:54:53.075048Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18443","last_updated":"2024-10-04T03:25:34Z","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T05:40:08Z","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18443","snapshot_observed_at":"2026-08-05T19:49:52.531389Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11779","last_updated":"2025-08-15T19:05:57Z","snapshot_observed_at":"2026-08-08T17:09:38.233329Z","submitted_at":"2025-08-15T19:05:57Z","title":"A Multi-Task Evaluation of LLMs' Processing of Academic Text Input","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T19:49:52.531389Z"},"links":{"cited_paper":"/paper/2404.18443","citing_paper":"/paper/2508.11779"},"observation_digest":"sha256:67c1a1399a469e78184aeb68f1f01b47573f28f3845b17ab5d0511b27587cffc","observation_id":"c07c9bdb-7766-4c8f-a89a-db01bd67f5b1","resolution":{"observed_at":"2026-08-05T19:49:52.531389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18443","last_updated":"2024-10-04T03:25:34Z","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T05:40:08Z","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18443","snapshot_observed_at":"2026-08-05T17:44:13.170068Z","title":"Ho, Chao Zhang, and Carl Yang","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15750","last_updated":"2025-08-28T19:15:55Z","snapshot_observed_at":"2026-08-08T02:14:03.268393Z","submitted_at":"2025-08-21T17:49:16Z","title":"Active Learning for Neurosymbolic Program Synthesis","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T17:44:13.170068Z"},"links":{"cited_paper":"/paper/2404.18443","citing_paper":"/paper/2508.15750"},"observation_digest":"sha256:b2b3e52aaf08170032fff87ab0dd467b4800eefbbeb7a6865411e68606eaaff9","observation_id":"996366ae-6382-4f80-9db8-bd9d4c9b7662","resolution":{"observed_at":"2026-08-05T17:44:13.170068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18443","last_updated":"2024-10-04T03:25:34Z","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T05:40:08Z","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18443","snapshot_observed_at":"2026-08-04T16:33:16.136080Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.15234","last_updated":"2026-06-01T07:20:14Z","snapshot_observed_at":"2026-08-08T07:09:49.009478Z","submitted_at":"2025-09-17T09:44:59Z","title":"Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T16:33:16.136080Z"},"links":{"cited_paper":"/paper/2404.18443","citing_paper":"/paper/2509.15234"},"observation_digest":"sha256:f654526eb3b48c87f88b20b0144efeef85ccdc287d8e705e4714b19f305b93c8","observation_id":"d5c9709c-47f3-4954-b0fe-718707e715e5","resolution":{"observed_at":"2026-08-04T16:33:16.136080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18443","last_updated":"2024-10-04T03:25:34Z","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T05:40:08Z","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","version":2},"cited_work":{"arxiv_id":"2404.18443","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.18443","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May D Wang, Joyce C Ho, Chao Zhang, and Carl Yang","venue":null,"work_id":"c1e2149a-925b-49d4-99ac-a970d1770b3c","year":2024},"citing_paper":{"arxiv_id":"2604.18146","last_updated":"2026-04-21T07:16:41Z","snapshot_observed_at":"2026-07-06T23:05:04.279268Z","submitted_at":"2026-04-20T12:08:58Z","title":"Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-10T04:09:10.125285Z"},"links":{"cited_paper":"/paper/2404.18443","citing_paper":"/paper/2604.18146"},"observation_digest":"sha256:bb316e4ca93e4c631fe0d5d1d073a89f6c6183790bad223b5640a31a77e9a3d8","observation_id":"6b6dbb95-a47d-4254-b5ea-702d45696ab4","resolution":{"observed_at":"2026-05-11T12:06:04.888724Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2404.18443/citation-record","integrity":"/paper/2404.18443/integrity","json":"/paper/2404.18443/citation-record.json","paper":"/paper/2404.18443"},"outbound":[],"paper":{"arxiv_id":"2404.18443","last_updated":"2024-10-04T03:25:34Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T05:40:08Z","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.18443."}