Paper Citation Record · LEDGER

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind

As of 19 August 2026, this Paper Citation Record lists 67 of 67 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.

pith.paper-citation-record.v1
2505.12207 v3

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:44:43.776033Z

measured 71 of 71 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T22:31:35.967550Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:34:01.406814Z

Reference resolution

67 of 67 outbound references displayed

verified exact5
verified fuzzy31
unresolved30
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f697720b-3c3c-4df7-9cc0-2b23183cb246 · outbound

This paper cites Remote sensing for agricultural applications: A meta-review,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Remote sensing for agricultural applications: A meta-review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.610919Z

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.

Observation 692f37c2-e072-4b68-a99e-af2c448d4b98 · outbound

This paper cites Zero hunger: future challenges and the way forward towards the achievement of sustainable development goal 2,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Zero hunger: future challenges and the way forward towards the achievement of sustainable development goal 2,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.600406Z

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.

Observation 125c0312-45dc-4605-9420-5da7e8cc189c · outbound

This paper cites Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the safy crop growth model,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the safy crop growth model,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.590362Z

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.

Observation ef43612f-ae2d-455c-80b9-868801ec410f · outbound

This paper cites Challenges and opportunities in remote sensing-based crop monitoring: A review,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Challenges and opportunities in remote sensing-based crop monitoring: A review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.579843Z

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.

Observation 82f1b7cd-e549-4f53-acbb-04b638b80670 · outbound

This paper cites A review of individual tree crown detection and delineation from optical remote sensing images: Current progress and future,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind A review of individual tree crown detection and delineation from optical remote sensing images: Current progress and future,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.569688Z

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.

Observation 258540a6-d650-44f6-bd21-bc559b2efae6 · outbound

This paper cites Progress and prospects of crop diseases and pests monitoring by remote sensing,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Progress and prospects of crop diseases and pests monitoring by remote sensing,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.558105Z

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.

Observation 845e2d05-b4ed-4664-8551-09a4b1e7c4d2 · outbound

This paper cites Advances in deep learning applications for plant disease and pest detection: A review,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Advances in deep learning applications for plant disease and pest detection: A review,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.547719Z

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.

Observation ac73e645-33da-40bc-84a7-9ed5c69de8f6 · outbound

This paper cites Evaluation of survey and remote sensing data products used to estimate land use change in the united states: Evolving issues and emerging opportunities,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Evaluation of survey and remote sensing data products used to estimate land use change in the united states: Evolving issues and emerging opportunities,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.537537Z

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.

Observation 72b681ce-6be8-4ce0-8486-400fd17caa30 · outbound

This paper cites FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:44.195809Z

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.

Observation a680ae04-b908-4e2d-a8c6-236359a0473a · outbound

This paper cites Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.527176Z

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.

Observation ba0563c7-05cc-4e4d-8e45-f4b895bca199 · outbound

This paper cites Hello gpt-4o,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Hello gpt-4o,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.516864Z

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.

Observation 5cc661c1-fbec-4fbd-8a8e-6110a841c4cb · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Gemini: A Family of Highly Capable Multimodal Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.577705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95bbac99-1bc3-4569-bf73-1e39a6a70bfa · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind LLaMA: Open and Efficient Foundation Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.581411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a15df018-ae5a-4abb-b778-97e508df4085 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.584689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cec67af7-7727-4142-9644-e13d45039cda · outbound

This paper cites H2rsvlm: Towards helpful and honest remote sensing large vision language model,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind H2rsvlm: Towards helpful and honest remote sensing large vision language model,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.588318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de2fc18f-1661-4ada-a68c-8f7a47edd58c · outbound

This paper cites Vision-language models in remote sensing: Current progress and future trends,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Vision-language models in remote sensing: Current progress and future trends,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.592136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e77b66b9-093a-414e-bddd-11e6d4fbdffd · outbound

This paper cites Urbench: A comprehensive benchmark for evaluating large multimodal models in multi-view urban scenarios,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Urbench: A comprehensive benchmark for evaluating large multimodal models in multi-view urban scenarios,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.493775Z

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.

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

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

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.

Observation 51132c66-6bae-46bb-b6d9-f6837c600f03 · outbound

This paper cites Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.482146Z

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.

Observation 935dba89-3363-4a0f-8481-c9873d081115 · outbound

This paper cites A multimodal benchmark dataset and model for crop disease diagnosis,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind A multimodal benchmark dataset and model for crop disease diagnosis,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.470949Z

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.

Observation 81f7c205-0497-4748-9ec4-3d6cffd92d0a · outbound

This paper cites Visual question answering model for fruit tree disease decision-making based on multimodal deep learning,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Visual question answering model for fruit tree disease decision-making based on multimodal deep learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.460357Z

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.

Observation 00bd4ed4-d206-42f4-8312-e759867a65b3 · outbound

This paper cites Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.614271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 981620e5-732a-46bf-897c-d5924e384ec9 · outbound

This paper cites AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.617655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda08dd6-a405-4de8-be9e-add82bd7609c · outbound

This paper cites AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.621024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d1b3ede-d2f8-4672-ae20-fc0d76d86b99 · outbound

This paper cites Rsgpt: A remote sensing vision language model and benchmark,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Rsgpt: A remote sensing vision language model and benchmark,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.448837Z

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.

Observation d441f3d2-dfc6-4911-a1bc-689d5a486c6b · outbound

This paper cites Earthvqa: towards queryable earth via relational reasoning-based remote sensing visual question answering,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Earthvqa: towards queryable earth via relational reasoning-based remote sensing visual question answering,

Reference 26

Resolution: verified exact; doi, observed 2026-08-15T20:44:43.808993Z

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.

Observation 9fb18c08-e19b-4ce0-8227-e428771df438 · outbound

This paper cites Lhrs-bot: Empowering remote sensing with vgi- enhanced large multimodal language model,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Lhrs-bot: Empowering remote sensing with vgi- enhanced large multimodal language model,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.437918Z

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.

Observation 0dc43abb-5a36-4fee-8687-0f762dd172de · outbound

This paper cites Agrogpt : Efficient agricultural vision-language model with expert tuning,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Agrogpt : Efficient agricultural vision-language model with expert tuning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.426907Z

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.

Observation 85f9deaf-7583-4036-9b6c-fd3f5f4378da · outbound

This paper cites GPT-4 Technical Report.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind GPT-4 Technical Report

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.639089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af598bc1-6abb-416b-ab0b-714e20ae000b · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.642389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 151b7615-1d8f-4729-85b3-97bca6c70690 · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.645653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2c2b996-2e3e-439d-971d-ea60266a18fc · outbound

This paper cites Improved baselines with visual instruction tuning,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Improved baselines with visual instruction tuning,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.649504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65cb4ec1-1960-4e1b-a267-031b63af15a9 · outbound

This paper cites Vila: On pre-training for visual language models,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Vila: On pre-training for visual language models,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.653014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b62de6e2-b00a-4a70-a33f-d0509cacfb82 · outbound

This paper cites Efficient prompt tuning of large vision- language model for fine-grained ship classification,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Efficient prompt tuning of large vision- language model for fine-grained ship classification,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.395774Z

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.

Observation 2aeed79d-edfe-417f-9f86-7a20cd24b4a1 · outbound

This paper cites Lhrs-bot: Empowering remote sensing with vgi-enhanced large multimodal language model,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Lhrs-bot: Empowering remote sensing with vgi-enhanced large multimodal language model,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.385068Z

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.

Observation d7f63bc7-3c74-4711-90f2-5424333363cc · outbound

This paper cites Geochat: Grounded large vision-language model for remote sensing,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Geochat: Grounded large vision-language model for remote sensing,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.663669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d90df507-686f-4e43-91f9-4eb9995c5d11 · outbound

This paper cites Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.667182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bd7efa1-10d3-4b2a-a952-fb0dc4605ef4 · outbound

This paper cites Integrating deep learning for visual question answering in agricultural disease diagnostics: Case study of wheat rust,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Integrating deep learning for visual question answering in agricultural disease diagnostics: Case study of wheat rust,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.363185Z

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.

Observation f4845ac9-d104-45ac-a635-50044e3bb5c3 · outbound

This paper cites Nocaps: Novel object captioning at scale,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Nocaps: Novel object captioning at scale,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.674409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dc5f878-943b-4dae-97fb-b276b61b4ebe · outbound

This paper cites Ok-vqa: A visual question answering benchmark requiring external knowledge,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Ok-vqa: A visual question answering benchmark requiring external knowledge,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.678061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b00d41fc-6530-48da-a56d-dd20f5b10df5 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answering,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Making the v in vqa matter: Elevating the role of image understanding in visual question answering,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.339636Z

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.

Observation b49fbbca-a4f8-4856-8b0d-89237b656308 · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Gqa: A new dataset for real-world visual reasoning and compositional question answering,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.328785Z

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.

Observation 151723f3-09df-42dd-bb9a-9149810740ec · outbound

This paper cites Tap: Text-aware pre-training for text-vqa and text-caption,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Tap: Text-aware pre-training for text-vqa and text-caption,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.317892Z

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.

Observation 0f4016df-612b-45c7-a68f-52145a216021 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.692226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ccb13b-1065-4b47-8a84-b390ece03a39 · outbound

This paper cites Seed-bench: Benchmarking multimodal large language models,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Seed-bench: Benchmarking multimodal large language models,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.301002Z

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.

Observation 05d5a28d-78ef-46e4-87e7-9f579b2fc602 · outbound

This paper cites MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.699366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11f515d5-fa07-4429-9bc2-c9ee9a943814 · outbound

This paper cites MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.702648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 569fbe55-d8de-4efe-a032-48c380ea04cc · outbound

This paper cites OAM-TCD: A globally diverse dataset of high-resolution tree cover maps.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind OAM-TCD: A globally diverse dataset of high-resolution tree cover maps

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:44.087748Z

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.

Observation 64538cee-8b6a-4d3b-b1ce-8d6863d232cc · outbound

This paper cites Growing status observation for oil palm trees using unmanned aerial vehicle (uav) images,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Growing status observation for oil palm trees using unmanned aerial vehicle (uav) images,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.290108Z

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.

Observation 2d940f55-35de-49d8-8a91-0718809a857c · outbound

This paper cites Agriculture- vision: A large aerial image database for agricultural pattern analysis,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Agriculture- vision: A large aerial image database for agricultural pattern analysis,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.278199Z

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.

Observation aec19df8-a4c2-4c30-9c23-3404eec3dacb · outbound

This paper cites PhenoBench — A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind PhenoBench — A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.267370Z

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.

Observation 27d544a8-7844-4f5a-84a0-ae46cebb5ecf · outbound

This paper cites Ip102: A large-scale benchmark dataset for insect pest recognition,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Ip102: A large-scale benchmark dataset for insect pest recognition,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.256424Z

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.

Observation 9363bcde-7669-406c-bbe5-fc68c2159f8f · outbound

This paper cites Deep Fruit Detection in Orchards.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Deep Fruit Detection in Orchards

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:44.073003Z

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.

Observation ea741b03-a55d-4eec-9160-e007f2a070ca · outbound

This paper cites Cropharvest: A global dataset for crop-type classification,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Cropharvest: A global dataset for crop-type classification,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.245693Z

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.

Observation 90644816-c143-400a-b292-506a6c4b0232 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind The claude 3 model family: Opus, sonnet, haiku,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.234965Z

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.

Observation b1d4b9f8-06ba-4a49-b02e-e6e9ece34d7d · outbound

This paper cites TinyLLaVA: A Framework of Small-scale Large Multimodal Models.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind TinyLLaVA: A Framework of Small-scale Large Multimodal Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.735012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 550a0bf7-a68f-48e4-b6cc-021b86d96a69 · outbound

This paper cites InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.738894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d9ef46f-229a-4fa4-9672-61c388c13a4e · outbound

This paper cites InstructBLIP: Towards general-purpose vision-language models with instruction tuning,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind InstructBLIP: Towards general-purpose vision-language models with instruction tuning,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.742793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 546fd704-f8b0-41f5-870d-dff1be5acf1c · outbound

This paper cites What matters when building vision- language models?.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind What matters when building vision- language models?

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.747231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 793974d0-9d2a-4633-984b-027cd81b1ea3 · outbound

This paper cites Geochat: Grounded large vision-language model for remote sensing,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Geochat: Grounded large vision-language model for remote sensing,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.751003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6b9c74e-ee63-41aa-ba9f-a870aa24fddf · outbound

This paper cites Geollava-8k: Scaling remote-sensing multimodal large language models to 8k resolution,.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Geollava-8k: Scaling remote-sensing multimodal large language models to 8k resolution,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.754518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a12dccf-ae67-4f72-a3ab-62f6d0c5d087 · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.758033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbf636e4-2de3-4951-90bb-752049acb7b7 · outbound

This paper cites MANTIS: Interleaved Multi-Image Instruction Tuning.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind MANTIS: Interleaved Multi-Image Instruction Tuning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.761621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32795418-00df-47b9-9eb9-54cdfafd60a8 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:43.765494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aebd6c7a-76a4-47ea-80c7-e0cd343c3e11 · outbound

This paper cites an unresolved cited work.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind Unresolved cited work

Reference 65

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:44:43.956799Z

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.

Observation b9b2a3c0-9be6-46d6-89e5-d92bb9b4556c · outbound

This paper cites com/PRBonn/ phenobench OAM-TCD Dataset.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind com/PRBonn/ phenobench OAM-TCD Dataset

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:44.205485Z

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.

Observation a97a32d1-1ee7-47ba-bc2a-c0d8f8ed19fb · outbound

This paper cites box-based.

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind box-based

Reference 67

Resolution: malformed identifier; no resolver link, observed 2026-08-15T20:44:43.776033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+00:00.