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AiraXiv: An AI-Driven Open-Access Platform for Huma... | AI Research

Key Takeaways

  • AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists introduces a new publishing paradigm designed to handle the rapid growth of research i...
  • 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.
Paper AbstractExpand

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 this https URL .

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists introduces a new publishing paradigm designed to handle the rapid growth of research in the age of artificial intelligence. As the volume of both human-authored and AI-generated papers increases, traditional academic journals and conferences are struggling with long review times and overwhelming submission numbers. AiraXiv addresses these bottlenecks by creating an open-access ecosystem where human and AI scientists can collaborate, publish, and refine their work through continuous, feedback-driven iteration.

How AiraXiv Works

The platform is built on a modular architecture that treats AI as a core participant in the research lifecycle. It supports two types of users: humans, who interact via a web interface, and AI scientists, who connect programmatically using the Model Context Protocol (MCP). The system performs four primary functions: it parses and summarizes submissions, provides automated peer reviews, offers personalized paper recommendations, and allows users to organize lightweight, topic-specific conferences. By using a "masterbrain" orchestrator, the platform coordinates these services to create a seamless, end-to-end publishing workflow.

AI-Driven Review and Iteration

Unlike traditional systems that rely on binary "accept or reject" decisions, AiraXiv uses AI to provide structured, multi-dimensional feedback. This allows authors to treat their papers as living documents that can be improved over time. The platform’s AI reviewers are designed to be prescriptive, offering actionable suggestions rather than acting as final gatekeepers. This approach encourages an iterative process where authors can update their work based on feedback from both AI agents and the community, accelerating the pace at which knowledge is shared and refined.

Real-World Performance

AiraXiv was tested as the official infrastructure for the 1st International Conference on AI Scientists (ICAIS 2025). During this deployment, the platform successfully processed 114 submissions, reducing the typical nine-month conference cycle to just 1.5 months. The results showed that AI reviewers were effective at aligning with human expert judgment, with a clear correlation between high AI scores and final acceptance. Furthermore, the platform fostered a culture of improvement: nearly 20% of authors updated their manuscripts during the conference, and these revised versions generally received higher scores from the AI reviewers.

Considerations and Limitations

While AiraXiv offers a scalable solution for modern research, the authors note several challenges. AI-generated reviews are not perfect and can be subject to bias or instability, meaning they should be used as advisory signals rather than definitive judgments. There are also concerns regarding the potential for adversarial content or low-quality feedback, which necessitates robust moderation. Additionally, while the platform performed well in its initial deployment, further research is needed to understand how these systems will function over the long term and across a wider variety of scientific disciplines.

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