Title: Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Video Misinformation Detection

Deep Reasoning
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

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.

1 Introduction

Platforms such as TikTok and YouTube have significantly reshaped the landscape of news consumption by enabling the rapid and large-scale dissemination of 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 to keep pace with the overwhelming volume and velocity of content, automated approaches have become increasingly essential for timely identification and mitigation of misinformation on 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 new challenges. Video-text misinformation is not only more engaging and fast-spreading but also harder to detect due to the complexity of heterogeneous 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 a few thousand videos collected over narrow timeframes

News Tittle:

now the Fate of tiktok in North America could be decided this

week in a decision due today a federal appeals Court is set to

say whether or not they uphold an appeal by tiktok over a US

law which demands the social media app is sold by its Chinese

parent company bite dance so what can we expect today that's

what I put to social media expert Jasmine engberg earlier it's

not looking good for tiktok right now given the difficult day

that it had in court in September and as you mentioned we ...

Decision in TikTok appeal against EU law which could ban app is due.

Fact-R1

<think>The video description indicates that it features a news segment from BBC News about the upcoming decision in the TikTok appeal against a US law that

could lead to the app\'s ban. The video shows scenes from TikTok, a reporter discussing the implications. The discussion centers around the potential ban in the

US. However, the news title, "Decision in TikTok appeal against EU law which could ban app is due," mentions the EU law, which is inconsistent with the

content of the video description. The video description clearly states that the discussion and the decision are about a US law, not an EU law.\n\n

Thus

, the news title

does not accurately reflect the content of the video description as it misleadingly refers to EU law instead of the US law discussed in the video.

In conclusion

, the event

discussed in the news title does not match the specific focus of the video description.\n</think>\n<answer>

Yes.

</answer>

Human

The news title is '

Decision in TikTok appeal against EU law

which could ban app is due.'

, the news audio is 'now the Fate of

tiktok in North America could be decided this week in a decision

due today a federal appeals Court is set to say whether or not they

uphold an appeal …', is this multimodal content manipulated?

GPT-4o

The images depict various users engaging with TikTok content-

showing both individuals using the app and scenes that presumably

relate to TikTok culture.

There are no obvious technical alterations

or digital manipulations presented in the content that would

imply deceit or misinformation

…Instead, it appears to visually

support the news topic by highlighting TikTok features and user

engagement, ... The answer is

No

.

QwenVL

<answer>

Yes

.</answer>

[21, 30]; (2) Lack of fine-grained 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 a 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 [23, 33].

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 1, 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 chain-of-thought processes, and efficient knowledge utilization [50], yet 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-thought capabilities tailored to the complexities of misinformation detection-a critical yet challenging direction.

To address these challenges, we introduce FakeVV, 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 and replace entities of four key types-persons, locations, events, and organizations-to fabricate realistic fake videos. Each fabricated sample is annotated with the manipulated entity, thereby supporting fine-grained reasoning supervision and interpretable evaluation.

Building upon the FakeVV dataset, we further propose Fact-R1, the first multimodal misinformation detection framework that integrates deep reasoning with collaborativ rule-based reinforcement learning. Fact-R1 is trained via a three-stage optimization pipeline tailored for misinformation scenarios: (1) misinformation Long Chain-of-Thought Instruction Tuning: We construct 85K CoT training samples using DeepSeek-R1 [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 hallucinations, 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 to evaluate both reasoning trajectories and entity-level correctness over 15K new samples and 5K auxiliary-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 to explore diverse and verifiable reasoning strategies beyond surface-level patterns and adaptively optimize for misinformation detection tasks.

Overall, our main contributions can be summarized as follows: (1) We construct FakeVV, the largest and most comprehensively annotated news-domain video misinformation dataset. It features highquality, 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 reinforcement learning. Fact-R1 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 a 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] leveraged audio and frame watermarks for cross-channel manipulation detection, while You et al. [44] proposed a 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 et al. [22], Hou et al. [15], and Shang et al. [30] collected data on multilingual news, prostate cancer misinformation, and COVID-19-related 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 FakeTT [6] address some of these issues but still face challenges in data diversity, temporal range, and interpretability-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 LLMs. The rapid progress of large language models (LLMs), such as OpenAI o1 [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 neuro-symbolic 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-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.

(a) Word cloud of video titles.

(b) Distribution of FakeVV over years.

Bidenmire

DonaldaTrump

woman

Iran

Boris_Johnson

man

10543

family

strike

report

deadly

Ukraine

case

Inside

Pu

hitluren

tep

react.protest

peopl

e

fight

gun

ChaldreiLmervlee

ussian

R

Covid

plan

Gaza

Leave

GOP

NN

father

year

ISIS

take

ection

win

①esplain

killedWi1l

dld

UK

speech

plan

break.

warn

back...

police

Former

Sm

video

claim

day

See

women

face

ki11

year

talk

death

eveal

moment

Russla

attack

tar

show

state

one

amid

make

die

hespita1

wantLn

New

Syria

first

NewYork

tell

time

Aa12

vote

world

homed

life

China

American

Number of Entries

10000

8000

6000

4000

2000

21

772

823

1499

1438

3253

5866.

8778

8694

8308

0M

5203

7F34

7919

9585

2217

Year

3 FakeVV Dataset Construction

In this section, we provide a detailed description of the construction process of the FakeVV dataset, as shown in Figure 3, which consists of the following components.

Data Collection. To ensure diversity and relevance, we collect the most popular videos from four official news accounts-BBC 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-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 conventional 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 captions help large language models (LLMs) achieve a deeper understanding of video news, supporting the generation of higher-quality reasoning chains. Our approach builds on Qwen-VL's [3] video processing strategy by first extracting 16 keyframes from each news video and identifying visual entities using the Google Vision API. We then employ GPT-4o [1] to generate instruction-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 misinformation samples, we propose a non-random entity matching strategy designed to introduce challenging yet realistic perturbations. Specifically, we generate four types of forged news samples by replacing different types of entities associated with similar news content: person-name inconsistency, location inconsistency, event inconsistency, and organization-name inconsistency.

Each news sample is represented as a 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 as a query, and based on the cosine similarity of the representations, the top-3 most semantically similar candidate samples are retrieved from the repository to serve as potential sources for entity substitution. The retrieval

process randomly selects one of the following five strategies: (1) Visual-to-Visual retrieval; (2) Visualto-Textual retrieval; (3) Textual-to-Visual retrieval; (4) Textual-to-Textual 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 generalization to unseen data.

Data Analysis. We present a comprehensive comparison between the FakeVV dataset and existing video misinformation detection datasets, as summarized in Table 1. FakeVV 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 FakeVV are derived from authentic videos, avoiding artifacts commonly introduced by web-scraped data. Figure 2 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, FakeVV spans from November 2006 to 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-R1 consists of three sequential stages designed to progressively endow the model with the ability to handle complex misinformation detection tasks.

4.1 Long-CoT Instruction Tuning

Existing open-source MLLMs exhibit limited reasoning capabilities in the misinformation domain. To address this, we construct a misinformation long-Chain-of-Thought (long-CoT) instruction tuning dataset from the FakeVV corpus and fine-tune an open-source MLLM to inject domain-specific reasoning knowledge and long-CoT capability, providing a foundation for subsequent high-level reasoning tasks. As shown in Figure 3, 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-R1. For each instance, the video title, caption, and audio are provided to DeepSeek-R1, which assesses the veracity and generates a 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-CoT 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) on a curated set of 5k human-annotated preference pairs. These samples specifically target common failure cases, such as inaccurate answers, hallucinated fake entity descriptions, and commonsense inconsistencies. Each pair consists of a human-preferred output y label and a suboptimal model-generated response y pred, 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 y label without requiring an explicit reward model. The loss is defined as:

Data Collection

120,000 raw data

Misinformation Data Construction

FakeVV Dataset Construction Process

Fact-R1 Pipeline

Preference Alignment via DPO

Data Filtering

Data Processing

Filtering target:

•

Noise Information

•

Duplicate data

•

Video length within 5 minutes

Sources

：

Retain high-quality

news samples

GPT-4o

News Database

Top k Similar

News Titles

Video news

Long-CoT Instruction Tuning

Designed

Prompt

Retrieve

GPT-4o

•

Fake entity

•

Forged type

GRPO with Verifiable Reward Function

Video Frames

News Title,

Visual Entity

News-domain

Video Caption

Designed

Prompt

News Title

Interpretability

Four types of

entity replacing

Forged News

(a) Misinformation Long-cot Generation

DeepSeek-R1

News-domain

Video Caption

Judge Fake

or Real

News Audio

Label

Explainability

<think>… <think>

<answer>…<answer>

Designed

Prompt

(b) Thinking SFT

Fact-R1

Human Correct

Long-cot

Prediction

Raw

Prediction

Correct

Prediction

Misinformation

Long-cot Data

News title:

The news title is 'British man captur-

ed while fighting with Russia.', the

news audio is 'British foreign..' is this

multimodal content manipulated?

LoRA

Projector

Visual Encoder

Embedding

<think>\nThe video…

News Title

(a) Human Preference Data Generation

Fact-R1

Generation:

𝑌 0

𝒀1

<think> The video depicts the well-

documented event of Trump supporters

storming the Capitol in

Washington

,

D.C., …</think> <answer> Yes</answer>

<think> The video shows Trump supporters

storming..., The entity in question is

Trump

the event is directly linked to his supporters,

</think><answer> Yes. </answer>

0.3

0.8

Maximum likelihood

Fact-R1

News Title

Normalized Verifiable Reward

𝑌 1

Response

𝑌 2

Response

𝑌 𝑛

Response

…

Policy

Optimization

Format Reward

Reasoning Keywords

Reward

Entity Grounding

Reward

𝑌 𝑛

Response

Judge model

Overall Reward:

News Tittle

YouTube

NEWS

BBC

CNN

The

Guardian

X

Main Task

Auxiliary Task

Misinformation Detection

News Video Caption

News Image OCR

News Video

News Audio

British foreign office is investigating reports that are former soldier fighting for Ukraine has been captured by Russian forces in the Curse region a video is circulating online which shows a

man dressed in military uniform by correspondence Simon Jones reports in that video he identifies himself as James Scott Rhys Anderson he says he's 22 years old and he says that he was

part of the British army for 4 years before being sacked he then apply to join the international Legion now that's part of the Ukrainian army made up of foreigners now he said he travelled to

Poland to get himself to Ukraine now tonight we've had a report from the news agency task the Russian news agency saying what it cause a British mercenary has been captured in…

Question:

The news title is 'British man captured while fighting

with Russia.', the news audio is 'British foreign office is

investigating reports that are…', is this multimodal content

manipulated? Output the thinking process in <think> </think> and final

answer in <answer> </answer>

'

Reasoning

:

<think>\

nThe video description details the capture

of a British man who was fighting for Ukraine and has since been

detained by Russian forces. The video shows him in military attire, sitting

against a plain background, and a news correspondent in a studio setting

providing further context about his situation. The narrative focuses on the

man\'s capture and detention by Russian forces, clearly indicating his

involvement in fighting for Ukraine.\n\nThe news title, "British man captured

while fighting with Russia," inaccurately represents the content of the video

description... It suggests that the man was fighting alongside Russian forces,

which contradicts the video description stating he was fighting for Ukraine

and was captured by Russian forces. Therefore, the title is misleading and

does not accurately reflect the content of the video description.\n\nIn

conclusion, the news title is inconsistent with the video description, as it

falsely implies the man was fighting with Russia when he was actually

fighting for Ukraine.\n

</think>\

n

<answer>

Yes

.</answer>

Question:

What is the text shown in the first image? Output the thinking process in <think>

</think> and final answer in <answer> </answer>

Reasoning

:

<think>

There is a BBC News header on the top left corner. At the bottom of the image, there is

a news headline in a red ticker bar. The headline states the main news story\n

</think>\

n

<answer>

British man fighting for Ukraine captured by Russia

.</answer>

Question:

Please describe this video from a news perspective. Output the thinking process in

<think> </think> and final answer in <answer> </answer>

Reasoning

:

<think>

The main objects in the video are the British man and the news correspondents. The

British man is dressed in a green military uniform and is seated. The news correspondents are speaking in a news

studio environment. The background setting includes a dark room for the footage of the captured man and a typical

news studio with screens and equipment in the background during the correspondent segments. The key actions involve

the captured British man sitting and speaking, and the news correspondents discussing the situation in the studio.

.

\n

</think>\

n

<answer>

The video shows a British man, wearing a green military uniform, seated in a dark room. He appears to be

speaking, though it is unclear what he is saying. The environment shifts to a news studio where correspondents are

explaining the situation. One correspondent, dressed in a suit, discusses the capture, providing details and context.

Another correspondent stands in front of a background filled with screens and equipment, typical of a news studio. The

focus remains on the report about the British man fighting for Ukraine who was captured by Russia

.  </answer>

000

Lme/

Lme/

sisa sa fyhcay ter Clenint aptemd ly ln

rmg  popy hoy e ee

Mosdh mas fighting fir Ulosite capfe

OCR

1

where r ( y ) denotes the reward computed from the log-ratio of likelihoods under the current policy and a frozen reference policy. This preference-based alignment encourages Fact-R1 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 rewardguided optimization, without reliance on supervised instruction.

61.1

67.3

62.7

58.8

56.4

76.4

20

30

40

50

60

70

80

90

100

Accuracy (%)

63.5

71.5

81.9

88.7

0

10

20

30

40

50

60

70

80

90

100

Event

Organization

Location

Person

Person

Location

Organization

Event

Group Relative Policy Optimization (GRPO). We adopt GRPO [32] to fine-tune the policy network without requiring an explicit value function or critic model. GRPO estimates relative output quality by comparing a group of candidate responses generated under the current policy. Given an input, the model samples a set of responses { y 1 , y 2 , . . . , y G } , each assigned a reward based on correctness and alignment with human preferences. The normalized advantage for each response y i is computed as:

This normalization mitigates reward variance and stabilizes training. The policy is optimized to maximize the importance-weighted advantage while regularizing deviation from a reference model:

Here, π θ is the current policy, π old the sampling policy, and π ref 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:

Accuracy Reward ( R acc): Measures whether the model's predicted class matches the ground truth.

Format Reward ( R format): 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 ( R word): Rewards reasoning traces that contain key reflective terms such as 'First', 'However', or 'In conclusion'.

Entity Grounding Reward ( R entity): 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 e ∗ :

Finally, the overall verifiable reward function R is defined as:

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-textual alignment, thereby improving its perception and understanding of manipulated content. The reward function for each auxiliary task consists of two components: Accuracy Reward and Format Reward, weighted at 0 . 9 and 0 . 1 , respectively. The accuracy reward R acc is computed using task-specific evaluation metrics:

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 to the [0 , 1] range to 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

Dataset. For evaluation, we use the FakeVV testing set along with two widely adopted benchmarks. FakeSV [24] is a large-scale Chinese short video dataset for fake news detection, enriched with social context features such as user comments and publisher metadata; video titles are translated into English for compatibility. FakeTT [6] is an English-language dataset from TikTok, covering a wide range of misinformation topics. Following [6], we adopt a temporal split by using the most recent 15% of samples from FakeSV and FakeTT as testing sets. All baseline models are trained on the FakeVV, FakeTT, and FakeSV 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 a comprehensive view of classification effectiveness. Baselines. We compare Fact-R1 against 15 competitive baselines in four groups: (1) single-modal methods including BERT [10]; (2) multimodal methods including TikTec [30], FANVM [9], SVFEND [24], and FakingRec [6]; (3) closed-source MLLMs including Gemini2-thinking [35], GPT-4o [1], and GPT-o1-mini [16]; (4) open-source MLLMs including Qwen2.5-VL [3], InternVL2.5 [8], QVQ-72B [36], InternVL2.5-MPO [39] and DeepSeek-R1 [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-o1-mini), 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 PyTorch on 8 × NVIDIA A100 GPUs,

with Qwen2.5-VL as the base MLLM and the default 8-frame sampling strategy. Stage 1 (Long-CoT Tuning). We freeze the visual encoder and apply LoRA ( r = 128 , α = 256 ), using learning rates of 2 × 10 -5 (LoRA) and 2 × 10 -6 (multimodal projector). Training is performed for 2 epochs with batch size 2, using AdamW optimizer. Stage 2 (DPO). We train for 1 epoch on 5k human preference pairs with batch size 4 and a learning rate of 2 × 10 -5 . The reference model is frozen, and gradient clipping is applied with a max norm of 1.0. Stage 3 (GRPO). We train for 172 steps using the verifiable reward function, sampling 5 candidate responses per input and applying a learning rate of 1 × 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 Fact-R1 achieves the best overall performance in video misinformation detection. Among baselines, BERT performs well, suggesting the strength of textual signals, while FakingRec benefits from modeling video production semantics. SV-FEND also performs 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-R1 and GPT-o1-mini show strong results, highlighting the power of high-level reasoning. Among open-source models, QVQ-72B leads, while InternVL2.5-MPO and Qwen2.5-VL-7B show promising but varied performance. In contrast, InternVL2.5-8B 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-oriented training. These results demonstrate that Fact-R1's 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 significantly hinders the development of trustworthy misinformation detection models. During evaluation, we retain both the reasoning traces generated by each model and the ground-truth manipulated entity (i.e., 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, we 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 in Table 3. w/o SFT denotes Fact-R1 without misinformation long-Chainof-Thought (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 a crucial 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.

6 Conclusion

In this work, we address the underexplored challenge of video misinformation detection by introducing FakeVV, a large-scale, diverse benchmark with fine-grained and interpretable annotations. To

effectively leverage this resource, we propose Fact-R1, a novel multimodal framework that unifies deep reasoning with collaborative rule-based reinforcement learning. Fact-R1 is trained through a three-stage pipeline, enabling the model to generate structured, explainable reasoning traces. Our approach not only advances the state of the art in multimodal misinformation detection, but also establishes a scalable, interpretable paradigm for aligning large multimodal models with complex real-world reasoning tasks. Comprehensive experiments validate the effectiveness of Fact-R1.[4]

7 Limitations

In this work, we propose Fact-R1, a deep reasoning model for video misinformation detection, which achieves state-of-the-art 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. Misclassifying authentic content as misinformation can suppress legitimate discourse and harm the reputations or economic viability of content creators. Conversely, undetected misinformation-especially when reinforced by flawed but confident reasoning-may 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.

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 as an 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 predictions-especially when accompanied by seemingly coherent but factually incorrect reasoning-could lead to misclassification of legitimate content or 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 WK2100000057.

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Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

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c

You are a video describer tasked with

providing a concise and accurate

description ofa news video. Use the

provided title and video image entity

as contextual referencesto support

your description.

# Entity: {entity}

# Instructions:1. Briefly describe the

main objects and their visual

characteristics as shown in thevideo.

2. Summarize the environment and

background setting…

❑

Task Definition

❑

Supporting Information

⚫

News Tittle

Biden waits in line and votes in Delaware.

Descriptive Accuracy

Fluency

In the video, President Joe Biden is seen outside on a sunny day,

wearing a dark suit with a white dress shirt and a red tie. His

appearance is formal and his suit lapel features a small American

flag pin. The setting appears to be a parking lot with several black

SUVs parked in the background, and there are a few trees with

autumn foliage visible. In the foreground, Biden gestures with his

hands while speaking. Around him, other people are present,

including one person wearing a dark suit and standing near one

of the vehicles. The background shifts to a news studio where

anchors discuss the events, before cutting back to Biden engaging

with those around him as he prepares to vote in Delaware.

⚫

Entity Extraction

Biden, Car, American Flag, CNN, etc.

…

poncs

Three found alive and four bodies recovered

after tourist boat capsizes in Red Sea.

That Time the Mediterranean

Sea Disappeared

Sea levels rose more than

expected in 2024

Antarctica's Weddell Sea

'deserves protected status

Query News Image

News Tittle

CLIP

Top Three Image

Similarity Scores

Designed

Prompt

Retrieved News Image

Retrieved News Image

Retrieved News Image

News Tittle

News Tittle

News Tittle

Rewrite

Three found alive and four bodies recovered after

tourist boat capsizes in Mediterranean Sea.

News Video

Fake News Tittle

Tourist boat sinks

Fourth body found after Egypt boat capsines

1211

(SPAIN)

BeneathAntarctica

The fightforMeWeddellSea

In 2011, paleontologists working on the Spanish

Rising sea levels

S

A Detailed Experimental Settings

A.1 Datasets

To evaluate the performance of our proposed framework Fact-R1 and several baseline models, we conduct experiments on three real-world short video misinformation datasets: FakeSV, FakeTT, and FakeVV. The statistics and key characteristics of these datasets are summarized in Table 1. Following the original setup in [24], we adopt a chronological split with a 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 are as 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.

FakeTT : 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.

FakeVV : 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.

(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.

FANVM [9]: A topic-agnostic fake news video detection model based on 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.

SV-FEND [24]: A multimodal approach for short video fake news detection that selects informative features via cross-modal correlation analysis and integrates social context using

News Tittle

Query  News Image

CLIP

Top Three Image-Text

Similarity Scores

Designed

Prompt

Rewrite

Biden waits in line and votes in California

News Video

Fake News Tittle

Biden waits in line and

votes in Delaware.

Biden delivers remarks in

California after deadly storms

and flooding

Watch the uncensored moment

on stage at the Oscars,

Biden falls at Air Force

Academy graduation ceremony

Retrieved  News Tittle

Retrieved  News Tittle

Retrieved  News Tittle

News Image

News Image

News Image

0p

made move to dismiss Trump's case

Query News Tittle

News Image

CLIP

Top Three Text-Image

Similarity Scores

Chaos in Washington as Trump

supporters storm Capitol and

force lockdown of Congress

President Putin announces

'Easter truce' in Ukraine until

end of Sunday

Commons for calling David

Cameron "dodgy Dave"

Retrieved News Image

Retrieved News Image

Retrieved News Image

News Tittle

News Tittle

News Tittle

Designed

Prompt

Rewrite

Honig explains why Jack Smith made

move to dismiss Putin's case

News Video

Fake News Tittle

US Capitol

UPCACLNELREOUEST

CRSE AUST GAANTED SAETH

BREAKNDININT

ONIRERON

CN

CN

Prosecutor drops federal criminal

cases against Trump.

News Image

Query News Tittle

CLIP

Top Three Text

Similarity Scores

Chaos in Washington as Trump

supporters storm Capitol and

force lockdown of Congress

exchange with veterans

US prosecutors dismiss election

interference, classified documents

cases against Trump

Retrieved  News Tittle

Retrieved  News Tittle

Retrieved  News Tittle

News Image

News Image

News Image

Designed

Prompt

Rewrite

Prosecutor drops federal

criminal cases against Biden

News Video

Fake News Tittle

Wastington

WHAT HAPPENSINAFEDERA'

CRIMNALCASE?

Prosecutors move to drop cases against Trump

co-attention and self-attention mechanisms. It captures inconsistencies between text, visual, and audio modalities to improve detection accuracy.

FakingRec [6]: A short video misinformation detection method focusing on the "creative process" behind fake content. It employs a 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]: A state-of-the-art closed-source multimodal large language model from OpenAI, supporting visual and text inputs, known for its strong reasoning and generation capabilities.

Gemini2-thinking [35]: A multimodal large language model developed by Google DeepMind, designed for advanced reasoning tasks across text and vision modalities.

GPT-o1-mini [16]: A smaller-scale version of GPT-o1, optimized for reasoning and generation tasks with strong performance on text-based 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) Open-source MLLMs :

Qwen2.5-VL [3]: An open-source multimodal large language model supporting image and text inputs, with competitive performance on vision-language understanding benchmarks.

QVQ-72B-preview [36]: A large-scale open-source vision-language model designed for visual reasoning tasks, preview version with 72 billion parameters.

InternVL2.5 [8]: A vision-language model optimized for cross-modal understanding, supporting fine-grained visual reasoning tasks.

InternVL2.5-78B-MPO [39]: A scaled-up version of InternVL2.5 with 78 billion parameters, incorporating multimodal pre-training and optimization strategies for enhanced reasoning.

A.3 Implementation Details

All experiments are conducted using PyTorch on 8 × NVIDIA A100 GPUs, with Qwen2.5-VL as the base multimodal large language model (MLLM) and the default 8-frame sampling strategy for video inputs.

Stage 1 (Long-CoT Tuning). Weapply LoRA for efficient fine-tuning and the LoRA hyperparameters are set as lora_r =128 and lora_alpha =256, with the learning rate for the mm_projector set to 2 × 10 -6 . The visual encoder remains frozen throughout this stage. LoRA fine-tuning is performed with a learning rate of 2 × 10 -5 and a batch size of 2. The model is trained for two epochs using the AdamW optimizer with a cosine annealing learning rate scheduler. This stage uses 85k highquality instances generated from the Misinformation Long-CoT Generation process, derived from the FakeVV 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 FakeVV training set, using a batch size of 4 and a learning rate of 2 × 10 -5 . The reference model is kept frozen, and gradient clipping is applied with a maximum norm of 1.0. We adopt the AdamW optimizer with a linear learning rate scheduler and warm-up over the first 500 steps. The DPO scaling factor β is set to 0.1. Training is conducted with mixed precision (fp16) for improved efficiency.

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 1 × 10 -6 . Following the GRPO framework [32], rewards are 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-weighted advantages. Policy updates are performed with clipped advantage weighting and KL regularization against a frozen reference model to ensure training stability.

This stage utilizes 15k news samples for the main task, including training data from the FakeTT 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 to jointly guide the optimization.

K

an Amba

BlobsThat ClosedAustralian

BeachesWereMadeof

and

ScientistsSay

CW

CNN

ND

ND

CW

Kim Kardashian joins Roy Wood Jr. and his panel to play 'Lie-curious.

(b) Person-type manipulation.

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-accuracy-centred for OCR and captioning, and multi-component for misinformation detection (accuracy, format, key-word usage, and entity grounding). The rewards are 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 a critical role in both the Misinformation Long-CoT Generation and the GRPO with Verifiable Reward Function stages. Unlike conventional video captions, these captions incorporate specific news-related entities, enabling large language models to 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 as 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 news image. Post-generation, we perform rigorous post-processing and validation to ensure the accuracy of the captions, confirming that the descriptions faithfully reflect the visual content and support effective learning of news-related semantics.

Figure 7 illustrates the data construction process and demonstrates how our approach produces accurate, fact-grounded video descriptions.

NEW DETAILS

REALLY

REALLY

REALLY

ITELCMTECHARCHNACYBERATTACKWORSTTELECOMHACKINU.S.STORY

CN Political & National Security Analyst

NEWSROOM

CW

NEWDETAILS

INTELCMTECHAR:CHNACYBERATTACKWORSTTELECOM HACXINUSHSTORY

NEW DETAILS

INTELCMTECHAIR:CHNACYBERATTACKWORSTTELECOMHACKINUS.HISTORY

CN

REALLY

REALLY

REALLY

NEW DETAILS

NTELCMTECHARCHINACYBERATTACKWORSTTELECOMHACKINUSHSTORY

ND

NEW DETAILS

NTELCMTECHAR:CHINACYBERATTACKWORSTTELECOMHACKINU.S.HSTORY

David Sanger 1OW Political & National Security Analyst

AV

NEWSROOM

ND

NEW DETAILS

INTELCMTECHAIR:CHINACYBERATTACKWORSTTELECOMHACKINUSHSTORY

CNN

(c) Location-type manipulation.

NEW TONIGHT

WAPO: TRUMP PLANS TO USE DOJ TOINVESTIGATE 2020 ELECTION

David Urban 1cw Senior Political Commentator

ND

NEWTONIGHT

WAPO:TRUMP PLANS TO USE DOJ TO INVESTIGATE 2020 ELECTION

David Urban 1 Former Trump Campaign Adviser

ND

NEW TONIGHT

WAPO:TRUMPPLANSTOUSEDOJTOINVESTIGATE2020 ELECTION

David Urban 1Former Trump Campaign Adviser

ND

NEWS

GHT

NEWSNIGHT

CW

NEW TONIGHT

WAPO: TRUMP PLANS TO USE DOJ TO INVESTIGATE 2020 ELECTION

ND

NEW TONIGHT

WAPO: TRUMP PLANS TO USE DOJ TO INVESTIGATE 2020 ELECTION

CNW

NEW TONIGHT

WAPO: TRUMP TO FIREJACK SMITH'S TEAM &

IISE DOITO INVESTIGATE THE 2O3O ELECTION

CNN

Trump is planning to use the FBI to address personal grievances.

(d) Organization-type manipulation.

C Analysis of FakeVV dataset

C.1 Analysis of Misinformation Data Construction

To generate fine-grained manipulated news samples, we propose a challenging non-random entity replacement strategy to construct semantically inconsistent fake data. The overall workflow is described as follows: each news sample is represented as a pair ( Img , Title ) , where Img denotes the first frame of the news video and Title 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 one 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 a fluent fake news title, such as 'Three found alive and four bodies recovered after tourist boat capsizes in Mediterranean Sea.'

Visual-to-Textual Retrieval : 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 a fake title like 'Biden waits in line and votes in California.'

Textual-to-Visual 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 case.'

critic/rewards/mean

0.8

0.75

0.7

0.65

0.6

Step

50

100

150

89

93

90

95

95

96

70

75

80

85

90

95

100

Accuracy (%)

GPT-40

nVL2.5-8B

en2.5-VL-7B

2.5-VL-72B

DeepSeek-R1

GPT-4o-mini

response_length/mean

230

220

210

200

Step

50

100

150

83

90

93

92

93

95

70

75

80

85

90

95

100

Accuracy (%)

GPT-40

nVL2.5-8B

en2.5-VL-7B

2.5-VL-72B

DeepSeek-R1

GPT-4o-mini

Textual-to-Textual Retrieval : As illustrated in 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 as 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 in 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

Prompt for News-domain Video Caption Generation. Figure 23 illustrates the prompt we 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.

Prompt for Explainability Evaluation (Judge Model). Figure 24 shows the prompt used to instruct GPT-4o to assess whether the model's reasoning process accurately identifies and describes the fake entity. This unified evaluation framework is used to obtain the interpretability metric for models on the FakeVV test set.

News Video

Case 1

Case 2

News Tittle:

iShowSpeed Meets DA ZHANG WEI!

News Audio

Super Idol, oh my god, Super Idol, idol sing a song,

everybody Super Idol, Super Idol, you put on the speaker, put

it on the speaker, put it on the speaker, hold on, hold on, wait,

hold on, wait, hold on, wait, we gotta recreate it, who got

water bottles, bottles, yo, Super Idol, here, hey, hey, this is a

do-all right, somebody played on the speaker, play it, Super

Idol, play Super Idol, let him in, let him in, let him in, no,…

News Video

News Tittle:

News Audio

breaking news coming into us in the last few moments because the

governor of Egypt's Red Sea region says three people have been found

alive in the rescue operation for the tourist boat which sank in the Red

Sea on Monday but… Hurley been Egypt there were 45 people on

board 31 tourists and 14 Egyptian crew members 28 people have been

rescued so far although that number changing all the time and they

were rescued further down the coast well let's go to Sal…

Three found alive and four bodies recovered after tourist boat

capsizes in Mediterranean Sea

Prediction:

"

<think>\

n The video shows a chaotic outdoor scene where

iShowSpeed interacts with an Asian man wearing a T-shirt with Chinese

characters, surrounded by a large crowd recording the moment on their phones.

The video captures unscripted, meme-like phrases repeatedly referencing

"Super Idol,"  The news title, "iShowSpeed Meets DA ZHANG WEI!," frames

the video as a formal or significant meeting between iShowSpeed and Da

However

, neither the visuals nor the audio confirm this person as

Da Zhang Wei, nor does the content focus on an actual meeting or

conversation of substance between two public figures.

Instead

, the focus is on

meme performance and crowd interaction. The title suggests an official meetup

but the video itself depicts a chaotic fan event or meme recreation, not a

formal encounter. This mismatch between the title's implication and the video's

actual content indicates manipulation through misleading framing rather than

technical editing or forgery.

.</think>\

n

<answer>Yes.</answer>

"

Prediction: "<think>\

nThe video description provides a detailed account of

a rescue operation following a capsizing incident in the Red Sea, specifically

in Hurghada, Egypt. It mentions the presence of a large white yacht, rescue

operations by uniformed personnel, and the recovery of bodies and rescue of

survivors.

Additionally

, the video includes a map showing the location of the

incident and a live report from the area.\n\nThe news title, \"Three found alive and

four bodies recovered after tourist boat capsizes in Mediterranean Sea,\" is

inconsistent with the video description. The title specifies the Mediterranean Sea,

whereas the video description clearly states that

the incident took place in the

Red Sea

. The Red Sea and the Mediterranean Sea are distinct bodies of water, and

this discrepancy suggests that the title does not accurately reflect the content of the

video description.\n\n

Therefore

, the multimodal content is manipulated as the title

provides incorrect information about the location of the

incident.\n

</think>

\n

<answer>Yes.</answer>

"

LdA53

(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 14 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 a gradual increase and eventual stabilization around a 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 a 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 MLLMs and human judgments. Specifically, we manually select 100 samples from the FakeVV test set where Fact-R1's reasoning correctly identifies the labeled fake entity (explainability-correct samples) and another 100 samples where the reasoning fails to identify the correct entity (explainability-error samples).

News Video

Case 1

Case 2

News Tittle:

News Audio

I'm not going to be that sort of Chancellor I faced a problem

and I faced into it and we've now drawn a line under the

fiction pedal by the previous government would put a public

finances back on a firm footing and we've Now set the budgets

for public services for the duration of this Parliament public

services now need to live within their means because…I will

only be doing one budget a year so that this government can

focus on what are number one priority…

News Video

News Tittle:

News Audio

you closed because of the Justice department's policy that Bans the

prosecution of a sitting President Donald Trump's team has hailed it as

a major victory let's get the very latest on this speech on senior North

America correspondent Gary O'Donoghue can join me now from

Washington Gary just took us through what has been said and what

this means we can also tell you that Jack Smith has asked Donald the

charges against Donald Trump in the other federal criminal…

Prosecutor drops federal criminal cases against Biden

Prediction:

"

<think>\

n The video description details that the UK

Chancellor of the Exchequer is speaking at an event organized by the

CBI, discussing her budget proposal. The description emphasizes her

confidence in not needing to present a budget like this again in the future. The

setting is professional and formal, indicating that the speaker is the

Chancellor.\n\nThe news title,

however

, states "UK prime minister says she

\'won\'t have to do a budget like this ever again.\'" This title incorrectly

identifies the speaker as the Prime Minister instead of the Chancellor of the

Exchequer.\n\nThis discrepancy between the video description and the news

title indicates that the title does not accurately reflect the content of the video

description. The title misleads by suggesting that the Prime Minister is the one

making the statement, whereas the actual statement is made by the

Chancellor.\n\n

Thus

, the multimodal content is manipulated because the title

does not accurately represent the content of the video

description

.</think>\

n

<answer>Yes.</answer>

"

Prediction: "<think>\

nThe video description details a live report from

Washington, focusing on the White House, with the correspondent

discussing the dismissal of federal criminal cases against Donald Trump. The

video shows Trump at a rally, emphasizing the legal developments concerning his

case.\n\n

However

, the news title, "Prosecutor drops federal criminal cases against

Biden," suggests that the focus is on Joe Biden and the dismissal of cases against

him.\n\nThere is a clear discrepancy between the video description and the news

title. The video content is about Donald Trump and the dismissal of cases against

him, whereas the title incorrectly mentions Joe Biden. This inconsistency indicates

that the title does not accurately reflect the content of the video description.\n

In

conclusion

, the title is misleading and does not match the specific focus of the

video content.\n

</think>

\n

<answer>Yes.</answer>

"

UK prime minister says she 'won't have to do a budget like

this ever again

CiB

CIE

CiB

CIB

CiB

CiB

BIII

BII

BI

CB

CB

CB

CIB

CIB

BIII

BII

BI1

News Video

Case 1

Case 2

News Tittle:

News Audio

grown men in small spaces pulling it open and shutting it in

the engineering in each took months and months to design

right one of the biggest things that Cynthia really wanted was

that she didn't just want to be a passenger on this you really

want to be a part of it she's not resisting it as much as in the

show in fact she's actually really enjoying this attention and

this friendship that mirror alone it's so heavy and has so much

engineering it's actually very very dangerous…

News Video

News Tittle:

News Audio

the tendency for overpacking I don't think you should overpack but I

don't want to have to be scrambling around in a city that I don't know

trying to find you know a pair of shoes to wear out because I'm so

dedicated to carrying my bag on the great thing about

The biggest travel debate: window seat or aisle seat

Prediction:

"

<think>\

n The video description indicates that it

showcases a scene from the musical "Wicked," featuring a blonde

woman in a vibrant pink dress engaging in various actions within a whimsical,

opulent room. The description highlights her movements, interactions with

other characters, and the ornate setting of the room, emphasizing the playful

and magical atmosphere of the scene.

The news title, "Lady Gaga swings from a chandelier in 'Wicked'," specifically

mentions a chandelier swing, which is not depicted in the video description.

The description does not mention a chandelier swing but instead focuses on the

blonde woman's actions and the room's whimsical decor.

Therefore, the news title does not accurately reflect the content of the video

description. The title is misleading by suggesting a scene involving a

chandelier swing, which is not supported by the description provided.

</think>\

n

<answer>Yes.</answer>

"

Prediction: "<think>\

The video features a discussion between two women

focusing on everyday life choices-specifically, their differing views on the

practicality and comfort of various types of footwear for travel. The setting is

carefully described, highlighting the minimalist and simple environment where the

conversation takes place. In contrast, the headline 'The Greatest Travel Debate:

Window Seat or Aisle Seat' points to a completely different topic-a familiar and

legitimate question about seat preferences during air travel. However, this is not

the subject of the video. The issue is not with the idea of a 'travel debate' itself,

but rather that the video does not actually present a debate about travel at all.

Instead, it revolves around a lifestyle discussion on shoe choices. In summary, the

true focus of the video is a conversation about everyday lifestyle choices-not

about travel. The disconnect between the headline and the video content leads to a

misrepresentation of what the video is actually

about...\n

</think>

\n

<answer>Yes.</answer>

"

Lady Gaga swings from a chandelier in 'Wicke'

Using the same prompt design shown in Figure 24, we evaluate the consistency of five models-GPT4o-mini, GPT-4o, DeepSeek-R1, Qwen2.5-VL-7B, and Qwen2.5-VL-72B-against human evaluations and report their accuracy on both types of samples.

As illustrated in Figure 16, for explainability-correct samples, DeepSeek-R1, GPT-4o-mini, and GPT-4o achieve high agreement with human evaluations, with negligible deviations. Figure 17 shows the results on explainability-error samples, where Qwen2.5-VL-72B, GPT-4o-mini, and GPT-4o again demonstrate high consistency with human assessments.

News Video

Case 1

Case 2

News Tittle:

News Audio

the police are the ones who kill women which is why they don't

want us to protect to protest against the killing of women

they're supposed to be protecting us but they're the ones

perpetrating violence against us so we want a accountability Ka

News Video

News Tittle:

News Audio

when's glacial pace a line from the 1993 chess Prodigy movie

searching for Bobby Fischer comes to mind you've lost you just don't

know it yet so Ellie explain why you Forshaw this all along and and

what you think could have been done differently what Alex Jackson

Smith was named special counsel in November of 2022 and so that

gave him exactly two years to get this case all done before the 2024

election anyone who's ever practiced criminal law…

Honig explains why Jack Smith made move to dismiss

Trump's case

Prediction:

"

<think>\

nThe video description outlines a series of

scenes featuring various groups of women marching globally to

commemorate International Day for the Elimination of Violence Against

Women. It includes details about the attire of the women, the banners they carry,

and the settings in which they march, including urban streets, historic buildings,

and public squares. It also describes interactions with police and the use of tear

gas in some instances. The video concludes with a call to action for ending

violence against women.\nThe news title, "World\'s women march to mark

International Day for the Elimination of Violence Against Women," succinctly

captures the essence of the video description. It accurately reflects the content

by mentioning the global participation of women in marches and the purpose of

these marches (to mark the International Day for the Elimination of Violence

Against Women).\n

Therefore

, the title accurately reflects the content of the

video description.

</think>\

n

<answer>No.</answer>

"

Prediction: '<think

>\nThe video description outlines a news segment

featuring Elie Honig discussing the legal actions taken by Jack Smith to

dismiss cases against Trump. It describes Honig\'s analysis, the various settings of

the participants, and the focus on the dismissal of Trump\'s election subversion

case and the dropping of the classified documents case. The news title, 'Honig

explains why Jack Smith made move to dismiss Trump's cases,' accurately

reflects the content of the video description, as it directly mentions Honig\'s

explanation and the context of Jack Smith\'s legal moves against Trump.

</think>

\n

<answer>No.</answer>

"

World's women march to mark International Day for the

Elimination of Violence Against Women

O

News Video

Case 1

Case 2

News Tittle:

News Audio

I imagine you want me to listen to the song one more time,

then you disappear without legal consequences, your news

News Video

News Tittle:

News Audio

ok so Disney world has set down indefinitely it was supposed to 10

hours ago that Disney World is now shut down and will not be

opening back up and that scary cuz Disney World only shut down for

Super major events by hurricane 911 stuff like that and see

Disney World shuts down for good

！

Prediction: <think>\

nThe video description implies that the footage

captures the flight of geese as they migrate southward. The title,

\"Yesterday, flights at Guangzhou Airport were grounded to…" suggests a

narrative where aviation activities are disrupted intentionally for wildlife,

presenting an idyllic and sentimental scenario. However, the title may mislead

viewers into thinking that the geese's flight is connected to operational changes

at the airport, which could imply a significant event or phenomenon. \n\nIn

actuality, the video appears to focus solely on the flying geese and does not

substantiate any claims of grounded flights or an intervention by airport

authorities. Therefore, the title is not aligned with the content, as it exaggerates

or manipulates the significance of the event while imposing a narrative that

isn't evident in the footage.\n\nThis discrepancy makes the multitasking

content manipulated, as it alters the perceived importance of both the moment

and the setting.\n

</think>\

n

<answer>Yes.</answer>

Prediction: <think>\

nThe title \"Disney World shuts down for good!\" is

sensational and suggests a permanent closure of Disney World, which is a

significant claim. It creates an impression that something drastic has occurred

affecting this well-known theme park. However, upon analyzing the video content,

it presents historical instances when Disney World has closed, such as following

natural disasters and significant events.\n\nFurthermore, the video appears to

culminate with recent news text referencing an indefinite shut down…. In fact, the

mention of historical closures emphasizes that they are event-specific and not

representative of a permanent closure.\n\nTherefore, the title does not accurately

reflect the video's focus on historical closure events and misleads viewers into

thinking that Disney World is closed indefinitely when the context shows a more

complex narrative regarding past, temporary shutdowns.\n\nIn conclusion, the title

misrepresents the content and intent of the video, qualifying it as manipulated

information

.</think>

\n

<answer>Yes.</answer>

Yesterday, flights at Guangzhou Airport were grounded to make way

for southbound geese. The magnificent scene of life was touching!

forgood!

for good!

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, FactR1 infers from the visual content that the video depicts 'Super Idol,' which contradicts the title's

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 to support your description.

# Instructions:

Briefly describe the main objects and their visual characteristics as shown in the video.

Summarize the environment and background setting.

Highlight key actions and interactions captured in the footage.

# Constraints:

Use a clear, narrative style. Avoid bullet points or lists.

Keep the description brief and focused on the most essential details.

Avoid speculation, subjective opinions, or emotional language.

The  provided  entity  may  contain  inaccuracies  and  should  be  treated  only  as  a reference.

reference to 'DA ZHANG WEI.' In Case 2, by extracting and interpreting key details from the news report, the model deduces that the event occurred in 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, we 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 term 'Chancellor' in the news audio provides critical information that aids accurate detection. In Case 2, the OCR-extracted text 'Trump' from the news image serves as essential visual evidence, effectively supporting the model's reasoning and final judgment. These examples highlight Fact-R1's ability to integrate cross-modal information for robust misinformation detection.

(3) Failure Cases in 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 is 'Lady Gaga,' yet the model mistakenly identifies 'chandelier' as the manipulated entity. In Case 2, the correct fake entity is 'window seat or 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 nor the OCR-extracted text from the news images provides effective supporting clues. This indicates a limitation in the model'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 on Real News Samples. As shown in Figure 21, we present two examples of real news videos correctly identified by Fact-R1. In these cases, the model's reasoning process accurately describes the events occurring in the video and aligns well with the given news titles. These examples demonstrate Fact-R1's capability not only in detecting manipulated content but also in validating authentic news through consistent and coherent reasoning.

(5) Real-world Cases. As shown in Figure 22, we present two real-world fake news cases that were successfully detected by Fact-R1. These cases are drawn from the FakeSV and FakeTT datasets, which

You are  an  AI  assistant  to  help  me  evaluate  whether  the  reasoning  process  in  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

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 FakeVV dataset is provided strictly for non-commercial research purposes and is accessible only to verified academic researchers under a research-specific license agreement. All samples have been carefully screened to remove any non-public personally identifiable information (PII), ensuring that no harmful, private, or illegal content is included. Fact-R1 is intended to function as an assistive component within human-in-the-loop 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 were anonymized, no personally identifiable information was collected, and compensation met or exceeded local fair-pay standards. The study adhered to institutional ethical guidelines and local labor regulations.

We collected a small annotation set (5k preference samples) under a transparent, compliant protocol. Given the non-sensitive content and minimal risk typical of standard annotation tasks, the study quali-

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, participant privacy, and societal safety.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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.\n\nQUESTIONS:\nWhat 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths :\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)\n\nQUESTIONS:\n1. 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..\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\nI 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}