VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics | Paper Citation Record

Observation 3de64b25-88e1-41f9-b307-935777e36455 · outbound

This paper cites Detecting ai-generated video: A vision–language dual-view survey.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Detecting ai-generated video: A vision–language dual-view survey

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.551518Z

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Observation 1f8b3410-e616-4f86-bcea-aeb357c851ab · outbound

This paper cites Cubecomposer: Spatio-temporal autoregressive 4k 360°video generation from perspective video.arXiv e-prints, pages arXiv–2603, 2026.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Cubecomposer: Spatio-temporal autoregressive 4k 360°video generation from perspective video.arXiv e-prints, pages arXiv–2603, 2026

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.532355Z

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Observation 6ffd2496-ddb2-4916-a621-fac9dbf6c8e9 · outbound

This paper cites Evatok: Adaptive length video tokenization for efficient visual autoregressive generation.arXiv preprint arXiv:2603.12267, 2026.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Evatok: Adaptive length video tokenization for efficient visual autoregressive generation.arXiv preprint arXiv:2603.12267, 2026

Reference 3

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Observation 03333bd2-c7b2-4602-b1d8-49ea40d60c0e · outbound

This paper cites Smrabooth: Subject and motion representation alignment for customized video generation.arXiv preprint arXiv:2512.12193, 2025.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Smrabooth: Subject and motion representation alignment for customized video generation.arXiv preprint arXiv:2512.12193, 2025

Reference 4

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Observation 1e0457a7-5d42-4719-88c4-e6baa67a11a3 · outbound

This paper cites Hunyuanportrait: Implicit condition control for enhanced portrait animation.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Hunyuanportrait: Implicit condition control for enhanced portrait animation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.514845Z

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Observation 2cfcbc61-3f74-42b7-92c8-f77dea5dcde7 · outbound

This paper cites Zo3t: Zero-shot 3d-aware trajectory-guided image-to-video generation via test- time training.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Zo3t: Zero-shot 3d-aware trajectory-guided image-to-video generation via test- time training

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.497252Z

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Observation d03707b8-4e84-4840-9564-dcca2729d7dd · outbound

This paper cites Perceptual judgments of video authenticity: An examination of viewing duration, confidence, content, and strategies.Law Review, 107:1753–1819, 2026.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Perceptual judgments of video authenticity: An examination of viewing duration, confidence, content, and strategies.Law Review, 107:1753–1819, 2026

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.478435Z

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Observation ed07736e-5741-4d46-b99f-b34aa8de0dae · outbound

This paper cites Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news.Social media+ society, 6(1):2056305120903408, 2020.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news.Social media+ society, 6(1):2056305120903408, 2020

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Observation d41f59ce-a7ce-455f-a8f8-697cca885771 · outbound

This paper cites Reducing risks posed by synthetic content an overview of technical approaches to digital content transparency.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Reducing risks posed by synthetic content an overview of technical approaches to digital content transparency

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.448784Z

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Observation ee0ba5a9-26c8-4588-9962-0b34e8c41920 · outbound

This paper cites DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T14:18:12.929450Z

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Observation c6dc54da-e672-4a87-81c1-bdda15206b70 · outbound

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

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T14:18:12.942040Z

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Observation 60af37fa-ea0e-41ae-b7df-8c11d3e649ef · outbound

This paper cites Videoveritas: Ai-generated video detection via perception pretext reinforcement learning.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Videoveritas: Ai-generated video detection via perception pretext reinforcement learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.430233Z

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Observation b79a7ff2-7ad5-461e-9c9c-f7737959404a · outbound

This paper cites Seeing what matters: Generalizable ai-generated video detection with forensic-oriented augmentation.arXiv preprint arXiv:2506.16802, 2025.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Seeing what matters: Generalizable ai-generated video detection with forensic-oriented augmentation.arXiv preprint arXiv:2506.16802, 2025

Reference 14

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Observation 00f1f3d2-f9fe-4977-ad57-40191d13e092 · outbound

This paper cites Chain-of-thought prompting elicits reason- ing in large language models.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Chain-of-thought prompting elicits reason- ing in large language models

Reference 15

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T14:18:14.410580Z

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Observation d7a131da-9e05-4dae-a5ff-42d44370b6bc · outbound

This paper cites Vidguard-r1: Ai-generated video detection and explanation via reasoning mllms and rl.arXiv preprint arXiv:2510.02282, 2025.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Vidguard-r1: Ai-generated video detection and explanation via reasoning mllms and rl.arXiv preprint arXiv:2510.02282, 2025

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T14:18:12.969352Z

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Observation ab21cdfc-a7da-4c4d-afcf-9a64f5a00168 · outbound

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

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM

Reference 17

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Observation 54989049-2b54-4509-9eb8-d9ca13ece505 · outbound

This paper cites Christiano, Jan Leike, Tom B.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Christiano, Jan Leike, Tom B

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.392735Z

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Observation 0988ad9b-3a54-4761-9d31-f98609e29172 · outbound

This paper cites Concrete Problems in AI Safety.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Concrete Problems in AI Safety

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Observation cbd9599d-0354-4e55-9ca5-7f25ccdc0011 · outbound

This paper cites an unresolved cited work.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Unresolved cited work

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Observation 057e4206-1d43-433d-8f62-bfc6a4a2b5be · outbound

This paper cites Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965, 2023.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965, 2023

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Observation 7beb9f0f-17ce-426b-b134-607df067891a · outbound

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

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Forgerynet: A versatile benchmark for comprehensive forgery analysis

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.349964Z

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Observation b0c75b10-21aa-48c1-8b28-fafbad6818ed · outbound

This paper cites Grounded-VideoLLM: Sharpening fine-grained temporal ground- ing in video large language models.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Grounded-VideoLLM: Sharpening fine-grained temporal ground- ing in video large language models

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Observation c4412164-5de1-4f83-baa6-bcd11092db9a · outbound

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

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Cnn- generated images are surprisingly easy to spot

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Observation 3a7b8e40-06cb-4cf2-932b-4e4a36048eb1 · outbound

This paper cites Towards universal fake image detectors that generalize across generative models.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Towards universal fake image detectors that generalize across generative models

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Observation cb481da3-5761-4bc5-becc-df97e7b1b5c0 · outbound

This paper cites Transcending forgery specificity with latent space augmentation for generalizable deepfake detection.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Transcending forgery specificity with latent space augmentation for generalizable deepfake detection

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Observation 5343d53d-2fba-4706-9a67-8db085c689ed · outbound

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

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection

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Observation b3e40501-9682-4c5f-8d2c-e876b5942933 · outbound

This paper cites Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake detection.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake detection

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This paper cites Exploring unbiased deepfake detection via token-level shuffling and mixing.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Exploring unbiased deepfake detection via token-level shuffling and mixing

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Observation 718fa667-41d4-4fc1-a795-86308e73b4da · outbound

This paper cites A sanity check for ai-generated image detection.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics A sanity check for ai-generated image detection

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Observation f5a89b47-ad8e-4f4b-80f9-fce27dcfc5dd · outbound

This paper cites All patches matter, more patches better: Enhance ai-generated image detection via panoptic patch learning.arXiv preprint arXiv:2504.01396, 2025.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics All patches matter, more patches better: Enhance ai-generated image detection via panoptic patch learning.arXiv preprint arXiv:2504.01396, 2025

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Observation 2d7d1138-fb3a-4499-9475-ab7edcc8c4ec · outbound

This paper cites BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation

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Observation 322761cf-b818-439f-8746-0d633e22eb29 · outbound

This paper cites OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration

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Observation 43558613-3c87-47f7-8d9d-d4e3fc685521 · outbound

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Generative universal verifier as multimodal meta-reasoner

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Observation ae4ce9fb-9e02-4e72-9933-7aa083daa193 · outbound

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Realcompo: Balancing realism and compositionality improves text-to-image diffusion models.Advances in Neural Information Processing Systems, 37:96963–96992, 2024

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Observation 30854198-d95c-43ba-826a-3c36232ddab6 · outbound

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Itercomp: Iterative composition-aware feedback learning from model gallery for text-to-image generation

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 72ad946e-43a7-4b5a-ba36-9d6d782dce41 · outbound

This paper cites Learning human-perceived fakeness in ai-generated videos via multimodal llms.arXiv preprint arXiv:2509.22646, 2025.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Learning human-perceived fakeness in ai-generated videos via multimodal llms.arXiv preprint arXiv:2509.22646, 2025

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Observation 18ed95a4-62a6-4ba8-87e9-a84d17624b76 · outbound

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Ai-generated video detection via spatial- temporal anomaly learning

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Observation dfb8102c-6e65-40f2-ac0a-49f58f564203 · outbound

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Detecting ai-generated video via frame consistency

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Genvidbench: A 6-million benchmark for ai-generated video detection

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Observation cbe65933-fc12-4ecd-a38d-bc7361a0328d · outbound

This paper cites Qwen3.5: Towards native multimodal agents.https://qwen.ai/blog?id= qwen3.5, February 2026.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Qwen3.5: Towards native multimodal agents.https://qwen.ai/blog?id= qwen3.5, February 2026

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Observation 81afcf7a-ba81-4ce2-b3ee-6327aa22a3b9 · outbound

This paper cites LTX-Video: Realtime Video Latent Diffusion.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics LTX-Video: Realtime Video Latent Diffusion

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Observation dea406f6-014b-41cc-9bea-cad01606c6e6 · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Wan: Open and Advanced Large-Scale Video Generative Models

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Observation 3cab0c11-efa9-4161-93fb-79230f0ca434 · outbound

This paper cites SkyReels-V2: Infinite-length Film Generative Model.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics SkyReels-V2: Infinite-length Film Generative Model

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Observation 6b5011b0-de9a-486f-a003-a1e922931603 · outbound

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VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Wan 2.7: Image-to-video api.https://www.alibabacloud.com/help/en/ model-studio/image-to-video-general-api-reference, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.102072Z

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Observation 913fc52c-6869-4435-abd0-c9306a7eb878 · outbound

This paper cites Seedance 1.0 Pro.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Seedance 1.0 Pro

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Resolution: verified exact; raw_fallback, observed 2026-08-15T14:18:13.383693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bda7bfde-d08f-43cf-8a7a-dc9928b5b519 · outbound

This paper cites Introducing gpt-5.https://openai.com/index/introducing-gpt-5/, 2025.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Introducing gpt-5.https://openai.com/index/introducing-gpt-5/, 2025

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T14:18:13.201227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3c47958-fb78-44ae-b977-b944bc58cea0 · outbound

This paper cites Gemini 3.1 Pro model card.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Gemini 3.1 Pro model card

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.071603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 63e01024-c60e-4870-910e-8c8f65e1e97b · outbound

This paper cites Dapo: An open-source llm reinforcement learning system at scale.Advances in Neural Information Processing Systems, 38:113222– 113244, 2026.

VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics Dapo: An open-source llm reinforcement learning system at scale.Advances in Neural Information Processing Systems, 38:113222– 113244, 2026

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:18:14.047933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers