Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
Authors: Fanrui Zhang
Dian Li
Qiang Zhang
Chenjun
sinbadliu
Junxiong Lin
Jiahong Yan
Jiawei Liu
Zheng-Jun Zha
Venue: NeurIPS 2025 poster ()
Keywords: Video Misinformation Detection
Deep Reasoning
Primary area: applications
TL;DR: We present Fact-R1, a reasoning-enhanced framework for video misinformation detection, along with FakeVV, a large-scale benchmark of over 100,000 annotated video-text pairs.

Abstract: The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large-scale, diverse datasets. Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content. To address these challenges, we introduce FakeVV, a large-scale benchmark comprising over 100,000 video-text pairs with fine-grained, interpretable annotations. In addition, we further propose Fact-R1, a novel framework that integrates deep reasoning with collaborative rule-based reinforcement learning. Fact-R1 is trained through a three-stage process: (1) misinformation long-Chain-of-Thought (CoT) instruction tuning, (2) preference alignment via Direct Preference Optimization (DPO), and (3) Group Relative Policy Optimization (GRPO) using a novel verifiable reward function. This enables Fact-R1 to exhibit emergent reasoning behaviors comparable to those observed in advanced text-based reinforcement learning systems, but in the more complex multimodal misinformation setting. Our work establishes a new paradigm for misinformation detection, bridging large-scale video understanding, reasoning-guided alignment, and interpretable verification.

FACT-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
Fanrui Zhang!.2. 1.2* Dian Li Qiang Zhang" Jun Chen Gang Liu Junxiong Lin", Jiahong Yan', Jiawei Liu Zheng-Jun Zha MMEE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, USTC SSanggai Innovation Institute Teeneett QQ *Fudan University
{zfr888, zq_ [eg_1266-000 126)-maal.....mmmmmmmmmmmaa.1.11 ustc. wuwt.ceu..c.. {goodli, albertjchen, sinbadliu, redyan/@tencent... com 1injx23@m. fudan, edu. cn
edu. en {jwliu6, zhazj]@ustc. edu. cn
Abstract
The rapid spread of multimodal misinformation on social media has raised grow- ing concerns, while research video misinformation detection remains limited due to the lack large-scale, diverse datasets. Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content. To address these challenges, we introduce FakeVVV, a large-scale benchmark comprising over 100,000 vidco-text pairs with fine- tne-grained, interpretable annotations. In addition, we further propose Fact-R1, novel framework that integrates deep reasoning with collaborative rule-based reinforcement learning. Fact-R1 R is trained through (Co'T) instruction tuning, (2) preference alignment via Direct Preference Optimization (DPO), and (3) Group Relative Policy Optimization (GRPO) using a novel verifiable reward function. This enables Fact-RI exhibit emergent reasoning behaviors comparable to those observed in advanced text-based reinforcement learning systems, but in the more complex multimodal misinformation setting. Our work establishes new paradigm for misinformation detection, bridging large-scale video understanding, reasoning-guide alignment, and interpretable verification.
three-stage process: (1) misinformation
Introduction
Platforms such TikTok and YouTube have significantly reshaped the landscape of news sumption by enabling the rapid and large-scale dissemination short-form videos. While this shift facilitates fast and wide-reaching information exchange, it also dramatically accelerates the spread of misinformation, thereby eroding the credibility and integrity of public discourse. As manual fact-checking struggles keep pace with the overwhelming volume and velocity of content, automated approaches have become increasingly essential for timely identification and mitigation of misinformation social platforms [5, 24, 45, 47].
While recent advances in multimodal misinformation detection have focused primarily on image-text modalities [47, 7]. the growing dominance of video content introduces challenges. Video-text misinformation is not only more engaging and fast-spreading but also harder to detect due to the complexity of heterogencous and temporally dynamic modalities [9, 48, 51]. The inherent complexity of video collection and annotation introduces several limitations in existing datasets [5]: (1) Limited scale and topic diversity, typically with only few thousand videos collected over narrow timeframes
Equal contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure I: While state-of-the-art multi-modal models like GPT-4o fail to consistently detect video misinformation, and template-finetuned finetuned systems such as Qwen VI. remain constrained by rigid response formats, Fact-RI establishes a novel paradigm by enabling deep, structured reasoning tailored for misinformation detection.
[21, 30]: (2) Lack of fine- ana-ggrined annotations and standardized interpretability metrics, resulting in fragmented research and inconsistent evaluation [43, 6]: (3) The presence of fact-checking watermarks or logos in some fake videos, which risks models overfitting to superficial visual patterns instead of learning semantically meaningful cross-modal inconsistencies [29, 52, 18, 19]. Among the various forms of misinformation, one of the most prevalent and deceptive strategies is the Out-of-Context misuse of real videos-misleading audiences by presenting authentic footage within fabricated or misleading narrative context [20, 46]. Owing to its high perceived credibility and low production cost, OOC has become a dominant tactic in the construction of persuasive misinformation campaigns 223,3).
The emergence of Multimodal Large Language Models (MLLMs) has advanced the integration of visual and textual modalities, thereby steering the field of misinformation detection toward MLLM-based frameworks [25]. However, as illustrated in Figure I, existing approaches still face critical limitations: (1) Many rely on rigid fine-tuning templates, resulting in overfitting to specific benchmarks; and (2) others resort to injecting excessive auxiliary prompts into general-purpose models that lack fine-tuning or the capability for deep misinformation reasoning. Large Reasoning Models (LRMs), such as Qwen3 [41, 42] and DeepSeek [14], represent the forefront of AI with advanced reasoning, deep their use remains largely confined to text-only domains. While recent open-source efforts have advanced deep reasoning on structured tasks like academic QA and single-image inference [13, 11], their application to misinformation detection remains underexplored. Future work should focus on equipping MLLMs with autonomous reasoning and long chain-of thothtttt capabilities tailored to the complexities of misinformation detection critical yet challenging direction.
processes, and efficient knowledge utilization [50], yet
To address these challenges, we introduce Fake the largest and most comprehensive dataset for video misinformation detection to date. It offers extensive topical coverage, a wide temporal range (2006-2025). and enriched annotations. We begin by collecting 100k high-quality video-text pairs from four official news channels on Internet, preserving associated metadata such as user comments, engagement metrics (e.g., likes), and timestamps. Given the frequent absence of video captions in the news domain, we develop a GPT-4o-based caption generation pipeline, guided by video titles and named entities, to generate high-quality captions that are essential for the subsequent training pipeline. To construct challenging misinformation samples, we propose a non-random entity replacement strategy that introduces semantically inconsistent video-text pairs. Leveraging CLIP-based similarity matching on video content, titles, and cross-modal representations, we retrieve similar news samples locations, events, and organizations-to to fabricate realistic fake videos. Fach fabricated sample is annotated with the manipulated entity, thereby supporting fine- grained reasoning supervision and interpretable evaluation.
and replace entities of four key
2

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Building upon the VV dataset, we further propose Rac the first multimodal misinformation detection framework that integrates deep reasoning with collaborativ rule-based reinforcement learn- ing. Fact-R1 I is trained via three-stage optimization pipeline tailored for misinformation scenarios: (1) misinformation Long Chain-of-Thousg
Instruction Tuning: We construct 85K CoT training samples using DeepSeek-RI [14] guided by prompts derived from the generated video captions. The outputs are filtered based on answer correctness and whether the reasoning correctly identifies the manipulated entity. This stage equips the model with the capability to perform deep, autonomous, and long-chain reasoning in the domain of misinformation. (2) Preference Alignment via Direct Preference Optimization (DPO) [28]: A curated 5K DPO dataset with human preference labels is used to refine the model's reasoning coherence and factual accuracy, addressing issues such as hallu- cinations, incorrect fake-entity attribution, and answer formatting inconsistencies. (3) Group- Relative Policy Optimization (GRPO) [32] with a Verifiable Reward Function: In the final stage, we define a task-specific, rule-based misinformation reward function evaluate both reasoning trajectories and entity-level correctness over 15K new samples and 5K auxiliary-tass -task samples. To further enhance visual reasoning, the auxiliary tasks include News Image OCR and News Video Caption, which are designed to improve visual-textual alignment and guide the model toward better interpretation of manipulated content. This encourages the model explore diverse and verifiable reasoning strategies beyond surface-level patterns and adaptively optimize for misinformation detection tasks.
Overall, our main contributions be summarized as follows: (1) We construct Fake VV, the largest and most comprehensively annotated news-domain video misinformation dataset. It features high- quality, generated video captions specifically designed to support misinformation reasoning tasks. (2) We propose a novel non-random entity replacement strategy that synthesizes semantically inconsistent misinformation samples, enabling explainable evaluation through fine-grained manipulated-entity annotations, supporting interpretable model behavior. (3) We introduce Fact-R1, the first multimodal misinformation detection model that combines deep reasoning with collaborative rule-based rein- forcement learning. Fact-RI establishes a new paradigm for misinformation detection by bridging video understanding, reasoning alignment, and verifiable explainability. (4) We design a task-specific reward function for misinformation detection and introduce novel reinforcement learning framework with multiple auxiliary tasks to enhance multimodal reasoning.
2 Related Works
Video Misinformation Detection. Existing approaches for video misinformation detection primarily focus on feature extraction and integration across multiple modalities, including linguistic patterns [29], emotional acoustic cues [15], and multimodal inconsistencies [9]. Tarhouni et al. [34] lever- aged audio and frame watermarks for cross-channel manipulation detection, while You et al. [44] proposed fusion model combining topic information and keyframe features. Qi et al. [24] explored cross-modal correlations with social context and further introduced the NEED framework [26] for neighborhood relationship modeling. Zong et al. [53] recently integrated large language models (LLMs) with a viewpoint evolution mechanism to support cross-modal reasoning. To facilitate this research, several dedicated datasets have been developed. Papadopoulou al. [22], Hou et al. [15], and Shang et al. [30] collected data on multilingual news, prostate cancer misinformation, and DWVID-19-relater content, respectively. However, these datasets often suffer from limited scale, narrow topical coverage, or lack of public availability. Later works such as FakeSV [24] and Fake [6] address some of these issues but still face challenges in data diversity, temporal range, and interpretabiliy--key factors for developing robust and generalizable misinformation detection models. Besides, current methods remain limited in reasoning capability, restricting their ability to interpret complex misinformation patterns and generalize across diverse manipulation strategies.
Reasoning in Multimodal LLMss The rapid progress of large language models (LLMs), such as OpenAl ol [16] and DeepSeek R1 [14], has fueled growing interest in complex reasoning tasks, while multimodal large language models (MLLMs) aim to extend this capability into the visual domain for cross modal applications [37]. Early work often relies on symrolic reasoning frameworks [38], such as the Differentiable Logical Formalism for VQA introduced by Amizadeh et al. [2]. Recent MLLMs further improve visual reasoning through techniques like Visual Chain-of of-Thought [31, 49], enhanced search strategies [40], reasoning transfer via data organization [12], and image-based reasoning trajectories [17], contributing to advances in both performance and interpretability. In this work, we adapt these reasoning capabilities to the task of video misinformation detection, aiming to uncover deceptive patterns in multimodal content.
3

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Table 1: Summary of datasets of video detection. Metadata refers to basic statistics such as # of likes/star//edit time. "_" represents the exact time range is not found in the paper.
Dataset
Video
Title
Metadata
Comment
#Fake
#Real
Time Range
Interpretability
Construction Mode
F22
2,916
2090
Web collection
VAYD [21]
123
423
0011209-2011//11
Web collection
YouTohe-Covidd [29]
/
113
67
2019/10-202004
X
Web collectioon
TokTok-Covid [30]
X
226
665
Web collection
[43]
/
383
collection
MYVC [9]
902
903
Web collection
PakeSV (24)
1,827
1,827
2012/10-202200
Weh collection
FakeTT [6]
1,172
819
201 2019005-202440
Web colleetion
VV (ours)
51,000
51,000
2006/11-2025002
Autotectonics
Dona kd
cump man
Puting
UK
Obama
ch
palice
stare New
call
attack
Ukraine an will
DN RUssia
Word doud of video
(b) Distribution of FakeV overy
Figure 2: The statisties of VV dataset.
Fake V Dataset Construction
In this section, we provide a detailed description of the construction process of the VV dataset, shown in Figure which consists of the following components.
Data Collection. To ensure diversity and relevance, we collect the most popular videos from four official news accounts
News, Guardian News, CNN, and The New York Times-covering November 2006 to February 2025, with approximately 120,000 raw samples. We filter out non-news content, exclude videos over five minutes, and remove near-duplicate events by clustering semantic representations of video titles, resulting in 100,000 curated news videos. We also collect metadata such as related articles, tags, timestamps, and user comments to support future research.
Data Pre- Pre-processing. To generate enriched video captions that provide detailed visual information for chain-of-thought reasoning, we propose a news-domain video captioning method. Unlike conven- tional approaches, our method produces captions that incorporate not only basic visual descriptions but also specific entities and semantically rich content tailored to news contexts. These enhanced cap- tions help large language models (LLMs) achieve deeper understanding of video news, supporting the generation of higher-quality reasoning chains. Our approach builds on Qwen- VL's [3] video pro- cessing strategy by first extracting 16 keyframes from each news video and identifying visual entities using the Google Vision API. We then employ GPT-4o ]] to generate instruction-bart based captions for these keyframes, guided by prompts that include auxiliary information such as the original news title and relevant entity names. Finally, we apply rigorous post-processing and validation to ensure that the enriched captions accurately reflect the core information of the news videos. Detailed data generation procedures can be found in the supplementary materials.
Misinformation Data Construction. To construct fine-grained and semantically inconsistent mis- information samples, we propose a non-random entity matching strategy designed to introduce challenging yet realistic perturbations. Specifically, replacing different types entities associated with similar news content: cermon-name inconsistency, location inconsistency, event inconsistency, and organization-nane name inconsistency.
generate four types of forged news samples by
Each news sample is represented as tuple (Img, Title), where Img denotes the first keyframe of the original news video, and Title refers to its corresponding news title. First, we utilize the CLIP [27] model to extract the visual and textual semantic representations of the news samples, forming a news semantic repository. Subsequently, any given news sample is used query, and based on the cosine similarity of the representations, top-3 most semantically similar candidate samples retrieved from the repository to serve potential sources for entity substitution. The retrieval

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process randomly selects one of the following five strategies: (1) Visual-to- Visual retrieval; (2) Visual- te-Textual retrieval; (3) Textual-to- Visual retrieval; (4) Textual-to- -extual retrieval; (5) Random selection. Using the retrieved three candidate samples, we input both the query sample and the candidate samples into the GPT-4o model. Through carefully designed prompts, a randomly selected target entity in the news headline of the query sample is replaced, thereby generating a forged news title that preserves surface fluency but violates factual consistency. In addition to the forged titles, we also prompt the model to produce corresponding metadata, including the manipulated fake entity and its type. Using this methodology, we generated 102,000 forged news samples, each containing the news video, title, forged entity, and manipulation type. The dataset was partitioned into 100,000 training samples (before 2025) and 2,000 testing samples (2025), enabling robust evaluation of the model's 's generalization to unseen data.
Data Analysis. We present a comprehensive comparison between the VV dataset and existing video misinformation detection datasets, as summarized in Table 1. Fake is an order of magnitude larger than prior datasets, with a balanced distribution (50% authentic, 50% manipulated) and no significant unimodal textual bias. Crucially, it provides fine-grained annotations of forged entities as explainability labels, along with manipulation types, offering explicit and interpretable evaluation signals. These features support rigorous evaluation of model reasoning, including the use of LLMs like GPT-4o and human evaluators to verify the accuracy of inferred manipulations. Moreover, all manipulated samples in VV are derived from authentic videos, avoiding artifacts commonly introduced by web-scraped data. Figure illustrates its thematic and temporal distribution. Thematically, the dataset focuses on political and international affairs, with frequent keywords such as "Trump." " "Russia," and "Ukraine," " covering high-impact misinformation topics. Temporally, FakeV spans from November 2006 February 2025, with notable peaks in 2016 and 2024 periods marked by major global events like U.S. presidential elections and the rise of social media-driven news.
4 Method
As illustrated in Figure 3, the training pipeline of Fact-Rl consists of three sequential stages designed to progressively endow the model with the ability to handle complex misinformation detection tasks.
4.1 Long-CoTT Instruction Tuning
Existing open-source MLLMs exhibit limited reasoning capabilities in the misinformation domain. To address this, we construct misinformation long-Chien sagg-Chiin-off Thought (long-CoT) instruction tuning dataset from the VV corpus and fine-tune an open-source MLLM to inject domain reasoning knowledge and long-CoT capability, providing a foundation for subsequent high-level reasoning tasks. As shown in Figure the long-CoT dataset is built using previously generated news-domain video captions and audio transcripts as textual proxies for video inputs, aligning with the text-only optimization of current deep reasoning models such as DeepSeek- RRI For each instance, the video title, caption, and audio are provided to RI,, which assesses the veracity and generates multi-step reasoning trace enclosed within <think> tags. We filter the generated responses by retaining only samples that meet two criteria: (1) the model correctly predicts the real/fake label, and (2) the reasoning trace explicitly and accurately identifies the manipulated entity. This process yields approximately 85,000 high-quality long-Co'I instances for the first-stage instruction tuning.
4.2 Preference Alignment via DPO
To further align the model's outputs with human expectations, as shown in Figure 3, we fine-tune Fact-R1 using Direct Preference Optimization (DPO) curated set of 5k annotted preference pairs. These samples specifically target common failure cases, such as inaccurate answers, hallucinated fake entity descriptions, and commonsense inconsistencies. Each pair consists of human-preferree output Ylahel and a suboptimal model-generated response Upred, where the preferred output demonstrates superior factual accuracy, reasoning coherence, and output formatting. Following the DPO objective [28], the model is optimized to assign higher likelihood to ylabel without requiring an explicit reward model. The loss is defined as:
exp (3 r r(Mabbll) ))
LDoo = loog exp (B r (abeel) ) + exp (3 r((prea))
(1)
5

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F Dataset Construction Process
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Figure 3: The overall architecture of the Fact-RI is illustrated, with the upper part showing the Fake dataset construction process and the lower part presenting the training pipeline of Fact-R1.
Main Task
Ausiliary Task
Misinfomation Detection
News Video Caption
News Image OCR
News Video
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Fact-RI incorporates News Video Caption and News Image OCR as auxiliary tasks to enhance its misinformation detection capability.
Figure 4:
where rly) denotes the reward computed from the log-ratio of likelihoods under the current policy and a frozen reference policy. This preference-basee alignment encourages Fact-R1 I to generate outputs that better match human expectations in reasoning accuracy and response structure, while maintaining stable training.
4.3 GRPO with Verifiable Reward Function
Inspired by recent advances in purely reinforcement-based training [14], we explore the potential of MLLMS to acquire sophisticated reasoning abilities through self-evolution driven solely by reward- guided optimization, without reliance on supervised instruction.

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Table 2: Performance comparison three real-works datasets. The best results are in red bold.
FakeSV
FakeTT
FakeVV
Model
Acc
Prec
Rec
F1
Ace
Prec
Rec
F1
Acc
Prec
Rec
F1
BERT [10]
65.4
66.0
66.5
66.2
68.7
67.5
67.5
67.5
60.4
$7.9
56.8
57.3
TikTcc [30]
64.8
63.2
61.9
62.5
61.1
64.8
64.2
64.5
59.3
59.1
59.5
59.3
FANVM [9]
65.4
66.1
64.3
65.2
68.9
64.7
68.8
67.1
61.9
60.7
60.8
60.8
SV-FEND [24]
67.1
67.4
66.3
66.8
67.6
72.2
69.0
70.6
70.9
71.4
71.3
71.3
FakingRec [6]
69.5
69.7
70.4
70.0
71.0
71.9
72.0
72.0
72.1
72.4
71.6
72.0
Gemini2-thinking [35]
63.1
61.8
61.9
61.9
56.6
55.2
55.3
55.3
51.5
46.0
46.0
48.6
GFT-4o [1]
66.6
65.2
64.7
64.9
57.9
57.8
62.9
63.7
56.0
60.4
35.0
44.3
GPT-o1-mini [16]
60.3
57.7
56.5
57.1
$2.5
51.6
51.7
51.7
47.5
46.9
37.6
41.8
DeepSeck-RT [14]
61.8
60.4
60.3
60.3
49.8
52.6
52.5
52.6
53.5
58.1
25.2
35.1
Qwen2.5-VL-7B [3]
55.6
55.5
55.7
55.6
54.9
54.0
54.1
54.0
52.9
51.1
51.1
51.1
Qwen2.5-VL-72B [3]
57.6
55.4
55.2
59.2
58.1
58.3
58.2
24.0
34.3
QVQ-72B-preview [36]
60.8
59.0
58.8
58.1
54.0
52.8
53.4
53.5
52.6
52.6
52.6
IntemVV.2.5-88 [8]
49.8
52.6
52.5
52.6
43.9
44.0
44.0
44.0
53.5
58.5
24.0
34.0
IncmmVL2.5-7BB-MP [39]
57.5
53.0
52.0
52.5
59.2
57.1
56.7
56.9
54.0
60.0
24.0
34.3
Fact-RI
75.6
77.7
72.0
74.7
74.4
77.8
68.3
72.7
81.2
84.5
76.4
80.3
Table 3: Ablation study on the contribution of key components in Fact-RI.
Table 4: Evaluating the Impact of the Reward Function in Fact-R
Variant
FakeTT
FakeVV
FakeTT ACC
FakeVV ACC
ACC
w/o SET
70.9
71.7
66.8
66.8
w/o Keywards
71.1
72.0
78.6
79.9
DPO
72.1
72.4
80.4
79.9
Faiity
71.4
GRPO
20.7
79.8
79.1
w/o Our
71.9
71.8
80.8
80.2
w/o Audio
73.0
72.3
79.0
77.7
w/o Caption
71.6
71.6
75.5
77.7
Fact-RI
74.4
80.3
Fact-RI
74.4
72.7
81.2
80.3
Event
63
54
Organiation
715
Lontion
Pazan
Wi pernee
lnnery Kapo
Goobal
Figure 5: The interpretabillty accuracy of the outputs from the six models.
Figure 6: Interpretability score distribution across forgery types.
Group Relative Policy Optimization (GRPO). We adopt GRPO (32) to fine-tune the policy network without requiring explicit value function critic model. GRPO estimates relative output quality by comparing group of candidate responses generated under the current policy. Given an input, the model samples of responses {y1, 1/,,..,.... ya each assigned reward based correctness and alignment with human preferences. The normalized advantage for each response is computed as:
A mean ((ri...,,,
std (r1....,,
This normalization mitigates reward variance and stabilizes training. The policy optimized to maximize the weightte advantage while regularizing deviation from reference model:
LGRPO = G 1=1
1 Σ
min
To (U:)) Totaa (y:)
As, clip
no (y:) (y:)
e, e, -
A.) - -rDDi.((T meer)
Here, Fa is the current policy, Tald the sampling policy, and कee a fixed reference model.
Verifiable Reward Modeling for Misinformation Detection To effectively adapt Reinforcement Learning (RL) to the misinformation domain, we design a rule-based verifiable reward function for video misinformation detection. The total reward R. is composed of four components:

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1. Accuracy Reward (Race): Measures whether the model's predicted class matches the ground truth. Format Reward (oooma)): Encourages proper formatting of reasoning content within the <think> and </think> tags, and the final answer within the <answer> and </answer> tags. Reasoning Keywords Reward (Rwwra): Rewards reasoning traces that contain key reflective terms such as "First", "However". or "In conclusion".
4. Entity Grounding Reward (Remity): Ensures the model correctly identifies manipulated entities in the reasoning trace. A judge model J is prompted to verify whether the predicted reasoning (within the <think> tag) contains a correct reference to the ground-truth fake entity ex:
[1,
(y e²) True
Remity (g. y)
(4)
0, otherwise
Finally, the overall verifiable reward function R is defined as:
R= ==50.8 10.8-Rac- 0.1 Riommat + + Rword
if ylabel real if ylabel fake
(5)
0.7--acc +0.1 Rforma + 0.1 Rword + Rentiyy
As illustrated in Figure 4, we introduce News Video Caption and News Image OCR as auxiliary tasks for misinformation detection, jointly trained to enhance the visual reasoning capability of Fact-R1. These auxiliary tasks explicitly guide the model to learn fine-grained visual-texture alignment, thereby improving its perception and understanding of manipulated content. The reward function for each auxiliary task consists two components: Accuracy Reward and Format Reward, weighted at 0.9 and 1, respectively. The accuracy reward Race is computed using task-specific evaluation metrics:
EdiiDistanceq.a)) max([4].lv]] if task OCR, ROUGE-L(q.V)) if task Caption.
Race (g. y) =
(6)
where q denotes the predicted sequence and y is the ground truth. For the OCR task, we adopt normalized edit distance to measure character-level accuracy, while for the caption task, we use the ROUGE-L score normalized the [0, 1] range evaluate sequence similarity. By incorporating these auxiliary reward functions into the reinforcement learning process, Fact-R1 achieves more stable training and effectively integrates OCR and caption- based reasoning, thereby enhancing its ability to detect and interpret video-based misinformation through stronger visual-textual alignment.
5
Experiments
5.1 Experimental Settings
the FakeVV testing set along with two widely adopted benchmarks. FakeSV [24] large-scale Chinese short video dataset for fake news detection, enriched with social context features such comments and publisher metadata; video titles translated into English for compatibility. FakeTT [6] is an Anglish-language dataset from TikTok, covering a wide range of misinformation topics. Following [6], adopt a temporal split by using the most recent 15% of samples from FakeSV and FT as testing sets. All baseline models are trained on the VV, FakeTT,, and VakeSV training sets, which provide balanced datasets with 53,800 real and 53,800 fake video news samples each. We report four standard evaluation metrics: Accuracy (ACC), Precision, Recall, and F1 Score, providing comprehensive view of classification effectiveness. Baselines. We compare Fact-R1 against 15 competitive baselines in four groups: (1) singlc-modal
Dataset. For evaluation,
methods including BERT [10]: (2) multimodal methods including TikTec [30], FANVM [9], SV- [35], GPT-4o
FEND [24], and FakingRec [6]; (3) closed-source MLLMs including Gemini2-thinll
[1], and GPT-ol-mini [16]; (4) open- MLLMs including Qwen2.5-VL. VL [3], Intern VL2.5 [8]. QVQ-72B [36]. Intern VL2.5-MPO [39] and DeepSeek-RI [14]. The first two groups are trained using the FakeVV training set, while the latter two are evaluated in a zero-shot setting. For models without native video support (e.g., DeepSeek-R1, GPT-ol-minit we use news-domain video captions as textual surrogates. Other closed-source or inaccessible methods are excluded from the evaluation. Implementation Details. All experiments are conducted using Py PoTch on 8 8XNVIDIA A100 GPUs,

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with Qwen2.5-VL as the base MLLM and the default 8-frame sampling strategy. Stage (Long-CoT Tuning). We freeze the visual encoder and apply LoRA (r 128, a 256), using learning rates of 2 x 10-$ (LoRA A) and 2 x 10- (multimodal projector). Training is performed for 2 epochs with batch size 2, using Adam W optimizer. Stage 2 (DPO), We train for epoch on 5k human preference pairs with batch size 4 and learning rate of 2 x 10-5. The reference model is frozen, and gradient clipping is applied with a max norm of 1.0. Stage (GRPO). We train for 172 steps using the verifiable reward function, sampling 5 candidate responses per input and applying learning rate of 1 x 10-6, The speech recognition system is employed to transcribe audio recordings into text.
5.2
Comparison to State-of-the-Art Approaches
As shown in Table 2, the proposed F achieves the best overall performance in video mis- information detection. Among baselines, BERT performs well, suggesting the strength of textual signals, while FakingRec benefits from modeling video production semantics. SV-FEND also per- forms competitively by leveraging multi-modal cues via attention mechanisms. For closed-source MLLMs, GPT-4o achieves the highest accuracy, with Gemini2-thinking following closely. Notably, text-only models like DeepSeek-RI and GPT-ol-ming show strong results, highlighting the power of high-level reasoning. Among open-source models, QVQ-72B leads, while VL2.r5VL2.55MM and Qwen2.5-VL-7B show promising but varied performance. In contrast, InternVL2.552 consistently underperforms. Overall, larger models tend to perform better, though performance varies across datasets, reflecting differing task difficulty. The generally weaker results of non-finetuned models further emphasize the need for reasoning- sassoning-oriented oriented training. These results demonstrate that Fact-RI 'ss superior performance arises from its tailored misinformation reasoning design, combining long-CoT instruction tuning, DPO-based preference alignment, and GRPO-driven policy optimization.
5.3
Explainability Analysis
Due to the lack of annotated manipulation process information in existing misinformation detection datasets, current explainability efforts suffer from inconsistent evaluation standards, which signifi- cantly hinders the development of trustworthy misinformation detection models. During evaluation, retain both the reasoning traces generated by each model and the ground-trit manipulated entity fake entity) for each fake sample. GPT-4o-mini is employed as a judge model to assess whether the fake entity is correctly identified and described within the reasoning process. Accuracy is used as the evaluation metric, with the prompt design detailed in the supplementary material. To decouple detection performance from reasoning quality, only evaluate samples in which the model correctly predicts the fake label. As shown in Figure 5, Fact-R1 demonstrates strong reasoning ability by consistently describing the correct fake entities rather than overfitting to specific patterns in the dataset. Figure 6 shows that describing manipulated Event and Organization types remains more challenging than other categories.
5.4 Ablation Study
We conduct ablation experiments to verify the contribution of each stage in our proposed three-stage training pipeline, as shown Table w/o SFT denotes Fact-R1 without misinformation long- ong-Chain-- of Th-uhht (CoT) instruction tuning, w/o DPO removes the preference alignment stage using Direct Preference Optimization, w/o GRPO removes Group Relative Policy Optimization with the verifiable reward function. Our observations confirm that the staged training design plays erucial role in progressively building up reasoning capability. Table 4 presents the effectiveness of our designed reward function and the two auxiliary tasks. Here, w/o Keywords and w/o Entity indicate the removal of the Reasoning Keywords Reward and Entity Grounding Reward, respectively, while w/o Ocr and w/o Caption denote the exclusion of the News Video Caption and News Image OCR auxiliary tasks. The results demonstrate that these components significantly contribute to enhancing the reasoning ability and overall performance of Fact-R1.
Conclusion
In this work, we address the underexplored challenge video misinformation detection by introduc- ing VV, large-scale, diverse benchmark with fineggrained and interpretable annotations. To

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effectively leverage this resource, we propose Fact-RI, novel multimodal framework that unifies deep reasoning with collaborative rule-based reinforcement learning. Fact-R1 is trained through three-stage pipeline, enabling the model to generate structured, explainable reasoning traces. Our approach not only advances the state of the art multimodal misinformation detection, but also establishes scalable, interpretable paradigm for aligning large multimodal models with complex real-world reasoning tasks. Comprehensive experiments validate the effectiveness of Fact-Rl.[4]
7 Limitations
In this work, we propose Fact-R1, 1 a deep reasoning model for video misinformation detection, which achieves state-of-the-ar performance on three benchmark datasets. Despite these promising results, the task remains inherently challenging and fraught with limitations.
First, the accuracy of misinformation detection remains generally low in real-world scenarios due to the complexity of video content, the ambiguity of factual claims, and the constantly evolving nature of misinformation strategies. While our model demonstrates strong performance on curated datasets, its generalizability and robustness in open-domain or adversarial environments require further investigation.
Finally, over-reliance on automated fact-checking systems carries significant societal risks. Misclassi- fying authentic content misinformation suppress legitimate discourse and harm the reputations or economic viability of content creators. Conversely, undetected misinfornationnespecida reinforced flawed but confident reasoning- erode public trust, amplify polarization, and destabilize information ecosystems. We acknowledge that our dataset, sourced exclusively from four Western news outlets, may introduce geographic and cultural biases; accordingly, the generalizability of our findings to other regions and contexts remains an open question.
when
8
Societal Impacts
Positive Societal Impacts
Our work aims to enhance the reliability and transparency of misinformation detection, particularly in the context of video content, which is increasingly prevalent and impactful in public discourse. By equipping multimodal language models with structured reasoning capabilities, Fact-R1 can serve effective assistive tool for human fact-checkers, enabling more informed decision-making in combating misinformation. The structured outputs may also contribute to media literacy by offering interpretable justifications, thereby supporting responsible content consumption and improving public trust in automated fact-checking systems.
Negative Societal Impacts
Despite its intended benefits, our approach may carry potential risks if deployed without proper safeguards. Incorrect eespeiaallleyellllllllllllll when accompanied by seemingly coherent but factually incorrect reasoning- could lead to misclassification of legitimate content failure to detect harmful misinformation. Over-reliance on automated systems may also reduce critical human oversight and unintentionally suppress diverse viewpoints. Moreover, hallucinated reasoning traces from large language models, if not clearly flagged, might mislead both users and reviewers in high-stakes scenarios.
Acknowledgment
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 62476260, 62225207 and 62436008, the Fundamental Research Funds for the Central Universities under Grant WK2 10000000
10

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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The abstract and introduction mention that this paper focuses on video
misinformation detection scenario. The paper's contributions are detailed line at the end of the introduction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
This paper discusses in Section the limitations of the work.
Guidelines:
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Task Definition
SF
Desctplive FREnxy
You video descriher tasked with providing a concise and acrurate description ofa news video. Use the image entiyy contextual reforenussto support description.
prouided
weerin Prmideet on Biden
sunay day, block
spperrance
lagel
# Title: (Bilc)
The
parking
* Entig: (entity)
with auturan follage visible. foregroand, Bitker gestares with his hank while speaking. Around him, other people are prosert, Incleding one penson weatiag dark aut standing near one of the weikee The backgreend shifts ao nees studle wihrre
SUVs parfed in the
Briefly descrihe main objects and their visual charactoristics shown in theviden.
Supporting Information
Rudly
2. Summarize the environment and background setting...
Bidcn, Cag Acrdinn Flag, CNX, etc.
anchees dis the events, betoon catting back. la Bidea engaçing with Boor around his as be perpaces to vote Delaware
Uides line and voter Delawars.
Figure 7: An example from the dataset to illustrate the News-domain Video Caption Generation.
very News Image
Retrieved News Image
Rotrioved News Image
Retrieved Neass Image
CLIP
Rising uea levels
Stallarity Scores
Tittle expoored
News
News Tittle
That Time Madilterssnion Ses Disappeared
Atomitiaas Widdelll Sea protacted
Three found alive and four bodies rocovened after touristbott copelaas in Red Sin.
Designed Prompt
Fake News Tittle
Three found slive and four bodies recovereed alled louist boot cxpsices in Medilenazeas Sea.
Rewrite
Figure 8: The candidate news sample retrieval pipeline based on Visual-to- Visual matching employed in the VV dataset.
A Detailed Experimental Settings
A.1 Datasets
To evaluate the performance of our proposed framework Fact-R1 and several baseline models, conduct experiments on three real-world short video misinformation datasets: FakeSV, Fake'TT, and VV. The statistics and key characteristics of these datasets summarized in Table 1. Following the original setup in [24], we adopt a chronological split with ratio of 70% for training, 15% for validation, and 15% for testing, simulating realistic scenarios where only past data is available for detecting future misinformation.
The details of each dataset
follows:
FakeSV: A dataset for Chinese short video misinformation detection, constructed by scraping news videos from Chinese short video platforms and enriching them with social context features such as user comments and publisher metadata.
FTkeTT:: A curated English short video misinformation dataset, focusing on misinformation detection on platforms like TikTok. Each sample includes the video content, title, and associated metadata, providing diverse and realistic misinformation scenarios from widely-used social media environments.
VV: Our proposed large-scale English video misinformation benchmark, featuring fine-grained forged entity annotations, manipulation types, and explainability labels.
A.2 Baselines
To verify the effectiveness of Fact-R1, we compare it against 15 competitive open-source accessible baselines across four groups. Other closed-source or inaccessible methods are excluded from the evaluation.
22

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Algorithm 1: GRPO with Task-Specific Reward Functions
Input: Mini-batch {(2,, q., fz) where task flag &, {MD, OCR, CAP): =D= Misinformation Detection, OCR Image OCR, CAP= Video Caption; current policy reference policy Trefs group size G.
Output: Updated policy TT.-
for each sample (It; qz. tz) do
(1) Sample candidate outputs: (Ma..... BBG)) ~ 9:);
(2) Compute tasc-fpecific reward I?:
for 1 to G do
if MD then
Rac + 1 [y/t = v.
Riormu t Itags correct];
Roodd
key words];
J [tikk (yhk.ccttar et),
Jubel fake, otherwise.
Reuity
0,
Roce + 0.1 Drocma + 0.1 Rwood + 0. O.IRemny
if Jabel fake,
0. + Ricema
0.1 Rwodd
if Vik
Jabel
real.
11
else if t, OCR then
Rux 1 EditDist(yaa v.
max(1y lnf
13
Rieeaa < [tags correct];
14
= R.9 + 0.1 Riormati
15
Rux ROUGE-L(MM.L yf");
Riorma <- (tags correct];
R 0.9 + 0.1 Rformai
R -
19
(3) Normalize rewards: A.
prs as are the mean/std of (Ri...., Haj:
σi
29
(4) GRPO loss:
G
Li
min
Te.
A., clip(
1 1 +)) DKL(TT Tee))
G
k=1
21 Update parameters: 0 <- 11 Vo V. La:
22 return
(1) Single-modal detection methods:
BERT [10]: A widely used pre-trained language model designed for natural language understanding tasks, employed here for text-based misinformation detection using video titles and descriptions.
(2) Multimodal detection methods:
TikTec [30]: A multimodal misinformation detection framework that leverages subtitles extracted from audio tracks and visual frames to effectively capture key information across modalities and improve detection performance.
A topic-agnostic fake news video detection model based adversarial learning and topic modeling. It dynamically adjusts the weight of comment features using stance estimation between titles/descriptions and comments through Gibbs sampling-based LDA.
FANVM [9]:
SV-FEND [24]: multimodal approach short video fake news detection that selects informative features via cross-modal correlation analysis and integrates social context using
23

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Query Image
News Image
News Image
CLIP
Top Throe Image-Tet Similarity Stares
Retrieved
litble
News T'itle Biddes walls in line and votes in Delaware.
Biden delivess ronaeks
Wanch the uncensored momeet
Biden ai Focte Academy gradustion cevestort
Caaffomis alter duadly flooding
Will Smith smarks Chrts Rock stago at
Designed Prampt
News Vileo
Boden wait in line and votes Califoria
Rewrite
Figure The candidate news sample retrieval pipeline based on Visual-to- -extuul Retrieval matching employed in the Fake VV dataset.
News Image
News Image
News Image
Retrieved
Top Thrse Toat-Ensage Siuillaity
News Tittle
News Tittle
Querry News Tittle
Chaos in Washingttn as Trump supporters Capital and lockdown Congress
President Iullin ennonnoss
Slonner Commons for calling David Camerve 'ddday Dave
Hosig expleins why Jack Smith mode dismins Trump's
'Ester unuce' U'kminne until end of Sunday
Designed
Prompt
Fake News Tittle
Honig explains why Jank Smith made dismues Putin's
Rewrite
Figure 10: The candidate news sample retrieval pipeline based on Textual-to- Visual Retrieval
matching employed in the Fake VV dataset.
News Image
News Image
News
News Image
CLIP
Prosecutor drape lodersl oriminal Cases apaizal Trunp.
Query Tittle
Top Three Teat Stnilarlly Starw
Trump suppocters sloms Capilol and force locisdown of Congress
Retrieved News l'ttle
Retrieved News NewsTitlle Trump's exchange with vetrans roasting Joe Biden
proseuttrrs dramiss
Retrieved News Tillle
interfeneree classified doruments crses against Trump
Designad Promitt
Video
Fake News Tüitle
Proseculor drops Cederal
Rewrite
crimtinal coses against Bidden
Figure 11:
The candidate sample retrieval pipeline based Textual-to-Textuall Retrieval matching employed in the Va dataset.
co-attention and self-attention mechanisms. captures inconsistencies between text, visual, and audio modalities to improve detection accuracy.
FakingRec [6]: short video misinformation detection method focusing on the "creative process" behind fake content. It employs dual-branch network to model both material

---

selection (emotional and semantic preferences) and editing behavior (spatial and temporal features).
(3) Closed-source MLLMs:
GPT-4o [1]: state-of-the-at closed-source multimodal large language model from Ope- nAI, supporting visual and text inputs, known for its strong reasoning and generation capabilities.
Gemini2-thinking [35]: A multimodal large language model developed by Google Deep- Mind, designed for advanced reasoning tasks across text and vision modalities.
GPT-ol-mini [16]: A amaller-scale version GPT-ol, optimized for reasoning and genera- tion tasks with strong performance on text-basee evaluations.
DeepSeek-R1 [14]: A powerful open-source multilingual large language model developed by DeepSeek, optimized for both natural language understanding and generation across diverse tasks.
(4) O Open-source MLLMs:
Qwen2.5-VL [3]: An open-source multimodal large language model supporting image and text imputs, with competitive performance vision-language understanding benchmarks.
QVQ-72B-previeys [36]: A serge-scale open-source vision-language model designed for visual reasoning tasks, preview version with 72 billion parameters.
Intern [8]: A vision-language model optimized for cross-modal understanding, supporting fine-grained visual reasoning tasks.
[39]: scaled-up version of InternVL2.55 with 78 billion pa- rameters, incorporating multimodal pre-training and optimization strategies for enhanced reasoning.
A.3 Implementation Details
All experiments are conducted using PyTorch on 8 8XNVIDIA A100 GPUs, with Qwen2.5-Vi VL as the base multimodal large language model (MLLM) and the default 8-frame sampling strategy for video inputs.
Stage 1 (Long-CoT) Tuningg) We apply LoRA for efficient fine-tuning and the LoRA hyperparameters are set as 1ora_r=122 and 1ora_ alpha=256, with the learning rate for the mm_ projector set to 2 x 10-6 The visual encoder remains frozen throughout this stage. LoRA fine-tuning is performed with a learning rate of 2 x 10-5 and a batch size of 2. The model is trained for two epochs using the AdamW optimizer with cosine annealing learning rate scheduler. This stage uses 85k high- quality instances generated from the Misinformation Long-CoT Generation process, derived from the VV training set, with an equal split between fake and real samples.
Stage 2 (DPO). We train for one epoch on 5k human preference pairs from the Fake'VV training set, using batch size of and learning rate of 2 x 10-5 The reference model is kept frozen, and gradient clipping is applied with a maximum norm of 1.0. We adopt the Adam optimizer with a linear learning rate scheduler and warm-up to 0.1. Training is conducted with mixed precision (fp16) for improved efficiency.
the first 500 steps. The DPO scaling factor 3 is set
Stage 3 (GRPO). We train for 172 steps using the verifiable reward function, sampling four candidate responses per input. The learning rate is set to 1x10 Following the GRPO framework [32], rewards computed based on reasoning correctness, entity-level alignment, and formatting consistency. Within each candidate group, rewards are normalized to estimate the relative quality of responses, which are then used to compute the importance-weightee weighted advantages. Policy updates are performed with clipped advantage weighting and KL. regularization against frozen reference model to ensure training stability.
This stage utilizes 15k news samples for the main task, including training data from the FTkeTT and FakeSV datasets. Additionally, we incorporate 3k News Image OCR and 3k News Video Caption samples as auxiliary tasks to enhance visual-textual reasoning. The rewards from auxiliary tasks are integrated with the main task rewards through a weighted sum jointly guide the optimization.
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Russia's ambassador to UK appears to laugh and dismiss Nord Stream inquiry.
(a) Event-type manipulation.
Blobs That Closed Australian Beaches Made of and
Scientists Sap
CNH
Kim Kardashian joins Roy Wood Jr. and his panel to play Lie-curious.
(b) Person-type manipulation.
Figure 12: Examples of Event-type and Person-type Manipulated Cases.
In Algorithm 1, we outline the GRPO training procedure. During training, the policy samples a group of responses for every input and assigns task-specific, verifiable rewards-accurace aceuracy-centred for OCR and captioning, and multi-component for misinformation detection (accuracy, format, key-word usage, and entity grounding). The rewards z-normalized within each group to obtain advantages, which are then optimized with a clipped GRPO objective regularized by a KL term to the reference model.
B Analysis of News-domain Video Caption Generation
We introduce News-domain Video Caption as an enhanced description for news videos, which plays critical role in both the Misinformation Long-CoTT Generation and the GRPO with Verifiable Reward Function stages. Unlike conventional video captions, these captions incorporate specific news-related entities, enabling large language models better understand multimodal video news.
We utilize the GPT-4o model to generate instruction-based news-domain descriptions for video keyframes, guided by auxiliary information such the original news title and associated entity names. Through carefully designed prompts in Figure 23, the generated captions are aligned with the factual content of each to ensure the accuracy of the captions, confirming that the descriptions faithfully reflect the visual content and support effective learning of news-related semantics.
image. Post-generation,
perform rigorous post-process post-processing and validation
Figure 7 illustrates the data construction process and demonstrates how our approach produces accurate, fact-grounded video descriptions.
26

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FLOROR 200 TOME
PIAA
CN
CNN
0. BUSINN BLLEEE Chi
Officials call Russia's cyberattack worst telecom hack' in US history.
(c) Location-type manipulation.
CNN
PARK
CNN
CH
Vot YUIP PJAB NUBEBA TA CNN
13 MODEEIOT
Trump planning use the FBI address personal grievances.
(d) Organization- type manipulation.
Figure 13: Examples of Location-type type and Organization-type Manipulated Cases.
C Analysis of VV dataset
C.1 Analysis of Misinformation Data Construction
To generate fine-grained manipulated news samples, we propose challenging non-random entity replacement strategy construct semantically inconsistent fake data. The overall workflow is described as follows: each news sample is represented as pair (Img. Title), where Img denotes the first frame of the news video and Tirle refers to the corresponding news headline. We first extract visual and textual embeddings of each sample using the CLIP model to build a semantic news database. For any given query sample, we retrieve the top three most similar candidates from this database based on cosine similarity, providing entity references for subsequent manipulation.
The retrieval strategy randomly selects of the following five methods:
Visual-to- Visual Retrieval: As shown in Figure 8, the query image is used to retrieve the top three most similar images, along with their associated news titles. The query title "Three found alive and four bodies recovered after tourist boat capsizes in Red Sea" and the retrieved titles are then fed into GPT-4o with the designed prompt (Figure 25) to generate fluent fake news title, such as "Three found alive and four bodies recovered after tourist boat capsizes in Mediterranean Sea."
As illustrated in Figure 9, the query image is used to retrieve the top three most similar text embeddings and their corresponding news titles. For example, given the query title "Biden waits in line and votes in Delaware," GPT-4o generates fake title like "Biden waits in line and votes in California."
Visual-to- -exetuall Retrieval:
Textual-to- Retrieval: As illustrated in Figure 10, the query text (news title) is used to retrieve the top three most similar image embeddings and their associated titles. For example, the query title "Honig explains why Jack Smith made move to dismiss Trump's case" leads to the generated fake title "Honig explains why Jack Smith made move to dismiss Putin's 's case."
27

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of to
reipeeo, SEETATIION
677
211
Figure 14: Reward Curve Over Training Steps.
Figure 15: Response Length Curve Over Training Steps.
100
100
65
05
12
Aepiinad
Oevat 1--1
Greal mpe
Deppind
cr Lrt-deadi
Opr
Figure 16: Consistency between MLLMs and human evaluation on explainability-coree samples from the VV test set.
Figure 17: Consistency between MLLMs and human evaluation explainability-emo sam- ples from the Fake VV test set.
Textual-to-Texteee Retrieval: As illustrated Figure 11, the query title is used to retrieve the top three most similar textual samples. For instance, the query title "Prosecutor drops federal criminal cases against Trump" results in the fake title "Prosecutor drops federal criminal cases against Biden."
Random Selection: Three random video-text pairs are selected from the database and used reference inputs to GPT-4o for editing the query news title.
Based on the retrieved candidates, the query sample and the selected samples are input to GPT-4o, which replaces one randomly selected target entity the query title using a carefully designed prompt, thereby generating manipulated news titles with semantic inconsistency.
C.2 Additional Dataset Details
In this study, we show four types of manipulated news samples by replacing different categories of entities drawn from semantically similar samples: Person, Location, Event, and Organization. Figure 12 and Figure 13 present example cases for each manipulation type. Notably, the manipulated news titles exhibit minimal detectable artifacts and maintain fluent semantic coherence, making the detection task challenging. This design requires models to perform robust reasoning to accurately identify the inconsistencies.
D Prompt Analysis
(1) Prompt for News-domain Video Caption Generation. Figure 23 illustrates the prompt designed for guiding GPT-4o to generate news-domain video captions. This prompt helps enrich visual information, enhancing the model's understanding of news content.
(2) Prompt for Explainability Evaluation (Judge Model). Figure 24 shows the prompt used to instruct GPT-4o to assess whether the model's 's reasoning process accurately identifies and describes the fake entity. This unified evaluation framework used to obtain the interpretability metric for models on the Fake VV test set.
28

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Case
Case 2
NewsTitttee SpandSpeed Mests ZHANG
News Tittles
found bodos receeaed after sourist boot
cipaiiess in Madiammoon Sea
News Video
News Video
Super idoi, oh goó, Seper kéel, riul ong e song evezytoyy Super liol. Super liel. you put on the spoaker, pat o0 the speakzt put on tae speazit kolc on, hald on, walt. kolé on, was,
trmaking neus comieg izte in the last fer zomers bocuse the goranee Ejypá' Rad Sea region agys paocile bean fourdl roccas opention for loutitt
News Auddoo
wall, we gatla nater betfie, bottles y0, Super dall,
get
AewsAudio
people people beon rescued faralthoug that number changing all and they the
Mondly Hartey Flpyer there wark Parptarr
right, somebody played speiker play super play Super soll,
Proclccione -alinkkk The slows a clartic andoorrscaan when- SSpnwsppen mnnotts with an can mas weatteg I dant with Chineso
Preelctions: -4hinkoe The vadeo deccrption on groviden detilled account of
mocce operitor Sallesingg coqnicing instiiend the speciiically
doscarters, sumeended a hage crewd mocoding the moment thee photes. The vidko captans unicriiiad cans like pluss reperaeds adfenrsiin "Super Stal." The saws iis, "Stow Spood Koes DA ZG WEll! trames the virten asa firmal serifiennt mesting betnven pShnndpped and
Earptaaa, Egppt. mettioes presence large white yactt.
openatcne by
Adhiiionlly personnel
recovery
mop
location
incilast live
Uix (Thae Sound alive cou'efaaaa Meditermnon Sea
Thonr Wei Howeorr seifve thevisall anain ondirmm parson Da zlanng (Wel,
morrend afee tounet boot
meeting
incersideet with video dosrtition. The tafe speeiiiig the Medivmmeen Sea, vitemes the vidoo desctgrio claaty stike that the incideat took place in the Red Sea The Red Siaa and the Meditecanion Sis are dostact bodres water and tlis duacquaary suggess Sux the lite thes ao accuradds ruflect th cosiant the vidko desciiption you Thanskan Sae caaiiinnial coctant is maxiigulind as the Gis
conversation
figurs. Insted, caeelap
but rideo dipicts charts The sugpasts
recrvaiion
fomal encortter The mosmorch betoven tilt mppication teweedeo's
wtrel curben rndraase ochiical ofting or Iorposy. -ttieiice sanmwrrttes +hamwer>
thrughh makeeshig froeiin nither that
provites tncomet information about the Jostion of the
tncideet in tithiiik canouertiin www.eero...teeeerr
Figure 18: In two illustrative cases, Fact-Ri exhibits impressive deep reasoning abilities. Its long-
form reasoning traces reveal not only high degree of logical rigor and depth, but also precise identification of the manipulated (fake) entity.
(3) Prompt for Misinformation Data Construction. Figure 25 presents the prompt we use to guide GPT-4o in generating fluent fake news titles. The generation process leverages information from three reference titles to modify the original title, ensuring semantic plausibility while introducing manipulation.
E
Additional Experiments
E.1
GRPO Training Curves
Figure and Figure 15 illustrate the reinforcement learning dynamics of Fact-R1 under the GRPO framework. During training, we monitor two key metrics: Overall Reward and Response Length. As shown in Figure 14, the overall reward exhibits a steady upward trajectory, indicating the model's continuous improvement in producing correct predictions throughout the reinforcement learning process. This trend suggests that our reward design effectively guides the model toward enhanced reasoning performance, thereby improving its capability in misinformation detection.
In addition, Figure 15 shows the dynamics of response length. Interestingly, we observe an initial decline in response length during the early stages of training. followed by gradual increase and eventual stabilization around consistent length. This behavior likely reflects an adaptive adjustment phase: the model first discards inefficient reasoning patterns inherited from supervised learning, then explores more concise responses during early reinforcement learning, and ultimately converges to stable reasoning strategy that balances informativeness and efficiency. The convergence of response length indicates the emergence of a consistent reasoning policy optimized under the reward framework.
E.2 Consistency Analysis Between Judge Model and Human Evaluation
To further assess the robustness of using GPT-4o-mini as the judge model in place of human evaluation for Fact-R1, we conduct a consistency analysis between several strong MILMS and human judgments. Specifically, we manually select 100 samples from the Va test set where Fact-R 's reasoning correctly identifies the labeled fake entity (explainabillty-corees samples) and another 100 samples where the reasoning fails to identify the correct entity (explainability-eruu samples).
29

---

Case
Case 2
News Tittle:
UK miniter won't have do budget Lise Stis over cssis
News Tittle:
Pronector drge fidkrdl crimiia coon Bizie
Gib
US
News Video
News Video
B
BiI
that Connodlor faced prokeen and fornd and werke drwwn line the fiction podid by the pootlous quvereeant would zuta public flaanoss badk on firm Socting and we've Now aut the budgets for peblit services for the chrotton of this Pariirment public sarviere ross nood to hive wathin their moon hacsse will only te doing one bradget a year that this grvemmeen can focas on vilatt are narbher one priority...
I'
closel of the departaenn's polics balked
proseattor
victory lef Pronidem Dorald "Inerp' sanior Narts very base this
News AudiAAddi
News-Audoo Amaka concspordent Caay O' O'Donogluz can joia me Sons
Wadhingtn Gary pus took us through what her been and and what this monts we cat also xel you tut Jack Sauth has zakod Donald the charges sgains. Derald Trurp in lae other federal custinal...
Proclictione -ahinkcis The video dosr ription decell the
Preelctions "dhinkey valen decrpptin on devils live report
Chanesllar of the Exch aquar is sppakingg el svent organiood by the
Wastiraton focusing
comeoondees dienuel canuaal caes aquinst Trunsp
CBI, discassing her budget propocal "The desctigton omploantie her
contideace tn not aeeding p a bodget bise this again
fistamm The
vickes Trunp ot arghasiring lega dovdsprent's oscoening
setting is peufissional forral, indicaiing that speakar
Solesa
Chancillocccattaa arws howover stales "IK
zustlerr says
Unden
focas Joe Uiden and demesol
idkerifiee speeker Pricse eeginn" The nourmelly
dleer
bebsoen video deseriton and the
Chencallor
ute The staco costrat is about Darald Toung and the dantisal of cass aquisst tim, vibereas the titl locomectly zoentoos: Joe Bidiee This lnconsstaccy indicato
otle trab cater far tille betwren viire deserittion
accuridyy refect content
that the dile not acsentaly niflec the corret of the vithes desciptiin yaln
descriptass. The lile misioas susppiiing that the Prizae Marislaria the ura
conclasion the file is misioding and doen no maaih tha spallii fooss the video contert bntthttee----aseerre streswrs
makia the atzleeent whenoss the actall stalanent is mada the Chancolloreter tame the multmmebi content is moriyelstd bacizne the ttle
does not wannddlly aemment the content of the vireen descnpbon -attrii-----ssoooo---- <tarswrr>
Figure 19: The figure presents two fake news cases successfully predicted by F1ct-R1. In Case 1, the key information "Chancellor" from the news audio aids accurate detection, while in Case 2, the OCR-extracted text "Trump" from the news image provides critical visual evidence that supports the model's reasoning process.
Case
Case 2
Tittle Gaça chandeierr "Wicke'
News Tittle The ligget buvdl dabake window aisle
Video
Video
growa anall spacos yulling opes shutticg It coptoeertag is monthe toonths to design nytt lagged frings that Cynitii
tenfeecy for eveppecingg dont you shosld evergeck bet don't
find koow shoss that dedicated log thing about
NewsAAdiio
diá't she's passangern this you sadly be
AuwsAudio
I'm
it
show shi't octeally relly enpeying time attentinn and this Zouddakk that nimme allne its Leary and has ao much cuaisoating setually vary vary dagasosa....
g Predictiont -sthmiicia be video descrppion indicates thatit
Prodictions *chinic The video Soature a discustion betwoen tvo vomte faig on overydas life daicss mastfially tsir dilfering viens co the
shzerczens
from the muscal tokodd fretaring bivnde
womann is vibeatt pick dress uncading vatious actions withis a vilinsia, opdeent coon. The descriptos highlights her novencnts, interacioon with
pandi and confatt of vasioss types of foon our for usdd. The setting
carefully desembed, highlghting the mummairr and emple enverrment when
other chirictes tin, and the smeds seting of the more, srrgineiiing my the playfu! and inueical dimcoplame the aoies
commmation tkks place coerner the beadlas "The Goutre Trooel Tutsare
Windon Aale Sout peints to complexly difecratt waic Comiller
The nows ttle. 'Lady Gage owings froer chandeler to "Wicked"" spentrrly
lestimatt question and prefermoes dutteg at settrrrd Howove,
mentoon chznoeleer soing whtch not depicted the vodeo descngroon
Tie disccipii
crsetionn chaddelar awing inotaad Docusss
poesen insted, menivve arvand lifestyle dicesson shon chocces. surmary the
vicao
about all
blonde wonarrs actiots mouth whinsccal dooce The
ascunraly
focen the vedoo
about moryybey lifesyle choioe
wxpnedigg
about tranel Tie discenect bewees the headlin and the video content leads to
chandclerr seitgg
suppored deseripbot prestded. <thirlci wesweri <tarwerss
mistesrreettztnn of what the viñeo actually about. ww-lhhikkoooeee -aaaearr "jumwee??
Figure 20: The figure presents two cases where Fact-RI achieves correct label prediction but fails to accurately identify the fake entity in its reasoning process.
Using the same prompt design shown in Figure 24, we evaluate the consistency of five 4o-mini, GPT-4o, DeepSeek-Ri. Doven.55VVL-72 and Qwen2.5-VLL-2B -against human evalua- tions and report their accuracy on both types of samples.
samples, DeepSeek-R1, GPT-4o-mini, and GPT-40 achieve high agreement with human evaluations, with negligible deviations. Figure 17 shows the results on explainability-ene samples, where Qwen2.5-VLL-72B, GPT-40-mini, and GPT-4o again demonstrate high consistency with human assessments.
As illustrated Figure 16, for explainability-corees
30

---

Case
Case
Tittke
nommn meatt mark Internbonnel Day for the Kirminator Volaann Araine Wormn
News Tittles
Hirigg roplara why Jack Smith made move to dianie Tramp's
News Video
News Video
the police the who kill womsnn dust
protect probest against killngg worure supposel proter
so axplain wis you Funihas aloog Audis you specia
ecandring for lusbby Fracher cammo mind you lewr you
wlee's glack paec lise fion the 1955 clew Prodige morie
News Audioo
parsetaalin agaird
Ko
drffemntly whet done before 2014
tim evactyy
election
Predlctione "slliekeeee deccipiion ouflitan Coutaring groupe
Prodiction: "oatskk-yll The video deaciption oatiines weyroed
csarchinu globally Elinasaiion Viclezsa Aquist
teatzinng Ube Uenng discesstg the legal actons takee by jack Seuth
Ieturational
demus case sguiee Trump. it desmbees lter''s ondyss, the varkoss seringgs
and tnchiies detri vbout the of the some, the larmers they
the purtsiparts and bae locas on the danisad Tharch a deetion subremionn
which morch including ither street Irotoric beiidrags and public oquarss 2 aho desothes intrratioos with police and the use of bar gas in some instasoss The vidao coodakss with cal to actioo for teding viblanae againd worsnn viT zaws libe, 'Wodalls worter seach mark Istenationaa Day for the Elinisation of Vallons A guitst Womnn" succiacdy cxpture the esurnce ot the video deserptorn. sccumatlyy rellets oostent menttorigg globel pertrigation wamen monche the perpose marths cark Interatiooal Agnlest Womenl herdoor: the socurned discrigtion <thhince
case and ix deoppeng of the clamiffed decumors cae The nows this, Teeti
enpliin way lock Smoth made mome to dionis Thomp's case," sooumedy
eets connent the vitken dieciptton, direlyy mentiors Horigh'
esploration the contrst of Seaittrss legal asvess sasinut Trump. <thimil-i--aamm wawweeeaaaaaaaa......a.a.a shaswers
Elmiittton Visience contect dfensnee
Figure 21: The figure presents two examples of real news cases successfully classified by Fact-R1.
Case
Case
News Tittke
Eittrday, Digis at Chaneaao Alppu war grocndad make war Sor aocilionaa gess. The sagiller soes Lit toathag!
News Tiltke
Doney World sizta deea for good)
News Video
News Video
ck ao Disras wodld las sett down indaficiiay was supposed to
time thas you dauppear without logal cotsaquatoos, your sews
imagtne litta
heurs ago that tienny Worli now shat down and scary cur lilewyy World only ahot down for Super majer cveats by humtezie 911 stuff lise soe
NewsAudd
Audl- speeing lock sp
Preeliction whirkcitTie has video discriptas inclies that the feotage captarss bh diett of goose as they nignde southwasd. The dlls, Moooecaas fight ei Graegolou Altoot were prounded to..." suggests a
Prafitions <BhinkohtTHe lile ('Diazay ahuts good!" is
raggess pemizneet clesare Uisney
ssynthont cmabe impreaaon somefinng the vodoo presets tistciial
whivliss ura disrrpeed intertiraaly wildiii may preaetit ding idylic settrnetal sessario Hoserve openstood changes greses
sarmatse when
bame
Diney Word has dlsod, raturd dioatees and sigilfiant bs sfs momo, the vidii aegars to fuet, the
following
voseers thminng mply a agrehcent sirpot,
phenemeeor.
cximinatc with rucett nown neferesingg indefinic ahst down
video appears
menbon of tistoccal dosams emphnsiers that they ever speethc and not myprn exbrive of peroneer cheam vnx Ihemfom, the trfe fnee not socumtely rdfect the video's locus os histedial dosus everts and nisceds viewess iato
subetasfala
granded Eight intevvstion rigeed with the conteet, stseypents
wethortiee Thamioor, the tile
litee Bisigeiiioanne of the woer while
sumitioe that
thinking that Diewsy World dosedd indecautdy whe the oustost shows a pacce
sell evier is the lootage/etallie ital disrpponcy makes the malttaddngg
eoogle is rumbiee regirding pad, terponnry snatiesers vyieln condusson, tille
oustr maripelnted. it the perzarred importance of both the moment
mnsosgronnts onckent and intent of the gudifying it manipulata informatton -thinkki cunserr> chanwer
and the adtiin in wtllrcke -csswws -iiis <lussux>>
Figure 22: The figure presents two real-world fake news cases that were successfully predicted by Fact-R1.
Considering both cost-effectiveness and robustness, we adopt GPT-4o-mini as our judge model. Its evaluation accuracy remains within an acceptable error range, providing reliable and stable judgment for assessing reasoning quality in our misinformation detection framework.
E.3 Case Study
(1) Successful Reasoning and Entity Identification. As shown in Figure 18, Fact-R1 demonstrates impressive deep reasoning capabilities, with key steps in its reasoning process highlighted in green. The model not only accurately determines whether a news video is manipulated but also generates structured and detailed reasoning chains that explicitly identify the fake entity. In Case 1, Fact- RI infers from the visual content that the video depicts "Super Idol," which contradicts the title's
31

---

You are a video describer tasked with providing a concise and accurate description of a news video. Use the provided title and video image entity as contextual references support your description.
#Title: {title}
# #Entity: {entity}
# Instructions:
Briefly describe the main objects and their visual characteristics as shown in the video.
2. Summarize the environment and background setting.
3. Highlight key actions and interactions captured in the footage.
# Constraints:
Use a clear, narrative style. Avoid bullet points or lists.
2. K eep the description brief and focused on the most essential details.
3. Avoid speculation, subjective opinions, emotional language.
4. The provided entity may contain inaccuracies and should be treated only a reference.
Figure 23: Prompt design for generating news-domain video captions.
reference to "DA ZHANG WEL." In Case 2, by extracting and interpreting key details from the news report, the model deduces that the event occurred the Red Sea, successfully identifying the inconsistency with the location mentioned in the title and confirming the video as manipulated.
(2) Contribution of Multimodal Signals. As shown in Figure 19, present two fake news cases correctly identified by Fact-R1, demonstrating the importance of multimodal evidence in the reasoning process. In Case 1, the presence of the key critical information that aids accurate detection. Case 2, the OCR-extracted text "Trump" from the image serves essential visual evidence, effectively supporting the model's 's reasoning and final judgment. These examples highlight Fact-R1's ability to integrate cross-modal information for robust misinformation detection.
"Chancellor" in the news audio provides
(3) Failure Cases Explainability. Figure 20 illustrates two cases where Fact-R1 successfully predicts the correct label but fails in its explainability by inaccurately identifying the fake entity. In Case 1, the ground-truth fake entity "Lady Gaga," yet the model mistakenly identifies "chandelier" the manipulated entity. In Case 2, the correct fake entity is "wwindow seat aisle seat," but the model incorrectly selects "travel debate" as the fake entity. Upon analysis, we observe that in both cases, neither the news audio the OCR-extracted text from the images provides effective supporting clues. This indicates a limitation in the model's 's reasoning ability when explicit cross-modal signals are absent, suggesting the need for enhanced entity grounding mechanisms and deeper semantic understanding.
(4) Accurate Reasoning Real News Samples. As shown in Figure 21, we present two examples of real news videos correctly identified by Fact-R1. these cases, the model's 's reasoning process accurately describes the events occurring in the video and aligns well with the given news titles. These examples demonstrate Fact-R1' capability not only in detecting manipulated content but also in validating authentic news through consistent and coherent reasoning.
(5) Real-world Cases. shown in Figure 22, we present two real-world fake news cases that were successfully detected by F.ct-R1. These cases are drawn from the FV and Fake datasets, which
32

---

You are an AI assistant to help me evaluate whether the reasoning process the prediction correctly identifies the labeled fake entity.
Task Description:
Determine if the following prediction mentions the fake entity: " "(entity)"
Prediction: (prediction)
Output "yes" if the prediction reasoning process mentions and describes the entity; otherwise, output "no".
Example Case:
Fake Entity: "French"
Prediction: "The news title, 'Storm Bert: drone footage shows extent of flooding at French holiday park,' suggests that the flooding occurred at a French holiday park, which is inconsistent with the video description indicating that the flooding occurred at an English holiday park. Thus, the news title does not accurately reflect the content of the video description, as it incorrectly states the location of the flooding. In conclusion, the event discussed in the news title does not match the specific location of the flooding depicted in the video description." Output: yes
Figure 24: Prompt design for the judge model to evaluate the accuracy of fake entity identification in the model's reasoning process.
consist of fabricated information posted by users. We observe that Fact-R1 is able to successfully capture the key indicators of misinformation in these examples.
F
Responsible Release and Safeguards
To mitigate potential misuse risks associated with misinformation detection models, the VV dataset provided strictly for non-commercial research purposes and is accessible only to verified academic researchers under research-specific license agreement. All samples have been carefully screened remove any non-public personally identifiable information (PIT), ensuring that no harmful, private, or illegal content is included. Fact-R1 is intended to function an assistive component within numan-in-the--lle workflows, rather than as a standalone decision-making or content moderation system. These measures are designed to balance research transparency with the responsible release of models and data.
G Ethics Statement
This work includes human evaluation via crowdsourced annotation. Annotators received clear task instructions and example interfaces, provided informed consent, and could withdraw at any time. Responses anonymized, no personally identifiable information collected, and compensation met or exceeded local fair-pay standards. The study adhered to institutional ethical guidelines and local labor regulations.
We collected small annotation set (5k preference samples) under transparent, compliant protocol. Given the non-sensitive content and minimal risk typical of standard annotation tasks, the study quali-
33

---

You are an Al assistant. Your task is to generate take news title by modifying the goven original title, ustnn the three provided unrelated reference tills inspiration.
Task Description:
You will peovided with:
original
2. T hree unrelated reference news tréles.
t-fake titler replacting information original tite. The replacement should. be inspired
eatlides context found the refeceuce lille.
Instractions:
Replace specitic decails in odiginal tide, which may include:
** Punan Information.*
Example:
From "Macron conrect Donald Trump on costs Ulkaine war and says: Peace must not meen swceendee"
To Anneea Merael oorrecss Donald Trump oosts of Ularaize war and says: Pesse must not mean sumender.""
**I Location Information:*
Example:
From "Volodymyr Zelenskty calls air defence hoost during A ntony Blinkan visi Ulmmmee
"Volody myp Zelenakly calls for air defence boost durtig Autony Blinken visit Russia."
Information.""
Pxample:
From "Myanmar titness coach accidentally captures military coup"
Myanmar
arcidentally captures pesceful protest""
**Orgonzzaionn Information""
Example:
pppe pulls protection tool government secutity tow"
"inomi pulls data protection tool after UK goverment scturity now.
After genemting the fake till, identify w*hhich category modifiration belongs (choose from: person, location, organization.
Example:
"Btle": "Thres found alive four boches recovered tourist beat capsizes Sea"
"efferecee tilel" "Taat Time the Mediterazeen Sea D Disuppeared"
"rfeennce tidl2". Soa levels
than exported 2024."
"referance trtes" ntbrrtica's
deserves protacted status. ",
"Generate Fake Title:: "Three found alive and four bodies recovered efter tourist bost capsizes in Meditersazeen Sea."
"Altenatonn Categury' "location"
# Now. process the following tngut: Original "[ttle)"
Restrmoe Title 1: "Rudiunucc tille)
Reference 1itl 2: (Reference trls2)"
[Rederence
Please output the result in the following J ISON format:
Generate Fake Title"; 1... Alteration Category"
Figure 25: Prompt design for generating fluent fake news titles by editing the original title based information from three reference titles.
fied for an IRB exemption under our institution's policy. We support transparency and reproducibility and have shared data and experimental details responsibly, prioritizing ethical considerations, partici- pant privacy, and societal safety.
34


Reviews:
[{"review_id": "OxOu6xoGzK", "reviewer": "Reviewer_B2W7", "summary": "This paper introduces Fact-R1, a new reasoning model for detecting video misinformation, and an accompanying dataset, FakeVV. The FakeVV dataset is a large-scale benchmark containing over 100,000 video-text pairs, created by sourcing videos from news channels and synthetically generating misinformation by replacing entities (e.g., persons, locations) in the titles to create semantic inconsistencies with the video content. The proposed model, Fact-R1, is trained using a three-stage pipeline: (1) Supervised fine-tuning (SFT) on long-chain-of-thought (CoT) instructions, (2) Direct Preference Optimization (DPO) for preference alignment, and (3) Group Relative Policy Optimization (GRPO) with a custom rule-based reward function to enhance reasoning. The authors position this work as a new paradigm for explainable misinformation detection by combining video understanding with deep reasoning.", "questions": "1. The core training paradigm (SFT, DPO, GRPO for reasoning) is heavily inspired by DeepSeek-R1. Could the authors clarify the key methodological novelty of Fact-R1 beyond the application of this existing pipeline to the video-text modality?\n2. The term \"misinformation detection\" implies an ability to check facts against reality. Given that Fact-R1 operates on a static dataset with no connection to external knowledge sources, how do the authors justify this claim? How would the model handle a news video about an event that occurred in March 2025? Wouldn't it be more accurate to frame the task as \"cross-modal consistency checking\"?\n3. Generalization Beyond Entity Replacement: Fact-R1 is trained specifically on entity replacement misinformation (person, location, event, organization). How does the model perform on other common video misinformation types such as: (a) Temporal manipulation (old footage presented as recent), (b) Context manipulation without entity changes, (c) AI related misinformation like Deepfakes or other synthetic content, (d) Misleading captions on authentic footage? Could you provide experiments or analysis showing Fact-R1's performance on these scenarios?\n4. Baseline Comparison Fairness and Real-world Performance: Several concerns about the experimental setup: (a) Many baseline models appear to be evaluated zero-shot or with suboptimal configurations - could you ensure fair comparison by providing task-specific fine-tuning for key baselines (at least for open-source models)? (b) While Fact-R1 does not currently support external search or retrieval, this is a critical limitation in real-world misinformation detection. There is no technical barrier preventing the model from calling a search API, and evaluating models that do so (e.g., retrieval-augmented systems) would offer a more complete understanding of Fact-R1’s practical utility.\n5. Missing Strong Internet-Augmented Baseline (e.g., Grok-3 with internet search): The current baselines omit strong internet-connected models like Grok-3, which has been fine-tuned on misinformation discrimination tasks and has demonstrated strong performance on platform X.", "limitations": "The authors should expand the limitations section to address several critical concerns:\n\nDataset Scope: The FakeVV dataset only covers entity replacement misinformation from four Western news sources, which may not generalize to other manipulation types (deepfakes, temporal manipulation, context changes) or diverse cultural/linguistic contexts. The synthetic nature of the fake content may not reflect real-world misinformation patterns.\nReasoning Quality: The paper doesn't adequately address the risk of confident-sounding but factually incorrect explanations that could mislead users more than simple classification outputs, or how to distinguish genuine reasoning from sophisticated pattern matching.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\nThe primary strength of this work is the introduction of FakeVV, a large-scale, diverse, and well-annotated benchmark dataset for video misinformation detection. With over 100,000 video-text pairs and fine-grained manipulated-entity annotations, FakeVV significantly improves the resources available for this underexplored task. Its inclusion of interpretable labels and support for explainable evaluation makes it a valuable contribution to the community.\n\nWeaknesses:\n1. Limited Methodological Novelty: The core training methodology (SFT -> DPO -> GRPO) is a direct application of techniques developed for text-only Large Reasoning Models, most notably DeepSeek-R1, which the authors themselves cite as inspiration. While applying this to the multimodal video domain is a valid engineering contribution, the paper lacks a fundamental novel algorithm or framework. The contribution is more an application of an existing reasoning RL pipeline to a new domain rather than a new method for misinformation detection itself.\n2. Static Knowledge and Misleading \"Misinformation\" Claim: A critical flaw is that the system operates in a closed world. Fact-R1 cannot verify information against the real, evolving world, as it has no connection to the internet or any live knowledge base. Its training data extends only to February 2025. This means the model is incapable of detecting misinformation about any event occurring after this date. The task it solves is not \"misinformation detection\" in a general sense, but rather \"detecting semantic inconsistency between a video and its accompanying static title.\" This is a much narrower and less impactful problem. True misinformation often requires external fact-checking, which this model cannot perform.\n3. \"Explainability\" is Not Verifiable: The paper claims its method is \"explainable\" because it generates a Chain-of-Thought (CoT) reasoning trace. However, this \"explanation\" is generated by the same model making the prediction and is prone to hallucination—producing a plausible-sounding justification for a potentially incorrect conclusion. There is no guarantee that the reasoning trace reflects the model's actual causal process. Evaluating these traces with another LLM (GPT-40-mini) is circular and does not constitute a robust verification of the explanation's faithfulness.", "was_revised": true, "final_justification": "I thank the authors for their detailed and thorough rebuttal, which addressed most of my experimental setup concerns — including fairness in baseline comparisons, evaluation on additional manipulation types, and clarification of retrieval‑augmented baselines. These revisions substantially improve the completeness and transparency of the empirical evaluation.\n\nHowever, my primary concern regarding limited methodological novelty remains. Fact‑R1’s three‑stage pipeline (SFT → DPO → GRPO) closely follows the DeepSeek‑R1 reasoning RL framework, with adaptations to a multimodal video–text setting. While these adaptations (task‑specific rewards, auxiliary tasks, dataset integration) are valuable engineering contributions, they do not amount to a fundamentally new learning paradigm. The main intellectual contribution here is the FakeVV dataset, which is large, well‑curated, and likely to be useful to the community — but dataset contribution alone does not, in my view, fully compensate for the lack of novel algorithmic ideas.\n\nGiven this balance, I raise my score to borderline reject, with the main reason being the lack of a sufficiently novel technical contribution despite strong dataset and experimental work.", "rebuttal": "Thank you very much for your thoughtful and constructive feedback. We sincerely appreciate your recognition of FakeVV, which greatly enriches resources for this underexplored task. Below are our point by point responses to your comments.\n>**Q1:** Limited Methodological Novelty...\n\n**A:** While our training paradigm is inspired by DeepSeek-R1, Fact-R1 is not a simple extension to video-text tasks but a task-specific framework designed for video misinformation detection: \n\n(1) **Research Motivation and Dataset Contribution:** In real world video auditing, users need not only labels but also verifiable reasoning traces. To address this, we introduce FakeVV for the first time, a dataset of 100k video–text pairs with fine grained metadata, providing a video domain resource to advance community research.\n\n(2) **Generalizable Data Pipeline:** Our non-random entity replacement pipeline can naturally extend to scenarios such as temporal and context manipulation, addressing the evolving challenges of video misinformation.\n\n(3) **Task-Specific Rewards & Auxiliary Tasks:** Fact-R1 introduces novel verifiable reward functions, including entity recognition ($R_{\\text{entity}}$) and reasoning keywords ($R_{\\text{word}}$), guided by a judge model to generate correct reasoning chains. Inspired by Figure 20, we also incorporate News Image OCR and video caption generation as auxiliary tasks with dedicated rewards. As shown in Table 4 of the manuscript, these domain specific designs substantially improve Fact-R1’s reasoning ability, making it a specialized framework rather than a direct reuse of existing RL pipelines.\n\n>**Q2:** The term \"misinformation detection\" implies an ability to check facts against reality…\n\n**A:** We sincerely thank the reviewer for their valuable feedback, which has prompted deeper reflection on the task definition and guided us in further improving Fact-R1 for video misinformation detection.\n\n(1) **Definition Consistency.** Our task definition follows prior work [1,2], which treats static multimodal datasets as benchmarks for misinformation detection. To ensure fairness and comparability, we strictly adopted the baseline setups of widely used datasets such as FakeSV  and FakeTT . Results on both FakeVV and these real world datasets show that Fact-R1 performs strongly in video misinformation detection.\n\n(2) **Generalization Beyond Training Data.** In fact, Fact-R1 was trained solely on samples dated before 2025 (**not including February 2025**), while the FakeVV test set contains events from 2025. As shown in Figure 19 (Case 1), the model accurately detects manipulations and produces valid reasoning for unseen events, indicating that it learns generalizable reasoning patterns rather than simply memorizing facts.\n\n(3) **Scalability to Fact‑Checking.** While trained on static data, Fact‑R1 is model‑agnostic and can integrate naturally with retrieval‑based fact‑checking pipelines. Using a retrieval augmentation method similar to 3MFact [4], retrieval significantly improved performance, as shown in the table below. This demonstrates that Fact‑R1 can serve as the reasoning core of a dynamic fact‑checking system enhanced by external evidence.\n\n|Dataset |Method|Acc|F1|\n|:-:|:-:|:-:|:-:|\n|FakeSV|Fact‑R1|75.6|74.7|\n||Fact‑R1+Retrieval|79.5(↑3.9)|78.3(↑3.6)|\n|FakeTT|Fact‑R1|74.4|72.7|\n||Fact‑R1+Retrieval|79.4(↑5.0)|78.9(↑6.2)|\n|FakeVV|Fact‑R1|81.2|80.3|\n||Fact‑R1+Retrieval|86.5(↑5.3)|84.5(↑4.2)|\n\n>**Q3:** Generalization Beyond Entity Replacement.\n\n**A:** To assess generalization beyond entity replacement, we constructed four representative benchmarks:  \n- (a) **Temporal Manipulation:** 500 samples from FakeVV with time references were modified by replacing timestamps using retrieved news titles (Figures 8–11). As shown in *Table Temporal*, this task is challenging, and Fact‑R1 performs well mainly when clear temporal cues are present.  \n- (b) **Context Manipulation without Entity Changes:** For 500 real samples, titles were replaced with mismatched real titles (following the procedure described in L604–L624 of the manuscript). *Table Context* shows that Fact‑R1 detects these out‑of‑context inconsistencies effectively.  \n- (c) **AI‑related Manipulation (Deepfake):** Using FaceSwap [3], we replaced the largest face with a random CelebA‑HQ face to generate 500 forged samples. *Table Deepfake* demonstrates Fact‑R1’s strong generalization to synthetic media.  \n- (d) **Misleading captions on authentic footage:** We extracted 50 fake samples from the test set and used Photoshop to modify keyframe captions to match fake news titles, creating 50 enhanced fake samples. *Table Captions* shows a ~5% accuracy drop, indicating the higher difficulty of this scenario.\n\n|Dataset|Method|Acc|F1|\n|:-:|:-:|:-:|:-:|\n|Temporal|GPT‑4o|62.2|61.3|\n||FakingRec|63.5|63.1|\n||Fact‑R1|69.7|67.8|\n|Context|GPT‑4o|78.2|78.0|\n||FakingRec|77.7|75.9|\n||Fact‑R1|86.4|85.9|\n|Deepfake|GPT‑4o|64.5| 62.6|\n||FakingRec|68.3|66.2|\n||Fact‑R1|76.8|76.4|\n|Captions|GPT‑4o|68.0|61.9|\n||FakingRec|64.0| 60.8|\n||Fact‑R1|73.0(↓5.0) |71.7(↓4.4) |\n\nAs noted in [1], “Unfortunately, most of the datasets are not released.” Inspired by this and the reviewer’s suggestion, we enriched FakeVV and proposed Fact‑R1 as a strong baseline with broad generalization. \n\n>**Q4:** Could you ensure fair comparison by providing task-specific fine-tuning for key baselines...\n\n**A:** We thank the reviewer for their valuable feedback on baseline fairness. In the revised version, we added fine-tuning experiments with open-source models, including InternVL2.5‑8B and Qwen2.5‑VL‑7B, using the same training data from FakeSV, FakeTT, and FakeVV with label-based answers. The experiments used the same LoRA parameters as Fact‑R1.\n\n|Dataset|Method|Acc|F1|\n|:-:|:-|:-:|:-:|\n|FakeSV|Qwen2.5-VL-7B-SFT|71.8|72.1|\n||InternVL2.5-8B-SFT|70.1|66.1|\n||Fact-R1|75.6|74.7|\n|FakeTT|Qwen2.5-VL-7B-SFT|68.8|66.4|\n||InternVL2.5-8B-SFT|67.7|68.0|\n||Fact-R1|74.4|72.7|\n|FakeVV|Qwen2.5-VL-7B-SFT|75.4|73.3|\n||InternVL2.5-8B-SFT|73.5|73.2|\n||Fact-R1|81.2|80.3|\n\nAs shown in the Table, fine tuning Qwen2.5-VL-7B achieved strong performance, further confirming its effectiveness as the backbone for Fact-R1.\n\n>**Q5:** And evaluating models that do so (e.g., retrieval-augmented systems) ... Missing Strong Internet-Augmented Baseline..\n\n**A:**  Following the fact‑checking setup [5], we adopted the evidence retrieval process from 3MFact [4] to enhance Fact‑R1. As shown in the table, the retrieval‑enhanced Fact‑R1 outperforms other methods, confirming its potential as the reasoning core of fact‑checking systems.\nWe also evaluated Grok‑3‑deepsearch and Grok‑4, which achieved strong zero‑shot performance, demonstrating the power of retrieval‑augmented models. However, unlike image fact‑checking, video fact‑checking incurs high API costs and raises privacy concerns. As noted in 3MFact [4], GPT‑4o‑mini was used as the LLM and tested on only a small number of samples, underscoring the need for efficient and accessible open‑source models. \n\n|Dataset|Method|Acc|F1|\n|:-:|:-|:-:|:-:|\n|FakeSV|Grok-3-deepsearch|68.8|69.2|\n||Grok-4|72.4|72.6|\n||3MFact|71.5|71.0|\n||Fact-R1|75.6|74.7|\n||Fact-R1+Retrieval|79.5(↑3.9)|78.3(↑3.6)|\n|FakeVV|Grok-3-deepsearch|69.2|70.1|\n||Grok-4|75.5|74.8|\n||3MFact|77.7|76.3|\n||Fact-R1|81.2|80.3|\n||Fact-R1+Retrieval|86.5(↑5.3)|84.5(↑4.2)|\n\n\n>**Q6:** \"Explainability\" is Not Verifiable...\n\n**A:** We thank the reviewer for this insightful question, which helps clarify our approach to explainability.\n\n(1) **Motivation.** In real-world misinformation auditing, reviewers need not only labels but also transparent reasoning. This aligns with Bu et al. [1], who emphasize predicting specific misinformation types with fine-grained annotations. FakeVV supports this by encouraging reasoning tied to manipulation types.\n\n(2) **Credibility.** Instead of style-sensitive metrics (e.g., ROUGE), FakeVV allows quantitative evaluation via entity-level accuracy. During GRPO, a judge model simulates human reviewers to guide reasoning. Table 4 in the manuscript shows that removing the judge lowers performance, and Figures 17–18 confirm GPT‑4o‑mini as judge aligns well with human evaluation.\n\n>**Q7:** Limitations\n\n**A:**(1) **Dataset and Generalization:** We provide an open-source data pipeline that generalizes to diverse manipulation types. Experiments show Fact‑R1 performs well on temporal, contextual, deepfake, and misleading captions.\n\n(2) **Real-World Performance:** Fact‑R1 achieves strong results on FakeSV and FakeTT. Retrieval augmentation further improves factual alignment, showing its reasoning goes beyond memorization.\n\n(3) **Incorrect Reasoning Risk:** As discussed in the original manuscript, current AI auditing models—like human auditors—are not flawless. While Fact‑R1 may occasionally produce confident but incorrect reasoning, these traces are valuable as diagnostic tools, helping human reviewers identify failure cases and guide model improvement. As shown in Figure 21, such errors are often easily detected by humans. We plan to integrate Fact‑R1 into a human‑AI collaborative auditing workflow, where its reasoning outputs assist reviewers, ensuring reliability in practical use. \n\n>**Q8:** Major Concern: Improper research involving human subjects... Major Concern: Human rights (including surveillance)\n\n**A:** We apologize for any concerns and confirm that this work involves no human-subject research, discrimination, bias,  fairness, human-rights issues, or surveillance.\n\n[1] Bu et al. Combating online misinformation videos: Characterization, detection, and future directions. \n\n[2] Shao et al. Detecting and grounding multi-modal media manipulation.\n\n[3] FaceSwap Contributors. Faceswap: Deepfakes software for face swapping. GitHub repository. \n\n[4] Niu et al. Explainable Video Fact-Checking with a New Dataset and Multi-role Multimodal Model Approach.\n\n[5] Braun et al. Defame: Dynamic evidence-based fact-checking with multimodal experts."}, {"review_id": "OR4DICCayA", "reviewer": "Reviewer_7T2b", "summary": "This paper studies the misinformation detection in the videos. This task is challenging due to two facts: 1. There is no large scale training datasets for support the chain-of-thought reasoning for misinformation detection. 2. There is no chain-of-thought MLLM that can excel this misinformation detection task. To address those two challenges, the paper first collects a large-scale datasets for the misinformation detection. The detailed caption and the audio is sent to the DeepSeek-R1 to generate the chain-of-thought thinking and the answer. The detailed caption is generated by using GPT-4o. To train the misinformation detection chain-of-thought model, the paper first leverages DPO to train a policy model with human preference alignment. Then GRPO with specifically designed tasks (e.g. News Video Caption and News Image OCR) are leveraged to further train the policy model. The proposed model acheives higher performance than the baseline approach on three benchmarks.", "questions": "I have several questions about the dataset construction and the ablation studies.\n\n1. For dataset construction: L187-188, what are the prompts used for Deepseek-R1?\n2. L191: It is easy to do answer verification. I wonder how to conduct the chain-of-thought verification specifically? How to check the reasoning trace is correct?\n3. For DPO, I understand this is to align with human preference. But I wonder whether this step is truly necessary? Given the ablation studies (Table 3), it seems w/o DPO, the model achieves quite good performance.\n4. For Table2 domain specific baseline. It is great to see BERT and VIT are leveraged as modality specific baseline. However, BERT is not an instruction following LLM. It merely fills the missing components in sentences. I wonder how to conduct the baseline studies by using BERT explicitly? \n5. Similar question above, I might miss some details, I wonder how to conduct the baseline studies with ViT? ViT is just a visual encoder, I wonder how would the Acc and F1 being calculated for the ViT model?\n6. One missing piece is the dataset scale studies. I wonder whether the current dataset size is enough or we still need more datasets to address the misinformation detection task. Specifically, I wonder whether the size of the dataset is enough and whether the topic coverage of the dataset is sufficient.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The large scale dataset is one of the major contributions. In the multimodal LLM domain, it is critical to have a large scale and high quality dataset for training and finetuing model. It is glad to see a carefully design and curated dataset.\n2. The ablation studies are conducted carefully with the modality speicfic baselines. Although I have some questions for how those modality specific baselines are being conducted, overall it is a quite careful design.\n3. The proposed approach achieves better performance than the baselines.\n\nWeakenss:\n1. Lacking some dataset scale studies.\n2. It seems not very clear whether chain-of-thought is strictly required for this task, although the model trained with GRPO achieves better perfomrance based on the ablation studies.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your careful reading of the manuscript and your constructive comments, which have been extremely helpful in improving our work. Below, we provide point‑by‑point responses to your feedback.\n\n>**Q1:** What are the prompts used for Deepseek-R1?.\n\n**A:** We apologize for the omission. The exact prompts for DeepSeek‑R1 are provided in the supplementary material (Section D, L647–L651), and Figure 27 further illustrates the prompt used to guide the model in determining the real/fake label. We will explicitly reference these prompts in the main text in the revised manuscript for clarity.\n\n>**Q2:** I wonder how to conduct the chain-of-thought verification specifically? How to check the reasoning trace is correct?\n\n**A:** We apologize for the lack of detail in the original manuscript. Below we clarify how we verify CoT reasoning.\nAfter Step 1 (label filtering), some Long CoT instruction tuning samples may still have correct labels but incorrect reasoning (e.g., Figure 21, where the model predicts the right label but fails to identify the manipulated entity).\nTo address this, we add an explainability filtering step. Using the prompt in Figure 25, GPT-4o-mini acts as a simulated human judge to check whether the reasoning correctly identifies the manipulation. As shown in Figures 17–18, its evaluations align closely with human judgments.\nThis two stage filtering yields a high quality set of 85k Long CoT samples, ensuring that the final training data includes both correct labels and faithful reasoning traces.\n\n>**Q3:** For DPO, I understand this is to align with human preference. But I wonder whether this step is truly necessary?\n\n**A:** (1) Thank you for your question. The original Fact-R1 pipeline did not include a DPO phase. Our initial SFT + GRPO approach struggled to produce reasoning traces aligned with human reviewer logic and was prone to errors. To address this, we curated 5k human‑annotated preference samples targeting common failure cases—such as incorrect answers, hallucinated entities, and commonsense inconsistencies. These samples simulate the decision‑making process of human auditors, guiding the model to produce more realistic and human‑aligned reasoning.\n\n|Dataset|Method|Score|\n|:-:|:-:|:-:|\n|Interpretability|w/o DPO|71.9|\n||Fact‑R1|76.4|\n|HallusionBench|w/o DPO|44.7|\n||Fact‑R1|48.8|\n\n(2) As shown in the Table, DPO also improves interpretability accuracy and boosts performance on HallusionBench [1], which evaluates hallucination. Importantly, DPO uses human edited model outputs, reducing annotation workload and mitigating issues from inconsistent annotation styles.\n\nOverall, the DPO phase is crucial for validating and enhancing chain of thought reasoning, enabling Fact-R1 to produce more accurate and reviewer aligned explanations.\n\n>**Q4:** I wonder how to conduct the baseline studies by using BERT explicitly?.\n\n**A:** (1) We sincerely apologize for the lack of detail regarding the BERT baseline training in the main text. In our setup, we use BERT to extract textual features from news titles. Specifically, we take the [CLS] token embedding and feed it into an MLP classifier for binary classification, truncating input sequences to a maximum length of 512 tokens. BERT and the MLP are trained jointly using the same training data as the baseline methods, which include the training splits of FakeSV, FakeTT, and FakeVV. A classification threshold of 0.5 is applied to compute Accuracy and F1 scores.\n\n(2) As shown in Table 2 of the manuscript, BERT performs reasonably well on the real world datasets FakeSV and FakeTT, where misinformation often appears through anomalous tone or unusual symbols in the titles. However, its performance degrades significantly on FakeVV, highlighting the difficulty of detecting fine grained textual manipulations and the necessity of incorporating multimodal reasoning.\nThese training details will be added to the final version of the manuscript for completeness.\n\n\n>**Q5:** ViT is just a visual encoder, I wonder how would the Acc and F1 being calculated for the ViT model?\n\n**A:** We sincerely apologize for not providing sufficient details on the ViT baseline training in the main text. \n\n(1) For keyframes, we used the ViT visual encoder to extract visual features from multiple frames. We then extracted the [CLS] token vector from each frame, averaged these vectors, and concatenated the averaged visual features with the [CLS] token vector from the pre-trained BERT. These concatenated features were then input into an MLP classifier.\nDuring training, we jointly trained ViT and MLP, while keeping BERT frozen. The training data and setup were consistent with the baseline, using the training sets from FakeSV, FakeTT, and FakeVV, with a 0.5 threshold for binary classification to compute Accuracy and F1 scores.\nAs shown in Table 2 of the manuscript, the ViT baseline performed poorly across all three datasets, indicating that relying solely on misaligned visual features is insufficient for video misinformation detection and can even degrade performance. \n\n(2) To further validate the importance of visual-text alignment, we added a CLIP baseline experiment. We used pre-trained and frozen CLIP vision and text encoders to extract the averaged visual keyframe features and news title features. The features were concatenated and then fed into a trained MLP classifier. The training data and threshold settings remained consistent. \n\n|Dataset|Method|Acc|F1|\n|:-:|:-:|:-:|:-:|\n|FakeSV|CLIP|68.7|66.9|\n|FakeTT|CLIP|69.2|68.8|\n|FakeVV|CLIP|70.0|70.1|\n\nAs shown in the results, the experiment confirmed the importance of introducing pre-trained visual-text alignment models.\nWe apologize again for the earlier omission, and we will include these progressive baseline design details and findings in the revised manuscript.\n\n\n>**Q6:** One missing piece is the dataset scale studies.\n\n**A:** We thank the reviewer for this valuable suggestion, which prompted us to further reflect on improving the FakeVV dataset. We plan to expand FakeVV from two perspectives: source diversity and content diversity.\n\n(1) **Scaling dataset size and source diversity.** The current FakeVV dataset contains over 100k high-quality video–text pairs from four official news channels, creating a resource similar to VisualNews [2] in the video domain, with the goal of advancing the community's technical development. We plan to expand FakeVV by adding more outlets (e.g., Fox News, ABC News) to increase source diversity. Our analysis shows health‑ and science‑related topics account for only 13.6% and 7.9% of samples, while most focus on social, political, and cultural issues. To address this imbalance, we will add more video–text pairs from these underrepresented categories. FakeVV will remain fully open‑source, and we will continue to update and maintain it to support large‑scale video misinformation research.\n\n(2) **Expanding manipulation types and topic coverage.** Our novel non-random entity-replacement pipeline is highly extensible to other types of misinformation. Using the retrieval-based pipeline described in Figures 8–11, we have already constructed benchmarks for (a) temporal manipulation and (b) context manipulation without entity changes (*see the experiments in Question 3 to Reviewer B2W7*). The results show that Fact-R1 generalizes well to these cases, demonstrating its strong generalization ability in handling various manipulation types.\n\n(3) Moreover, we further enriched FakeVV with new manipulations, such as (c) numeric changes in news titles. For instance, the title  “Woman has over 1000 butt surgeries”  was modified to  “Woman has over 10000 butt surgeries” . If no retrieved titles contained numeric information, we performed random numeric substitutions. This resulted in 500 manipulated samples paired with 500 real cases. \n\n|Dataset|Method|Acc|F1|\n|:-:|:-:|:-:|:-:|\n|Number|GPT-4o|55.3|53.2|\n||FakingRec|61.4|60.6|\n||Fact-R1|67.7|65.5|\n\n\nAs shown in the Table，Experimental results reveal that such manipulations remain challenging, motivating us to enhance Fact-R1’s ability to reason about numeric information.\nAll enriched benchmarks and analyses will be released publicly to further support open source model training and research. \n\n>**Q7:** It seems not very clear whether chain-of-thought is strictly required for this task.\n\n**A:** We sincerely thank the reviewer for raising this important question.\n\n(1) **Qualitative Motivation for Chain‑of‑Thought Reasoning.** In real‑world misinformation auditing, reviewers require not just binary labels but transparent reasoning to understand and verify model outputs. Compared to many tasks in the community, misinformation detection relies more heavily on a verifiable and explainable decision-making process. Therefore, incorporating strong reasoning capabilities into open-source models is especially important, as it helps enhance the model's value in practical applications.\n\n(2) **Quantitative Benefits of Chain-of-Thought Reasoning.** In addition to significantly improving prediction performance, the ablation study in the table below demonstrates that the introduction of GRPO-based chain-of-thought reasoning training leads to a substantial improvement in interpretability accuracy. \n\n|Dataset|Method|Acc|\n|:-:|:-:|:-:|\n|Interpretability|w/o GRPO|67.1|\n||Fact-R1|76.4|\n\nThis shows that Fact‑R1 demonstrates emergent reasoning abilities similar to advanced text‑based RL systems, but in the more challenging context of video misinformation detection. Overall, the results highlight that chain‑of‑thought reasoning is particularly valuable for misinformation detection, where transparent and verifiable reasoning is essential for real‑world use.\n\n\n[1] Guan et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models\n\n[2] Liu et al. 2021. Visual News: Benchmark and Challenges in News Image Captioning."}, {"review_id": "d04oPVNhfr", "reviewer": "Reviewer_19bT", "summary": "This paper introduces FakeVV and Fact-R1, a large-scale dataset of 100,000 video-text pairs designed to advance multimodal misinformation detection with a novel framework combining deep reasoning and collaborative reinforcement learning. FakeVV features realistic misinformation samples created through semantically consistent entity replacements, supporting interpretability of the method. Experimental results confirm that Fact-R1 significantly surpasses existing methods in detection accuracy and explainability.", "questions": "1. How well would Fact-R1 generalize to non-news domains?\n\n2. Can you provide detailed computational analysis or complexity metrics for each component?\n\n3. Can you give comparisons with recent methods using variants of GRPO? DeepVideo-R1 https://www.arxiv.org/abs/2506.07464, Video-R1 https://arxiv.org/abs/2503.21776 etc..", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths :\n\n- FakeVV is a large-scale dataset covering a broad range of topics and time frames, significantly advancing the field. And integration of deep reasoning with reinforcement learning (Long-CoT, DPO, GRPO) is effectively addresses critical limitations of existing methods. They're technically sounds.\n- Fine-grained annotation enables interpretability of this method, which is crucial for practical misinformation detection.\n- Extensive evaluations clearly show Fact-R1’s state-of-the-art performance compared to baselines\n- They validate each methodological component through ablation studies.\n- Extensive supplementary materials and scripts for reproducing their results.\n- Writing is clear and easy to follow.\n\n\nWeaknesses :\n\n- The proposed training approach, while helpful, may require significant computational resources and extensive training time. There should be some computational analysis too.\n- Heavy reliance on external models for caption generation make concerns on reproducibility and independence.\n- Need further evaluation on even broader real-world datasets and scenarios beyond short news clips might be necessary to ensure generalizability.\n- The dataset primarily focuses on political and news-related topics, limiting applicability to other misinformation types (scientific or healthcare misinformation)", "was_revised": true, "final_justification": "The additional evaluations in the medical domain and multiple benchmarks strengthen the generalization claim, and the computational breakdown addresses efficiency and reproducibility concerns. Comparisons with GRPO variants are convincing, I will reflect this and keep my final score as accept.", "rebuttal": "Thank you for your constructive comments and suggestions, and they are exceedingly helpful for us to improve our paper. We have carefully incorporated them in the revised paper. In the following, your comments are first stated and then followed by our point-by-point responses.\n\n>**Q1:** How well would Fact-R1 generalize to non-news domains? Need further evaluation on even broader real-world datasets and scenarios beyond short news clips might be necessary to ensure generalizability.\n\n**A:** We sincerely thank the reviewer for their valuable comments. We have evaluated the generalization capability of Fact-R1 in non-news domains from the following two aspects:\n\n(1) **Performance on Non-News Misinformation Datasets.** We tested Fact-R1's applicability in the medical domain on the Covid-VTS [1] dataset. Each video in Covid-VTS contains a long real or fake claim. Following the experimental setup of TwtrDetective [1], we used video frames, OCR text, speech text, and claim text as inputs for binary misinformation detection. \n\n|Dataset|Method|Acc|F1|\n|:-:|:-:|:-:|:-:|\n| COVID-VTS |GPT-4o| 55.9 |55.7|\n||TwtrDetective|68.1|67.9|\n||Fact-R1|71.6|69.8|\n\nThe results show that Fact-R1 performs at a level close to task-specific baseline models for medical-related video misinformation. In the future, we plan to enrich FakeVV with additional medical domain data to better support training and evaluation of open-source models.\n\n(2) **Preservation of Generalization Capability.** We further evaluated Fact-R1's generalization capability on several open-source benchmarks. \n|Dataset | Method| Score|\n|:-:|:--:|:--:| \n|OCRBench|Qwen2.5-VL-7B|888|\n| | Fact-R1|875|\n| HallusionBench| Qwen2.5-VL-7B| 51.9   |\n| |Fact-R1| 48.8|\n| Mathvista| Qwen2.5-VL-7B| 68.1|\n| | Fact-R1| 62.9|\n\nAll evaluations follow the OpenCompass framework to ensure fairness and reproducibility. As shown in the table, Qwen2.5-VL-7B reflects the pre trained model’s baseline performance. Thanks to the introduction of the DPO phase and the enhancement of GRPO auxiliary tasks, Fact-R1 performed well on HallusionBench [6] and OCRBench [5], tasks that are highly relevant to reasoning and multimodal misinformation understanding. However, we observed a noticeable decline in performance on MathVista[7], indicating a reduction in its mathematical reasoning capability.\n\nWe will include these results in the final version of the manuscript and make the evaluation code publicly available for the community to reproduce and validate.\n\n>**Q2:** Can you provide detailed computational analysis or complexity metrics for each component? The proposed training approach, while helpful, may require significant computational resources and extensive training time. There should be some computational analysis too.\n\n**A:** Thank you for your suggestion. In designing Fact-R1, we carefully considered both practical efficiency and reproducibility. We used a 7B parameter backbone model and avoided expensive pre-training. All experiments were conducted using PyTorch on multiple 8 × A100 GPUs. Our framework is lightweight and fully transparent. The computational cost for each stage is summarized as follows:\n\n| Training Stage| Dataset Size|Trainable Params|BatchSize|Time|GPU|\n|-|-|-|-|-|-|\n| Stage 1 (Long CoT Tuning) |85k instances|LoRA|2| ~22 h| 8 × A100|\n| Stage 2 (DPO)| 5k preference pairs|LoRA| 1 | ~1 h| 8 × A100|\n| Stage 3 (GRPO)| 15k main + 5k auxiliary | Full model fine-tuning |1| ~9 h| 8 × A100|\n\nOverall, the total training time is under 35 hours, and the framework does not require any large-scale pre-training, making it both highly efficient and easy to reproduce, which is convenient for community researchers.\nWe will include this computational analysis table in the final version to enhance transparency.\n\n\n>**Q3:** Can you give comparisons with recent methods using variants of GRPO?\n\n**A:** We sincerely thank the reviewer for their valuable suggestions. Recently, several impressive works have explored variants of GRPO:\n- DeepVideo-R1 [2] redefined the traditional GRPO reinforcement learning objective as a regression task, directly predicting the advantage values of a sample set, and dynamically adjusted the difficulty of the video-text input by incorporating reasoning hints or altering the video content. We highly appreciate its contributions in long video reasoning and plan to conduct detailed comparative experiments once its algorithm is open-sourced.\n- Video-R1 [3] enhanced temporal modeling capabilities and designed a reward function with timeline clue enhancement to improve the model's understanding and reasoning of dynamic visual content, providing valuable insights for video reasoning.\n- DAPO [4] is an improved reinforcement learning method that decouples clipping, dynamic sampling, token-level loss, and length-aware reward shaping, significantly improving large model stability and accuracy in complex chain-of-thought reasoning tasks. \n\nTo ensure fair comparison, We conducted the same reinforcement training on the third-stage data for Video-R1 to assess its performance on our task. At the same time, we replaced the third-stage GRPO algorithm in the Fact-R1 pipeline with DAPO to directly compare the differences between the two optimization methods. \n\n| Dataset | Method              | Acc   | F1    |\n| :-----: | :-----------------: | :---: | :---: |\n| FakeSV | Video-R1  | 69.9  | 68.4  |\n|  | DAPO  | 72.4  | 72.1  |\n|  | Fact-R1  | 75.6  | 74.7  |\n| FakeVV | Video-R1  | 74.3  | 72.7  |\n|  | DAPO  | 79.1  | 78.8  |\n|  | Fact-R1  | 81.2  | 80.3  |\n\n\nThe experimental results show that Fact-R1 still maintains a leading position across all metrics. We attribute this to our task-specific reward functions designed for misinformation detection, as well as the novel reinforcement learning framework that integrates multiple auxiliary tasks. For example, the timeline clue enhancement reward in Video-R1 mainly improves the model's fine-grained understanding of temporal information but provides limited benefits to the misinformation detection task, while our task-specific GRPO design significantly enhances the model's reasoning ability in video misinformation detection.\nWe will include these experimental results and relevant references in the revised manuscript.\n\n\n\n>**Q4:** Heavy reliance on external models for caption generation make concerns on reproducibility and independence.\n\n**A:** We sincerely thank the reviewer for raising this important question.\n- Reproducibility. Our news video captioning process is fully transparent. In *Section Data Pre-processing (L130)*, we provide a detailed description of the process for generating News-domain Video Captions and showcase the specific prompts used with GPT-4o in Figure 24. Additionally, we have already made part of the generated training data available in the supplementary materials, and we will fully release all video captions after the review period to ensure the reproducibility of our results.\n- Independence. We understand the reviewer’s concern regarding the reliance on closed-source models. In fact, the process of generating News-domain Video Captions in FakeVV is model-agnostic, meaning that any open-source captioning model can be used to replace GPT-4o without affecting the subsequent training framework. With the full release of FakeVV, researchers can either directly use the publicly available caption data or regenerate captions using any model, ensuring both the dataset and methodology remain independent.\n\n\n\n>**Q5:** The dataset primarily focuses on political and news-related topics, limiting applicability to other misinformation types (scientific or healthcare misinformation)\n\n**A:** (1) We sincerely thank the reviewer for their valuable feedback. Currently, the FakeVV dataset contains over 100k high-quality video–text pairs from four official news channels, aimed at advancing the development of community technology. We plan to expand the dataset by including more diverse news outlets, such as Fox News, ABC News, and others, to further enhance the variety of data sources.\n\n(2) Our preliminary analysis of the news topics in FakeVV shows that health and science account for only 13.6% and 7.9%, respectively, while the majority of samples focus on social, political, and cultural issues. To address this imbalance, we are expanding the dataset with additional science- and healthcare-related video–text pairs from specific news categories. The data collection pipeline is easily adaptable to various domains, and the code for collection and processing will be released in the supplementary materials. Additionally, the experiments in *Question 1* demonstrate that Fact-R1 has shown good applicability in the medical domain using the Covid-VTS [1] dataset.\n\n(3) Our long-term goal is to build and release a large-scale, fully open-source video dataset encompassing political, scientific, and healthcare information to support research on various types of misinformation manipulations. This effort aims to address the current gap in high-quality open-source datasets in the field. We are committed to continually updating and maintaining this dataset to foster the advancement of video misinformation detection.\n\n\n[1] Liu et al.. Covid-vts: Fact extraction and verification on short video platforms.\n\n[2] Park et al.. DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO.\n\n[3] Feng et al. Video-r1: Reinforcing video reasoning in mllms.\n\n[4] Yu et al. Dapo: An open-source llm reinforcement learning system at scale.\n\n[5] Liu et al. Ocrbench: on the hidden mystery of ocr in large multimodal models.\n\n[6] Guan et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.\n\n[7] Lu et al. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts."}, {"review_id": "zKvBnQCG1b", "reviewer": "Reviewer_riKt", "summary": "The paper tackles an important problem, video misinformation detection. The paper argues that the most common and misleading types of misinformation involve using real videos out of context that is taking genuine footage and placing it within a false or distorted narrative to deceive viewers. To build/train models to detect such content, there is a lack of datasets especially for multi-modal large language models (MLLMs). Hence the paper proposes a multi-modal dataset with 100,000 video-text pairs.\n\nThe authors collected 100,000 video-text pairs from four news outlets, BBC News, Guardian News, CNN, and The New York Times, from November 2006 to February 2025. Along with the videos, they preserved metadata like user comments, likes, and timestamps. They used a GPT-4o to generate detailed, news-specific captions using video titles and named entities.\n\nTo create misinformation examples, they introduced a method that swaps key entities (like people, places, events, and organizations) with semantically inconsistent alternatives. This was done using CLIP-based similarity matching to find closely related news content, making the fake samples appear plausible. The dataset was also refined by removing non-news videos, filtering out long content, and eliminating near-duplicates through clustering of video titles. \n\nThen they train the Qwen2.5-VL model in three stages, tuning LORA layers using long-chain-of-thought (long-CoT) instruction tuning (85k samples), DPO tuning on 5k samples, and then GRPO training with Accuracy formate, reasoning, and entity reward while making the reward verifiable by which the authors mean that reasoning traces are only useful if model made a correction decision about the video being fake or real.", "questions": "What is the input resolution of evaluation videos? What is the training input resolution for your model? Are all the models in Table 2 trained and tested on the same resolutions?\n\nWhat is the generalization trade-off of the Qwen2.5-VL model? How much does Qwen2.5-VL lose in terms of generalization after tuning on the proposed dataset?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "The tackled problem is significant. The paper is well-written and organized. The generated data pipeline is clearly described and reproducible, and so are the training details. The results are favorably better than baseline Qwen 2.5-VL and similar approaches on three benchmarks/datasets (FakeSV, FakeTT, FakeVV).\n\nThe originality of this work mainly lies in creating a large-scale dataset and fine-grained reward modifications by invoking semantic consistency  (that is, reasoning matters when the model is accurate) during GRPO training. The paper is not technically novel as it uses existing approaches to train the model on the proposed dataset.", "was_revised": true, "final_justification": "My main concerns, especially regarding generalization, have been addressed by the rebuttal. Considering this, I have improved my rating.", "rebuttal": "Thank you very much for your thoughtful and constructive feedback. Your insightful comments have been extremely valuable in helping us improve our work, and we have carefully addressed them in the revised version. Below, we restate each of your comments followed by our point by point responses. We are deeply grateful for the valuable time and thoughtful effort you devoted to reviewing our paper.\n\n>**Q1:** What is the input resolution of evaluation videos? What is the training input resolution for your model?\n\n**A:** Thank you for your question. Unlike the approach of forcing all inputs to a fixed resolution (e.g., 224×224), Fact‑R1, like Qwen2.5‑VL, uses a dynamic resolution handling mechanism. This allows us to process video frames at their original resolution and map them into visual patch tokens without the need for forced resizing. This design helps retain the spatial proportions and contextual information better during encoding.\n- For each video, we sample key frames as input, following Qwen‑VL's default video processing strategy.\n- The sampled frames are input at their original resolution and are dynamically mapped into patch tokens by Qwen2.5‑VL.\n- We do not resize the frames to 224×224, thus maintaining the strategy consistent with the pre-training configuration of Qwen2.5‑VL.This approach avoids the distortion caused by fixed resizing and aligns with the pre-training configuration of Qwen2.5‑VL.\n\nTo summarize, Fact‑R1 uses original resolution for input during both training and evaluation, and the dynamic resolution handling ensures consistency with Qwen 2.5‑VL's pre-training setup.\n\n>**Q2:** Are all the models in Table 2 trained and tested on the same resolutions?\n\n**A:** (1) We sincerely thank the reviewer for their valuable suggestions. Except for the closed-source MLLMs and open-source MLLMs, we performed fine-tuning on all other baselines using the same training set, which includes portions of FakeSV, FakeTT, and FakeVV. The input resolution for both the training and testing datasets remains consistent with the original news video. During the training and testing process, we followed the appropriate visual encoder and cropping strategies for each baseline to ensure optimal performance.\n\n(2) Thank you for your insightful comments. We have added fine-tuning experiments with open-source MLLMs, including Qwen2.5-VL-7B and InternVL2.5-8B, using label data for the answers. Due to practical application scenarios and computational resource constraints, we have not yet conducted full fine-tuning on large-scale models.\n\n| Dataset | Method              | Acc   | F1    |\n| :-----: | :-----------------: | :---: | :---: |\n| FakeSV | Qwen2.5-VL-7B-SFT              | 71.8  | 72.1  |\n| | InternVL2.5-8B-SFT         | 70.1  | 66.1  |\n|  | Fact-R1  | 75.6  | 74.7  |\n| FakeTT  | Qwen2.5-VL-7B-SFT              | 68.8  | 66.4  |\n|   | InternVL2.5-8B-SFT         | 67.7  | 68.0  |\n|  | Fact-R1  | 74.4  | 72.7  |\n| FakeVV | Qwen2.5-VL-7B-SFT              | 75.4  | 73.3  |\n|  | InternVL2.5-8B-SFT         | 73.5  | 73.2  |\n|  | Fact-R1  | 81.2  | 80.3  |\n\nAs shown in the table, fine-tuning Qwen2.5-VL-7B achieved impressive performance, further validating its suitability as the backbone for Fact‑R1. The best performance of Fact‑R1 demonstrates the importance of incorporating deep reasoning in misinformation detection.\n\n>**Q3:** What is the generalization trade-off of the Qwen2.5-VL model?\n\n**A:** (1) Thank you for your question. The strong performance of Fact‑R1 is largely attributed to Qwen2.5‑VL, a model that introduces extensive reasoning-focused data during its pre-training phase, making it highly suitable as the backbone for deep reasoning tasks. Several studies have shown that Qwen-based models excel in multi-step reasoning and complex multimodal understanding tasks, which further validates the choice of Qwen2.5‑VL as the backbone for our work.\n\n(2) To analyze the trade-offs in its generalization ability, we conducted ablation experiments using different open-source Multimodal Large Language Models, including InternVL2.5‑8B and LLaVA‑Video‑7B [9]. \n\n| Dataset | Method              | Acc   | F1    |\n| :-----: | :-----------------: | :---: | :---: |\n| FakeSV | LLaVA-Video-7B              | 72.4  | 70.9  |\n| | InternVL2.5-8B         | 72.1  | 71.7  |\n| | Fact-R1  | 75.6  | 74.7  |\n| FakeTT  | LLaVA-Video-7B              | 69.0  | 68.3  |\n|  | InternVL2.5-8B         | 72.0  | 71.8  |\n|  | Fact-R1  | 74.4  | 72.7  |\n| FakeVV | LLaVA-Video-7B              | 74.9  | 73.8  |\n| | InternVL2.5-8B         | 77.7  | 76.4  |\n|  | Fact-R1  | 81.2  | 80.3  |\n\n\nAs shown in the table, the results indicate that when Qwen2.5‑VL is used as the backbone, Fact‑R1 achieves the best performance in terms of prediction accuracy, demonstrating the model's excellent chain-of-thought reasoning quality and factual generalization ability.\n\n>**Q4:** How much does Qwen2.5-VL lose in terms of generalization after tuning on the proposed dataset?\n\n**A:** We sincerely thank the reviewer for their valuable feedback. We evaluated the generalization performance of Fact‑R1 after fine-tuning on our dataset using several widely adopted open-source benchmarks.\n- OCRBench [1] evaluates optical character recognition and visual-text understanding.\n- HallusionBench [2] measures model hallucination and factual consistency in multi-modal reasoning.\n- MMMU_VAL [3], MMStar [4], and MMVet [5] are multimodal evaluation benchmarks covering reasoning, commonsense, and world knowledge.\n- AI2D [6] and MathVista [7] assess diagram and math-related reasoning.\n- MME [8] is a comprehensive multi-modal evaluation benchmark across multiple tasks.\n\nAll evaluations follow the OpenCompass framework to ensure fairness and reproducibility. \n\n| Dataset| Method| Score  |\n| :---: | :--: | :--: |\n| OCRBench | Qwen2.5-VL-7B   | 888    |\n|   | Fact-R1|875|\n| HallusionBench| Qwen2.5-VL-7B   | 51.9   |\n| | Fact-R1| 48.8|\n| MMMU_VAL| Qwen2.5-VL-7B| 58|\n| | Fact-R1| 54|\n| MMStar        | Qwen2.5-VL-7B   | 64.1   |\n|               | Fact-R1         | 62.6   |\n| MMVet         | Qwen2.5-VL-7B   | 69.7   |\n|               | Fact-R1         | 62.9  |\n| AI2D          | Qwen2.5-VL-7B   | 84.3   |\n|               | Fact-R1         | 83.0   |\n| Mathvista     | Qwen2.5-VL-7B   | 68.1   |\n|               | Fact-R1         | 62.9   |\n| MME           | Qwen2.5-VL-7B   | 2312.1 |\n|               | Fact-R1         | 2209.3 |\n\nAs shown in the table, Fact-R1 retains most of Qwen2.5-VL's original capabilities, with only a small drop in overall performance due to the lightweight nature of our training pipeline.\nNotably, Fact-R1 performs well on HallusionBench and OCRBench, which is consistent with the additional reasoning-related and OCR-related data introduced in the DPO and GRPO stages. This indicates that our fine-tuning successfully preserved capabilities closely related to misinformation reasoning, while sacrificing performance on tasks like MathVista and MMVet, which are less relevant to our target application.\nWe will include a more comprehensive analysis of these results and release the evaluation code in the final supplementary material for community verification.\n\n>**Q5:** The paper is not technically novel as it uses existing approaches to train the model on the proposed dataset.\n\n**A:** Thank you for recognizing the Fact‑R1 pipeline and providing a clear summary of our work. Fact‑R1 is not a simple application of GRPO to a classification task, but rather a systematic exploration of video misinformation detection, incorporating several key innovations:\n\n- **Task‑specific verifiable rewards.** Unlike [10], which adopts uniform classification rewards, Fact‑R1 introduces entity recognition rewards ($R_{\\text{entity}}$) and reasoning keyword rewards ($R_{\\text{word}}$). A judge model is employed to simulate human auditors, guiding the model to produce reasoning chains that are both accurate and interpretable.\n\n- **Auxiliary tasks for multimodal enhancement.** Motivated by failure case analysis and inspired by the reasoning process in Figure 20, we first incorporate News Image OCR and News Video Caption generation as auxiliary tasks, designing dedicated rewards for each. These tasks strengthen the model’s ability to understand multimodal information and handle entity‑level reasoning in manipulated content.\n\n- **Progressive reinforcement learning framework.** The three‑stage pipeline (Long‑CoT → DPO → GRPO) is carefully designed to combine explainable data construction, preference alignment, and task‑specific reward optimization. Each stage progressively improves reasoning quality, factual consistency, and interpretability.\n\nOverall, Fact‑R1 contributes a large‑scale, finely annotated dataset (FakeVV) together with a task‑specialized reinforcement learning framework. These innovations enable Fact‑R1 to achieve strong predictive performance while generating verifiable, human‑aligned reasoning chains, advancing practical and explainable video misinformation detection.\n\n[1] Liu et al. Ocrbench: on the hidden mystery of ocr in large multimodal models.\n\n[2] Guan et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.\n\n[3] Yue et al. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi.\n\n[4] Chen et al. Are we on the right way for evaluating large vision-language models?\n\n[5] Yu et al. Mm-vet: Evaluating large multimodal models for integrated capabilities.\n\n[6] Kembhavi et al. A diagram is worth a dozen images.\n\n[7] Lu et al. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts.\n\n[8] Fu et al. Mme-survey: A comprehensive survey on evaluation of multimodal llms.\n\n[9] Canhui Tang. Open llava-video-r1. Open-LLaVA-Video-R1, 2025.\n\n[10] Liu et al. Visual-rft: Visual reinforcement fine-tuning."}]

Author rebuttal:

Meta-review: The manuscript investigates detecting video misinformation, proposing a reasoning model and a large-scale benchmark comprising over 100k video-text pairs. The dataset is constructed by sourcing videos from from the web (e.g., news channels), whereas the proposed model leverages a three-stage pipeline.  The manuscript received ratings of three accept and one borderline reject. Reviewers appreciated the proposed dataset, extensive ablation studies, and performance of the proposed model against the baseline. Reviewers also raised several questions about dataset construction and ablation studies along with one reviewer raisning ethical concerns. While the ethics reviewers acknowledged some of these concerns, they generally consider these concerns to be addressable suggesting recommendations including, providing more details (Ethics Reviewer dQpE), addressing discrepancies in the checklist along with moving limitations (Appendix F) and societal impacts (Appendix G) to the main paper (Ethics Reviewer P7BD), and discussing potential dataset bias in more details (Ethics Reviewer uGv2). Given that three reviewers are positive about the manuscript, authors response raised in the initial review, and overall reviews, the recommendation is accept. Authors are strongly encouraged to take into account all the recommendations of the reviewers in the final manuscript.
Decision: Accept (poster)