AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists

2026-05-20

AI Summary

This work addresses the inadequacy of traditional academic publishing systems in managing the surge of research outputs generated through human–AI collaboration, which has led to overwhelming submission volumes, excessive reviewer burdens, and limited scalability. To overcome these challenges, we propose and implement an open research platform that unifies human researchers and AI agents within a shared environment. The platform integrates an open preprint architecture, AI-augmented analytical peer review, interoperable interfaces based on the Model Context Protocol (MCP), and a dynamic paper lifecycle model enabling continuous iterative refinement of manuscripts. Deployed as the official submission system for the ICAIS 2025 conference, the platform demonstrates its viability as a fast, inclusive, and scalable infrastructure for scientific communication in the AI era.

Abstract

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.

Problem

• academic publishing
• AI-generated research
• scalability
• reviewer workload
• open-access platform

Innovation

• AI-driven publishing
• open-access platform
• Model Context Protocol
• AI-augmented review
• continuous iteration