{"as_of":"2026-08-05T09:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b1f7a3f579617bc89e6326ca8b847ec782d2a711d5af2fa8d498dd6e1183b8a6","coverage":[{"denominator":10,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T23:44:44.026159Z","state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2602.12833/citation-record","integrity":"/paper/2602.12833/integrity","json":"/paper/2602.12833/citation-record.json","paper":"/paper/2602.12833"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.19457","last_updated":"2026-02-14T11:42:30Z","snapshot_observed_at":"2026-07-06T22:02:56.216824Z","submitted_at":"2025-07-25T17:42:32Z","title":"GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.19457","snapshot_observed_at":"2026-08-02T23:44:42.739735Z","title":"A., Tan, S., Soylu, D., Ziems, N., Khare, R., Opsahl-Ong, K., Singhvi, A., Shandilya, H., Ryan, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:42.739735Z"},"links":{"cited_paper":"/paper/2507.19457","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:021905c393b08d316c431599fb7555fef931f15e4c241f0934d643504afc322d","observation_id":"06561b5c-5099-4bd5-9d9b-1d86c21c5736","resolution":{"observed_at":"2026-08-02T23:44:42.739735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.13375","last_updated":"2023-04-12T16:48:39Z","snapshot_observed_at":"2026-07-06T15:07:11.623462Z","submitted_at":"2023-03-20T16:18:38Z","title":"Capabilities of GPT-4 on Medical Challenge Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.13375","snapshot_observed_at":"2026-08-02T23:44:43.362107Z","title":"M., Carignan, D., and Horvitz, E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:43.362107Z"},"links":{"cited_paper":"/paper/2303.13375","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:206cda47c56b0aacf7248e257f733323fb973a38d23d548821e64ad5ff35a032","observation_id":"4d13c08d-4e46-4abb-97ea-2856fc8043a9","resolution":{"observed_at":"2026-08-02T23:44:43.362107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.07952","last_updated":"2025-04-10T17:57:33Z","snapshot_observed_at":"2026-07-06T21:07:29.062389Z","submitted_at":"2025-04-10T17:57:33Z","title":"Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.07952","snapshot_observed_at":"2026-08-02T23:44:43.670735Z","title":"Dynamic cheatsheet: Test-time learning with adaptive memory.arXiv preprint arXiv:2504.07952,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:43.670735Z"},"links":{"cited_paper":"/paper/2504.07952","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:06e8b7c1d3864350fa4886e8eb2769af11d742f038b5d7b4c9c428b56e9266ce","observation_id":"41206ed7-c57b-4b6f-ad6f-7f57ee5d0ea2","resolution":{"observed_at":"2026-08-02T23:44:43.670735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:44:43.821415Z","title":"Generative medical event models improve with scale.arXiv preprint arXiv:2508.12104,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:43.821415Z"},"links":{"citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:77a68ea7181510e8d4016887676e5b1096b94d82283cb30e2d1d5e14b5f65003","observation_id":"e5452b87-71a6-4877-8349-360e385be58f","resolution":{"observed_at":"2026-08-02T23:44:43.821415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16178","last_updated":"2025-03-18T18:04:32Z","snapshot_observed_at":"2026-08-03T01:56:47.537884Z","submitted_at":"2024-12-09T21:58:27Z","title":"Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16178","snapshot_observed_at":"2026-08-02T23:44:43.935604Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:43.935604Z"},"links":{"cited_paper":"/paper/2412.16178","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:93832fa622ee7a78fa2862b349f31682c0f75efb95b789c6183e6e06583ef3ce","observation_id":"1fda7dea-be5a-4b4d-82d1-a733c0f7b49e","resolution":{"observed_at":"2026-08-02T23:44:43.935604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.04618","last_updated":"2026-03-29T09:18:02Z","snapshot_observed_at":"2026-07-06T22:31:49.574184Z","submitted_at":"2025-10-06T09:30:18Z","title":"Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.04618","snapshot_observed_at":"2026-08-02T23:44:44.026159Z","title":"Agentic con- text engineering: Evolving contexts for self-improving language models.arXiv preprint arXiv:2510.04618,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:44.026159Z"},"links":{"cited_paper":"/paper/2510.04618","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:226f2a140fdf4a0b35248ba81c1b126df178d9963327e162f5f59b0b20cf09f7","observation_id":"3f37bbc0-430f-4432-a0b5-a871e6936c42","resolution":{"observed_at":"2026-08-02T23:44:44.026159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.20822","last_updated":"2026-05-07T15:02:00Z","snapshot_observed_at":"2026-07-06T22:39:58.137482Z","submitted_at":"2025-12-23T22:52:24Z","title":"MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.20822","snapshot_observed_at":"2026-08-02T23:44:43.486727Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:43.486727Z"},"links":{"cited_paper":"/paper/2512.20822","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:7f1245e45159604944c604085025e56e151bbdd5e40d6756880fc69f6e98a951","observation_id":"c91d4b2c-2cf0-42d7-a563-9139c1770aa5","resolution":{"observed_at":"2026-08-02T23:44:43.486727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02060","last_updated":"2024-06-12T02:46:16Z","snapshot_observed_at":"2026-07-06T17:54:39.685251Z","submitted_at":"2024-04-02T15:59:11Z","title":"Long-context LLMs Struggle with Long In-context Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02060","snapshot_observed_at":"2026-08-02T23:44:43.039263Z","title":"D., Yue, X., and Chen, W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:43.039263Z"},"links":{"cited_paper":"/paper/2404.02060","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:921d155a929c55e2763a7d4a36df45e589c0c83a063c0adb6aa364c80ecbe2be","observation_id":"3036ea04-0192-46b7-9790-df5fde4432fd","resolution":{"observed_at":"2026-08-02T23:44:43.039263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:44:43.203896Z","title":"A comprehensive survey on long context language modeling.arXiv preprint arXiv:2503.17407,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:43.203896Z"},"links":{"citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:681bad5eb6f1bd71e8d962e13024da60294193eae20334df07236f6ff769afaf","observation_id":"77d05dbc-5b00-48d3-a278-396216f3d944","resolution":{"observed_at":"2026-08-02T23:44:43.203896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16079","last_updated":"2023-11-27T18:49:43Z","snapshot_observed_at":"2026-07-06T16:53:18.275859Z","submitted_at":"2023-11-27T18:49:43Z","title":"MEDITRON-70B: Scaling Medical Pretraining for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16079","snapshot_observed_at":"2026-08-02T23:44:42.849813Z","title":"H., Romanou, A., Bonnet, A., Ma- toba, K., Salvi, F., Pagliardini, M., Fan, S., K ¨opf, A., Mohtashami, A., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-02T23:44:42.849813Z"},"links":{"cited_paper":"/paper/2311.16079","citing_paper":"/paper/2602.12833"},"observation_digest":"sha256:975c51ac18f2065e7b3af5ffbd6c5e8adf03e2ed2a6f5461f62ecd1523d1f78f","observation_id":"e905fa49-c81f-4e34-b251-4e1d17ed70e1","resolution":{"observed_at":"2026-08-02T23:44:42.849813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.12833","last_updated":"2026-05-26T12:29:12Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T01:58:30.373491Z","submitted_at":"2026-02-13T11:39:19Z","title":"Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories"},"reference_resolution":{"displayed":10,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":10},"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 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2602.12833."}