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Paper Citation Record · LEDGER

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2501.18033.

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

pith.paper-citation-record.v1
2501.18033 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:58:34.816586Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:05:35.078581Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:50:54.825239Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d2ec1ba-9818-4141-b1c4-60a0ad8879fd · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Flamingo: a Visual Language Model for Few-Shot Learning

Reference 1

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Observation dc59e2d9-dd8a-48bf-91b6-74cafde60306 · outbound

This paper cites Wasserstein GAN.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Wasserstein GAN

Reference 2

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Observation 75dbb572-e3ac-4817-96df-3bfb978c375e · outbound

This paper cites Agentic systems: A guide to transforming indus- tries with vertical ai agents, 2025.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Agentic systems: A guide to transforming indus- tries with vertical ai agents, 2025

Reference 3

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Observation 415d0328-c0c8-47c0-a7c1-f4ae09f35438 · outbound

This paper cites Physical ai agents: Integrating cognitive intelli- gence with real-world action, 2025.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Physical ai agents: Integrating cognitive intelli- gence with real-world action, 2025

Reference 4

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Observation 47df9200-8030-464c-971d-e9ef9060208e · outbound

This paper cites Large scale gan training for high fidelity natural image synthesis.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Large scale gan training for high fidelity natural image synthesis

Reference 5

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Observation bd8ef176-e7e2-47f5-ac8b-04c75b823e8c · outbound

This paper cites InstructPix2Pix: Learning to Follow Image Editing Instructions.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications InstructPix2Pix: Learning to Follow Image Editing Instructions

Reference 6

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Observation 59bde726-9b30-4c72-ba35-2e3082da7b75 · outbound

This paper cites Janus-pro: Unified multi- modal understanding and generation with data and model scaling, 2025.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Janus-pro: Unified multi- modal understanding and generation with data and model scaling, 2025

Reference 7

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Observation deff94b1-4d1e-4efa-b91a-c0e873fdc51f · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 8

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Observation a79ad726-70a4-483f-bcd1-f5c9c8574a6a · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 9

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Observation ff498176-8f78-4915-b3c2-e93c5bf47d5b · outbound

This paper cites Diffusion models beat gans on image synthesis.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Diffusion models beat gans on image synthesis

Reference 10

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Observation eee4d4c0-061c-4bae-ade4-b0d68f96a9fb · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 11

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Observation 1c75d7f9-d1a3-4517-8078-41958eb99500 · outbound

This paper cites Gen- erative adversarial nets.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Gen- erative adversarial nets

Reference 12

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Observation cc8e0ba7-c444-46ed-b1ca-c2ceae6a765a · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 13

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Observation a9a2a6ff-9ab1-437d-accf-91485c90f2d5 · outbound

This paper cites Denoising diffusion proba- bilistic models, 2020.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Denoising diffusion proba- bilistic models, 2020

Reference 14

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Observation 06d2087a-f973-4cde-973b-0cba28826119 · outbound

This paper cites Crossing Over from Attractive to Repulsive Interactions in a Tunneling Bosonic Josephson Junction.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Crossing Over from Attractive to Repulsive Interactions in a Tunneling Bosonic Josephson Junction

Reference 15

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Observation ce86ef70-141e-4ee5-9746-78c31da1afe0 · outbound

This paper cites Image- to-image translation with conditional adversarial networks.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Image- to-image translation with conditional adversarial networks

Reference 16

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Source-reported events for the cited work

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Observation 0a5f1184-abc0-4e8e-81b3-c905ef66edf7 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Scaling up visual and vision-language representation learning with noisy text supervision

Reference 17

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Observation 2399bf68-2f4b-4cbd-9e0e-b97d3eaea610 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications A style-based generator architecture for generative adversarial networks

Reference 18

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Observation 498897eb-c13c-43f6-87a0-c1e2857edc16 · outbound

This paper cites Auto-Encoding Variational Bayes.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Auto-Encoding Variational Bayes

Reference 19

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Observation 9c5a2deb-4c78-475d-ae9f-b11e1182d931 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 20

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Observation bcc98676-e740-4654-af3f-0db467b66fdc · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Photo-realistic single image super-resolution using a generative adversarial network

Reference 21

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Observation f10a70b6-0a7e-48e1-a9ce-3aae5d7e0072 · outbound

This paper cites BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Reference 22

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Observation 58abe1d0-0a2c-450a-a940-9e27201acb4b · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 23

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Observation f1087d8a-57ca-4dd4-a066-1a2b3adb23ca · outbound

This paper cites Pose Guided Person Image Generation.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Pose Guided Person Image Generation

Reference 24

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Observation aacc12b8-a051-4268-9037-612cbc9589fc · outbound

This paper cites Adversarial Autoencoders.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Adversarial Autoencoders

Reference 25

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Observation 0d1db8f7-12a9-4f40-8e7c-bee10848611c · outbound

This paper cites T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Reference 26

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Observation bd822de9-8a7f-444f-aa8a-88b819e37529 · outbound

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Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Neural discrete representation learning

Reference 27

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Observation 6fc63e86-f7e9-4889-8bf6-d2c9043b81f7 · outbound

This paper cites Gpt-4 technical report, 2024.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Gpt-4 technical report, 2024

Reference 28

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Observation 2bb465e6-bd40-4f5f-b612-8f8ea1ef5184 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Learning Transferable Visual Models From Natural Language Supervision

Reference 29

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Observation d621d4b5-2a13-4fba-81e9-b249644f0887 · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Learning transferable visual models from natural language supervision, 2021

Reference 30

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Observation fac6f3a2-b451-4e97-8d72-0c20ac03f842 · outbound

This paper cites Language models are unsupervised multitask learn- ers.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Language models are unsupervised multitask learn- ers

Reference 31

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Observation 76670c2e-5d86-4030-8ce0-a403033a60ca · outbound

This paper cites Hierarchical text-conditional image generation with clip latents, 2022.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Hierarchical text-conditional image generation with clip latents, 2022

Reference 32

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Observation 3e7e4d89-555a-487c-9449-06c44ac4f311 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications High-resolution image synthesis with latent diffusion models

Reference 33

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Source-reported events for the cited work

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Observation 2c379c1c-6b4f-4061-843d-f6ad3dccef73 · outbound

This paper cites High-resolution image synthesis with latent diffusion models, 2022.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications High-resolution image synthesis with latent diffusion models, 2022

Reference 34

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Source-reported events for the cited work

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Observation fb485ef0-41f9-4fe4-8eee-63a8642eb62b · outbound

This paper cites DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

Reference 35

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source=pdf_text observed=2026-08-10T00:58:34.784678Z digest=sha256:3af667e85660ffda3356f77d9465682f6766b3b3b6177c68230570054c301e59

Observation 19aa62e2-cd83-4882-ab65-ecef2649dc3b · outbound

This paper cites Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d24e3f00-1a77-4052-b0fe-e81466be31c9 · outbound

This paper cites Imagen: Text-to- image diffusion models with large pretrained models.Advances in Neural Information Processing Systems, 35, 2022.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Imagen: Text-to- image diffusion models with large pretrained models.Advances in Neural Information Processing Systems, 35, 2022

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 2a4ad4ab-efd4-4071-9b1f-d0bd22dd724d · outbound

This paper cites FLAVA: A Foundational Language And Vision Alignment Model.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications FLAVA: A Foundational Language And Vision Alignment Model

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a80f88a3-8c7c-4a77-a1c4-aaa534016149 · outbound

This paper cites Learning structured output representation using deep conditional generative models.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Learning structured output representation using deep conditional generative models

Reference 39

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0936282b-17bd-4ddc-b3b8-95b893c7af5b · outbound

This paper cites Ladder variational autoencoders.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Ladder variational autoencoders

Reference 40

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 085aa662-56ef-4f77-b426-c197fb0dc97c · outbound

This paper cites Consistency Models.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Consistency Models

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f2f8f35e-b839-4bd0-a964-adc4af66232f · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Score-based generative modeling through stochastic differential equations

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T00:58:35.015841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 83c0e36e-ce39-4a08-ba39-a7fa2e028f30 · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models, 2023.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Llama 2: Open foundation and fine-tuned chat models, 2023

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T00:58:35.007420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 37c5718c-6765-48c1-94bf-70d0fb120a0b · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications, 2024.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Diffusion models: A comprehensive survey of methods and applications, 2024

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T00:58:34.998570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 8f252cd2-c921-47f5-bcde-66c5e87bd54d · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-10T00:58:34.811605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 00492d8a-8c24-4082-984e-6e01e45f1b21 · outbound

This paper cites Adding Conditional Control to Text-to-Image Diffusion Models.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Adding Conditional Control to Text-to-Image Diffusion Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-10T00:58:34.814064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T00:58:34.814064Z digest=sha256:96c14954e4d2bafd7e28f693f0b995d7466a46633d863b60c12d373d2a93149b

Observation d3769457-8161-4e98-ba7c-02fa9e82d4c1 · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks.

Generative AI for Vision: A Comprehensive Study of Frameworks and Applications Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T00:58:34.989782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T00:58:34.816586Z digest=sha256:984bdb925b81754ebe87c5b8c5222616b5ed4d26cf2fc43c6d22b8ab132d3da7

Pith citing papers

Observation 8322d647-05dc-419e-a2bc-c0c386bab16e · inbound

GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design cites this paper.

GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design Generative AI for Vision: A Comprehensive Study of Frameworks and Applications

Reference 12

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:05:35.078581Z digest=sha256:3e73de3dc41fef283e334153e0f65d76bbe5f7dce408c912a42ede7bf2a965d0

Observation 8afc7f2f-4da9-4872-af67-7a4e0756d540 · inbound

Generative AI for Industrial Contour Detection: A Language-Guided Vision System cites this paper.

Generative AI for Industrial Contour Detection: A Language-Guided Vision System Generative AI for Vision: A Comprehensive Study of Frameworks and Applications

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:50:54.900599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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