FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection | Paper Citation Record

Observation 90cb1b5c-2f61-4c11-ade8-06535f65176c · outbound

This paper cites Generative adversarial nets,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Generative adversarial nets,

Reference 1

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Observation f4334d7c-03a4-4fe0-a9fe-1e2e3491893d · outbound

This paper cites Denoising Diffusion Probabilistic Models.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Denoising Diffusion Probabilistic Models

Reference 2

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Observation 7cd98e7a-7bad-4595-9fe6-342988dbf508 · outbound

This paper cites Improving image generation with better captions,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Improving image generation with better captions,

Reference 3

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Observation 8155c72b-e2cc-4bb6-8665-2aac173ad74f · outbound

This paper cites Z-image: An efficient image generation foundation model with single-stream diffusion transformer,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Z-image: An efficient image generation foundation model with single-stream diffusion transformer,

Reference 4

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This paper cites Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

Reference 5

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Observation c0365c7a-19fc-4ca1-b79b-01a888d2d7b0 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Diffusion models beat gans on image synthesis,

Reference 6

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Observation b062e9cc-5e64-43da-b35c-1b98f6468ba8 · outbound

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

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection High- resolution image synthesis with latent diffusion models,

Reference 7

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Observation fe47d8a1-993a-4662-bd80-d43f28af4c70 · outbound

This paper cites Diffusion models in vision: A survey,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Diffusion models in vision: A survey,

Reference 8

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Observation 69afdfa5-cf3f-4b9b-a5b5-a0ad6696c6f0 · outbound

This paper cites Realgen: Photorealistic text- to-image generation via detector-guided rewards.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Realgen: Photorealistic text- to-image generation via detector-guided rewards

Reference 9

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Observation d20fdc83-d91f-410e-9248-57b23476e84c · outbound

This paper cites MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs

Reference 10

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Observation 2bd0ee09-c45e-4b27-9fa0-be919d7c4800 · outbound

This paper cites Deepfakes: Deceptions, mitigations, and opportunities,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Deepfakes: Deceptions, mitigations, and opportunities,

Reference 11

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Observation 62e0da64-24af-4736-8925-f43c3c43dcd3 · outbound

This paper cites Leveraging representations from intermediate encoder-blocks for synthetic image detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Leveraging representations from intermediate encoder-blocks for synthetic image detection,

Reference 12

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Observation d335d59e-83c3-4a35-bde2-33cb48a7cbdc · outbound

This paper cites Seeing before reasoning: A unified frame- work for generalizable and explainable fake image detection.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Seeing before reasoning: A unified frame- work for generalizable and explainable fake image detection

Reference 13

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Observation 764130cc-c9df-4b7c-a278-b194d35b679d · outbound

This paper cites Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection

Reference 14

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Observation 09129206-9d1b-40e5-8b81-726fbd553db2 · outbound

This paper cites OmniAID: Decoupling Semantics and Artifacts for Universal AI-Generated Image Detection in the Wild.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection OmniAID: Decoupling Semantics and Artifacts for Universal AI-Generated Image Detection in the Wild

Reference 15

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Observation 14e610bd-63b2-483b-b5bf-811c3cc02405 · outbound

This paper cites Forgerynet: A versatile benchmark for comprehensive forgery analysis,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Forgerynet: A versatile benchmark for comprehensive forgery analysis,

Reference 16

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Observation 1d6f4a5a-d33a-44f2-96ba-39ff1a8f37ef · outbound

This paper cites Genimage: A million-scale benchmark for detecting ai- generated image,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Genimage: A million-scale benchmark for detecting ai- generated image,

Reference 17

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Observation c28bbe51-0b24-42c7-a45a-733b271bbee9 · outbound

This paper cites Drct: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Drct: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images,

Reference 18

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Observation a5aaa5d4-52ce-4b39-bb08-01660c19923f · outbound

This paper cites Wildfake: A large-scale and hierarchical dataset for ai-generated im- ages detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Wildfake: A large-scale and hierarchical dataset for ai-generated im- ages detection,

Reference 19

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Observation 969170c7-79ed-4d11-9b72-5a95764cd372 · outbound

This paper cites Frepgan: robust deepfake detection using frequency-level perturbations,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Frepgan: robust deepfake detection using frequency-level perturbations,

Reference 20

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Observation bf494dfb-b7fe-4bfc-84ee-c8e4ad750127 · outbound

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

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection A Single Simple Patch is All You Need for AI-generated Image Detection

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.118417Z

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Observation ad597bb8-e11d-4235-ad69-cf64b26d5614 · outbound

This paper cites Fakescope: Large multimodal expert model for transparent ai-generated image forensics.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Fakescope: Large multimodal expert model for transparent ai-generated image forensics

Reference 22

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Observation 5b502b2b-797a-4b71-a39c-cef04b56f3e5 · outbound

This paper cites Legion: Learning to ground and explain for synthetic image detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Legion: Learning to ground and explain for synthetic image detection,

Reference 23

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Observation e0619ebe-961a-4747-b9d3-4612b46d67d7 · outbound

This paper cites Can chatgpt detect deepfakes? a study of using multimodal large language models for media forensics,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Can chatgpt detect deepfakes? a study of using multimodal large language models for media forensics,

Reference 24

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Observation 6099b895-e1b0-43ce-9129-36a22e311265 · outbound

This paper cites LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.161507Z

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Observation feeb5e46-1070-4f5b-b996-58985151555c · outbound

This paper cites Fakebench: Probing explainable fake image detection via large mul- timodal models,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Fakebench: Probing explainable fake image detection via large mul- timodal models,

Reference 26

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Observation 4364bf9b-9111-49f5-8d41-72df3f4f8639 · outbound

This paper cites X2-DFD: A framework for eXplainable and eXtendable Deepfake Detection.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection X2-DFD: A framework for eXplainable and eXtendable Deepfake Detection

Reference 27

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Observation 89a006a7-49af-477e-935f-31873bfcb361 · outbound

This paper cites Aigi-holmes: Towards explainable and generalizable ai-generated image detection via multimodal large language models,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Aigi-holmes: Towards explainable and generalizable ai-generated image detection via multimodal large language models,

Reference 28

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Observation 2a756def-6d1e-4cf9-bafc-504d93f623ab · outbound

This paper cites Spot the fake: Large multimodal model-based synthetic image detection with artifact explanation.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Spot the fake: Large multimodal model-based synthetic image detection with artifact explanation

Reference 29

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Observation eb71130f-ce3b-4a68-b831-1d10a5fb60da · outbound

This paper cites Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate

Reference 30

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Observation 4242dd8e-c300-451e-b413-a591eeb46da1 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Chain-of-thought prompting elicits reasoning in large language models,

Reference 31

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Observation c0059742-8e6d-4142-a723-dbbc63bd864c · outbound

This paper cites Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation

Reference 32

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Observation 9473925c-7c64-4d67-91a0-00120a4efe12 · outbound

This paper cites GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation

Reference 33

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Observation 7336c536-2a78-4aa9-9ac5-435b3ef3755d · outbound

This paper cites Mind-brush: Integrating agentic cognitive search and reasoning into image generation.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Mind-brush: Integrating agentic cognitive search and reasoning into image generation

Reference 34

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Observation 8fccfff4-7ae0-41f8-b4be-852428e012ab · outbound

This paper cites Cnn- generated images are surprisingly easy to spot... for now,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Cnn- generated images are surprisingly easy to spot... for now,

Reference 35

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Observation 88d487ce-f1e6-4d3d-bf0a-622ca525e1ba · outbound

This paper cites Towards universal fake image detec- tors that generalize across generative models,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Towards universal fake image detec- tors that generalize across generative models,

Reference 36

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Observation 254a7253-5384-4838-b7a4-7b86e47f80a3 · outbound

This paper cites Detecting Generated Images by Real Images Only.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Detecting Generated Images by Real Images Only

Reference 37

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Observation 30ab31bb-efd9-47da-bb8c-5bb24113383f · outbound

This paper cites Generative adversarial networks,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Generative adversarial networks,

Reference 38

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Observation ee265c3a-23dd-4b1b-b5aa-129f425849e4 · outbound

This paper cites PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

Reference 39

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Observation b1ae7d38-cfd3-4d8c-9610-47ddfede2039 · outbound

This paper cites Fakecatcher: Detection of synthetic portrait videos using biological signals,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Fakecatcher: Detection of synthetic portrait videos using biological signals,

Reference 40

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Observation 1ff3f7d2-081d-478e-bda2-1f1b75aa2728 · outbound

This paper cites Wavelet-packets for deepfake image analysis and detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Wavelet-packets for deepfake image analysis and detection,

Reference 41

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Observation cd8b8329-984c-45e1-831f-def4dd15ab7d · outbound

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

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Rethinking the up- sampling operations in cnn-based generative network for generalizable deepfake detection,

Reference 42

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Observation e126eaa4-b37b-412b-8379-3906baad1f5c · outbound

This paper cites Visual veracity: Advancing ai-generated image detection with convolutional neural networks,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Visual veracity: Advancing ai-generated image detection with convolutional neural networks,

Reference 43

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Observation 14368e87-3caf-4f53-8880-72ab4b3b8449 · outbound

This paper cites Compar- ative analyais of cnn architectures for deep fake detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Compar- ative analyais of cnn architectures for deep fake detection,

Reference 44

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Observation 16db207d-c463-4560-9621-3fc5362f88f9 · outbound

This paper cites Detec- tion of ai-generated synthetic images with a lightweight cnn,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Detec- tion of ai-generated synthetic images with a lightweight cnn,

Reference 45

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Observation 8ff2f72d-e201-4ced-b5cc-2e70920fb741 · outbound

This paper cites Advancing ai-generated image detection: Enhanced accuracy through cnn and vision transformer models with explainable ai insights,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Advancing ai-generated image detection: Enhanced accuracy through cnn and vision transformer models with explainable ai insights,

Reference 46

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Observation c49e502f-9ec7-4405-bafc-0728ddb6193c · outbound

This paper cites AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.104112Z

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Observation cbf4c9bb-5928-4ee9-be40-641db7026547 · outbound

This paper cites Fad-net: Fake images detection and generalization based on frequency domain transformation,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Fad-net: Fake images detection and generalization based on frequency domain transformation,

Reference 48

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Observation a44a0414-ff0f-4aa1-9671-1cd8623ed5b1 · outbound

This paper cites Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning,

Reference 49

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Observation c370916b-ced7-4e87-88b2-c5d240c5a7ea · outbound

This paper cites Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection,

Reference 50

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Observation c9947770-2873-468b-b82f-1b81b07a13fe · outbound

This paper cites an unresolved cited work.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Unresolved cited work

Reference 51

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Observation 0f98e4fa-0b67-4cef-866f-e518a02024e6 · outbound

This paper cites Gemini 2.5 pro,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Gemini 2.5 pro,

Reference 52

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Observation b657680d-886f-461b-95f0-e9c75da18489 · outbound

This paper cites Visual instruction tuning,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Visual instruction tuning,

Reference 53

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Observation c72acc55-2be5-4c34-a8b1-87a968bd6f5f · outbound

This paper cites Qwen2.5-VL Technical Report.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Qwen2.5-VL Technical Report

Reference 54

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 47474e1b-89ee-4079-99a1-68f079ed7a45 · outbound

This paper cites Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.129255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc759154-1dbc-46a4-b87c-d42f9cd33273 · outbound

This paper cites Why are visually-grounded language models bad at image classification?.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Why are visually-grounded language models bad at image classification?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 748d961b-87e0-4c69-974c-ba8c66d45ca4 · outbound

This paper cites FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.077799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 89d73619-3926-4d62-b88a-09f54201bab8 · outbound

This paper cites Common sense reasoning for deepfake detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Common sense reasoning for deepfake detection,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dc22535-7bcc-49b1-9692-36565716264a · outbound

This paper cites FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.142644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8f4c0406-e867-48aa-98bf-742f54babe6a · outbound

This paper cites BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:03:14.120575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f01430db-4270-4354-beba-f61a7e1697a8 · outbound

This paper cites Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:03:14.152909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e30b255b-4673-442b-aa7d-d7cd6f4b2d3a · outbound

This paper cites ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:03:14.074457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 053141ef-af96-4d27-bba0-fb12c75fbb3f · outbound

This paper cites So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-08-03T03:15:17.929242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d7f50cb7-cc2c-4666-8e35-2172f433ba72 · outbound

This paper cites Sida: Social media image deepfake detection, localization and explanation with large multimodal model,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Sida: Social media image deepfake detection, localization and explanation with large multimodal model,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58cc4f53-35da-4001-bb63-1841114ba6c8 · outbound

This paper cites International Journal of Computer Vision 128(7), 1956–1981 (Mar 2020).

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection International Journal of Computer Vision 128(7), 1956–1981 (Mar 2020)

Reference 65

Resolution: metadata mismatch; doi, observed 2026-06-29T08:03:13.656774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 07e80e04-3bf5-4520-8956-af2b39382d46 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Efficient memory management for large language model serving with pagedattention,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09f13c35-e48f-46a0-8d69-c4ad3a33d1ea · outbound

This paper cites Sglang: Efficient execution of structured language model programs,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Sglang: Efficient execution of structured language model programs,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2f514ee-081e-4b02-9e7b-cc5ce9af4656 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:03:14.106857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3e59ff88-379e-45cd-afe2-0fa3ba4a3964 · outbound

This paper cites Sophiavl-r1: Reinforcing mllms reasoning with thinking reward.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Sophiavl-r1: Reinforcing mllms reasoning with thinking reward

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.126779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 147e8b69-828f-45fc-a50e-a244baabc5c1 · outbound

This paper cites On the detection of synthetic images generated by diffusion models,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection On the detection of synthetic images generated by diffusion models,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24ebeb57-0bfd-4b92-9836-1ad66bc60da4 · outbound

This paper cites Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:03:14.109387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a6c3b986-30a3-4631-be24-78698bdff06b · outbound

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

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection A Sanity Check for AI-generated Image Detection

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.145411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ddfe369-9bf4-4fcb-9034-aa1cc11ed27c · outbound

This paper cites Gpt-5 system card,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Gpt-5 system card,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc97621d-93ec-4a11-94fe-3e4627891ec3 · outbound

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

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:03:14.158918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b2a2c78f-72ef-4087-8190-204561be5383 · outbound

This paper cites Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f6d22a9-6731-46d4-83b0-05335c15ee82 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 76

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T08:03:14.117991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c52195c7-0daf-41db-9893-90d9ade1d42f · outbound

This paper cites Global texture enhancement for fake face detection in the wild,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Global texture enhancement for fake face detection in the wild,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 180f86e5-724b-4f31-a6b6-cdb1ecc1dbec · outbound

This paper cites Fusing global and local features for generalized ai-synthesized image detection,.

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection Fusing global and local features for generalized ai-synthesized image detection,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-29T07:59:44.436264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Outbound references

Pith citing papers