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

arXiv: https://arxiv.org/abs/2605.21481 · HTML: https://mlsanity.com/paper/2605.21481/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search](https://mlsanity.com/paper/2504.08066/) (2025)
- [Can large language models provide useful feedback on research papers? A large-scale empirical analysis](https://mlsanity.com/paper/2310.01783/) (2023)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [CycleResearcher: Improving Automated Research via Automated Review](https://mlsanity.com/paper/2411.00816/) (2024)
- [AutoSurvey: Large Language Models Can Automatically Write Surveys](https://mlsanity.com/paper/2406.10252/) (2024)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Large Language Models for Automated Open-domain Scientific Hypotheses Discovery](https://mlsanity.com/paper/2309.02726/) (2023)
- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [PaSa: An LLM Agent for Comprehensive Academic Paper Search](https://mlsanity.com/paper/2501.10120/) (2025)
- [SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing](https://mlsanity.com/paper/2503.04629/) (2025)
- [The Impact of Large Language Models in Academia: from Writing to Speaking](https://mlsanity.com/paper/2409.13686/) (2024)
- [OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists](https://mlsanity.com/paper/2511.16931/) (2025)
- [Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking](https://mlsanity.com/paper/2505.21815/) (2025)
- [aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists](https://mlsanity.com/paper/2508.15126/) (2025)
- [Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference](https://mlsanity.com/paper/2508.04586/) (2025)
- [Publication Trends in Artificial Intelligence Conferences: The Rise of Super Prolific Authors](https://mlsanity.com/paper/2412.07793/) (2024)
- [SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search](https://mlsanity.com/paper/2507.15245/) (2025)
- [Deep Literature Survey Automation with an Iterative Workflow](https://mlsanity.com/paper/2510.21900/) (2025)
- [SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts](https://mlsanity.com/paper/2604.26506/) (2026)
- [Airalogy: AI-empowered universal data digitization for research automation](https://mlsanity.com/paper/2506.18586/) (2025)

## Abstract (arXiv)

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.