Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2505.12207.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T22:31:35.967550Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T22:34:01.406814Z
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 bb39dfde-fb40-4cd6-a7db-b6b77dc19a05 · inbound
AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture cites this paper.
AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T18:00:26.927424Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8ab42fb7-eae2-4e35-a89d-faba5bb31b6f · inbound
HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing cites this paper.
HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:30:57.695458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation eecb98c2-802f-4748-9c77-08aac7d822ef · inbound
AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture cites this paper.
AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T07:51:16.383604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 55f06c66-96e7-41a9-8ace-4dffebf7d04b · inbound
VertiCue-Bench: Diagnosing Whether MLLMs Use Height Cues to Resolve 2D Ambiguity in Remote Sensing Natural Scenes cites this paper.
VertiCue-Bench: Diagnosing Whether MLLMs Use Height Cues to Resolve 2D Ambiguity in Remote Sensing Natural Scenes Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:34:01.409852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.