AI水论文还得AI治：西湖大学首次模拟人类专家思考链，AI审稿分钟级给出全面反馈

Core Viewpoint - The article discusses the launch of AiraXiv, an open preprint platform for AI-generated academic papers, and DeepReview, an AI review system that simulates human expert evaluation, addressing the challenge of distinguishing high-quality research from a surge of AI-generated content [1][6][21]. AiraXiv Overview - AiraXiv is designed to manage and showcase AI-generated papers, reducing interference with traditional peer review processes [2][8]. - The platform provides a dedicated channel for high-quality AI research, allowing researchers to efficiently access valuable work [9]. - AiraXiv supports seamless integration with arXiv, enabling users to view original papers and AI review comments by entering the arXiv ID [10]. DeepReview Functionality - DeepReview is the first multi-stage AI review system that mimics human expert thought processes, aiming to provide systematic and interpretable paper evaluations [12]. - The review process includes three core stages: novelty verification, multi-dimensional assessment, and reliability validation [12][13][14]. - DeepReview can deliver comprehensive review feedback in minutes, significantly reducing the time required compared to traditional methods [19]. Performance Metrics - The DeepReviewer-14B model, trained on the DeepReview-13K dataset, outperforms the CycleReviewer-70B model while using fewer tokens [3]. - In optimal conditions, DeepReviewer-14B achieved a win rate of 88.21% and 80.20% against GPT-o1 and DeepSeek-R1, respectively [4]. Future Prospects - AiraXiv and DeepReview represent initial steps towards a broader exploration of AI's role in academic research, with plans to expand beyond computer science into other disciplines [21][22]. - The platforms aim to enhance the visibility and dissemination of quality research outcomes, reflecting potential changes in the research ecosystem where AI plays a larger role in various research stages [23]. Laboratory Background - The Westlake University Natural Language Processing Laboratory, established in September 2018 and led by Professor Zhang Yue, focuses on foundational and applied research in natural language processing and aims to advance the development of AI scientists [24].