aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
Pengsong Zhang, Xiang Hu, Guowei Huang, Yang Qi, Heng Zhang, Xiuxu Li, Jiaxing Song, Jiabin Luo, Yijiang Li, Shuo Yin, Chengxiao Dai, Eric Hanchen Jiang, Xiaoyan Zhou, Zhenfei Yin, Boqin Yuan, Jing Dong, Guinan Su, Guanren Qiao, Haiming Tang, Anghong Du, Lili Pan, Zhenzhong Lan

TL;DR
aiXiv is an innovative open-access platform designed to facilitate autonomous scientific discovery by integrating human and AI scientists in a scalable ecosystem for submitting, reviewing, and refining research proposals and papers.
Contribution
The paper introduces aiXiv, a multi-agent platform enabling AI and human scientists to collaboratively generate, review, and improve scientific research content in a scalable, open-access environment.
Findings
aiXiv significantly improves quality of AI-generated research after iterative review.
The platform demonstrates robustness and reliability through extensive experiments.
aiXiv accelerates dissemination of high-quality AI research content.
Abstract
Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of high-quality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be…
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Taxonomy
TopicsScientific Computing and Data Management · Machine Learning in Materials Science · Artificial Intelligence in Healthcare and Education
