Paper Citation Record · LEDGER

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2508.07020.

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

pith.paper-citation-record.v1
2508.07020 v1

Coverage vector

measured 31 of 31 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-05T22:26:40.829467Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

31 of 31 outbound references displayed

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Outbound references

Observation 6856a0a3-9925-4b27-856f-48a70440556f · outbound

This paper cites GPT-4 Technical Report.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders GPT-4 Technical Report

Reference 1

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Observation 041867c6-1d6c-4b4e-80af-ed3416e6a642 · outbound

This paper cites Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI

Reference 2

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Observation 07bb3db2-8014-464a-b326-b05f0a479425 · outbound

This paper cites Can large language models challenge CNNs in medical image analysis? In IEEE International Conference on Image Processing (ICIP), 2025.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Can large language models challenge CNNs in medical image analysis? In IEEE International Conference on Image Processing (ICIP), 2025

Reference 3

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Observation 54e6cc2b-8235-44c6-a76e-5c7bf727f12d · outbound

This paper cites Al-Yasriy.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Al-Yasriy

Reference 4

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Observation dc7f0871-c204-460b-8a2b-2600d45773f5 · outbound

This paper cites A framework to assess clini- cal safety and hallucination rates of LLMs for medical text summarisation.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders A framework to assess clini- cal safety and hallucination rates of LLMs for medical text summarisation

Reference 5

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Observation 7dad49f6-0ca2-4643-84ca-8c9a1f8d1e05 · outbound

This paper cites Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

Reference 6

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Observation 7f3a3373-044d-4cc6-9ca0-d03c960bf095 · outbound

This paper cites Breaking the shield: Vulnerabilities in content moderation for multi- modal language models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Breaking the shield: Vulnerabilities in content moderation for multi- modal language models

Reference 7

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Observation bc5692b7-7986-471c-af9f-2d399a6117df · outbound

This paper cites Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models

Reference 8

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Observation ed5d554b-7f57-45c2-a527-163702b4bb99 · outbound

This paper cites Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer

Reference 9

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Observation 132dd4a7-8acd-489d-ae79-bd02b2a8786b · outbound

This paper cites MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

Reference 10

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Observation 9e0df14e-efb7-4a17-a8f1-1ea7867b6703 · outbound

This paper cites ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

Reference 11

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Observation 8958df86-8678-4615-90ad-7fe812929d83 · outbound

This paper cites FactCheXcker: Miti- gating measurement hallucinations in chest X-ray report gen- eration models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders FactCheXcker: Miti- gating measurement hallucinations in chest X-ray report gen- eration models

Reference 12

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Observation 8ffec8df-7571-41d2-a039-ba8b6b047736 · outbound

This paper cites DALL-M: Context-aware clinical data augmentation with large language models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders DALL-M: Context-aware clinical data augmentation with large language models

Reference 13

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Observation 4e10332b-4cec-4b12-b2d3-c47d2476c7b9 · outbound

This paper cites Evaluation of SVM performance in the detection of lung cancer in marked ct scan dataset.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Evaluation of SVM performance in the detection of lung cancer in marked ct scan dataset

Reference 14

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Observation c047cc7c-2da1-4ca4-b7a2-0066f399c1f3 · outbound

This paper cites Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS

Reference 15

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Observation 5124e28e-58b2-4dcd-8a47-ad25a2f9b67b · outbound

This paper cites Medical hallucinations in foundation models and their impact on healthcare.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Medical hallucinations in foundation models and their impact on healthcare

Reference 16

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Observation b0c198ee-c064-4058-8cc9-479304a01135 · outbound

This paper cites Mitigating structural hallucination in LLMs with local diffusion.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Mitigating structural hallucination in LLMs with local diffusion

Reference 17

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Observation 58d8db1c-e1fd-4c9e-9f28-cd397d6dcac0 · outbound

This paper cites LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation

Reference 18

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Observation fbc80399-b4b3-4d82-974b-1ba64bee1aab · outbound

This paper cites Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation

Reference 19

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Observation f42509ce-0b02-44dc-8118-86ac967f31d8 · outbound

This paper cites Prompt-guided generation of structured chest X-ray report using a pre-trained LLM.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Prompt-guided generation of structured chest X-ray report using a pre-trained LLM

Reference 20

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Observation 45d012b7-ae69-4ac9-835b-be873d46a7be · outbound

This paper cites Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval

Reference 21

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Observation ad5f96c1-ab70-4a39-b9ed-9f6300a435c6 · outbound

This paper cites Indiana university chest x- ray.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Indiana university chest x- ray

Reference 22

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Observation b28f2f61-f351-406d-ad89-f2f0183af618 · outbound

This paper cites Med-HALT: Medical Domain Hallucination Test for Large Language Models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Med-HALT: Medical Domain Hallucination Test for Large Language Models

Reference 23

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Observation 5d1b0511-ac37-4f50-b960-ef756ea44bdc · outbound

This paper cites Leveraging large language models to foster equity in health- care.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Leveraging large language models to foster equity in health- care

Reference 24

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Observation 7fccf8ea-dc5a-4a14-b68e-9b1678dc65ec · outbound

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

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Gemini: A Family of Highly Capable Multimodal Models

Reference 25

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Observation 0e1a827a-adea-4f00-840a-4e4afe52e54e · outbound

This paper cites Hallucination index: An image quality metric for gener- ative reconstruction models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Hallucination index: An image quality metric for gener- ative reconstruction models

Reference 26

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Observation 071568f0-6a16-46fc-bf6a-bbdba9341b2d · outbound

This paper cites On large visual language models for medical imaging analysis: An empirical study.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders On large visual language models for medical imaging analysis: An empirical study

Reference 27

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Observation 900b83b3-9e7e-4f39-a214-8c12e18a2922 · outbound

This paper cites Rodney Long, and George R.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Rodney Long, and George R

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:40.819700Z

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Observation e2957301-ec11-4dc4-b42e-438cac3c0c06 · outbound

This paper cites Med-hvl: Au- tomatic medical domain hallucination evaluation for large vision-language models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Med-hvl: Au- tomatic medical domain hallucination evaluation for large vision-language models

Reference 29

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Observation 84d671a0-3789-48b0-b0d7-78efdf937a6e · outbound

This paper cites RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:40.826225Z

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Observation 038607a1-13e2-41aa-a690-ab5f2d7550b7 · outbound

This paper cites MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models.

TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:40.829467Z

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Pith citing papers

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