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Paper Citation Record · LEDGER

XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.23771.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2503.23771 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:44:43.599067Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T09:47:59.465812Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8fd26173-5223-4deb-a2b0-7700721627cd · inbound

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind cites this paper.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T20:44:43.599067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:44:43.599067Z digest=sha256:8a1f1c238e67757e90afc93be24d92a15370923131436570dbfbb274f80e8084

Observation 43f5b33d-fb80-43de-9d4d-209176a8bfe2 · inbound

RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking cites this paper.

RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:24:47.520906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T00:05:26.906011Z digest=sha256:9f8afef7c54216642fa6ebc8126370bb0f4e120b05baf951bca9b90158e0181d

Observation 124b307f-5770-4447-8603-695d158deabc · inbound

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA cites this paper.

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

Reference 73

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T09:47:59.467156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-27T10:21:12.782864Z digest=sha256:1678c4a79709d6eb26c1c44b60f9511877ddcae36b1249bc1875f819d1217117

Observation 52b5e044-50a9-4d0c-b289-e25af79c9551 · inbound

Beyond Zooming: Learning Multi-Tool Visual Reasoning for Ultra-High-Resolution Remote Sensing cites this paper.

Beyond Zooming: Learning Multi-Tool Visual Reasoning for Ultra-High-Resolution Remote Sensing XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-01T00:59:16.958440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:59:16.958440Z digest=sha256:9182047ee48573397812c0e0f42067d00596bf38c958b87a4ca9858d4faee9ee

Observation 121c2f26-fe75-467d-96aa-348bd73e7c38 · inbound

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams cites this paper.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-04T02:47:25.515864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T02:47:25.515864Z digest=sha256:c27b41167fb4f13fe2808f39a3807372a434b610a47430c9ddafa409ed00afdb