Reasoning-Aware AIGC Detection via Alignment and Reinforcement

Computer Science > Artificial Intelligence [Submitted on 21 Apr 2026] Reasoning-Aware AIGC Detection via Alignment and Reinforcement Zhao Wang, Max Xiong, Jianxun Lian, Zhicheng Dou The rapid advancement and widespread adoption of Large Language Models (LLMs) have elevated the need for reliable AI-generated content (AIGC) detection, which remains challenging as models evolve. We introduce AIGC-text-bank, a comprehensive multi-domain dataset with diverse LLM sources and authorship scenarios, and propose REVEAL, a detection framework that generates interpretable reasoning chains before classification. Our approach uses a two-stage training strategy: supervised fine-tuning to establish reasoning capabilities, followed by reinforcement learning to improve accuracy, improve logical consistency, and reduce hallucinations. Extensive experiments show that REVEAL achieves state-of-the-art performance across multiple benchmarks, offering a robust and transparent solution for AIGC detection. The project is open-source at this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.19172 [cs.AI]   (or arXiv:2604.19172v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.19172 Focus to learn more Submission history From: Zhao Wang [view email] [v1] Tue, 21 Apr 2026 07:29:55 UTC (594 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)