Xijin Tang — accepted AI-conference papers

2026

Beyond Detection: Exploring Evidence-based Multi-Agent Debate for Misinformation Intervention and Persuasion

Chen Han, Yijia Ma, Jin Tan, Wenzhen Zheng, Xijin Tang

AAAI 2026technical

Multi-agent debate (MAD) frameworks have emerged as promising approaches for misinformation detection by simulating adversarial reasoning. While prior work has focused on detection accuracy, the importance of helping users understand the reasoning behind factual judgments has been overlooked. The de

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2025

Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models

Chen Han, Wenzhen Zheng, Xijin Tang

EMNLP 2025

The proliferation of misinformation in digital platforms reveals the limitations of traditional detection methods, which mostly rely on static classification and fail to capture the intricate process of real-world fact-checking. Despite advancements in Large Language Models (LLMs) that enhance autom

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