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[2508.15126] aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

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Type: Web Article Original link: https://arxiv.org/abs/2508.15126 Publication date: 2025-09-04


Summary
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WHAT - aiXiv is an open-access platform for the publication and review of AI-generated scientific content. It allows for the submission, review, and iteration of research proposals and articles by human and AI scientists.

WHY - It is relevant for the AI business because it solves the problem of disseminating AI-generated scientific content, offering a scalable and high-quality ecosystem for publishing AI research.

WHO - The main authors are researchers from academic and research institutions, including Pengsong Zhang, Xiang Hu, and others. The platform is supported by a community of human and AI scientists.

WHERE - It positions itself in the market of scientific publication platforms, competing with arXiv and traditional journals, but with a specific focus on AI-generated content.

WHEN - It is a project in the development phase, with a preprint currently under review. The temporal trend indicates a growing need for platforms dedicated to AI-generated research.

BUSINESS IMPACT:

  • Opportunities: Collaboration with academic institutions to validate and publish AI research, expanding the reach and impact of the company’s AI solutions.
  • Risks: Competition with existing platforms like arXiv and traditional journals, which could adopt similar technologies.
  • Integration: Possible integration with existing AI research and development tools to automate the review and publication of scientific content.

TECHNICAL SUMMARY:

  • Core technology stack: Uses Large Language Models (LLMs) and a multi-agent architecture for managing scientific proposals and articles. API and MCP interfaces for integration with heterogeneous systems.
  • Scalability: Designed to be scalable and extensible, allowing the integration of new AI agents and human scientists.
  • Technical differentiators: Automated review and iteration of scientific content, improving quality and publication speed.

Use Cases
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  • Private AI Stack: Integration into proprietary pipelines
  • Client Solutions: Implementation for client projects
  • Strategic Intelligence: Input for technological roadmaps
  • Competitive Analysis: Monitoring AI ecosystem

Resources
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Original Links #


Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-09-04 19:00 Original source: https://arxiv.org/abs/2508.15126

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