FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning

Zehao Li; Hongwei Yu; Hao Jiang; Qiang Sheng; Yilong Xu; Baolong Bi; Yang Li; Zhenlong Yuan

cs.AIFeb 26, 2026

FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning

Authors: Zehao Li, Hongwei Yu, Hao Jiang, Qiang Sheng, Yilong Xu, Baolong Bi, Yang Li, Zhenlong Yuan,

Organizations: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · University of Science and Technology Beijing, Beijing, China · The University of Queensland, Brisbane, Australia

Abstract

Multimodal large language models (MLLMs) have substantially advanced video misinformation detection through unified multimodal reasoning, but they often rely on fixed-depth inference and place excessive trust in internally generated assumptions, particularly in scenarios where critical evidence is sparse, fragmented, or requires external verification. To address these limitations, we propose FactGuard, an agentic framework for video misinformation detection that formulates verification as an iterative reasoning process built upon MLLMs. FactGuard explicitly assesses task ambiguity and selectively invokes external tools to acquire critical evidence, enabling progressive refinement of reasoning trajectories. To further strengthen this capability, we introduce a two-stage training strategy that combines domain-specific agentic supervised fine-tuning with decision-aware reinforcement learning to optimize tool usage and calibrate risk-sensitive decision making. Extensive experiments on FakeSV, FakeTT, and FakeVV demonstrate FactGuard's state-of-the-art performance and validate its excellent robustness and generalization capacity.

Figures & tables

Figure 1: Comparison of video misinformation detection methods and our proposed FactGuard in terms of (a) explainability analysis and (b) comprehensive performance analysis.

Figure 2: Pipeline of FactGuard . The upper part illustrates the inference-time agentic verification process, where FactGuard assesses uncertainty based on the ambiguity of the input and selectively invokes external tools to acquire additional evidence before refining its reasoning and producing a final decision. The lower part depicts the training pipeline, which combines supervised fine-tuning with decision-aware reinforcement learning to reinforce structured reasoning, calibrated tool usage, and risk-sensitive verification behavior.

Figure 3: Key advantages of FactGuard. (a) MLLM-based methods with enhanced reasoning may induce cross-modal hallucination in ambiguous cases by over-relying on internally generated assumptions, treating them as grounded evidence without acquiring or validating critical supporting information. (b) FactGuard formulates misinformation verification as an uncertainty-aware, tool-assisted decision-making process that adaptively refines its conclusions, enabling reliable verification in open and dynamic environments.

Model	FakeSV				FakeTT				FakeVV
Model	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1
BERT ( Koroteev, 2021 )	65.4	66.0	66.5	66.2	68.7	67.5	67.5	67.5	60.4	57.9	56.8	57.3
TikTec ( Shang et al., 2021 )	64.8	63.2	61.9	62.5	61.1	64.8	64.2	64.5	59.3	59.1	59.5	59.3
FANVM ( Choi & Ko, 2021 )	65.4	66.1	64.3	65.2	68.9	64.7	68.8	67.1	61.9	60.7	60.8	60.8
SV-FEND ( Qi et al., 2023a )	67.1	67.4	66.3	66.8	67.6	72.2	69.0	70.6	70.9	71.4	71.3	71.3
FakingRec ( Bu et al., 2024 )	69.5	69.7	70.4	70.0	71.0	71.9	72.0	72.0	72.1	72.4	71.6	72.0

Table 1: Performance comparison on FakeSV, FakeTT and FakeVV datasets. We highlight the improvements achieved by FactGuard.

Figure 4: Qualitative analysis of model reasoning. Representative reasoning traces under correct predictions show that FactGuard produces more coherent and evidence-grounded reasoning than Qwen2.5-VL and Fact-R1, highlighting improved interpretability.

Cost Ratio	FakeSV		FakeVV
( $\alpha:\gamma$ )	Precision	Recall	Precision	Recall
1:2	80.8	82.1	83.2	83.7
1:1	82.2	80.6	85.8	82.1
2:1	83.2	75.0	86.6	79.3

Table 2: Cost-sensitive precision and recall achieved by FactGuard on FakeSV and FakeVV under different asymmetric error cost ratios.

Model	FakeSV		FakeTT
Model	Acc	F1	Acc	F1
FactGuard (Ours)	79.3	81.4	75.3	75.2
w/o SFT	73.1	74.7	71.6	68.2
w/o RL	62.0	63.2	67.9	65.7
w/o $R_{tool}$	77.7	78.5	74.7	72.9
w/o $R_{risk}$	78.5	78.9	74.9	73.8

Table 3: Ablation study of FactGuard on FakeSV and FakeTT datasets. We report Accuracy (%) and F1-score (%) to evaluate the contribution of each component.

Variant	FakeSV		FakeTT
Variant	Acc	F1	Acc	F1
(a) w/. Rollouts = 6	77.3	78.9	72.6	69.4
(b) w/. Rollouts = 8 (Ours)	79.3	81.4	75.3	75.2
(c) w/. Rollouts = 12	79.5	81.5	75.2	74.3
(d) w/. $\beta$ = 0.02	78.2	79.5	74.6	73.4
(e) w/. $\beta$ = 0.04 (Ours)	79.3	81.4	75.3	75.2

Table 4: Performance of GRPO variants on FakeSV and FakeTT.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Additional Case Study of FactGuard.

Figure 6: FactGuard Two-Turn Prompting.

Figure 7: GPT-4o reasoning audit prompt. The prompt evaluates whether a model’s reasoning and prediction are supported by explicit and relevant evidence, serving as an external assessment of reasoning faithfulness.

Automated Fact-Checking

Automated fact-checking detects checkable claims, retrieves evidence, and assesses whether that evidence supports or refutes them.

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MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning

Peipei Li, Shuhan Xia, Shengyang Liu, Zekun Li+1

Beijing University of Posts and Telecommunications · Minzu University of China · NLPR, Institute of automation, Chinese academy of science, Chinese Academy of Sciences

Multimodal Deception Detection Multimodal Tool Use Multimodal Agents RL for Tool Use

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Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented methods rely on predefined workflows or inference-time planning, limiting adaptability or increasing inference cost. To address this issue, we introduce MM-VeriAgent, which learns to verify mixed-source multimodal misinformation with tools. We first build MM-VeriTools, a specialized toolkit for misinformation detection agents. By benchmarking various candidate models and methods on the sub-tasks required by mixed-source detection, we select the strongest for textual, visual, and cross-modal forgery analysis and encapsulate them as callable tools with a unified interface. On top of this toolkit, we train the LVLM agent with reinforcement learning to teach it how to use these tools to better solve mixed-source detection. Since many of the tools are specialized models whose online execution at every rollout severely limits RL efficiency, we further introduce Tool-Execution Cache, which pre-executes candidate tool calls and reuses their cached outputs during training. This preserves multi-step rollouts while reducing online tool execution, largely improving the training efficiency.Experiments on MMFakeBench demonstrate substantial accuracy gains over the base model without explicit tool search at inference time. Ablation and efficiency analyses further validate the learned tool-use policy and show that Tool-Execution Cache reduces online tool executions during training.

Abstract

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