AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866911702622142464 |
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| author | Pan, Junshu Lu, Panzhong Weng, Yixuan Sun, Qiyao Guo, Fang Yang, Zijie Zhou, Qiji Zhang, Yue |
| author_facet | Pan, Junshu Lu, Panzhong Weng, Yixuan Sun, Qiyao Guo, Fang Yang, Zijie Zhou, Qiji Zhang, Yue |
| contents | Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs, placing increasing strain on traditional academic publishing systems and challenging the scalability of conference- and journal-centered paradigms amid rising submission volumes, reviewer workload, and venue size. To address these challenges, we explore an AI-era publishing paradigm in which both human and AI scientists participate as authors and readers, and papers evolve through continuous, feedback-driven iteration. We propose AiraXiv, an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback. AiraXiv supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions. We validate AiraXiv through real-world deployments, including serving as the submission platform for ICAIS 2025, demonstrating its potential as a fast, inclusive, and scalable research infrastructure for the AI era. AiraXiv is publicly available at https://airaxiv.com. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists Pan, Junshu Lu, Panzhong Weng, Yixuan Sun, Qiyao Guo, Fang Yang, Zijie Zhou, Qiji Zhang, Yue Artificial Intelligence Computation and Language Machine Learning Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs, placing increasing strain on traditional academic publishing systems and challenging the scalability of conference- and journal-centered paradigms amid rising submission volumes, reviewer workload, and venue size. To address these challenges, we explore an AI-era publishing paradigm in which both human and AI scientists participate as authors and readers, and papers evolve through continuous, feedback-driven iteration. We propose AiraXiv, an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback. AiraXiv supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions. We validate AiraXiv through real-world deployments, including serving as the submission platform for ICAIS 2025, demonstrating its potential as a fast, inclusive, and scalable research infrastructure for the AI era. AiraXiv is publicly available at https://airaxiv.com. |
| title | AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.21481 |