Paper Citation Record · LEDGER

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios

As of 8 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 2 inbound Pith citation observations for arXiv:2509.09172.

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

pith.paper-citation-record.v1
2509.09172 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:37:02.250303Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T00:49:22.183457Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T10:41:29.506096Z

Reference resolution

63 of 63 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0749b45-61cd-45ef-bcc0-fe4addcbf59b · outbound

This paper cites GPT-4 Technical Report.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios GPT-4 Technical Report

Reference 1

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Observation d99da56e-1179-4718-b814-23d0039503bf · outbound

This paper cites Introducing the next generation of claude.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Introducing the next generation of claude

Reference 2

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Observation 19deff02-3006-4137-887b-e6d8de6f7fb7 · outbound

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

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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Observation c5df39ca-94ce-4bc1-ba44-75ad07daba63 · outbound

This paper cites Cifake: Image classifica- tion and explainable identification of ai-generated synthetic images.IEEE Access, 2024.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Cifake: Image classifica- tion and explainable identification of ai-generated synthetic images.IEEE Access, 2024

Reference 4

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Observation b3bc495c-b74b-4c08-a019-039812bf3128 · outbound

This paper cites Real-time deepfake detection in the real-world, 2024.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Real-time deepfake detection in the real-world, 2024

Reference 5

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Observation b50e4041-fb9d-4501-9932-fecf098cd1ce · outbound

This paper cites What makes fake images detectable? understanding proper- ties that generalize.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios What makes fake images detectable? understanding proper- ties that generalize

Reference 6

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Observation 4b8c090e-e552-4f7b-a8a3-9e173fffa6cc · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts

Reference 7

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Observation 9eebc9d9-6584-4302-8d89-0b506eda50b2 · outbound

This paper cites DRCT: diffusion reconstruction contrastive training towards universal detection of diffusion generated images.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios DRCT: diffusion reconstruction contrastive training towards universal detection of diffusion generated images

Reference 8

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Observation 529607ac-8da3-424c-8e22-5dcfeb2a7365 · outbound

This paper cites A Single Simple Patch is All You Need for AI-generated Image Detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios A Single Simple Patch is All You Need for AI-generated Image Detection

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Observation a4c11670-7538-4185-9dd4-219f8d7f20a4 · outbound

This paper cites On the detection of synthetic images generated by diffusion mod- els.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios On the detection of synthetic images generated by diffusion mod- els

Reference 10

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Observation 762c53b5-d782-4f9d-ad45-91eb6976554a · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Imagenet: A large-scale hierarchical image database

Reference 11

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Observation fdfddca6-bbb8-4a89-b79d-99d54b6912c7 · outbound

This paper cites Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions

Reference 12

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Observation aa360687-7f79-4a7b-9cf9-48642ceea2bf · outbound

This paper cites Witherden.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Witherden

Reference 13

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Observation 3271de46-887e-4001-9745-ae01031e2cda · outbound

This paper cites Fake-gpt: Detecting fake image via large language model.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Fake-gpt: Detecting fake image via large language model

Reference 14

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Observation 7f9d0131-3eb0-48b7-8a77-8ae4a356342b · outbound

This paper cites Leveraging fre- quency analysis for deep fake image recognition.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Leveraging fre- quency analysis for deep fake image recognition

Reference 15

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Observation bfd27ed2-239c-4b1c-953b-6ffd8708577f · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 16

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Observation 44c63182-7222-4f6f-948c-c34cecdf10f2 · outbound

This paper cites grok-2.https://x.ai/blog/grok-2, 2025.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios grok-2.https://x.ai/blog/grok-2, 2025

Reference 17

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Observation a8e10bf7-d84e-4120-bceb-051ca725a3f3 · outbound

This paper cites WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection

Reference 18

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Observation 9538e24b-46b4-4a84-899e-caa7f7ebc857 · outbound

This paper cites hunyuan-vision.https : / / hunyuan.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios hunyuan-vision.https : / / hunyuan

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Observation c589ef71-be30-4e88-b097-2b0a2403a7d5 · outbound

This paper cites Fusing global and local features for gen- eralized ai-synthesized image detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Fusing global and local features for gen- eralized ai-synthesized image detection

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Observation 06ebf391-d7f0-49e3-8c04-bf0b58274439 · outbound

This paper cites Evolution of Detection Performance throughout the Online Lifespan of Synthetic Images.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Evolution of Detection Performance throughout the Online Lifespan of Synthetic Images

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Observation 29bfbe6b-ede2-41af-99c6-cc38e62e1e52 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Progressive Growing of GANs for Improved Quality, Stability, and Variation

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Observation d1436673-5723-4b82-8386-77f593645c10 · outbound

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

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios A style-based generator architecture for generative adversarial networks

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Observation 32294f7a-1edd-4de8-9921-11316d049837 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

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Observation 769c78f1-2e6b-4323-9113-e256927168d6 · outbound

This paper cites Harnessing the Power of Large Vision Language Models for Synthetic Image Detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Harnessing the Power of Large Vision Language Models for Synthetic Image Detection

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Observation 29b69589-54e9-452a-83ac-f9816bd4c36f · outbound

This paper cites Clip- ping the deception: Adapting vision-language models for universal deepfake detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Clip- ping the deception: Adapting vision-language models for universal deepfake detection

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Observation d8ddebcb-9ac3-4a89-9818-991c330e0576 · outbound

This paper cites Leveraging rep- resentations from intermediate encoder-blocks for synthetic image detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Leveraging rep- resentations from intermediate encoder-blocks for synthetic image detection

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Observation 665b7d74-d049-4cd0-875f-fe0e5d094d0d · outbound

This paper cites Flux.https://github.com/ black-forest-labs/flux, 2024.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Flux.https://github.com/ black-forest-labs/flux, 2024

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Observation 9b7bca3d-26d6-4281-b86d-46226ba8893e · outbound

This paper cites Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective

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Observation f32f6f8b-8f60-4575-8cd8-bd428f7d7ce8 · outbound

This paper cites FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models

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This paper cites Microsoft coco: Common objects in context.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Microsoft coco: Common objects in context

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Observation 9bd8d8c1-66ae-4037-9884-ff1e7e369603 · outbound

This paper cites Detecting generated images by real images.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Detecting generated images by real images

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Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Unresolved cited work

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Observation 96319216-7f78-4710-8bd3-b5b566a91c8f · outbound

This paper cites Seeing is not always believing: benchmarking human and model perception of ai-generated images.Advances in Neural Information Processing Sys- tems, 36, 2024.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Seeing is not always believing: benchmarking human and model perception of ai-generated images.Advances in Neural Information Processing Sys- tems, 36, 2024

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This paper cites Detecting gan-generated images by orthogonal training of multiple cnns.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Detecting gan-generated images by orthogonal training of multiple cnns

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Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Midjourney, 2024

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This paper cites moonshot-preview-vision.https://www.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios moonshot-preview-vision.https://www

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Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Towards uni- versal fake image detectors that generalize across generative models

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Observation 624af3c5-c06e-42d6-a232-def3b803ce99 · outbound

This paper cites Semi-truths: A large-scale dataset of ai-augmented images for evaluating robustness of ai-generated image detectors.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Semi-truths: A large-scale dataset of ai-augmented images for evaluating robustness of ai-generated image detectors

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Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Scalable diffusion models with transformers

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Observation c8ffe7bf-496a-4e3b-9b64-6f1b66aea7d2 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Learning transferable visual models from natural language supervi- sion

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Hierarchical Text-Conditional Image Generation with CLIP Latents

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Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Aer- oblade: Training-free detection of latent diffusion images using autoencoder reconstruction error

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Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios High-resolution image synthesis with latent diffusion models

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Observation 548222c2-c8aa-4d1d-b403-7e48729d8f99 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

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Observation f8977232-9c3c-41f7-8787-138a5a9ecb59 · outbound

This paper cites Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now

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This paper cites De-fake: Detection and attribution of fake images generated by text- to-image generation models.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios De-fake: Detection and attribution of fake images generated by text- to-image generation models

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This paper cites Learning on gradients: Generalized ar- tifacts representation for gan-generated images detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Learning on gradients: Generalized ar- tifacts representation for gan-generated images detection

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Observation 64f3cecf-7b9d-471d-be4d-723c90639f05 · outbound

This paper cites C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection

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Observation f4462c4b-822c-4b55-82d8-4b2a5627f01f · outbound

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Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Frequency-aware deepfake de- tection: Improving generalizability through frequency space domain learning

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Observation bf9ddeb2-df79-4fbb-86eb-82839ab1fd53 · outbound

This paper cites Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection

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Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.213914Z

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Observation f37a3110-8418-415e-8493-fd50cc15f779 · outbound

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

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Gemini: A Family of Highly Capable Multimodal Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.216996Z

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Observation a30ae9ec-ce2d-4853-94f0-a8ef8a45a444 · outbound

This paper cites an unresolved cited work.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Unresolved cited work

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.220343Z

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Observation 0aeb3391-8922-4498-8ee7-7b8fd988dd8c · outbound

This paper cites Dire for diffusion-generated image detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Dire for diffusion-generated image detection

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.226099Z

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Observation ac0f21cd-6fd0-4e5b-9aa9-f948fba4ab0e · outbound

This paper cites F3net: fusion, feedback and focus for salient object detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios F3net: fusion, feedback and focus for salient object detection

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.228998Z

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Observation 6c01c7a5-d88c-4ba5-a5ce-b4d04c2fa69a · outbound

This paper cites A Sanity Check for AI-generated Image Detection.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios A Sanity Check for AI-generated Image Detection

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.232099Z

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Observation 416d5f05-811b-4d38-a48d-fe1cc574c4b9 · outbound

This paper cites Qwen2.5 Technical Report.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Qwen2.5 Technical Report

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.235158Z

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Observation a36350c1-7741-454b-bcb4-737d200b4f4f · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Yi: Open Foundation Models by 01.AI

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Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.238296Z

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Observation 8cface01-7e6d-4870-bce0-446883076a88 · outbound

This paper cites Detecting and simulating artifacts in GAN fake images.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Detecting and simulating artifacts in GAN fake images

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.241770Z

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Observation 15ac8e52-7923-4973-835c-be06f3c9d253 · outbound

This paper cites Diffusion noise feature: Ac- curate and fast generated image detection.arXiv preprint arXiv:2312.02625, 2023.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Diffusion noise feature: Ac- curate and fast generated image detection.arXiv preprint arXiv:2312.02625, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.244594Z

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Observation c3a19d96-1a4c-4f07-9d39-3b165c5f6515 · outbound

This paper cites Patchcraft: Exploring texture patch for efficient ai-generated image detection, 2024.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Patchcraft: Exploring texture patch for efficient ai-generated image detection, 2024

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.247473Z

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Observation 5266768c-5dc9-4d0c-b894-e2af7717643e · outbound

This paper cites Genimage: A million-scale benchmark for de- tecting ai-generated image.Advances in Neural Information Processing Systems, 36, 2024.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Genimage: A million-scale benchmark for de- tecting ai-generated image.Advances in Neural Information Processing Systems, 36, 2024

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.250303Z

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Observation 868fb70a-d559-499b-8335-d5caa306a221 · outbound

This paper cites an unresolved cited work.

Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios Unresolved cited work

Reference 8701

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:02.223229Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 39b6abe6-fdcb-4ee0-941b-1163e3792461 · inbound

Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection cites this paper.

Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:41:29.508874Z

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Observation 87e78a62-950d-4165-9001-5097ddc444a3 · inbound

SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection cites this paper.

SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:51:15.703391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.