{"as_of":"2026-08-16T13:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:429e3987cf5f5dfb55a0b6b487886e4b3b89e0d460b73cb6abd0a470bbf07fdc","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:05:48.431995Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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":"2026-08-06T15:28:47.162607Z","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":"2026-05-19T10:47:15.104724Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"cited_work":{"arxiv_id":"2504.13650","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.13650","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Eyecaregpt: Boosting comprehensive ophthalmology understanding with tailored dataset, benchmark and model","venue":null,"work_id":"a6829804-5bd7-45e6-85ff-b25fb07132b3","year":2025},"citing_paper":{"arxiv_id":"2506.05831","last_updated":"2026-04-07T10:06:53Z","snapshot_observed_at":"2026-08-11T06:23:45.741698Z","submitted_at":"2025-06-06T07:56:41Z","title":"HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-19T10:44:01.880405Z"},"links":{"cited_paper":"/paper/2504.13650","citing_paper":"/paper/2506.05831"},"observation_digest":"sha256:1516f55b1af43c020ee7f995a34a341504510ad1374c0a3aa6cfa59a5edf768a","observation_id":"7841898c-32a2-49f9-95e1-2135d941e24b","resolution":{"observed_at":"2026-05-19T10:47:15.106185Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13650","snapshot_observed_at":"2026-08-06T15:28:47.162607Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model [Internet]","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15717","last_updated":"2025-07-21T15:27:32Z","snapshot_observed_at":"2026-08-12T16:46:35.278321Z","submitted_at":"2025-07-21T15:27:32Z","title":"BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:28:47.162607Z"},"links":{"cited_paper":"/paper/2504.13650","citing_paper":"/paper/2507.15717"},"observation_digest":"sha256:073502c79bf31ae81744238c1a732853779c16c474d09c73fb3b2de00b34a7eb","observation_id":"620c27bb-70d6-4918-8fb5-5eef302943b4","resolution":{"observed_at":"2026-08-06T15:28:47.162607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13650","snapshot_observed_at":"2026-08-04T19:27:40.699503Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.09254","last_updated":"2025-09-11T08:39:08Z","snapshot_observed_at":"2026-08-16T11:25:33.002146Z","submitted_at":"2025-09-11T08:39:08Z","title":"Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset for Panoramic X-ray Analysis","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:40.699503Z"},"links":{"cited_paper":"/paper/2504.13650","citing_paper":"/paper/2509.09254"},"observation_digest":"sha256:9364ad179bcb8d03f772eece98c4b23cb2fbab5e603ac6856397e6bfcc5a2361","observation_id":"67a3dc25-dc94-4109-ad6d-81c2ee77e8ae","resolution":{"observed_at":"2026-08-04T19:27:40.699503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.13650/citation-record","integrity":"/paper/2504.13650/integrity","json":"/paper/2504.13650/citation-record.json","paper":"/paper/2504.13650"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.113881Z","title":null,"venue":null,"work_id":"6d7d9b6b-3367-49f3-9181-25747380a27e","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.249467Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:6346c348f569c38df2f177794a38f8c9bde13ee3adaa6a4f0def143d5a9dd19f","observation_id":"92c8f34e-022a-4e9f-9689-3d92c8ce4ba1","resolution":{"observed_at":"2026-08-16T12:05:49.118219Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.09838","last_updated":"2025-02-21T17:39:29Z","snapshot_observed_at":"2026-08-16T12:46:53.434136Z","submitted_at":"2025-02-14T00:42:36Z","title":"HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.09838","snapshot_observed_at":"2026-08-16T12:05:48.238230Z","title":"Nature medicine, 30(10): 2886–2896","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.238230Z"},"links":{"cited_paper":"/paper/2502.09838","citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:b83157ae1581cf8f5887f479b2ef1e098e9d82ce4482a120a7c691cb34936a8e","observation_id":"aa66a86d-2283-4561-9b72-e07778dd50c2","resolution":{"observed_at":"2026-08-16T12:05:48.238230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05530","last_updated":"2024-12-16T17:39:39Z","snapshot_observed_at":"2026-08-14T18:15:53.516440Z","submitted_at":"2024-03-08T18:54:20Z","title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05530","snapshot_observed_at":"2026-08-16T12:05:48.244083Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.244083Z"},"links":{"cited_paper":"/paper/2403.05530","citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:03b52d159024e67885d8940682b0c6f31fec1eef315d9f5bc3b095b40d8e20e2","observation_id":"0c04d133-b1df-4c4f-b1f1-e3ecfe6da1f0","resolution":{"observed_at":"2026-08-16T12:05:48.244083Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.067692Z","title":null,"venue":null,"work_id":"b967af26-a71d-41ae-a951-fb8a17e4461d","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.263814Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:5759e39a64ee9056ba8c99de40f9cc1119b20f5fc72719b8b0f0d99d8fc08d6d","observation_id":"f45d4162-d83f-4383-ae40-6662ccc3f069","resolution":{"observed_at":"2026-08-16T12:05:49.072626Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.099730Z","title":null,"venue":null,"work_id":"dba59168-3740-46ca-aa68-ede149b8f1de","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.254181Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:04a67bb1aa32375c03140281f23a381499ecb5b8982d53821a734036a4f6885a","observation_id":"41389fc4-b3d0-43d9-a4c5-138718d20f81","resolution":{"observed_at":"2026-08-16T12:05:49.103778Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.083971Z","title":null,"venue":null,"work_id":"1ff6e869-8176-4cb6-85b3-b995bd9b5497","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.259131Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:c58bbb5915da4f9a1892b1e0b9b7335c49140cd91e9aaa4953626b7546511f32","observation_id":"6fca5f96-fcec-417d-9223-44702a961a18","resolution":{"observed_at":"2026-08-16T12:05:49.088857Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.019658Z","title":null,"venue":null,"work_id":"fb20734a-c351-4f77-9de8-3bebf57d2552","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.278184Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:dce0a46dd5b18da8e0936cced1700d5d0b2bcb3ade9fdec14614b4d33972a70b","observation_id":"da6212e9-5470-49d8-ab00-e85a6a722f86","resolution":{"observed_at":"2026-08-16T12:05:49.024416Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.052182Z","title":null,"venue":null,"work_id":"e3fe6cda-fb74-4285-b7e6-fb195d060f24","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.268418Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:a0c0b133fafb4596cb1ee11f899d0700913afb3db39ccafe78386fd29305e53a","observation_id":"a9067815-fc8b-4231-9a30-b06b52d109a9","resolution":{"observed_at":"2026-08-16T12:05:49.056698Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.036457Z","title":null,"venue":null,"work_id":"22df8f8c-a794-439c-b3da-a06650c0a486","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.273128Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:7bad3effb5d612e3278b5f9e94b3e01bd298b4aefc72662fd928ddc884ea5dbb","observation_id":"49fe863c-709e-434f-8ef8-7f65dd2aff93","resolution":{"observed_at":"2026-08-16T12:05:49.041352Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:49.002491Z","title":null,"venue":null,"work_id":"18c491f3-dce8-483b-b689-5231677e5fcd","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.282542Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:f898fb04b1ede189ad36af11086b04985d1c533c4e70f2f2e703eedb33125dc4","observation_id":"3b95b629-8b7f-4257-859d-312fab2e653a","resolution":{"observed_at":"2026-08-16T12:05:49.008425Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.986994Z","title":null,"venue":null,"work_id":"8999ca14-99b7-4f13-bbff-27494a9661c4","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.286978Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:2ce9268c8cb8139152a1f1ac6a97472491773b4480120e3181468d95dc4947a7","observation_id":"70876880-bdf2-422a-9629-bb837dc898e5","resolution":{"observed_at":"2026-08-16T12:05:48.991737Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.971298Z","title":null,"venue":null,"work_id":"780837de-5cd9-488a-bbaa-bea987c8f800","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.291662Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:debb64fc715b1fbe55865d03be4746cf6c26d0f3c19b3487ebed84867d43b44d","observation_id":"29aa55fe-37c0-4c25-ad42-f1a325676757","resolution":{"observed_at":"2026-08-16T12:05:48.975915Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.956774Z","title":null,"venue":null,"work_id":"cc053bf0-99d4-440a-a8aa-3831d11b16a9","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.296516Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:5020cf986b645edc065fa0c4106d78454d8573348968a5a6bc657c06e9e16481","observation_id":"0147d97f-0aaf-4c50-8d2f-1a2fe9596b32","resolution":{"observed_at":"2026-08-16T12:05:48.961401Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.941989Z","title":null,"venue":null,"work_id":"cb81df05-4c84-4209-a7ec-f4118384f6cf","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.301122Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:6b03378b83342997c718cdff377f3848bc9b30dd50107c5cdb667a31d447fcd8","observation_id":"1a26660c-adc5-47cd-a5a0-133a97c1ef14","resolution":{"observed_at":"2026-08-16T12:05:48.946887Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.926908Z","title":"Negative ¡condition¿:","venue":null,"work_id":"b052ed75-eaef-4c4a-9122-ad9378bfd5a2","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.306069Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:3f24716fcfd92e730979d2dda84cb1907edd1fb8c151626f192db24b95de9648","observation_id":"e672124c-9284-4f5e-841f-a158b289b1c8","resolution":{"observed_at":"2026-08-16T12:05:48.931792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.912012Z","title":null,"venue":null,"work_id":"f035dd36-a4fe-413c-be89-648720090744","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.311178Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:6137abd40283ea76d209c533a3f5d6f456223b1daa578a2436f5a0d49fbf8fc7","observation_id":"9f6e20fc-41a1-4ceb-bb32-c92ad5d3cac3","resolution":{"observed_at":"2026-08-16T12:05:48.916580Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.896671Z","title":"This image shows that the retina looks normal, with no hemorrhages, exudates or other signs of abnormality","venue":null,"work_id":"5edd6436-784e-47ec-a58b-0773fc6e1d58","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.316613Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:0f1eeee823aab0f8ec6f86ce4da0eca7b2ba2a2c0ef2d9fe00e72a446b01d30f","observation_id":"c713c11f-f34a-44c9-b621-f9b0783f5508","resolution":{"observed_at":"2026-08-16T12:05:48.901578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.881517Z","title":null,"venue":null,"work_id":"e9d1af18-6efe-4651-909b-0bedcb5eef38","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.321015Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:698fdddfbba5ca3c3642b856dd697fb909486003e339b6bf7418a0c4775b257a","observation_id":"8ccb02d6-5c9c-4cf3-bcea-78be0870347f","resolution":{"observed_at":"2026-08-16T12:05:48.886116Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.866604Z","title":null,"venue":null,"work_id":"8572bb7b-e98b-4355-8463-25f08fdedfe6","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.325827Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:90060a1018dc3ed0d6ca4259cc8eeb013211818e5c4a02c7b8686118b1114152","observation_id":"9985024e-c58f-490b-8940-a3c41edfbef8","resolution":{"observed_at":"2026-08-16T12:05:48.871051Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.851313Z","title":null,"venue":null,"work_id":"5a40f270-04ac-479f-bd68-7d146a47e580","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.330176Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:b25c00f6d2d33e762b4d547525c98717bf715cc24554ca5116ab3d234d04e5be","observation_id":"662b6398-5424-4149-9fab-6a7570d4eba7","resolution":{"observed_at":"2026-08-16T12:05:48.856051Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.836345Z","title":null,"venue":null,"work_id":"fec44f7d-7836-4cc4-9d0d-c84a67635e42","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.335028Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:fb43b06876529222a7918049ec02ec5f76e64bd1eef7b2ca69f4901d0fd8bd77","observation_id":"ffdd3352-908c-4af6-a67d-baf7407c8317","resolution":{"observed_at":"2026-08-16T12:05:48.840984Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.821546Z","title":"Question2:","venue":null,"work_id":"d776903e-71ca-4aa6-b938-730dbab84085","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.339694Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:2b3efc20d8d05d77310f5bbff2ce089f7a2acd2839911dd7c3b88364bcc86e1f","observation_id":"72cb4412-654c-43f8-b5bb-79c17ee07d6a","resolution":{"observed_at":"2026-08-16T12:05:48.826260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.806478Z","title":null,"venue":null,"work_id":"dac68d55-475f-4718-b996-0470ab8953aa","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.344151Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:467fbff7cd65e0896e6db6de68e11024d8ba671f4355efa98a6fd5a3f5ba033f","observation_id":"959dbefc-f750-458d-8a7d-4887760355a8","resolution":{"observed_at":"2026-08-16T12:05:48.811031Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.791796Z","title":null,"venue":null,"work_id":"c6a67726-bc5d-4415-addf-4a677fc70b8c","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.349123Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:d83dfc3b4d2cff691319940f425e20a1c0cda6a855fdb3aae108295ed023b61b","observation_id":"164f4510-77d3-4686-9cbc-f50f86db2374","resolution":{"observed_at":"2026-08-16T12:05:48.796494Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.776588Z","title":null,"venue":null,"work_id":"10c296de-ce4b-4c5d-9a29-ca7c2025a74a","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.353779Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:6607f33cdfa70568b0c8fbbb85f836acfe246a9f8fd8786e8e07667c36c65a6d","observation_id":"b64e41e4-8624-45e6-8c18-55c149ec870d","resolution":{"observed_at":"2026-08-16T12:05:48.781328Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.761705Z","title":null,"venue":null,"work_id":"4cf03359-0473-4f18-a327-555911eb07c4","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.359126Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:3df2161a5d0014d21d8e6c4f06602fbfd65e8a22d136247b451334a404cee650","observation_id":"14cd8e26-6927-4c97-b5a6-99305c53d23b","resolution":{"observed_at":"2026-08-16T12:05:48.766319Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.746968Z","title":null,"venue":null,"work_id":"78cc3feb-b64e-4364-b6c6-ad32500fccef","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.363770Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:26e49839c22476809b05ecf76b9addac9039361f53bdb9183a22df9b5baeac0d","observation_id":"2b379f67-40de-4ce9-8836-5b84e8c9db28","resolution":{"observed_at":"2026-08-16T12:05:48.751445Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.731644Z","title":null,"venue":null,"work_id":"7c5d895c-f402-4025-96e2-02b085b12d91","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.368276Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:9efd9bb921e612aa1fac5738d435b3c3c06eea6c73f20fd1c0a43d67c0f6c1b9","observation_id":"893adc4f-c866-4f19-8071-bf4aee3d2d27","resolution":{"observed_at":"2026-08-16T12:05:48.736087Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.717034Z","title":null,"venue":null,"work_id":"042b2a30-fa2e-44fe-86e1-4a7cc38eddab","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.372721Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:e2ea6a932c3d4b59cbf16f41bd19a69c3b9bf87eaed92b174466a0272a5b0a86","observation_id":"392db78b-5e30-4f57-8955-88d74d99253b","resolution":{"observed_at":"2026-08-16T12:05:48.721651Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.701415Z","title":null,"venue":null,"work_id":"89f5455e-7164-4a25-9d80-27bae15088cc","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.377387Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:09c481e6e951001215e0848e086be3ef14f8272dcfeeb8b9a627406c78afd084","observation_id":"d54b5213-f183-44d6-bded-5abfe69514e7","resolution":{"observed_at":"2026-08-16T12:05:48.707114Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.686584Z","title":null,"venue":null,"work_id":"5086c4a8-128d-49b3-b1a1-1793718534ac","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.381984Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:3704a0b54fec2a67959544962b8a407e03b83d96624c516325a5c8233121c9ea","observation_id":"431cd308-5342-4c62-bc96-99e0d59ca07d","resolution":{"observed_at":"2026-08-16T12:05:48.691448Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.671597Z","title":"2.{condition}is evident in the eye depicted in the image","venue":null,"work_id":"430f5207-f1ca-458b-a8d8-d7bb56a8086b","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.386871Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:3b6eb70aec32286503bfd956dac7a5cbf7f750d18680d9294dafc53ffb908cdd","observation_id":"8bac8f28-1440-4b1a-b946-4e6adb408863","resolution":{"observed_at":"2026-08-16T12:05:48.676395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.656118Z","title":null,"venue":null,"work_id":"7d71fa13-bd67-4152-9c84-a3e2a69487c3","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.391358Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:73c5a3d0dc836c0b5c72987054e6f334dfee69f3e61d52a9a9b6af59b61f4d2b","observation_id":"c7021d5b-2467-4911-83c8-0fde9453b2e5","resolution":{"observed_at":"2026-08-16T12:05:48.661058Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.640549Z","title":null,"venue":null,"work_id":"c0a4c1cf-6b50-40fa-95bf-0e234687d8f8","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.396130Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:7fa97673b6ab4912bd245f4ddeaa74ee17bd08776901e99e9703d95f612f9511","observation_id":"2f368978-f74d-44d4-9eec-4a92096b8b06","resolution":{"observed_at":"2026-08-16T12:05:48.645890Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.624936Z","title":null,"venue":null,"work_id":"008910a9-6b9a-42e5-8c23-017dcabf96bc","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.400794Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:26662a7045e70645a8d15c7b1c57a8245af06b746c7e7202f873858c5c9dd9a9","observation_id":"c8a00b99-08ac-4b07-b03f-cf4a6e18b66a","resolution":{"observed_at":"2026-08-16T12:05:48.629768Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.609250Z","title":"7.{condition}is visible in the eye from this picture","venue":null,"work_id":"1886a942-a43b-47a5-9234-49034f2b0150","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.405036Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:e5084e88d9528af294dc827f30ca8961667847b833af1ed9aa8e92ed5da62958","observation_id":"efa68a81-8af3-41ad-a2ba-ccddd561c6b7","resolution":{"observed_at":"2026-08-16T12:05:48.613947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.593736Z","title":null,"venue":null,"work_id":"8ed3f41a-2676-4408-a44e-4bcd35339465","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.409361Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:397e0fe5158aebf560f8aaf4703cfccccfdbbe571d530b1323354d34ac6f5ebf","observation_id":"d7b94968-6f3e-4d47-a333-3173346176cb","resolution":{"observed_at":"2026-08-16T12:05:48.598786Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.577500Z","title":null,"venue":null,"work_id":"3927f4de-3774-4fb7-b208-6a632808e88c","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.413903Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:a4a9bf47269e64984730004e75d3c68cce06312400ba688029f894e5012796e5","observation_id":"40a86745-cffd-4dfa-83e9-2f86868a661e","resolution":{"observed_at":"2026-08-16T12:05:48.582332Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.562860Z","title":null,"venue":null,"work_id":"532f08a8-4ff8-427f-9215-9e450bdb58a0","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.418443Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:fe41829cfddba2796a96b4c6d13990992154c769f84466c846f1786197d0fadf","observation_id":"8710c7a8-50af-446c-b794-309679f8943f","resolution":{"observed_at":"2026-08-16T12:05:48.567424Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.547300Z","title":"It’s very healthy","venue":null,"work_id":"3930eb7d-4979-4a77-bceb-a783645c2ff3","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.423051Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:6ec539d0931c06a8284f9b4234f8cbab13027e138837f44cee1e5713e911e89a","observation_id":"91c7c0e6-b945-4a7e-9019-8a0f1102b52b","resolution":{"observed_at":"2026-08-16T12:05:48.552208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.531974Z","title":null,"venue":null,"work_id":"2a161f49-84ab-406d-b8d7-6b9ff0633cc0","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.427537Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:71c1b3be895353fb6591bbf2f0dfaeed4db575e60e2e2e6422223d1ca4c6e648","observation_id":"37c9789f-2408-4691-9ccc-60fb053ac788","resolution":{"observed_at":"2026-08-16T12:05:48.536658Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:05:48.513825Z","title":"16 Table 10: Other evaluation metrics for the open-ended question answering task in the main experiment","venue":null,"work_id":"2351dfbc-b9a2-4c67-8c07-714ec02680bd","year":null},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.431995Z"},"links":{"citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:f57dfc2b5a11500bd7ae2e2326c5667eb8a537d36e8b85f40e1ab958c749bb2a","observation_id":"b9101b9c-7e1e-4438-8a76-49603e131cc2","resolution":{"observed_at":"2026-08-16T12:05:48.520388Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-08-15T14:02:47.366139Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-16T12:05:48.232455Z","title":"In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition, 22170–22183","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T12:05:48.232455Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2504.13650"},"observation_digest":"sha256:b8a4d4f48dfc2c00a9007d5d38e94b3dfb78f8847cfb5fc567893679dd7f889b","observation_id":"c4a24096-3c97-4bf4-a16b-4a9e3f29a31b","resolution":{"observed_at":"2026-08-16T12:05:48.232455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.13650","last_updated":"2025-04-18T12:09:15Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T12:00:42.709123Z","submitted_at":"2025-04-18T12:09:15Z","title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":0,"verified_fuzzy":6},"total_outbound_references":43},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 3 inbound Pith citation observations for arXiv:2504.13650."}