{"as_of":"2026-08-13T12:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e4b34dc304eb657383e300e292519d7e398bd50cb99bebf38ac953c960eb1376","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T02:47:25.515864Z","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":"2026-07-03T09:47:59.465812Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.23771","last_updated":"2025-03-31T06:41:18Z","snapshot_observed_at":"2026-08-11T17:33:19.993684Z","submitted_at":"2025-03-31T06:41:18Z","title":"XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?","version":1},"cited_work":{"arxiv_id":"2503.23771","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.23771","snapshot_observed_at":"2026-07-03T09:47:59.465812Z","title":"Xlrs-bench: Could your multimodal llms understand extremely large ultra-high-resolution remote sensing imagery?","venue":null,"work_id":"33998986-1700-4162-a283-1ac7b447621c","year":2025},"citing_paper":{"arxiv_id":"2604.20623","last_updated":"2026-04-22T14:38:41Z","snapshot_observed_at":"2026-07-31T18:27:34.438640Z","submitted_at":"2026-04-22T14:38:41Z","title":"RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T00:05:26.906011Z"},"links":{"cited_paper":"/paper/2503.23771","citing_paper":"/paper/2604.20623"},"observation_digest":"sha256:7a85f46e8c89e049a3ea9dae207de79517fa5c1baa77f9021b1e83edd66b26f0","observation_id":"43f5b33d-fb80-43de-9d4d-209176a8bfe2","resolution":{"observed_at":"2026-05-10T00:24:47.520906Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.23771","last_updated":"2025-03-31T06:41:18Z","snapshot_observed_at":"2026-08-11T17:33:19.993684Z","submitted_at":"2025-03-31T06:41:18Z","title":"XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?","version":1},"cited_work":{"arxiv_id":"2503.23771","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.23771","snapshot_observed_at":"2026-07-03T09:47:59.465812Z","title":"Xlrs-bench: Could your multimodal llms understand extremely large ultra-high-resolution remote sensing imagery?","venue":null,"work_id":"33998986-1700-4162-a283-1ac7b447621c","year":2025},"citing_paper":{"arxiv_id":"2606.11740","last_updated":"2026-06-10T07:16:27Z","snapshot_observed_at":"2026-08-02T02:17:16.809620Z","submitted_at":"2026-06-10T07:16:27Z","title":"UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-06-27T10:21:12.782864Z"},"links":{"cited_paper":"/paper/2503.23771","citing_paper":"/paper/2606.11740"},"observation_digest":"sha256:f4a561405d4a2bb86d5df0dfb7f8e8b349cdb202187410ccb5c9a6d60641005f","observation_id":"124b307f-5770-4447-8603-695d158deabc","resolution":{"observed_at":"2026-07-03T09:47:59.467156Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.23771","last_updated":"2025-03-31T06:41:18Z","snapshot_observed_at":"2026-08-11T17:33:19.993684Z","submitted_at":"2025-03-31T06:41:18Z","title":"XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.23771","snapshot_observed_at":"2026-08-01T00:59:16.958440Z","title":"Xlrs-bench: Could your multimodal llms understand extremely large ultra-high-resolution remote sensing imagery?arXiv preprint arXiv:2503.23771, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25993","last_updated":"2026-07-28T17:09:16Z","snapshot_observed_at":"2026-08-12T12:27:46.074162Z","submitted_at":"2026-07-28T17:09:16Z","title":"Beyond Zooming: Learning Multi-Tool Visual Reasoning for Ultra-High-Resolution Remote Sensing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T00:59:16.958440Z"},"links":{"cited_paper":"/paper/2503.23771","citing_paper":"/paper/2607.25993"},"observation_digest":"sha256:dfec5ef060014101d76e46314b1e4c772c4bdf4b2bd7ee25f0836f14783e8838","observation_id":"52b5e044-50a9-4d0c-b289-e25af79c9551","resolution":{"observed_at":"2026-08-01T00:59:16.958440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.23771","last_updated":"2025-03-31T06:41:18Z","snapshot_observed_at":"2026-08-11T17:33:19.993684Z","submitted_at":"2025-03-31T06:41:18Z","title":"XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.23771","snapshot_observed_at":"2026-08-04T02:47:25.515864Z","title":"Xlrs-bench: Could your multimodal llms understand extremely large ultra-high-resolution remote sensing imagery?","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00012","last_updated":"2026-06-24T07:51:49Z","snapshot_observed_at":"2026-08-12T05:04:49.131230Z","submitted_at":"2026-06-24T07:51:49Z","title":"Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T02:47:25.515864Z"},"links":{"cited_paper":"/paper/2503.23771","citing_paper":"/paper/2608.00012"},"observation_digest":"sha256:41e5087a4f45a5bb77281ed92aa89c197d2caa2472c4f3b87424579bfdf91e90","observation_id":"121c2f26-fe75-467d-96aa-348bd73e7c38","resolution":{"observed_at":"2026-08-04T02:47:25.515864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2503.23771/citation-record","integrity":"/paper/2503.23771/integrity","json":"/paper/2503.23771/citation-record.json","paper":"/paper/2503.23771"},"outbound":[],"paper":{"arxiv_id":"2503.23771","last_updated":"2025-03-31T06:41:18Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T17:33:19.993684Z","submitted_at":"2025-03-31T06:41:18Z","title":"XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2503.23771."}