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    <abstract>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 https://aka.ms/reveal</abstract>
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%0 Conference Proceedings
%T Reasoning-Aware AIGC Detection via Alignment and Reinforcement
%A Wang, Zhao
%A Xiong, Max
%A Lian, Jianxun
%A Dou, Zhicheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-reasoning
%X 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 https://aka.ms/reveal
%U https://aclanthology.org/2026.findings-acl.1043/
%P 20813-20829

Markdown (Informal)

[Reasoning-Aware AIGC Detection via Alignment and Reinforcement](https://aclanthology.org/2026.findings-acl.1043/) (Wang et al., Findings 2026)

• Reasoning-Aware AIGC Detection via Alignment and Reinforcement (Wang et al., Findings 2026)

ACL

• Zhao Wang, Max Xiong, Jianxun Lian, and Zhicheng Dou. 2026. Reasoning-Aware AIGC Detection via Alignment and Reinforcement. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20813–20829, San Diego, California, United States. Association for Computational Linguistics.