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

Problem
This paper addresses the increasing strain on traditional academic publishing systems due to the rapid growth of both human-authored and AI-generated research outputs. The authors highlight the limitations of existing conference and journal-centered paradigms, which struggle with rising submission volumes, reviewer workloads, and venue sizes. They propose a novel solution in the form of AiraXiv, an AI-driven open-access platform that facilitates collaboration between human and AI scientists. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution of this paper is the design and implementation of AiraXiv, which integrates several innovative features to enhance the academic publishing process. AiraXiv is built on an open preprint model, allowing for continuous iteration of research papers through feedback-driven mechanisms. The platform employs AI-augmented analysis and review processes, leveraging a Model Context Protocol (MCP) to facilitate interactions between human and AI authors. The authors validate AiraXiv through real-world deployments, including its use as the submission platform for the ICAIS 2025 conference. The platform’s architecture is designed to support an interactive user interface (UI) that caters to both human and AI scientists, promoting a collaborative research environment.

Results
The authors demonstrate the effectiveness of AiraXiv through its deployment at ICAIS 2025, showcasing its ability to handle a high volume of submissions efficiently. While specific quantitative metrics (e.g., submission throughput, reviewer engagement rates) are not detailed in the abstract, the authors claim that AiraXiv provides a “fast, inclusive, and scalable research infrastructure.” The implications of this deployment suggest that AiraXiv can significantly reduce the time and effort required for the peer review process compared to traditional models, although exact effect sizes and comparisons to baseline systems are not provided.

Limitations
The authors acknowledge several limitations, including the need for further empirical validation of AiraXiv’s effectiveness across diverse academic fields and the potential biases introduced by AI in the review process. They also note that the platform’s reliance on user feedback may lead to variability in the quality of the iterative improvements made to papers. Additionally, the paper does not address the challenges of ensuring the integrity and reliability of AI-generated content, which could impact the credibility of research published on the platform. An obvious limitation not discussed is the potential resistance from traditional academic institutions and stakeholders who may be hesitant to adopt a radically different publishing model.

Why it matters
The introduction of AiraXiv has significant implications for the future of academic publishing, particularly in the context of the growing role of AI in research. By facilitating a collaborative environment where both human and AI scientists can contribute to and refine research outputs, AiraXiv could help alleviate the bottlenecks currently faced by traditional publishing systems. This platform may pave the way for more dynamic and responsive research dissemination practices, ultimately enhancing the speed and accessibility of scientific knowledge. The work encourages further exploration of AI’s role in academia and could inspire similar initiatives aimed at modernizing the publishing landscape.

Authors: Junshu Pan, Panzhong Lu, Yixuan Weng, Qiyao Sun, Fang Guo, Zijie Yang, Qiji Zhou, Yue Zhang
Source: arXiv:2605.21481
URL: https://arxiv.org/abs/2605.21481v1