FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning

2026-02-26

AI Summary

This work addresses the limitations of existing multimodal large language model–based approaches to video misinformation detection, which rely on fixed-depth reasoning and struggle with scenarios involving sparse evidence or requiring external verification. The authors propose an agent-based iterative reasoning framework that formulates fact-checking as a dynamic process: by assessing task ambiguity, the system adaptively invokes external tools to gather critical evidence and iteratively refines its reasoning path. A two-stage training strategy is introduced—combining domain-specific agent supervised fine-tuning with decision-aware reinforcement learning—to enhance tool-calling efficiency and enable risk-sensitive decision calibration. Evaluated on the FakeSV, FakeTT, and FakeVV benchmarks, the method significantly outperforms current state-of-the-art approaches, demonstrating superior robustness and generalization capability.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• video misinformation detection
• multimodal large language models
• external verification
• reasoning ambiguity
• evidence sparsity

Innovation

• agentic reasoning
• reinforcement learning
• multimodal large language models
• external tool invocation
• video misinformation detection