VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL
Authors: Kyoungjun Park
Yifan Yang
Juheon Yi
Shicheng Zheng
Muhammad Muaz
Yifei Shen
Dongqi Han
Caihua Shan
Lili Qiu
Venue: ICLR 2026 Poster (2026)
Keywords: Discriminator
MLLM
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We present VidGuard-R1, the first multimodal LLM fine-tuned with reinforcement learning to detect and explain AI-generated videos, achieving state-of-the-art accuracy with interpretable reasoning.

Abstract: The rapid proliferation of AI-generated video necessitates robust detection tools that offer both high accuracy and human-interpretable explanations. While existing MLLM-based detectors rely on supervised fine-tuning (SFT) or direct preference optimization (DPO), these methods are often bottlenecked by static, pre-labeled datasets that fail to capture the evolving, multi-step physical inconsistencies of modern generative models. To bridge this gap, we introduce VidGuard-R1, the first video authenticity detector to utilize group relative policy optimization (GRPO). Moving beyond passive preference matching, VidGuard-R1 employs a reinforcement learning framework that encourages the model to explore and rank multiple reasoning paths. By introducing specialized reward models for temporal stability and diffusion-aware complexity, we incentivize the model to discover 'physics-grounded' artifacts. Our contributions include: (1) a curated dataset of 140,000 challenging real/fake video pairs; (2) a GRPO-based training paradigm that achieves state-of-the-art zero-shot performance; and (3) a reasoning-first architecture that provides precise, verifiable rationales for its forensic judgments. Project website: https://vidguard-r1.github.io

Published as a conference paper at ICLR 2026
VIDGUARD-RI: AI-GENERATED VIDEO DETECTION AND EXPLANATION VIA REASONING MLLMS AND RL
Kyoungjun Park', Yifan Yang Yi², Muhammad Muaz', Shicheng Zheng", Yifei Shen², Dongqi Han?, Caihua Shan?, Lili Lili
Qiu
'Tee University of Texas at Austin "Microsoft Research (kjpark, lili} @cs. Pees.utexas.edu yifanyang @ microsoft.com
Prorpt: Detarmine whether the video real or generated by an AI model.
The first thing is the motion consistency padlock would not nove in such smooth manner without any external suggesting villttinn. lighting appears consistent, but there's slight unnatural glow around the padlock that night be result of the AI generation. noticeable. The padlock surface it's attached to have very smooth for- texture natural. Hin, I see. The combination of these factors nakes clear that this video likely AI-generated.
applied
Multi-Mada Modal Large Language Model
arlffats
Physical violations
Content-matinn consistency
doesn't look
Lighting consistency
Texture artifacts
ofain-of-Thought
NE
Overall framework of VidGuard-R1. We present the first video authenticity detector based on multi-modal large language models (MLLMs), which generates a chain-of-thought rea- soning process along with the final answer.
Figure 1:
ABSTRACT
The rapid proliferation of Al-generated video necessitates robust detection tools that offer both high accuracy and cumaa--nterpretable explanations. While exist- ing MLLM- detectors rely on supervised fine-tuning (SFT) or direct pref- erence optimization (DPO), these methods are often bottlenecked by static, pre- labeled datasets that fail capture the evolving. multi-step physical inconsisten- cies of modern generative models. To bridge this gap, we introduce VidGuard- R1, the first video authenticity detector to utilize group relative policy optimiza- tion (GRPO). Moving beyond passive preference matching, VidGuard-R1 em- ploys a reinforcement learning framework that encourages the model to explore and rank multiple reasoning paths. By introducing specialized reward models for temporal stability and diffusion-aware complexity, we incentivize the model discover physics-grounder
artifacts. Our contributions include: (1) rated dataset of 140,000 challenging real/fake video pairs; (2) a GRPO-based training paradigm that achieves state-of-the-arr zero-shot performance; and (3) a reasoning- ceasoning-first first architecture that provides precise verifiable rationales for its foren- sie judgments. Project website: htttps://viduardd hit..u.uuu................... io/
INTRODUCTION
In the past year, have witnessed unprecedented progress in video generation models, with dra- matic improvements in realism and quality. The release of powerful models such as Sora (Brooks) al. 2024), Wan (wan 2025). and Hunyuan Video (Kong al. 2024) has made Al-generated videos more accessible to the public, further blurring the line between synthetic videos and real ones. At the time, these advancements have led series of social risks, including the spread of misin- formation, violations of privacy rights, damage to personal reputations, and increased susceptibility scams and fraud.
"oorrespnniing authors.

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Published as a conference paper at ICLR 2026
Motivated by its practical significance, several pioneering works have been developed to detect AI- generated videos. Early approaches primarily targeted DeepFake-styy facial forgeries (Qian et al, 2020 Tan | 2024 Gu et al. 202I), which often assumed single-subject, frontal-face scenarios under constrained settings. These assumptions diverge significantly from open-domain, multi-scene videos produced by modern generative models. More recent detectors leverage spatial-temporal consistency (Ma et al. [222; [aii et al.) [0244] Liu et al., 2024]; however, such methods are limited in capturing higher-level semantic or causal inconsistencies and can be easily bypassed by post- processing techniques. Other methods are trained on curated fake video detection datasets (Chen al., [024a] Ni et al., [025]] Kundu et al. (025). but these benchmarks often lack coverage of newly emerging models and fail to refiect the full diversity of generative capabilities. A recent benchmark (Chen et al.) 2024a) shows that even state-of-the-art detectors still struggle to reliably identify videos from advanced models like Sora. Furthermore, these detectors typically offer only binary decisions without accompanying explanations, which raises concerns for transparency, es- pecially when detection outcomes affect content moderation or legal accountability. Users are also more likely to trust detection systems that provide interpretable reasoning.
Recent advances in multi-modal large language models (MLLMs) have significantly enhanced video understanding, enabling detailed explanations of model decisions (Bai et al.] 2023] ZZhang et al.] 2024b). This makes them promising candidates for detecting and explaining Al-generated videos. However, directly applying existing MLLMMs, including advanced models like GPT-4o, yields subpar performance on current benchmarks, underscoring the need for supervised fine-tuning (SFT). As an initial step, we applied SFT to the Qwen2.5-VL. V- model (Bai et al.] (025). While the model achieved strong overall performance, remained limited in its ability to explain why a video is fake, revealing shortcomings in its reasoning capability.
To address this, we adopt reinforcement learning (RL), which has shown promise in enhancing LLM reasoning (Guo et al., (025). Notably, Video-R1 (Feng et al., (025) outperforms commercial models on video reasoning tasks. RI. enables MI.LMs to develop self-improving reasoning via outcome- based rewards. We hypothesize that RI. fine-tuning can help models detect subtle temporal and generative artifacts. Key to this is designing effective reward models. Simple binary rewards (c.g., for real, 0 for fake) are insufficient. Instead, we propose two strategies: (1) injecting temporal artifacts into both real and fake videos to encourage temporal reasoning, and (2) assigning higher rewards to videos generated with more diffusion steps, which are harder to detect. Incorporating these into a group relative policy optimization (GRPO) framework yields over 86% accuracy on our dataset and 95% on two benchmarks.
We introduce VidGuard-Ri -11, the first video authenticity detector that fine-tunes the MLLM using GRPO. The model leverages the pretrained knowledge of MLLMs for accurate clas- sification and employs reinforcement learning for effective exploration. To further enhance performance, we design two specialized reward models that target temporal artifacts and generation complexity based on diffusion steps.
We construct a challenging dataset of 140k real/fake video pairs for Al-generated video detection. By employing ttate-of-the-art generation models and carefully controlling the process, we ensure that distinguishing real from fake is non-trivial.
VidGuard-R1 achieves state-of-the-art zero-shot performance existing benchmarks, with accuracy exceeding 95%. Case studies further highlight its ability to produce accurate and interpretable explanations.
2 RELATED WORKS
2.1 AL-GENERATED VIDEO DETECTION METHOD
Recent research on Al Al-generated video detection has largely focused on deepfake videos with syn- thetic faces (Pei et al. 2024), using spatial-temporal consistency, frequency artifacts, or data-driven approaches. These methods often struggle to generalize beyond face-centric content to more di- verse, real-world videos. Recently, general video detection methods have emerged: AIGDet (Bai et al. 2024) captures tency, and diffusion--aar based representations track temporal dynamics (Liu et al. 2024). Other works identify appearance, motion, and geometry key factors for classifier training (Chang al. 2024).
anomalies, DeCoF (Ma et al., 2024) exploits frame consis-
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Faee loght
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Red Traegrt Kenwor Rood
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Figure 2: The overall training framework of VidGuard-R1, consisting of two stages: (1) supervised fine-tuning (SFT) for chain-of-thougee (CoT) initialization, and (2) reinforcement learning-baad -baed fine-tuning to enable deeper reasoning.
Recent efforts in forgery detection leverage multimodal LLMs, including FakeShield (Xu et al., 2024), which employs supervised fine-tuning, and SafcWatch (Chen al. 2024b), which applies direct preference optimization (DPO) for video guardrails. However, DPO relies on static prefer- ence pairs that struggle to capture subtle temporal inconsistencies in evolving generative models. We introduce the first application of group relative policy optimization (GRPO) to Al-generated video detection. By enabling iterative exploration and ranking of multiple reasoning paths, our method promotes deeper physics-aware understanding of video consistency, leading to improved generalization across frontier generative models and diverse benchmarks.
2.2
AI-GENERATED VIDEO DETECTION DATASET
Given the recency of this research area, only a limited number of benchmarks have been introduced. The generated video dataset (GVD) (Bai al. 2024) (1 samples) and Vedeo (Chen et al., 2024a) (with millions of samples) consider settings where both training and test videos are gen- erated by the same series of models. However, these benchmarks lack prompt/imaee-vide pairs, semantic labels, or cross-source settings. GVF (2.8k samples) contains prompts/images-vieee viieo pairs and semantic labels, but does not provide cross-source settings. Gen VidBench (Ni et al., (025) con- sists of 100k videos and incorporates cross-source settings, but the video generation models used are less advanced, such as Cog Video and SVD.
Moreover, existing datasets often contain shortcuts in resolution, frame rate, bitrate, data-source imbalance, enabling models to exploit superficial cues rather than learn intrinsic visual realism. To address these limitations, construct curated dataset of 140,000 real-fake videos generated with state-of-the-ant vidco generation models: Humyuan Video (Kong et al. 2024) and Cog VideoX (Yang al. 2024). Our dataset explicitly standardizes bitrate, resolution, frame rate, and content distribu- tion, resulting in a shortcut- free benchmark that encourages models to rely on semantic and temporal realism rather than on metadata artifacts.
3 METHODOLOGGY
Figure 2 illustrates the VidGuard-RI framework, which consists of two stages. We first apply supervised fine-tuning (SFT) to the multimodal large language model (MLLM), followed by di- rect preference optimization (DPO) and group relative policy optimization (GRPO) based the collected datasets. We further develop two GRPO variants by introducing temporal artifacts and leveraging videos generated with varying diffusion steps.
3.1 DATA COLLECTION
3.1.1 DATA CONSTRUCTION FOR VIDEO REALISM DISCRIMINATION
High-quality training data is essential for video reasoning in MLLMs. However, many existing benchmarks for real vs. generated video classification, such as Gen (Chen et al., 2024a and Gen VidBench (Ni et al., 2025) exhibit uncontrolled discrepancies
basic metadata e.g., real videos are often longer than 10 seconds, whereas generated videos are typically under 4 seconds in Gen Video. Moreover, they reveal clear modality-leve gaps in motion dynamics and content con- trasts between real and generated videos. These differences introduce unintended shortcuts, enabling models to rely on superficial cues like duration or resolution rather than actual visual realism. As a result, VidGuard-R1 attains over 96% accuracy both Gen Video and Gen VidBench by exploit- ing such artifacts. To mitigate this reward hacking behavior, we construct a curated dataset with standardized video properties, encouraging models to focus intrinsic visual content.
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We collect real videos from the Intern Vid (Wang al. 2024) and ActivityNet (Caba Heilbron al. 2013 datasets and generate their corresponding fake counterparts using Hunyuan Video (Kong et al, 2024 and Cog VideoX (Yang et al.] 2024). We specifically choose these two models because they support conditioning on both the firmt-frame image and text description-ane essential requirement for generating videos that contextually aligned with their real counterparts. To achieve such alignment, we provide the generation models with the first frame of each real video along with textual caption describing its content. For ActivityNet, which lacks native captions, we extract concise descriptions using Qwen2.5- L-L 72B. This pairing strategy mitigates content-based biases and forces the model reason over subtle visual details.
3.1.2 COLLECTING CHAIN OF THOUGHT (COT) ANNOTATION
Eliciting deliberate, step-by-step reasoning in MLLMS requires high-quality CoT supervision. To this end, we leverage Qwen-2.5-VL (72B) extract salient visual cues from each video and guide the model toward a deeper understanding. Specifically, we query the model with critical factors known to distinguish real from generated content motion consistency, lighting consistency, texture artifacts, and physical plausibility violations. These targeted prompts encourage detailed reasoning grounded in visual evidence.
However, current MILMs lack the capacity to reliably distinguish real from fake videos on their own To compensate, we provide ground-truth labels during prompt construction and instruct the model to generate CoT rationales conditioned on the given label. While these rationales do not re- flect genuine discrimination ability, they capture rich contextual cues such object interactions, background details, and lighting inconsistencies that are highly informative. These CoT annota- tions serve as useful clues for subsequent reinforcement learning fine-tuning. For prompt templates used in CoT generation, please refer to our supplementary materials.
3.2 SUPERVISED AND RL FINE-TUNING
We begin with SFT, where the model is trained to mimic the ground-truth reasoning process, Given a video x and its annotation from the collected dataset, the model parameters 0 are optimized by minimizing the negative log-liked likliiil LSFT (0) outputs with human preferences. we apply DPO, which updates the model based on pairwise pref- erence data without explicit reward modeling. Given a preferred response yo and a less-preferred response yt for the same video x, the DPO loss encourages the model to prefer you over compared to reference model Pref
> t=1 Pg z)). To align the model
Liso( = - log (3 (Bog
Po Po (z)) Per(y (x) Piet
B log Po Po(ux(z)) (=:)=)
where (.) is the sigmoid function and 3 controls the preference strength. This method allows fine-tuning using preference comparisons without requiring scalar rewards.
Finally, we adopt GRPO from DeepSeek R1 (Guo et al. 2025), which generalizes RLHF to group- level comparisons. Given query video and group of generated outputs {or Jo the model is trained to assign higher probabilities to outputs with higher rewards. The GRPO objective is:
Σ min po(or)a))
CCRrO(f) = --.E G
A, clip
DKL (po Pwr)
Pre((o.(2))
where e is clipping threshold and | regularizes the policy stay close to the reference modeï. The advantage term A, normalizes the reward r for output o, within the group, computed as
A, T= where f4 and on are the mean and standard deviation of {r:)e GRPO thus enables dx learning from relative ranking among multiple responses, capturing nuanced distinctions in quality across outputs.
3.3
VIDGUARD-RI
3.3.1 OVERVIEW
Figure illustrates the training pipeline of VidGuard-R1. Following the data collection procedure, construct two datasets of different scales: VidGuard-Rl-Coo- and WidGuard-R1-RL-DE We adopt Qwen2.5-VL-7B as the base MLLM and train using our proposed fine-tuning framework.
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The first stage is supervised fine-tuning initialization using the VidGuard-RL-CoT-- dataset, which contains videos paired with chain-of- tfothht (CoT) annotations. This stage establishes foun- dational reasoning ability and equips the model with basic cross-modal alignment and visual under- standing. The resulting model is referred to as VidGuard-RI (CoT).
In the second stage, we apply two reinforcement learning methods DPO and GRPO to further refine the model on a larger and more diverse dataset, model with high-quality preference signals via pairwise comparisons, requiring the construction of preference pairs. Specifically, since our dataset includes pairwise real and fake videos, each sample annotated with CoT rationales for both perspectives. For DPO training. we construct preference pairs by swapping these CoTss For real video, the CoT supporting its authenticity with the answer "real" serves as the positive annotation, while the CoT from the paired fake video with the answer "fake" is used as the negative annotation. In contrast, GRPO encourages consistent performance across grouped outputs by leveraging structural regularization.
DPO aligns the
it does not rely on preference annotations, video labels are directly used as reward signals. The resulting models are denoted as VidGuard-RI (DPO) and VidGuard-RI (GRPO).
We introduce two variants, GRPO-TA and GRPO-Q, to further enhance detection performance. These methods extend the original GRPO framework by adjusting reward values according to the difficulty of detecting fake videos. Detailed descriptions provided in the following sections.
3.3.2 GRPO WITH TEMPORAL ARTIFACTS (GRPO-TA)
While standard GRPO performs well in vidco discrimination by leveraging local visual cues-such as pixel distortions and lighting inconsistencies
often overlooks temporal inconsistencies, which are crucial for detecting generated videos. To address this limitation, we introduce GRPO with temporal artifacts (GRPO-TA), variant that explicitly promotes temporal reasoning through contrastive reward adjustment.
We apply two common temporal artifacts: (1) repeating specific video segment and (2) reversing the frame sequence within a segment. These manipulations are applied probabilistically. with the manipulated region selected based on Gaussian distribution over the video timeline.
Specifically, for each input query, we generate two sets of model outputs: video, and (ö; ( for the corresponding manipulated video with temporal artifacts. These videos should be classified as fake videos. In GRPO-TA, we assign additional rewards when the model correctly classifies temporally manipulated videos as fake. Consider two numbers, 01 > 02- De- tecting temporal artifacts videos manipulated from real content tends to more challenging than identifying those derived from fake videos. This is because real videos typically exhibit coherent and natural motion, so temporal manipulations such as frame shuffling or repetition can be subtle and difficult to detect. In contrast, generated videos often contain artifacts like unstable motion or low temporal consistency, which make further manipulations more visually salient. To reflect this asymmetry in difficulty, we assign the model higher reward on when the original video o; is real, and a moderate reward 02 when the original video is fake. This is defined as:
{o,1E= for the original
if ō, = fake and ys real 02 if ō. fake and yı fake 0, otherwise
Q1
Wi:
where y; denotes the label of the i-th video. In the experiments, set the hyperparameters to 0n = 0.5 and 0: = 0.3. This additional reward, Wi, is designed to be applied conditionally. Specifically, for given sample, only add w; to the original GRPO reward if two conditions met: the model's prediction on the original video (O;) must be correct, and the overall accuracy on the group of manipulated videos (p) must exceed predefined threshold μ. This ensures that we only reward the model for temporal reasoning when it already has solid baseline performance. The final reward of GRPO-TA given by
GRPO + Wi, if or is correct and p > μ T y GRPO
GRPO.TA 17
otherwise
where r; denotes the original GRPO reward, set if the model prediction on the original video is correct, and 0 otherwise. The additional reward w; is applied only when both the original pre- diction is correct and the group of responses for the temporally manipulated videos achieves higher accuracy. In the experiments, we set μ = 0.8.
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3.3.3 GRPO WITH QUALITY EVOLUTIONARY VIDEOS (GRPO-Q)
Our motivation is to extend the model's capability to detect videos based quality. Given the sub- jective nature of quality assessment, we avoid relying on large-scale human annotations. Instead, we leverage diffusion-
video generation by systematically varying the number of reverse diffusion steps to produce videos with distinct quality levels.
As in GRPO-TA, O; E Y and y/ y denote the model output and ground-truth label, with J freal} U Uffake-s), where s is the diffusion step. A reward is given for an exact match, and no reward assigned if the real/fake classification is incorrect. In GRPO-Q. Q, if the model correctly classifies fake video but selects an incorrect diffusion step, we assign a partial reward based on the distance between the predicted and ground-truth diffusion steps. The GRPO-Q reward is defined follows:
0, GRPO-Q
if (o: real and yı # real) or (o, * real and y; real)
o; = y4 lg (o,, 80],, if oz, E y {real}.
(3)
The first scenario occurs when the model fails to correctly classify the video as real or fake. The second scenario, where = 1, represents exact match in prediction, including the diffusion progression. In the third case, the function g(:-)) maps the step distance to a scalar reward, enabling fine-grained credit assignment based on the similarity in quality. Specifically, we define a progress value s() in the range [0. 1] to indicate the fraction of diffusion steps used, where 0 denotes zero steps, and denotes full completion of the steps. The ground -rouu value s(y.). and the model will estimate a progress value. We define the reward function as g Vi) = 6 (1 (s({)) - s(y:)l).
This reward formulation enables the model to move beyond binary discrimination and perform fine- grained analysis of video quality. By learning to associate subtle differences in generation steps with quality variations, the model develops deeper understanding of the diffusion process and its impact on perceptual realism. As result, it can not only detect whether a video is fake, but also infer and estimate the degree of quality degradation generated videos. This facilitates more interpretable and controllable evaluation of generated content quality.
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
4.1.1
DATASET
Our dataset contains 140k videos, balanced between 70k real and 70k generated samples, orga- nized into contextual pairs. The real set comprises 55k videos from Intern Vid and 15k from Activ- ityNet, while the generated set includes 50k samples synthesized by Hunyuan Video-12V (Kong et al.] 2024) and 20k by CogVideoX-SB (Yang et al., (024). We allocate 130k samples for training and 10k for testing, with the latter evenly split between real and generated videos. Within the training data, 30k samples are reserved for as
(CoT) learning, denoted Sina------o---00 and the remaining 100k are used for reinforcement learning fine- tuning, denoted diaGuard-R1-RL-1000
Since the state-of-the-art generative models still produce relatively short videos (~~299 frames) at modest resolutions, we standardize all real videos match generated ones by enforcing 49 frames, 8 FPS, 480 resolution, and YUV420p format.
For GRPO-Q fine-tuning, we augment the training set with intermediate generations sampled from diffusion steps 10 to 50. These are labeled with approximate quality levels (20%, 40,, 60%, 80%, and 95%). Specifically, we use 12k real videos, each paired with five generated variants at different diffusion steps, resulting in 72k samples per generation model.
4.1.2 EVALUUTIOON PROTOCOL
We evaluate three datasets- VidBench (Ni et al., 2025), and Gen Video (Chnn et al.) 2024a) 2024a)-usine using the metrics and baselines defined by their respective benchmarks. For ours and report mean Top-1 accuracy, the average correctness over all predictions. For Gen Video, we follow the original protocol and report recall and Fl score. All evaluations adhere to the official settings of each benchmark to ensure fair comparison.
Gen VidBench,

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Table 1: Comparison of models on our dataset, reported as mean Top-1 accu-
Table 2: Extended Gen VidBench results with VidGuard-R1 and additional MILMs, reported as
racy (%). TF denotes transformer.
mean Top-1 accuracy (%). TF denotes transformer.
Method
Eye
CogVidooX
Hun Video
SowFut
CSN
77.57
77.03
Method
MuseV
CogVideo
HD-VV
B3D
CNN
64.78
62.13
SlowFast
12.25
15:34
TRN
CNN
65.73
09.87
CNN
8.15
8.20
60:11
49.25
Uniforser V2
1F
73355
11.02
CNN
26.44
91.34
71.26
TimeSlomer
76.53
24.55
45.21
Vilaskain
70.71
73.14
VideoSwin
8.01
67.27
V2
47.50
Qwen2.5 Dwcn2.5-VLL.7
MILM
Qwm1255V11778
mini
MILM
4.1
26.07
96.07
GPT
MILM
56.61
57.42
36.52
VilGued-RI (GxT)
MLLM
06.18
63.19
VidCrard-RI
VicGuund-t KI (DP0)
MILMM
79.13
W9.81
97.24
96.59
98.14
96.37
Nalthord-Rt RI (GRPP))
MILIM
81.M0
81.90
Zem-leet
ViaGuard RJ (GRRPO TA)
MLLM
82.17
83.72
ValGund-RI (CRPP)
97.38
94.08
97.53
Vi0Coard RJ (GRPO Q)
MLLM
84.32
86.17
4.1.3 TRAINING SETUP
We employ Qwen2.5-V VL-BBB the base MLLM and conduct all experiments on four NVIDIA A100 GPUs (80GB). Each video is represented by up to 16 frames, each resized to 28 x 28 and mapped to 128 feature channels for encoder input during both training and inference. For Gen Video and Gen- VidBench, we follow their official evaluation protocols and adopt 8-frame inputs. In GRPO training, sample 8 responses per input; for GRPO-TA, we additionally sample 4 responses from tempo- rally manipulated variants of the input to enhance robustness against temporal artifacts. Training proceeds in two stages: first, the base model fine-tuned for one epoch on the CoT dataset, yielding the SFT-CoT MLLM; second, we initialize VidGuard-RI with SFT-CoT and perform reinforce- ment learning for approximately 2,000 steps.
4.2
MAIN RESULTS
4.2.1
OUR DATASET
We evaluate VidGuard-R1 on our dataset with several methods, including CNN-based models
(SlowFast (Feichtenhofer et al. 2019). I3D (Carreira & /isserman| TRN (Zhou et al. 2018
Transformer- based models (UniFormer V2 Li et al. (2022a), TimeSformer (Bertasius et al. 2021
VideoSwin (Liu al. 2022), MVIT V2 (Li et al. 2022b)). and MLLM-based models (Qwen2.5-
VL (Bai et al. 2025) and GPT-4.1 mini (OpenAl 2025)). For CNN and Transformer models, we
use the default training settings provided by the MMAction2 framework (Contributors 2020).
As shown in Table I CNN- and Transformer- based models achieved 53-79% accuracy, with Slow- Fast and TimeSformer among the top performers. In contrast, VL--2.5-VL-7B and GPT-4.1 mini exhibited near-random performance, highlighting their limited capability in distinguishing fake videos. VidGuard-R R/ (CoT)), trained via supervised fine-tuning (SFT) on Qwen2.5-VV VL--BB, substan- tially improved accuracy from around 51 to over 66%, yet remained less competitive compared to advanced SOTA methods. This result aligns with the intended role of the SFT stage-as as a cold start phase to guide the model toward structured think + answer responses, emphasizing the extraction of salient visual cues.
In the subsequent RL stage, both DPO and GRPO further improved performance by roughly 2% over the best baseline. Our proposed approximately 2% and 5% over GRPO, respectively. demonstrating the effectiveness of temporal artifact supervision and quality-aware reward modeling in enhancing detection accuracy.
and GRPO-Q EeRPo-Q-achieved additional gains of
4.2.2 GENVIDBENCH BENCHMARK
The Gen VedBeednnch dataset comprises approximately 87k training samples and 82k testing sam- ples, with fake videos generated by models such as Muse (Xia et al. 2024). SVD (Blattmann) et al.] 2023), CogVideo (Hong et al.] 2022). and Mora (Yuan et al. 2024). and real videos sourced from HD-VG (Wang et al. 2023b). We conduct training and evaluation under the cross-source and cross-generator settings as proposed in their benchmark. In addition to the models originally reported in Gen VidBench. we evaluate VidGuard-R1 using the same model families as in our dataset experiments CNNN-assed Transformer-busedd and MLLM-based expertee
models-including two MLLMs: Qwen2.5-VL and GPT-4.1 mini VidGuard-R1 (GRPO, GenVideo-preeral
zero-shot) denotes the zero-shot model pretrained on Gen Video
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Table 3: Extended VenVodeo results with VidGuard-R1 and additional MLLMs, evaluated by Fl
score and recall (R)
Method
Detection level
Metrie
Sura
Morch Studio
Gen2
LottSoo
Lavie
Show-1
Moon Valley
Craftr
Model Scope
Wild Scrape
Mean
NPR Tan ml. 2004
Image
F1
0:91
0.27
0.90
0.84
0.91
0.30
0:.34
0.36
0.57
0.59
0:97
0.51
0.91
0:99
[156
0.81
0.31
0.87
0.64
0.71
VideoMAE
Tong ct
Valto
0.67
0.9%
0.98
0.56
0:72
0.80
0.97
0.96
0.56
0.68
0.87
MINTIME-C.JIP Cascamini al. 2004
Vilan
R
0.89
0.49
0.95
1.00
0.96
0.98
0.26
0.37
0.96
0.94
0.92
0.96
0.59
0.92
0.56
1.00
0.86
0.84
0.35
0.82
0.82
0.87
PIUN-CLIP Zhopg al. 2221
Video
F1
0.87
0.78
0.98
1,00
0.96
0.98
0.29
0:00
0.98
0.94
0.98
1.00
0.99
0.50
0.89
0.82
0.86
0.87
DeMamboa XCLIP
20124
Video
0.8
1.00
0.99
0.65
0.94
0.98
1.00
0.92
0.30
0.91
0.91
0.90
Owen2.5 VL 7B Bai
MLLM
F1
0.74
0.72
0.70
0.49
0.60
0.55
0.90
0.77
0.68
0.82
0.70
GPT-4.1
OpenAl
MLLM
0.43
0.60
0.67
0.72
0.56
0.54
0.70
0.63
0.77
0.72
0.55
0.92
0.67
0.80
0.69
0.82
0.64
0.82
0.68
0.77
VelGand-RR (CrT)
MILM
F1
R
0.90
0.92
0.89
0.91
0.95
0.91
0.39
0.50
0.98
0.99
0.91
0.79
0.95
0.99
0.85
0.89
0.89
0.85
0.38
0.57
0,90
6.90
TenVi-lllench pretriine Zem shot)
ViJGauss-RI (CILPO.
MLLM
0.95
0.93
0.93
0.96
0.91
0:97
0.99
0.2
0.05
0.93
0.89
0.85
0.38
0.91
0.92
VaGuun-RR (GRPO)
MLLM
0.5
1.0
0.08
0.54
0.98
0.97
0.99
0.98
0.91
0.95
and evaluated on Gen VidBench. As shown in Table both the zero-shot model and two fine-tuned variants achieve over 15% higher accuracy compared to prior SOTA methods. Notably, the zero-shot model demonstrates strong generalization, highlighting the effectiveness of pretraining on diverse generative content. Complete detection model results are provided in Appendix B
4.2.3
GENVIDEO BENCHMARK
The Gen Video dataset comprises approximately 2.2M training samples and 20k testing sam- ples, with generated videos sourced from diverse set of models, including Sora (sor 2025) MorphStudio (mor 2025), Gen2 (Esser et al.] 2023), HotShot (hot) 2025). Lavie (Wang et al, 2025). Show-1 <Zhang et al. 2024a), Moon Valley (moo) 2025). Crafter (Chen et al.) 2023). 3), Mod- elScope (Wang et al. 2023a). and WildScrape (Wei et al. 2024). Following the official evalu- ation protocol, we benchmark two MLLM baselines and three variants of VidGuard-R1, Among these, VidGuard-BB -R1 (GRPO) consistently outperforms almost all prior detection methods across videos generated by the various models. As shown Table it achieves an Fl score improvement of 0,06 compared to DeMamba-XCLII Complete detection model results are provided in Appendix B
4.2.4 PERFORMANCE GAP BETWEEN OUR DATASET AND BENCHMARKS
While VidGuard-R1 achieves approximately 85% accuracy on our dataset, it obtains significantly higher accuracy accuracy-exceeding 95%-on on the two benchmark datasets. This discrepancy arises from two key differences. First, the benchmarks exhibit clear discrepancies in video metadata-such as resolution, duration, and frame rate-between real and fake videos, which models can exploit as su- perficial cues. In contrast, we standardize all videos in our dataset by matching resolution, FPS, and format, thereby forcing models to rely on actual visual content. Second, our dataset ensures strong contextual alignment by conditioning generation on the first frame and the corresponding caption of a real video, resulting in more realistic and semantically consistent outputs. In comparison, bench- mark datasets often generate fake videos from unrelated prompts and synthetic images, leading to artifacts that make detection casier.
4.2.5 ABLATION STUDY
Explanation quality and accuracy comparison. Table
Table 4: LLMM-as-a-judge explanation
presents results on the Hunyuan Video (Kong et al. 2024
and CogVideoX (Yang et al. (024)) datasets. We report ex- planation quality scores, which are rated on 1-10 scale (with 10 indicating excellent quality and full alignment) by GPT-4.1 mini using the LLL--as-a-judge prompt de- scribed in Appendix D Compared to bascline models such as Qwen2.5-VL-7B and GPT-4.1 mini, our VidGuard-RI GRPO variants achieve consistent improvements in both classification accuracy and explanation quality.
scores our dataset
Method
(Hdeyaanideo)
Expl. Score
Expl. Score (Cog/Vdeeo))
Owen2.5-V1-98
588
5.6
GPT-4.1 misi
5.9
VidGaedRR (CuT)
6.3
6.9
VidGinansRi (D00))
3.1
VidKinedRt (CRIPD)
3.0
ViKiinedit (CRPPO-TA)
Vidluad-Rii (CRPP--)
5.5
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Published as a conference paper at ICLR 2026
Table Accuracy (%) for GRPO-
Table 7: Cross-dataset evaluation for VidGuard-RI
Q with varying number of interme- diate diffusion steps
Test Dataset
Training Source
Accuracy (%)
Ours
VidGuard-R1 (Ours only)
81.65
steps (step numhers)
Accuracy (%)
Ours
VidGuard-R (Ours Gen Viden)
82.97
Vedei
VidGuard-RI (GenVideo only)
97.53
(50) 3 (10, 30,
81.63
Gen Video
VidGuard-RI (Ours GenVideo)
97.98
83.21
5 (10, 20, 30, 50)
85.80
Table Label-rationale consistency and expla- nation quality for 20 videos
Table Human ranking of explanation quality
Model
Avg. Rank
Metric
Value
VidGuard-R1 (GRPO)
1.67
Annotators
GPT-4o
2.08
cabel-rational alignment
89%
Qwen2.5-VI.-72B
2.22
Rationale score (0-5)
3.9
Table Accuracy (%) for GRPO- TA under different reward function parameters a₁ and 022
GRPO-TA reward ablation. Table reports an ablation study of GRPO-TA on our dataset by varying the weight pa-
Accuracy (58
rameters a1 and ag2 which control the relative importance of
0.3
0.1
81.31
different temporal artifact types. The highest classification ac- curacy of 83.57% is achieved with = 0.5 and = 0.3, while the threshold y is fixed at 0.8 across all experiments.
0.1
0.5
0.1
0.3
82
R3S87
0.5
0.5
85.12
0.7
0.
82.33
GRPO-Q reward ablation. Table 6 presents an ablation study on GRPO-Q conducted on our dataset by varying the number of intermediate diffusion steps included per real video during fine- tuning. Using more steps provides richer supervision of video quality progression, improving detec- tion accuracy. The best accuracy of 85.80% is obtained with five steps, which is the setting used in our main experiments.
Cross-dataset complementarity. To assess whether training on a limited generative source in- duces overfitting, conduct dataset-mixing experiments using VidGuard-RI (GRPO), As shown in Table augmenting our dataset with Gen Video leads to consistent performance gains across both evaluation sets, suggesting that the model benefits from heterogeneous training data and does not rely on artifacts from any single source. These findings indicate that incorporating diverse genera- tive sources enhances overall accuracy, reinforeing that VidGuard-RR learns generalizable detection cues rather than dataset-specific patterns.
4.3 HUMAN EVALUATION OF EXPLANATION QUALITY
To examine the coherence and interpretabiliiy of CoT rationales, conducted a human evaluation of VidGuard-R1 (GRPO). Tables and summarize the two complementary studies.
Consistency and quality. Five annotators evaluated twenty randomly selected fake videos that the model correctly identified. Annotators judged whether each rationale was consistent with the predicted label and assigned quality score on 0-5 scale after watching the corresponding video. As shown in Table 8, annotators reported 89% label-rationale agreement with an average quality score of 3.9. Lower scores (< 2) occurred primarily when the model emphasized subtle texture- level artifacts that were difficult for human raters perceive.
Explanation quality ranking. We conducted blind ranking study which five participants eval- uated explanations from three models across twenty videos, consisting of ten real and ten synthetic videos. Table 9 shows that VidGuard-R R1 achieved the strongest human preference, with the lowest average rank (I.67), outperforming GPT-4o and Wwen2.5-vL-72BB Participants consistently judged VidGuard-BR 's explanations to be more informative and higher quality. This improvement is likely because, although GPT-40 and D0ven2.5-VL--72. also describe video features well, their explana-

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Published as a conference paper at ICLR 2026
Question: Does this video look real or gereraaee by en Al world model?
Answer: Generated
Question: Does this video look real or generated by an Al world model?
Answer: Real or Alguneerated with xor6 quality
think this video. The first paper is sind is aation consistancy.
sc chink about this. nction sspears sonowhat Jonky Inconsistent which wight be sign Al-generated foolage, Nexs, Lighting no drasatic charges brightness shadows However, this could due to the nature of the equipeent and evviroment so it's not definitive indicator.
like Next, Highing highlights tua would suggest real-world setting. Texture. artifacts are anothen aspect to consider. be a result of AT generatioon. sdolacions. The way the paper is being folded seens to defy the natural physics of how paper behaves....
precise consistent,
nachine sould perfore the task. consistency. in no harsh shadoes
would sugpest real-1lfu scanario.
have a very seooth texcure, which wight let's think about physics
Texture actilacts the way the hand interacts with the equipment seces bit off, The hand is novement looks bit too smooth and precise i Lastly, let's think sbout prysics viclations. The equipment and the hand sean to interact In nay that follows the loss of physics.
Hin, perfect. the precision physics the folding seen to violste natural paper behavior. All these points suggest this video is likely aT-genarated.
Let's break it
The motion paper
Considering all these factors, The video could still be real, but the inconsistencies in the hand's i sovenent and the mverall feel of the footage suggest that it night be aI-
generated with 05% quality
Figure 3: VidGuard-R1 (GRPO): reasoning
Figure 4: VidGuard-RI (GRPO-Q): temporal in-
about an origami folding sequence
consistency observed as unnatural movements
tions are less effective at distinguishing real from synthetic content, whereas VidGuard-RI better highlights the discriminative aspects relevant to this task.
Interpretation and broader implications. Although the CoT rationales align closely with human judgments, they are not guaranteed to be correct in every case. Hallucinations may arise, and the ra- tionales should therefore be shown with an appropriate disclaimer noting their Al-generated nature. Nevertheless, presenting interpretable cues helps users more effectively assess whether video may be Aeggeerrted,, supporting a practical human- in- in-the-loop verification process.
4.4
CASE STUDIES ON EXPLANATIONS
Figures 3 and 4
illustrate cases where VidGuard-RI correctly identifies videos generated. The reasoning across motion, lighting, texture, and physical plausibility
model performs before arriving final decision. Notably, it does not rely single cue, but instead accumulates evidence across frames, resembling how humans distinguish fake videos. In each figure, pink high- lights denote cues suggesting realism, red indicates artifacts indicative of generation, yellow marks intermediate reasoning steps, and underlines represent several key factors.
For instance, in Figure 3l the smooth hand motion initially suggests realism; however, once the origami folds in a physically implausible manner, the model revises its judgment. In Figure 4 although the lighting and shadows are consistent-tyyically cue for authenticity-the model rec- ognizes that this is insufficient in largely static scene with only a stationary machine and a human hand. In particular, even in its final prediction, the model reflects on earlier realistic cues and ac- knowledges that the video could still be real, underscoring its nuanced, human-like reasoning in assessing video quality. Additional case studies are provided the Appendix F
5 CONCLUSION
We propose VidGuard-R1, an MLLL-bbssed discriminator that not only detects Al-generated videos with high accuracy but also provides interpretable reasoning. By leveraging reinforcement learning with reward models targeting temporal artifacts and generation quality, VidGuard-R1 achieves 85% accuracy on our dataset, 97% on GenVideench, and 96% on Gen Video, substan- tially surpassing prior ofte-oof-the-nn methods. We expect this work to advance MLLM-based video analysis and foster future research on strengthening MLLMs' reasoning.
5.1 LIMITATIONS
Our dataset currently contains fake videos generated by Hunyuan Video and Cog the main open source models that support large-scale text-image joint conditioning. Most other diffusion models offer only text image conditioning, making them less suitable for our pairwise data struction. Although this design ensures strong contextual alignment, adding outputs from more generative models would increase diversity and robustness, improving real-world applicability.
10

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Published as a conference paper at ICLR 2026
ETHICS STATEMENT
This work does not involve personally identifiable information or sensitive user data. All datasets used in experiments are publicly available and constructed in accordance with their licenses and usage guidelines. VidGuard-R1 designed to mitigate societal risks associated with Al-generated videos, such misinformation and reputational harm, by providing interpretable CoT reasoning to assist human verification. To the best of our knowledge, the method does not introduce risks related fairness, safety, or privacy.
ACKNOWLEDGEMENT
This work supported in part by the National Science Foundation under Grant CNS-2212297. We are grateful to the anonymous reviewers for their insightful suggestions, which helped improve the quality and clarity of this paper.
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Table 10: Extended GenVidBench results with VidGuard-RI and additional MLLMs, reported as
mean Top-1 1 accuracy (%). TF denotes transformer.
Method
Type
Muse
SVD
CogVideo
Mora
HD-VG
Mean
SlowFast (Feichtenhofer al. 2019
CNN
12.25
12.68
38.34
45.93
93.63
41.66
P3Nel (Qian et al. 2020
CNN
37.43
37.27
36.46
39.59
52.76
42.52
I3D
(Carreira & Zisserman
2017
CNN
8.15
8.29
60.11
59.24
93.99
49.23
CFV2
CVVZoNguycn (guycn ct al. 2019
CNN
86.26
86.53
10.10
16.90
88.40
60.53
TPN
Yang et al.
2020
CNN
37.86
8.79
68.25
90.04
97.34
61.52
TIN
Shao el al.
2020
CNN
33.78
21.47
81.59
79.44
97.88
63.97
TRN
/Zhou
2018
CNN 38.92
26.64
91.34
93.98
93.97
71.26
TSM [Ln
2019
CNN
70.37
54.70
78.46
70.37
96.76
76.40
X3D (Feichtenhoter
2020
CNN
92.39
37.27
65.72
49.60
97.51
77.09
UniFormer V2 ( al. 2022a
TF 20.05
14.81
45.21
99.21
96.89
57.55
TimeSformer [Bertasms et al. 2021
TF 73.14
20.17
74.80
39.40
92.32
64.28
VideoSwin (L.u et al. 2022
TF
62.29
8.01
91.82
45.83
99.29
67.27
MVIT V2 Li al. 20226
TF
76.34
98.29
47.50
96.62
97.58
79.90
Owen2.5-VI--7B (Bai et al. (2025)
MILM
25.86
27.06
68.51
43.26
71.15
47.30
GPT-4.1 mini (OpenAl 2025
MLLM
26.07
33.78
94.07
57.19
87.64
59.62
VidGuard (Col)
MLLM
36.52
16.02
99.35
76.94
99.94
66.09
VidGuard-R1 (GRPO,
MLLM
97.24
96.59
99.88
99.93
88.14
96.37
Gen Gen Video-pretrained, Zero-shot) VidGuard-RI (GRPO)
MLLM
97.38
94.98
99.90
99.99
95,46
97.53
A ADDITIONAL SETUP
To further guide the model during RL training, we incorporate a length-based reward strategy. We promote informative yet concise reasoning by rewarding outputs that neither brief exces- sively long. Specifically, if the model predicts the correct answer and the length of the response falls within the range [/min m an additional reward assigned. Let be the length of the model's response for the i-th video. The reward is defined as:
pooal
rr+ton w,
if o; correct and 4min S 44 S 4max
(4)
rss
otherwise
where we w 0.1, Imin 320, and 4max 512.
B COMPREHENSIVE BENCHMARK EVALUATION
In this section, we provide extended benchmark results for VidGuard-R1 alongside additional MLLMs. Table I0 presents mean Top-1 accuracy on Gen VidBench across multiple video datasets, including CNN and Transformer baselines as well as selected MLLM variants. Table [T reports comprehensive F1 and recall scores on the GenVided dataset, including all models provided in the official benchmark alongside our MILMM variants. These extended tables offer complete compari- son of performance across all evaluated models.
C ZERO-SHOT GENERALIZATION TO UNSEEN GENERATIVE MODELS
To evaluate the robustness of VidGuard-RI beyond the curated training sources, we assess its zero- shot performance on a diverse set of recently released generative video models that were not used during training, including Gen-3 Alpha (run) 2025). Pika (pik] 2025). Pika 2.2 (pik] 2025). Luma Ray2 (lum 2025), Sora (sor 2025), Veo2 (veo) 2025). Veo3 (veo 2025), and Wan 2.1 (wan 2025).
Table 12 summarizes performance across these unseen systems. VidGuard-R1 achieves accuracy above 80% in all cases, reaching up to 96.36%, demonstrating strong generalization to more recent and increasingly realistic generative models.
These analyses demonstrate that VidGuard-RI generalizes effectively beyond the curated genera- tive sources and remains robust across wide range of unseen, high-quality video generation models.
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Published as a conference paper at ICLR 2026
Table 11: Extended Gen Video results with VidGuard-RI and additional MLLMs, evaluated by Fl and recall scores
Model
Desection level
Mestc
Sora
Morph Studio
Gen2
Hotthor
Lavie
Show
Moon Valley
Crafter
Model Scage
Wild Seraee
Mean
F3Nec Qua et
20220
Image
0.83
0.50
0.99
0.94
0.96
0.98
0.77
0.81
0:57
0.69
0.49
0.36
0.99
0.93
0.59
0.56
0.58
0.30
0.76
0.82
0.81
0,80
NPR Jan et al.
2024
Image
0.91
0.27
0.99
0.84
0.91
0.99
0.30
0.24
0.89
0.86
0.57
0.59
0.97
0.51
0.99
0.11
0.51
0.94
0.81
0.87
0.71
0.84
STIL
Gu et
2021
Video
0.78
0.38
0.98
0.90
0.98
0.94
0.76
0.7M
0.72
0.61
0.53
0.02
0.99
0.90
0.97
0.94
0:94
0.58
0.65
0.72
0.82
0.78
VileMAF Tong ot all
2022
Vilao
0.62
0.67
0.96
0.95
0.98
0.98
0.96
0.96
0.77
0.86
0.80
0.87
0.97
0.96
0.56
0.77
0:36
0.96
0.79
0.68
0.77
0.89
MINTIMF-CLIP Cozzomini et al
2024
Vidao
0.49
0.99
0.95
1.00
0.98
0.96
0.26
0.37
0.96
0.94
0:98
0:92
0.99
0.92
0.56
1.00
0.54
0.54
0.85
0.82
0.87
0.82
FICN-CLIP Zhesg ot X011
Vilas
0.78
0.87
0.98
1.00
0.98
0.98
0.17
0.29
0.97
0.98
0:91
0.94
0.98
1.00
0.99
1.00
0.90
0.85
0.82
0.89
0.87
0.86
TAL. X al 2023
Vilas
0.26
0.1
0.98
0.82
0.97
0.89
0.55
0.74
0.77
0.76
0.79
0.72
0.99
0.81
0.98
0.90
0.50
0:94
0.66
0.67
0.74
0.88
CLIP Radionl et
X011
Image
0.94
0.28
0.99
0.84
0.91
0.86
0.77
0.72
0.85
0.88
0.76
0.86
0.99
0.82
0.59
0.91
0.76
0.54
0.79
0.84
0.76
0,90
DeMManba-C1.JP Chen el. 2024g
Vidas
F1
0.95
0.64
0.96
1.00
0.98
0.97
0.78
0.69
0.94
0.92
0.90
0:93
0.95
1.00
0.98
1.00
0.87
0.53
0.82
0.87
0.91
0.89
XCLIP Ni al
2025
Vilas
F1
R
0.82
0.31
0.99
0.88
0.90
0.93
0.65
0.61
0.79
0.82
0.89
0.70
0.86
0.97
0.59
0.93
0.77
0.75
0.83
0.82
0.84
0.76
DcMamiooXCCL.PP IP Chen al,
3024a
Vidas
F1
R
0.64
0.98
0.96
L.00
0.99
0.97
0.75
0.65
0.95
0.94
0.96
0.95
0.95
1.00
0.97
1.00
0.93
0.90
0.89
0.91
0.90
0.93
Qwen2.5-VL.c Bai
2025
0.58
0.54
0.43
0.38
0.54
0.70
mini OpenA)
MULM
0.45
VidGuard (CoT)
0.96
0.79
0.9
0.91
0.95
0.99
0.51
0.35
0.88
GenVidBench presziindd shot)
VilCOune-RI (GRPO.
MLLM
0.3
0.93
0.96
0.99
0.52
0.35
0.91
Vi8Guad (GRPO)
0.97
0.89
0.95
0.90
Table 12: Zero-shot detection accuracy on unseen generative models
Model
Total
Correct
Incorrect
Accuracy (%)
Gen-3 Alpha
56
49
7
87.50
Pika
110
101
9
91.82
Pika 2.2
110
106
4
96.36
Luma Ray2
110
98
12
89.09
Sora
110
102
8
92.73
Veo2
52
45
7
86.54
Veo3
55
45
10
81.82
Wan2.1
55
46
9
83.64
D PROMPT
Figure 5 shows the base prompt used for the real-vs-fake classification task. Annotators are in- structed to assess whether a video real or Al-generated by analyzing key visual and physical properties.
Figures [ and provide category-specific rationale collection prompts. In particular, Figure presents the prompt for identifying visual cues of realism in real videos, while Figure focuses on spotting artifacts in Al-generated videos. Both prompts guide annotators to evaluate videos across four diagnostic categories: motion consistency, lighting consistency, texture artifacts, and physics violations.
Figure illustrates the LLM-as-a-judee prompt used to evaluate rationale quality. In this setting, GPT-4.I mini rates the quality of model-generated explanations on a 1-10 scale, where a score of 10 corresponds to excellent quality and full alignment with the ground truth rationale.
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formpt Distinguishing Real from Al-Generated Content
SYSTEM:
A conversation between User and Assistant. The user asks question, and the Assistant solves it. The assistant first thinks about the reasoning process the mind and then provides the with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, le., <think> reasoning process here </think><answer>> answer here </answer>
USER: <video> Decide whether a video looks a real one or generated from the Al world model.
Figure 5: Prompt for identifying realism cues in real videos across four categories
Rationale Collection Prompt for Real Videos
<video> This is real-world video. Your task is to provide a detailed guide of which specific parts of the video should be examined identify signs of real across four key categories: Motion Consistency, Lighting Consistency, Texture Artifacts, and Physics Violations. For each category, highlight critical areas or elements within the video.
Figure 6: Prompt for identifying realism cues in real videos across four categories
Rationale Collection Prompt Al-Generated Videos
<video> This video has been generated by an Al model. Your task is to provide detailed guide which parts of the video identify signs of generation across four key categories: Motion Consistency, Lighting Consistency, Texture Artifacts, and Physics Violations. For each category, highlight critical areas or elements within the video.
Figure 7: Prompt for identifying artifacts in Al-generated videos four categories
E CASE STUDIES ON EXPLANATIONS
E. GENVIDBENCH
Figures 12 present inference examples for videos synthesized by four distinct Al models in- cluded in the Gen VidBeech testing dataset: Muse (Xia al. 2024), SVD (Blattmann al. 2023). Cog Video (Hong al. 2022). and Mora (uaa et al. 2024).
E.2 GENVIDEO
Figures [3 22 show inference examples for videos generated by ten different Al models included in
the Gen Video testing dataset: Sora (sor) 2025), Morph Studio (mor) (025),, Gen2 (Esser al. 2023),
HotShot (hot 2025). Lavie (Wang et al. (2025). Show-1 (Zhang et al.) 2024a), Moonvalley (moo
2025).
Crafter (Chen al. 2023), ModelScope (Wang al. (023a). Dream Video (Wei al.
2024
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Published as a conference paper at ICLR 2026
LLM-as-a-Judge formpt Rationate Quality Evalustion In Real Generated Video Classification
SYSTEM:
You are an expert judge evaluating the explanation quality of a vision-language model (VLM) that decides model outputs binary decision or fake) along with rationale explaining the basis of its decision.
whether video real or Al-generated.
The rationale should focus on four visual diagnostic categories:
Your evaluation should consider the following five criteria:
Accuracy: Does the rationale identify the key generation artifacts or natural signals relevant to the decision?
Alignment with Ground Truth: Does the rationale emphasize the same visual evidence? Specificity: the rationale grounded the visual content (i.e., free from hallucinations vague generalities)?
Conciseness: Is the rationale clearly and efficiently worded without unnecessary length or redundancy? Consistency with Model's Answer: Is the rationale logically coherent with the model's predicted label (real/fake)? For example, the model said "fake," does the rationale convincingly support this?
Please provide your evaluation in the following format:
Start with "ANALYSIS:" and write concise paragraph analyzing the rationale with respect to the five criteria.
Then, on the next line, write "SCORE:" followed by single integer from 10 (10 excellent quality and full alignment).
USER:
Let's evaluate the model's rationale!
Ground truth answer (real/fake):
(ground truth answer)
Ground truth rationale:
(ground truth rationale)
Model answer (real/fake):
(model answer)
Model rationale:
(model rationale)
Figure -a--ug-a prompt for rationale quality evaluation
Question: Does this video look real generated by an Al world model? Real Generated
<thiniolet ne think about this, when evaluating whether this video is real or AI generated, I'll consider the four factors mentioned: Motion Consistency, Lighting Consistency, Texture Artifacts, and Physics Violations.
"Mettoon Consistency**: the video, there noving oojects that allow static, with visible difficult deternine
any
lack astion it notion consistency alone.
""Lighting Consistency**; Highting Al-generated
video appears consistent with what
expect
scenario. The shadows and highlights on the buildings and uater reflect natural lighting canditions. Hassver, without nore context conparisen other videos, it's hard to definitively if the lighting is perfectly consistant with real: world conditions.
"Texture "rtifacts**
expect buildings, sater, photograph.
elesents appear
uniforn
generat for process, where textures sight not perfectly realistic.
4. exptystes Violations*:; There are na ahvious physics violations in this scene, The pasitianing of the buildings, water, and bosts seens plausible and follows the expected physics of harbor or cityscape. However, the lack of notion and the unifornity textures could still indicative Al generation. Considering these
the video could suggest generation. However, conclude.
exhibit clean absence
textures and lighting sonswhat uniforn, which challenging to definitively
In cone lusion,,n, while the video does not exhibit clean notion on physics violations, the uniforsity of textures and the lack of fine detail suggest that it could be AI-generated. However, nore context or comparison to other videos would be needed for a nore cefinitive assessrent.c/hhe c/thhink
Generated c/ansver>
Figure 9: VidGuard-Ri (GRPO) inference on MuseV-geenernn video
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Published as a conference paper at ICLR 2026
Question:
Does this video look real or generated by an Al model?
Real or Generated
<thiniolet
appears analyze video, consider Lighting Consistency, Texture Aetifacts, and Physics Violations.
passibly
Intervention,
sencioned: Motion Consistency,
1. "Motion consistency":; The individuals in the video soen to be noving in say that is somewhat realistic. However, the speed and direction of the sovesent appear slightly inconsistent at times. This could be due to the Al's attempt to simulate husan rovement, but the slight discropancies night indicate lack of perfect synchronization.
"Lighting Consistency":: The Highting the video changes brightness expected patterns for outdoor scene.
daylight. consistent with highlights lighting.
suggest
3. "Texture Artifacts**: Uponn closer inspection, there are sone subtlc artifacts in the textures of the clothing and the environment. The edges of objects and people see bit jagged, which is conmon issue with AI- Al-generated inages. This could the artifacts noticeable. violations in video. people environeent typical generraateed content.
due the Al's attespt render textures realistic
**Physics Viclations**: There
noving
plausible Considering these osservations, motion and texture artifacts are subtle but present, and the overall scene is consistent with what one night expect from real-world event. However, the slight disenspancies in notion consistency and the presence of texture artifacts suggest that this video likely generated while video AI model rather than real recording.
interactions between video exhibit inconsistencies
seen
realistic depiction chaotic texture facts towards heing Al-genenated c/thinks canswor> Generated c/answers
subtle inconsistancies notion
Figure 10: VidGuard-R1 (GRPO) inference SVD-generated video
Question: Does this video look real or generated by an Al world model? Real or Generated
<thinioLet think
AI-generated, consider steststency, Highting consistency, texture artifacts, and physics violations.
evaluating whather
1. "Hotion consistancy**; The video shous a plush toy dinosaur inside box. The sovasent of the toy appears to be smooth and natural, suggesting that it eight be real video. However, the toy is not roving in that would be expected in real-uorld scenarto,
seess to floating or noving
that deffes gravity. This could be physirs violation. good sign
"Lighting consistency":;
lighting the video appears consistent
highlights real.
suggest
lighting post-processing.
3. "Texture Artifacts**: The texture of the plush toy and the box looks realistic, There are no visible artifacts or distortions that would indicate the use of AI generation. The fabric of the toy and the cardboard of the box have natural.
**Physics Vielations**:
The striking physics. anoraly.
novenent
floating
significant
Al-gererated content,
highly unlikely
video capture
Considering all these points, the video seens to have a physics violation that is not typical in real-world footage. While the notion and lighting appear consistent, the unnatural novement of the toy suggests that this video night be Al-generated. The coneination these factors rakes it highly improbable that this is real video.</thints Generated
Figure 11: VidGuard-RI (GRPO) inference CogVideo-generatte video
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3. TTexture Artifacts**: " The texture of the radio and the cassette tape looks very detailed and realistic. There are no visible pixelation distortion that would suggest it's Al-genersted. The lines and edges sharp and well-difined. cassette Published as a conference paper at ICLR 2026
Question: Does this video look real generated by Al world model?
Real Generated
image provided playen. It's a detailed and realistic-looking illustration, but need to consider the factors nentioned to deternire if it's real or Al li-ggeerated
<thinioLet think about
appears vintage or cassette
1. "*lctoon Consistency**: The image is still frons, there's no notion to analyze. However, if this were video, buttons,
would look for inconsistencies the novement of the objects, such the tape there "Lighting Consistency":: farring notions.
if they
lighting
image appears consistent acress entire frane.
highlights that seen out place. this were a real video, I would expect the lighting to change slightly as the canera noves or as the scene changes.
"Dhyyics Violations":;
buttons violations this inage. Everything leaks logical arrangeeent.
cornect pesition
Given these diservations, the isage appears the consistent lighting, the detafled textures, and the absence of physies violations all point towards this boing an At- generated image rather than real video.
high-quality illustration rather
real video. lack son,on,
Hmm, let's break down. The still nature of the image, the detailed and realistic textures, and the absence of notion and physies violations dynaric qualities canswer> Generated c/answer>
support conclusion this Ar-generated Irage, It's canvincing illustration,
doesn't
cideo.</thinko
Figure 12: VidGuard-RI (GRPO) inference on a Mora-generatee video
generated by an Real Generated
<thiniotet ne think about this. when evalusting whether video is Al-generated, consider several factors: motion consistency, lighting consistency, texture artifacts, and ics violations. Let's break it down.
1. "Metion consistency**. In the video, the volves are shown in natural, fluid notion. Their asvements appear smooth and sudden jerks other realistic.
consistent how anirals sould Indicate
wild,
that would typically
The "LLighinng consistency": The lighting
interact with
would expect night scene. noonlight illuninates the solves and the surrounding enviromment in way that is natural and not overly artificial. The shadows and highiights are well-balane balanced, which adds to the realisn of the scone.
video consistent
3. "Texture *rtifacts**; The texture of the volves and the enviromeet appears smooth and natural. There are no visible pixelation unusual textures that are often ceen Al geseeettddd inages. The fur of the wolves looks realistie, and the enviroreent, including
ground,
texture that
with
night.
say that physically plausible. abvious physics viclations, such as the wolves floating or noving in an unrealistic sanner. The houling and the novensnnt of the wolves are in syne with their natural behavior.
"shyystes Violations**;
chown ing noving
no
considering all these factors, the video does not exhibit any significant notion inconsistaacies lighting inconcistenc ies, setting, captured in
texture artifacts, physics violations. photography video.
appeans be real
way Therefore, based the analysis, seers this video likely real video rather than Al-generated one. c/thinkt conswer> Generated </answer>
Figure 13: VidGuard-RI (GRPO) inference on a Sora-generated video
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Published as a conference paper at ICLR 2026
Question: Does this video look real or generated by an Al world model?
Real or Generated
<thinik Let think about this, when evaluating whether this video is real or AI Ml-generated, I'11 consider the four factors nentioned: Mction Consistency, Lighting Consistenty, Texture Artifacts, Physics Vislations. their real-worid appear sorexhat natural. Howvver, speed the nnvesent, which can subtle noticeable.
the video, and fluidity their noverents right not entirely consistent
"Motion Consistancy"'
notion. AI-generated videos
slightly artificial
2. "Lighting Consistency*; The lighting in the video soens to be consistent with what you'd expect in real-vorld setting. The shadows and highlights the people and buildings appear natural, suggesting that the lighting was carefully simulated natch real-sorld conditions.
"Texture
lipon
Inspection,
textures of people's clothing variation
anviroment
and building facades) appear sonsehat uniform real photograph video. could
typically
sign that the textures are generated by an sodel.
4. "*pbysics Violations** There are na ahvious physics violations in this video. The people are walking in way that is consistent with how they would move in real-world scenario. The shadows and reflections also align with what you'd dou'd expect real enviroment. Considering these
the video
artificial qualities, particularly
notion Al-generated video, subtle inconsistencies in texture
consistency. Mhile notion suggest that right be. However, without more advanced analysis tools, it's challenging to be centain. The video could still be a high- qughituaitty real-world recording that has been edited or enhanced in some way, </think canswer> Generated </answer>
not definitively
Figure 14: VidGuard-RI (GRPO) inference on a Morph Studio-generated video
Question: Does this video look real or generated by an Al world model? Real or Generated
appears animated video. considering the Factors Notion Consistency, Lighting Consistancy, Texture Artifacts, Physics Violations, I'll analyze the inage step step.
<thiniolet think
The provided
1. "Hotion Consistency"*; The character in the image is in a dynanic pose, suggesting sovasent. Houaver, since this is
still frone, cannot deternine notion consistency. The character's 's pose looks smooth and well-aninsted, which is typical for high-quality anteation.
"Lighting consistency**; The lighting
cansistent with well-lit would suggest real-world envirovennt.
evenly distributed,
shadows inconsistencies "'eetture factsftts* The texture the characten and the background appears seooth consistent with digital animation. There are no visible signs of pixelation or inregularities that would indicate a real-world video.
scene.
4. **physics violations** The character is depicted in wsy that defies real-world physics. For examle,, the character is floating the sir, which not possible real life. this video.
clear indication that the
is from aninsted
considering these points, seens shysies involved all point towards anivation rather than real-world video. absence sotion in not affect this conclusion, as the analysis is based on the visual elements present.
image
Al-gererated video. character's pose, lighting,
does
In sunnary, the irage eshibits characteristies typical of Al-generated animation, such as smooth textures, consistent lighting, and physies viclations, which are not present in noal-world sideos.</thinn canswers Generated c/ansver>
Figure 15: VidGuard-RI (GRPO) inference on a Gen2-generated video
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Published as a conference paper at ICLR 2026
Question: Does this video look real or generated by an Al world model?
Real or Generated
<thinkolet think about this, when evalusting whether video real Al-generated, consider several factors: notion consistency, lighting consistency, video, "Mstion consistency"::
artifacts, physics
apverent seens sonswnat jerky inconsistent. Real-world objects, especially large structures like Ferris wheels, typically exhibit smoother notion. The wey the gondolas are roving right suggest lack of natural fluidity, which is comson sign of Al-generated content.
appears
nation,
2. "Lighting Consistency"*: : The Lighting the video sppears to be natural, with shadous and highlights that align with artifacts ancrolies Ar-generated videos.
from sunlight. There artificial lighting techniques typically
would
lighting that would
texture the that would be expected in real-world photograph. AI ge-gerertted inages often have slightly artificial look, with less variation in texture and detail.
"Texture Artifacts"
gordolas locks quite unifors
fine details
4. "*Physics Violations"'; The Ferris wheel is rotating, and the gondolas reving in that seens plausible. However, bit too perfect and lacks the slight ieperfactions wnuld expect in real real-wonld physics inconsistencies nction texture
the notion the gondolas
way
video exhibits generated content. the ration the Ferris wheel and gondolas is not entirely natural, and texture of objects is commuat artificial. Mhile the lighting is consistent with natural sunl unlttgh, the overall impression is that this video Is likely < </thinks canswer> Generated c/answer>
Considering these deservations,
characteristicc of
Figure 16: VidGuard-R1 (GRPO) inference HotShot geeeertteeee video
Question: Does this video look real or generated by an Al world model? Real or Generated
<thiniolet think about this video and the factors you' nentioned. The video shows and blue bost floating caln analyze it, artifacts, and ghysics violations.
consistency, lighting consistency,
1. "Motton consistency**: The beat appears to be noving smoothly across the water, There are no abrupt thanges in its position orientation, which sugests consistent notion. However, the lack of visible ripples or waves around the bost night indicate slight inconsistency in the water's novenent, which could be result of the AI generation process.
"Lighting Consistency**: The lighting direction the light
cansistent that lighting would suggest video AT-generated. 3. "Toxture Artifacts**; The water's surface looks snooth and lacks the fine details that would be experted in real photograph on video. The beat's surface also appears conuhat uniforn, which night be result of the Al' atteept to simolify textures for aesthetic purposes.
with
with expect
surny day. There are noticeable artifacts anmnlless appear
esphysics violations**; The bost flosting uster without visible ripples waves, scenanto.
propulsion, which is plausible
the
any
interaction disturbance
the
physics
violation.
scenario, the considering these observations, the video seers to have sone inconsistencies rotion and texture, which cormon in At- generated irages. The lighting and physirs violations are subtle but noticeable. Therefare, it is likely that this video has been generated by aI rodel, but it still quite plausible that it could be real video with some post-processing or sirolification.. </thinko
sater, even it's noving slouly.
Figure 17: VidGuard-RI (GRPO) inference on a Lavie-generated video
22

---

Published as a conference paper at ICLR 2026
Question: Does this video look real or generated by an Al world model? Real or Generated
<thinkolet think about this video carefully. lighting. analyze Artifacts, and Physics The dancers appear to be noving in a coordinated ranner, which is typical choreographed
of dancers performing Consistenry, Lightingg consistency,
with vibrant
1. "*pttoon Consistency*
perfornance. However, upon closer inspaction, the novenents seen slightly unnatural. The fluidity of the dancers' novenents is not entirely consistent with what I would expect from real huran dancers. There are some abrupt transitions that night entirely natural.
the video "Lighting
The lighting
colorful,
performances. lighting changes
way inconsistencies are obvious 3. "Texture Artifacts"* The dancers' costumes and the stage itself appear to have realistic taxture. There are no visible artifacts distortions that would indicate the video is AT-generated. The textures of the costures and the stage are smooth and consistent with what would expect in real video.
visually appealing consistent suggest lighting that
what
expect is Al-generated.
professional stage setup.
"*byysics V
generally consistant with
dancers.
floating roving ways and coordinated, which typiral for well-rebeaseed perforrance.
physics
defy gravity.
After considering all these factors, I's inclined to think that this video is likely AI- AI-generated. The slight inconsistencies in the dancers' apvements and the lack of completely natural flox sugpest that the video night be generated trage heavily edited video. The lighting and testure appear consistent with real footage, but the notion indicator that elenents,
inconsistencies
video.
conclusion, this video ti-geeerated.c/thieee conswer> Generated </answer>
inconsistencies significant enough suggest
Figure 18: VidGuard-R1 (GRPO) inference Show- -generate video
Question: Does this video look real or generated by an Al world model? Real or Generated
cthiniolet ne think about this video carefully. The inage shows scone with toy cars, and need to deternine If it's real AE-generated. Let's break it down by the factors nentioned:
1. "Motion Consistency" The cars
be noving in that looks sorewhat realistic, but the notion soons bit suggests
stiff and not entirely natural. pasitions content.
positioned quite align real-sorld physies. This forward, sign Al-generated
turning and
"Lightinn consistancy"*; The Highting in the scone is quite uniforn and doesn't ' show any significant shadows on hight highlights that would be expected in a real-world scenario. This could be result of the At model trying to create concistent Lighting environsent, but it right not natch the natural lighting conditions.
and the background real-world objects, which usually generation.
**Texture Artifacts**; The
smooth, alrost plastic-like texture. This is typical of
Indicaton
**gbysics violations**: The cars are positioned in a way that suggests they are roving, but the physits of their novenent don't it seen to follow real-sorld rules, for exmple, the cars appear to be roving without visible force or interaction with the ground, which is not how real cars would behave.
Considering all these points, artifacts, along
the video
inconsistencies that typical of AI-genersted content. The likely an Al-generated
physics violations, sugpest sodels sonetines produce content that notion, lighting, real-world recording. However, inportant note video
closely ninies woal-world scenarios, so the distinction night not always be clear-cut.<<thhnke canswers Gonerated c/answers
Figure 19: VidGuard-RI (GRPO) inference on a Moonvalley-genenaal video
23

---

Published as a conference paper at ICLR 2026
Question: Does this video look real or generated by an Al world model? Real or Generated
AI-generated, I'll consider four you nentioned: Motion consistency, Lighting Consistency, Texture Artifacts, and Physics Violations.
evaluating whetner viceo
1. "Mecton consistency*:: In the video, the waan appears to be welking through cave, leer apverent looks smooth and naturai, wilch geod sign. However, the notion is sonswaat oxaggerated, which right be result of the AI generstion Reai-life videos typically Consistency" The natural, possibly sunlight flitering through opening,
subtle and less
consistent with
would highlights
light source appears the idea that this could be real viceo.
realistic, which supports
3. "Texxtrre :rtifacts*: The textures of the cave walls and the wcnan's dress appear quite detailed and realistic. There ane devious signs pixelation distontion that one would typically associate with AI: di-generated inages. The colors and details the environent look natural and well-rendered.
"Dbyysics violations**;
follow would suggest physics violation. way salks
of physics. way light
unreallstic noverents the enviroment are consistent with world physics.
with
considering all these points, the videa seess to be quite realistie. The notion is smooth and natural, the lighting is consistent with real cave, the textures are detailed and realistic, and there are no physics violations. However, the slight exaggeration the wosan's sovesent night subtle indicator that this is Al-generated video. generated by elight exaggeration sotion suggest
tesclusion, while nodel. </thinks canswer> Generated s/answer>
appears
Figure 20: VidGuard-RL (GRPO) inference on a Crafter-generatel video
Question: Does this video look real or generated by an Al world model? Real or Generated
evaluating whather video
AI-gonerated, I'll considor
you' pentiored: Motion tonsistency, Lighting consistency, Texture Artifacts, and Physics violations.
1. "Mpoton consistency**: In the video, the astronaut appears to be in static position, suggesting to notion. If this were a real video, sould here. this lack sction could indicate that the video Al-generated. scenario.
sase subtle sovenents, such as breathing or slight shifts in pasture, which are not
lighting suggest
entire video, lighting naturally very
noticeable shadows highlights depending on the environrent and the time of day.
s. "Texturee artifactss*; The astronaut's 's suit and helnet appear to have a very smooth and unifars texture, which is typical of At-generated images. Real-life textures usually have nore variation and detail, such as wrinkles, reflections, or slight imperfectiors.
and the uniforn Lighting The astronaut physics violation
lack of notion dynanic Considering these observations, it seers that the video lacks the subtle details and natural variations that would be expected in real video. The uniforsity and lack of notion suggest that this video is likely AI generated.. The absence of realistic lighting and texture artifacts further supports this conclusion. The
""ttysics Violationg**:
upright,
realistic
real-world scenario. videos
interactions with the environsent, such as novenent or interaction with objects.
exhibit unifors lighting are strong indicators canswers Gererated c/answer>
variations subtle details would be AI-generated.e/thins.
Figure 21: VidGuard-R1 (GRPO) inference ModelS video
24

---

Published as a conference paper at ICLR 2026
Question: Does this video look real genersted by Al world model?
Real Genersted
<thinioLet think about this, when evaluating whether this video is real or AI-generated, I'll consider the factors of notion consistancy, Lighting consistency, texture artifacts, and physics violations.
"Hotion Consistency*:: The astronaut
The horse's
say sign Al-generated content.
aligo
physies.
The
slightly jarky inconsistent, "Lighting to The Highting in the video seens to be consistent with what one right expect in real outooor cetting. Howver, the lighting on the astronaut and the honse is somwhat flat and lacks the depth and variation that would present real scene. This could be due to the AT nodel's Hnitations in generating realistic lighting. artificial.
"Texture : The
wisible
sould suggest striking aspect
indicator phystes violation. astronaut sight Al-generted. spacesuft
*xphystes Violations**
ride a horse in a real-world scenario. The combination of these clements sugpests that this is not real video but nather an Al t--generated one.
concidering all these points, seers that this video likely falls into the category of AI- Al-generated rostent. The anconsistencies rotion, the flat lighting, the artificial textures, the physics violations closely towards this sources experts possible.</thnnts
isportant cross-reference scenarios, always canswery Generated </answer>
However,
produce
Figure 22: VidGuard-RI (GRPO) inference on Dream Video seieeo-geeerted video
25


Reviews:
[{"review_id": "GHLTBRk4TG", "reviewer": "Reviewer_acmo", "summary": "This paper introduces VIDGuard-R1, a video authenticity detector leveraging multi-modal large language models (MLLMs) and reinforcement learning with group relative policy optimization (GRPO). The approach involves fine-tuning Qwen-VL via supervised and reinforcement learning with two reward models: one designed to incentivize temporal artifact detection and another to guide the model towards assessing generation quality using diffusion step progression. The authors also curate a challenging dataset of 140k real and AI-generated videos. Extensive experiments show VIDGuard-R1 outperforms prior methods and baselines on multiple benchmarks and provides interpretable, step-by-step rationales for its decisions.", "questions": "1. How is the detection capability for ood videos?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper advances the state-of-the-art by developing the first MLLM-based, RL-enhanced video authenticity detector that not only improves accuracy but also generates detailed, interpretable reasoning, as supported by the architecture overview in Figure 1 and illustrated in Figures 3 and 4 showing stepwise explanations.\n\n2.  Introduction of specialized reward models one that encourages careful temporal reasoning (GRPO-TA) and another that uses diffusion step quality signals (GRPO-Q) is a thoughtful step toward aligning model incentives with the challenges of real/fake video discrimination. The mathematical formulations are clearly presented, especially the explicit conditions and reward assignment logic.\n\n3. The authors curate a large, carefully controlled real/fake video dataset (140k paired samples) where low-level cues and trivial metadata are equalized, thus demonstrating scientific rigor in experimental setup. The commitment to removing “shortcut” biases strengthens the validity of their results.", "weaknesses": "1. The article proposes a method for identifying fake videos using an enhanced reasoning model and incorporates additional video-related designs in the reward scheme. Although it cannot be ignored the author's contribution of applying enhanced reasoning training to a specific field, the overall approach still follows common training techniques, limiting its overall innovativeness.\n\n2. While the curated dataset is a step forward, its scope is limited by only including videos generated by HunyuanVideo and CogVideoX. As acknowledged in Section 5.1, this restricts the external validity and robustness of the detector. The lack of inclusion of generative outputs from other recent or diverse models (such as those evaluated in the GenVideo or GenVidBench test sets) in the training phase could lead to overfitting to specific generation artifacts. The importance of this limitation is amplified by the single-source pairing and should be further addressed experimentally.\n\n3. The use of reward thresholds and hyperparameters, for example $\\alpha_1$, $\\alpha_2$, and $\\mu$ in Equation (GRPO-TA, Section 3.3.2) or step mapping function $g(\\cdot,\\cdot)$ in GRPO-Q (Section 3.3.3), is not sufficiently justified theoretically nor by ablation for practical robustness. While Table 5 presents some grid search results for $\\alpha_1$ and $\\alpha_2$, the rationale for selecting these ranges and assessing sensitivity is shallow—potentially undermining reproducibility if the optimal region is narrow.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8UxT3SsTkM", "reviewer": "Reviewer_AX7C", "summary": "The authors propose a detector finetuning MLLM using GRPO producing 2 reward models, one based on temporal artifacts and the other focused on image generation quality. They also produced an additional dataset with standardised formatting of the videos so as to prevent models from taking shortcuts to easily identify real and fake videos. The authors also produce CoT data that supports the production of text that can be used as a possible explanation.", "questions": "Q1: Could the authors discuss or introduce quality checks to ensure the CoT data is high quality?\n\nQ2: The idea of using the number of diffusion steps to control the quality is interesting, have you considered also manipulating other hyperparameters (e.g. changing the number of frames?)\n\nQ3: Is there a specific rationale for the hcoice of those 2 specific temporal artifacts? Indeed they're quite different from naturally occurring ones. Is there a chance that applying more realistic ones could result in even better performance?\n\nIn relation to W1,W2,Q1: It would be interesting if the authors could show 1) human studies to assess the quality of the explanations, 2) produce a study on how many times the explanations are in disagreement with humans and whether the disagreement is caused by an hallucination or by the fact humans leverage different cues. These would also guarantee the produced CoT data is high quality and can be more broadly useful for the community. \n\nI am very favourable to increasing my score if these concerns are addressed, as I think it will greatly increase the impact of the author's work.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1: The way the finetuning procedure is performed is non-trivial and proposes interesting ideas. The methodology is simple but effective. \n\nS2: The way the dataset is constructed, and the special attention in avoiding models taking shortcuts is appreciated.\n\nS3: The availability of rich CoT data could be really impactful and useful for many applications, but ...", "weaknesses": "W1: The way the CoT data is generated is likely to produce hallucinations. The models may easily base their explanations based on prior knowledge about video artifacts, and the model could be correct at random. \n\nW2: Claims about interpretability should be made cautiously: the generated text is simply a sequence of tokens, not really an explanation of the model's decision.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ib9TQCAV3J", "reviewer": "Reviewer_1fnv", "summary": "This paper presents VidGuard-R1, a multi-modal large language model (MLLM)–based system for AI-generated video detection and explanation. The work addresses growing societal risks from realistic video generation by developing a reasoning-capable authenticity detector that produces both accurate judgments and interpretable explanations. The model fine-tunes Qwen-VL using a two-stage framework: supervised chain-of-thought (CoT) initialization followed by reinforcement learning with Group Relative Policy Optimization (GRPO) and two specialized reward models—one emphasizing temporal artifacts (GRPO-TA) and another focusing on generation complexity (GRPO-Q). The authors construct a large dataset of real and synthetic videos generated by recent diffusion-based models and demonstrate that VidGuard-R1 achieves strong zero-shot and fine-tuned performance on several benchmarks, accompanied by qualitative examples illustrating its reasoning process.", "questions": "- The statement that “directly assigning a reward of 1 to real videos and 0 to fake ones presents challenges” is not justified theoretically or empirically; a short ablation or analysis on this design choice would clarify its necessity.\n\n- Will the dataset and code be publicly released for community use?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper tackles an important societal problem by adapting reasoning MLLMs for deepfake and synthetic video detection.\n- The combination of supervised CoT initialization and RL-based GRPO fine-tuning is conceptually sound and clearly explained, with reward models thoughtfully designed to encourage temporal and quality-aware reasoning.\n- The curated dataset is large, diverse, and standardized to minimize shortcut cues such as duration or resolution differences, making it well suited for robust learning. The dataset can be of good value to the community as well.\n- Experimental results show consistent and often state-of-the-art accuracy across multiple benchmarks, including GenVideo and GenVidBench, with competitive zero-shot generalization.\n- The inclusion of reasoning traces and qualitative explanations enhances transparency and potential user trust, distinguishing this work from prior black-box detectors.\n\n- The paper is well written and easy to follow, with clear explanations of both the methodology and its motivation. Figures and examples  effectively convey the reasoning process and highlight the interpretability of the model’s predictions.", "weaknesses": "- Although interpretability is a stated goal, the paper does not evaluate the quality of explanations beyond qualitative examples. It remains unclear whether the rationales are causally consistent with correct predictions or merely plausible-sounding. Established metrics (e.g., LLM-as-judge, human evaluation, or coherence scores) should be adopted to substantiate this claim.\n\n- Several key ideas have been explored in prior work on image and video authenticity detection (e.g., SafeWatch, DeMamba-XCLIP, or recent surveys on AI-generated media detection). The paper could more clearly articulate what is fundamentally new in its contribution beyond integrating existing elements.\n\n- The related work section omits relevant prior literature on video safety and explainable detection, such as SafeWatch (ICLR 2025) and other text-to-video safety models, which share similar objectives and techniques. A more explicit comparison—methodologically and empirically—would strengthen positioning.\n\n- The training dataset, while large, is largely synthesized from one or two generative sources (e.g., HunyuanVideo-I2V, CogVideoX). This raises concerns about generalization to unseen generation models. Evaluating across broader generative distributions or conducting cross-model tests would better support claims of robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JXrM0jguGY", "reviewer": "Reviewer_3kcH", "summary": "The paper introduces VidGuard-R1, a multimodal large language model (MLLM)-based system designed for detecting AI-generated videos and providing interpretable explanations for its decisions. The model utilizes reinforcement learning (RL) and group relative policy optimization (GRPO) to fine-tune a Qwen-VL model for better accuracy and reasoning capability. VidGuard-R1 targets temporal artifacts and generation complexity to improve detection performance. It achieves high accuracy on several benchmarks and presents detailed case studies showing its ability to generate interpretable rationales behind predictions.", "questions": "1. Have you tested the model on unseen AI-generated video models? How does the performance of VidGuard-R1 compare when it is trained on AIGV model-generated videos from one source and tested on videos generated from other unseen AI models?\n2. Could you provide more details on how VidGuard-R1 performs when trained on your proposed dataset but evaluated on other datasets or with video content generated by different AI models?\n3. Given that the CoT rationales are conditioned on ground-truth labels from a more powerful model (Qwen2.5-VL 72B), how do you ensure that VidGuard-R1 can develop independent reasoning without overfitting to the potentially biased or predefined cues provided by the larger model?\n4. Given that similar approaches, such as Q-insight and VQ-insight, have used GRPO for optimizing multi-modal models, can you clarify the novel contributions of your work, especially in relation to GRPO? How does the reward method or the new dataset significantly advance the existing literature?\n5. Could you provide a more comprehensive comparison of VidGuard-R1’s explanation quality against state-of-the-art models (such as GPT-5 or Qwen2.5-VL 72B) to give a clearer understanding of how VidGuard-R1’s interpretability holds up against larger, more capable models?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Dataset Scale: The paper introduces a dataset of 140k real and AI-generated videos designed to challenge current detection methods.\n2. Introduce Explanation in AIGV Detection: the ability to explain why a video is classified as real or fake.\n3. Advanced Fine-Tuning Techniques (GRPO and RL): The use of GRPO (Group Relative Policy Optimization) and reinforcement learning for fine-tuning is a notable strength. The GRPO-TA and GRPO-Q variants further enhance performance by targeting temporal artifacts and generation complexity, offering additional levels of refinement that set VidGuard-R1 apart from previous methods.\n4. Paper Clarity: The paper presents a clear and structured methodology for VidGuard-R1, and the results are presented clearly in tables and figures that allow for easy comparison of VidGuard-R1’s performance against other methods.", "weaknesses": "1. Dataset Generalization: While the paper introduces a well-curated dataset, its generalizability could be questioned, as it only includes videos generated by HunyuanVideo and CogVideoX. The paper does not explore the performance of models when trained on the proposed dataset and evaluated on others. \n2. Model Generalization: the lack of cross-dataset validation raises concerns about the model’s ability to generalize to unseen data or models outside of these two specific generative systems. The paper does not explore the performance of VidGuard-R1 when trained on one dataset and evaluated on others, or trained on some AIGV model generations and tested on other unseen AIGV model generations.\n3. Ground-Truth Labeling Reliability: The method of conditioning CoT rationales on ground-truth labels from a more powerful model may compromise the genuineness of the model's discrimination ability. Since the annotations are directly influenced by the 72B model’s reasoning, the 7B model might struggle to perform true reasoning on its own, leading to potential overfitting to the provided cues rather than developing independent judgment.\n4. Innovation in the framework: Existing works such as Q-insight and VQ-insight have already employed similar reinforcement learning-based approaches GRPO for optimizing Qwen2.5-VL 7B models and improving their reasoning abilities. The main difference in this paper seems to be the use of a new reward method or a new dataset rather than introducing a fundamentally new approach or technique.\n5. Limited Explanation Comparison and Model Comparison: The explanation comparison fails to include a more powerful model like Qwen2.5-VL 72B, GPT5, Grok, etc., which is crucial for a more comprehensive evaluation of the explanation quality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)