T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search

TL;DR

This paper introduces T^2Agent, a tool-augmented multimodal misinformation detection system that uses Monte Carlo Tree Search to dynamically select and coordinate multiple verification tools for improved detection accuracy.

Contribution

The paper presents a novel agent framework combining an extensible toolkit with MCTS and a multi-source verification strategy for effective multimodal misinformation detection.

Findings

Outperforms existing baselines on challenging benchmarks.

Effectively integrates multiple verification tools via a greedy selector.

Demonstrates strong potential as a training-free detector.

Abstract

Real-world multimodal misinformation often arises from mixed forgery sources, requiring dynamic reasoning and adaptive verification. However, existing methods mainly rely on static pipelines and limited tool usage, limiting their ability to handle such complexity and diversity. To address this challenge, we propose \method, a novel misinformation detection agent that incorporates an extensible toolkit with Monte Carlo Tree Search (MCTS). The toolkit consists of modular tools such as web search, forgery detection, and consistency analysis. Each tool is described using standardized templates, enabling seamless integration and future expansion. To avoid inefficiency from using all tools simultaneously, a greedy search-based selector is proposed to identify a task-relevant subset. This subset then serves as the action space for MCTS to dynamically collect evidence and perform multi-source verification. To better align MCTS with the multi-source nature of misinformation detection, \method~ extends traditional MCTS with multi-source verification, which decomposes the task into coordinated subtasks targeting different forgery sources. A dual reward mechanism containing a reasoning trajectory score and a confidence score is further proposed to encourage a balance between exploration across mixed forgery sources and exploitation for more reliable evidence. We conduct ablation studies to confirm the effectiveness of the tree search mechanism and tool usage. Extensive experiments further show that \method~ consistently outperforms existing baselines on challenging mixed-source multimodal misinformation benchmarks, demonstrating its strong potential as a training-free detector.