Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:46:03.748195Z
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 2 inbound Pith citation observations for arXiv:2506.18985.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:46:03.748195Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-10T19:59:19.379119Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
28 of 28 outbound references displayed
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Observation 9368b046-2675-46dd-b26f-c9dd94caf847 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Quantifying attention flow in transformers
Reference 1
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Observation dad3ad63-80a2-480c-a477-43c140ab9e35 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers
Reference 2
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Observation bfce766f-b504-4d1e-ac24-49f90eba467d · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models XAI for trans- formers: better explanations through conservative propaga- tion
Reference 3
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Observation c302c794-ca24-4bc0-8f2d-7ee72c1b7db3 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Reference 4
Source-reported events for the cited work
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Observation cc955556-c5c7-42e0-b57a-da82e4a71a53 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Qwen2.5-VL Technical Report
Reference 5
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Observation f17b3e68-7802-453c-93ee-23e3b4df9b09 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Unresolved cited work
Reference 6
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Observation b3a63cf5-3d53-471f-bc0b-0ce2f469bbae · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Visual Explanations via Iterated Integrated Attributions
Reference 7
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Observation 1eedf591-3e3b-409f-b668-3ac954c14642 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers
Reference 8
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Observation 8a60b89c-3f04-4ab7-bed2-be1f59881448 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Human attention in visual question answer- ing: do humans and deep networks look at the same regions? In Proc
Reference 9
Source-reported events for the cited work
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Observation 3698a970-62c3-4bf5-bbd6-1a3a5a009400 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation
Reference 10
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Observation 3b3c9461-e966-43be-ac55-28178f7d9727 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Turtles, Hats and Spectres: Aperiodic structures on a Rhombic tiling
Reference 11
Source-reported events for the cited work
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Observation 49838183-700a-4569-8e91-10c49ea97e7d · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models iGOS++: inte- grated gradient optimized saliency by bilateral perturbations
Reference 12
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Observation 4948d703-030d-414c-8f24-d2cee8ca793c · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models From representa- tion to reasoning: Towards both evidence and commonsense reasoning for video question-answering
Reference 13
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Observation 0925e4f2-94ce-49c2-a15e-37135eb8dbfb · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Visual Instruction Tuning
Reference 14
Source-reported events for the cited work
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Observation b30bf151-85f1-44f2-9379-fe6a621791d2 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Lundberg and Su-In Lee
Reference 15
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Observation 6f305ed1-19a6-4792-aff2-1ffc1df838cc · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Mortality Forecasting using Variational Inference
Reference 16
Source-reported events for the cited work
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Observation 8625d7cc-78aa-4e18-9c69-5d185cb40c03 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Exploring human-like attention supervision in visual question answer- ing
Reference 17
Source-reported events for the cited work
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Observation 8e4f297d-0d47-4626-af6e-e971c00aff3c · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Reference 18
Source-reported events for the cited work
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Observation 85c84dc7-508d-4685-8e72-1e1ea0405777 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling
Reference 19
Source-reported events for the cited work
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Observation 4810cd64-9b2c-47a6-a56f-ca0ebda08dab · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Ba- tra
Reference 20
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Observation ab0d3a7d-9336-465e-83f1-0972c0f63b06 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Ba- tra
Reference 21
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Observation 13db61c4-7aed-41f1-821b-af02a44434df · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Deep inside convolutional networks: visualising image clas- sification models and saliency maps
Reference 22
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Observation d3968f7e-926d-4681-8dc7-aa014e420dcc · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models On numerical solutions of the time-dependent Schr\"odinger equation
Reference 23
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Observation 01581318-b063-4710-9727-44e317e01030 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models
Reference 24
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Observation ef6f40f4-bdf6-47ab-8fde-1911bff2fc9b · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Axiomatic attribution for deep networks
Reference 25
Source-reported events for the cited work
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Observation 5c4d78c7-4ea7-4683-866c-3774b2dd847d · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models Attention, Please! PixelSHAP Reveals What Vision-Language Models Actually Focus On
Reference 26
Source-reported events for the cited work
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Observation 9758877d-3a24-40fc-b4b4-0f5d28450645 · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models VQA-MHUG: human gaze supervision for visual question answering
Reference 27
Source-reported events for the cited work
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Observation b27832b6-71d2-45dd-9a4b-b6ff578538dc · outbound
GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models What if the tv was off? examining counterfactual reasoning abilities of vision-language models
Reference 28
Source-reported events for the cited work
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Observation 2bfed174-41f7-409f-974f-a06262405880 · inbound
Saliency-R1: Enforcing Interpretable and Faithful Vision-language Reasoning via Saliency-map Alignment Reward GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models
Reference 61
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Observation 79adc347-769b-4532-bf89-f49ad36076e7 · inbound
Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models
Reference 25
Source-reported events for the cited work
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