RAAR: Retrieval Augmented Agentic Reasoning for Cross-Domain Misinformation Detection

• URL: http://arxiv.org/abs/2601.04853v1
• Date: Thu, 08 Jan 2026 11:43:16 GMT
• Title: RAAR: Retrieval Augmented Agentic Reasoning for Cross-Domain Misinformation Detection
• Abstract summary: RAAR is a retrieval-augmented agentic reasoning framework for cross-domain misinformation detection.<n> RAAR retrieves source-domain evidence aligned with each target sample's semantics, sentiment, and writing style.<n> RAAR constructs verifiable multi-step reasoning paths through specialized multi-agent collaboration.
• Score: 31.765619582229146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-domain misinformation detection is challenging, as misinformation arises across domains with substantial differences in knowledge and discourse. Existing methods often rely on single-perspective cues and struggle to generalize to challenging or underrepresented domains, while reasoning large language models (LLMs), though effective on complex tasks, are limited to same-distribution data. To address these gaps, we introduce RAAR, the first retrieval-augmented agentic reasoning framework for cross-domain misinformation detection. To enable cross-domain transfer beyond same-distribution assumptions, RAAR retrieves multi-perspective source-domain evidence aligned with each target sample's semantics, sentiment, and writing style. To overcome single-perspective modeling and missing systematic reasoning, RAAR constructs verifiable multi-step reasoning paths through specialized multi-agent collaboration, where perspective-specific agents produce complementary analyses and a summary agent integrates them under verifier guidance. RAAR further applies supervised fine-tuning and reinforcement learning to train a single multi-task verifier to enhance verification and reasoning capabilities. Based on RAAR, we trained the RAAR-8b and RAAR-14b models. Evaluation on three cross-domain misinformation detection tasks shows that RAAR substantially enhances the capabilities of the base models and outperforms other cross-domain methods, advanced LLMs, and LLM-based adaptation approaches. The project will be released at https://github.com/lzw108/RAAR.

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