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Proceedings of the AAAI Conference on Artificial Intelligence

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T2Agent: A Tool-augmented Multimodal Misinformation Detec...
Xing Cui, Yu · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Xing Cui Beijing University of Posts and Telecommunications
  • Yueying Zou Beijing University of Posts and Telecommunications
  • Zekun Li University of California, Santa Barbara
  • Peipei Li Beijing University of Posts and Telecommunications
  • Xinyuan Xu Beijing University of Posts and Telecommunications
  • Xuannan Liu Beijing University of Posts and Telecommunications
  • Huaibo Huang MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v40i1.36977

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 T2Agent, 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, T2Agent 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 T2Agent consistently outperforms existing baselines on challenging mixed-source multimodal misinformation benchmarks, demonstrating its strong potential as a training-free detector.

How to Cite

Cui, X., Zou, Y., Li, Z., Li, P., Xu, X., Liu, X., & Huang, H. (2026). T2Agent: A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 175–183. https://doi.org/10.1609/aaai.v40i1.36977

Issue

Section

AAAI Technical Track on Application Domains I

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