{"as_of":"2026-08-05T14:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:685c078cbc73953cfc013f5893d5fc84ecbce4fd66707c3d073138025c9089f0","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":"2026-08-05T06:32:48.257954+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":"2026-08-04T00:00:00.169975Z","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":"2026-07-03T18:08:45.686200Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.18421","last_updated":"2024-03-27T10:18:21Z","snapshot_observed_at":"2026-07-06T17:51:48.378864Z","submitted_at":"2024-03-27T10:18:21Z","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","version":1},"cited_work":{"arxiv_id":"2403.18421","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.18421","snapshot_observed_at":"2026-07-03T18:08:45.686200Z","title":"Biomedlm: A 2.7 b parameter language model trained on biomedical text","venue":null,"work_id":"eacb5aff-ebda-464d-a6fb-b9414866fd51","year":2024},"citing_paper":{"arxiv_id":"2503.08223","last_updated":"2026-04-09T15:28:27Z","snapshot_observed_at":"2026-07-06T20:50:33.513259Z","submitted_at":"2025-03-11T09:41:29Z","title":"Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices","version":3},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-23T01:03:26.037233Z"},"links":{"cited_paper":"/paper/2403.18421","citing_paper":"/paper/2503.08223"},"observation_digest":"sha256:6295a573cd21ef89cf80e647ec428ffd948cb99541eb237e0d1158828b713072","observation_id":"d489b56c-99f3-4f88-8aed-6721e1e8cdc9","resolution":{"observed_at":"2026-05-23T01:05:16.214995Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18421","last_updated":"2024-03-27T10:18:21Z","snapshot_observed_at":"2026-07-06T17:51:48.378864Z","submitted_at":"2024-03-27T10:18:21Z","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","version":1},"cited_work":{"arxiv_id":"2403.18421","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.18421","snapshot_observed_at":"2026-07-03T18:08:45.686200Z","title":"Biomedlm: A 2.7 b parameter language model trained on biomedical text","venue":null,"work_id":"eacb5aff-ebda-464d-a6fb-b9414866fd51","year":2024},"citing_paper":{"arxiv_id":"2509.07177","last_updated":"2026-04-14T15:07:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-08T19:48:52Z","title":"Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-18T17:39:17.456350Z"},"links":{"cited_paper":"/paper/2403.18421","citing_paper":"/paper/2509.07177"},"observation_digest":"sha256:17f8c98d0cc8f2f5c67783633ab5b557179910883fb6140a1f31a92a86c499fe","observation_id":"d3a15b1a-4564-47af-bd88-55dc225f1487","resolution":{"observed_at":"2026-05-18T17:42:47.516113Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18421","last_updated":"2024-03-27T10:18:21Z","snapshot_observed_at":"2026-07-06T17:51:48.378864Z","submitted_at":"2024-03-27T10:18:21Z","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.18421","snapshot_observed_at":"2026-08-04T00:00:00.169975Z","title":"Biomedlm: A 2.7 b parameter language model trained on biomedical text.arXiv preprint arXiv:2403.18421, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.03354","last_updated":"2026-07-23T13:45:54Z","snapshot_observed_at":"2026-08-03T23:59:51.497110Z","submitted_at":"2025-11-05T10:48:36Z","title":"Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T00:00:00.169975Z"},"links":{"cited_paper":"/paper/2403.18421","citing_paper":"/paper/2511.03354"},"observation_digest":"sha256:05bc538fb7cc3b0587ab5c78d7389536059846a7ed9853ede10ad7e5874c93d8","observation_id":"3d7ec0f9-a267-47c8-9234-805336aa81ca","resolution":{"observed_at":"2026-08-04T00:00:00.169975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18421","last_updated":"2024-03-27T10:18:21Z","snapshot_observed_at":"2026-07-06T17:51:48.378864Z","submitted_at":"2024-03-27T10:18:21Z","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","version":1},"cited_work":{"arxiv_id":"2403.18421","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.18421","snapshot_observed_at":"2026-07-03T18:08:45.686200Z","title":"Biomedlm: A 2.7 b parameter language model trained on biomedical text","venue":null,"work_id":"eacb5aff-ebda-464d-a6fb-b9414866fd51","year":2024},"citing_paper":{"arxiv_id":"2605.11774","last_updated":"2026-05-12T08:46:40Z","snapshot_observed_at":"2026-08-05T02:44:45.527899Z","submitted_at":"2026-05-12T08:46:40Z","title":"From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-13T06:44:04.493625Z"},"links":{"cited_paper":"/paper/2403.18421","citing_paper":"/paper/2605.11774"},"observation_digest":"sha256:726f6a75c13eb7dd1c87813e3dc9c0054b4258b65333aa87e2e8d3eeb5b85b29","observation_id":"f86d9df5-da80-41ea-8f93-40d1eaebedf3","resolution":{"observed_at":"2026-05-13T06:47:27.380219Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18421","last_updated":"2024-03-27T10:18:21Z","snapshot_observed_at":"2026-07-06T17:51:48.378864Z","submitted_at":"2024-03-27T10:18:21Z","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","version":1},"cited_work":{"arxiv_id":"2403.18421","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.18421","snapshot_observed_at":"2026-07-03T18:08:45.686200Z","title":"Biomedlm: A 2.7 b parameter language model trained on biomedical text","venue":null,"work_id":"eacb5aff-ebda-464d-a6fb-b9414866fd51","year":2024},"citing_paper":{"arxiv_id":"2606.17213","last_updated":"2026-06-15T18:51:31Z","snapshot_observed_at":"2026-08-02T10:09:29.451711Z","submitted_at":"2026-06-15T18:51:31Z","title":"Revisiting LLM Adaptation for 3D CT Report Generation: A Study of Scaling and Diagnostic Priors","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T03:21:31.374575Z"},"links":{"cited_paper":"/paper/2403.18421","citing_paper":"/paper/2606.17213"},"observation_digest":"sha256:e0522e41d4e3ad3d46e006fc9ecdb9c90420d5711a008e73fe28dd28a3c36808","observation_id":"cddea931-d44e-4395-88e4-0bd0d06104fd","resolution":{"observed_at":"2026-07-03T18:08:45.688157Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18421","last_updated":"2024-03-27T10:18:21Z","snapshot_observed_at":"2026-07-06T17:51:48.378864Z","submitted_at":"2024-03-27T10:18:21Z","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.18421","snapshot_observed_at":"2026-08-02T09:48:00.974261Z","title":"BioMedLM: A 2.7B parameter language model trained on biomedical text,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16256","last_updated":"2026-07-21T15:34:43Z","snapshot_observed_at":"2026-08-05T02:48:01.925897Z","submitted_at":"2026-06-28T05:01:32Z","title":"Discovery by Dreaming: Cross-Domain Recombination in Artificial Memory","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T09:48:00.974261Z"},"links":{"cited_paper":"/paper/2403.18421","citing_paper":"/paper/2607.16256"},"observation_digest":"sha256:9bf690ac3dda9c44a2aac0eb0a9304c24374cec003c27ee5aa550d524feb4c4a","observation_id":"02daba06-2825-450b-848b-4340214e24e4","resolution":{"observed_at":"2026-08-02T09:48:00.974261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2403.18421/citation-record","integrity":"/paper/2403.18421/integrity","json":"/paper/2403.18421/citation-record.json","paper":"/paper/2403.18421"},"outbound":[],"paper":{"arxiv_id":"2403.18421","last_updated":"2024-03-27T10:18:21Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:51:48.378864Z","submitted_at":"2024-03-27T10:18:21Z","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text"},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.18421."}