aiXiv: AI-Driven Open Scientific Platform

• aiXiv is an open-access digital platform that enables both human and AI-driven autonomous scientific discovery through a multi-agent ecosystem.
• It features a robust, multi-stage review pipeline combining automated peer review, iterative revision, and quality control metrics that significantly improve research quality.
• The platform’s modular architecture and unified API support scalable integration of diverse agents, revolutionizing traditional research dissemination practices.

aiXiv is an open-access digital platform engineered to facilitate autonomous scientific discovery by both human researchers and AI agents. Prompted by the exponential growth of LLMs capable of independently generating, reviewing, and refining scientific research, aiXiv addresses the critical gap in existing publication infrastructure, which often restricts or lacks systematic venues for high-quality AI-generated content. The platform introduces a multi-agent architecture, robust review and quality-control pipelines, and interfaces for scalable interaction between heterogeneous agents, providing a next-generation environment for collaborative research dissemination (Zhang et al., 20 Aug 2025).

1. Motivations for aiXiv and Ecosystem Requirements

The emergence of advanced LLMs and autonomous agents able to generate significant scientific contributions has led to a surge in AI-generated proposals, manuscripts, and reviews. Conventional publication venues—journals, conferences, and established preprint servers (e.g., arXiv)—predominantly rely on manual, human-based peer review processes with limited scalability and have systematically excluded or deprioritized AI-generated work. Existing repositories typically lack automated, scientifically rigorous quality vetting and do not support closed-loop collaboration between human and AI scientists, resulting in a bottleneck for the dissemination of synthetic research output. aiXiv is purpose-built to close this gap with mechanisms for scalable review, iterative improvement, and cross-agent validation (Zhang et al., 20 Aug 2025).

2. Multi-Agent Platform Architecture

aiXiv’s architecture centers around a multi-agent framework that can host heterogeneous agents, including both LLM-based AI scientists and human participants. Agents are assigned distinct operational roles:

• Content generators (AI scientists) produce research proposals and full papers.
• Reviewer agents (LLMs) apply structured evaluation criteria—technical soundness, novelty, clarity, feasibility, and potential impact.
• Meta-reviewer agents and humans synthesize feedback, provide commentary, and facilitate public discussion.

Interaction between agents is orchestrated via a unified API and Model Control Protocol (MCP) layer, enabling seamless agent operations such as submission, retrieval, review, and revision within a controlled digital ecosystem. Human researchers use public interfaces to comment, endorse, and validate AI-generated research through direct engagement (Zhang et al., 20 Aug 2025).

3. Review Pipeline, Iterative Refinement, and Quality Control

aiXiv implements a multi-stage, closed-loop review system combining LLM-based automated peer review with robust quality defense mechanisms:

• Submissions undergo an initial triage by panels of reviewer agents using both “Direct Review” and “Pairwise Review” modes, the latter explicitly comparing the original and revised versions for improvement.
• Reviews rely on retrieval-augmented generation (RAG) pipelines that ground critiques in external literature, ensuring alignment with the current state of the art.
• A specialized prompt injection defense pipeline parses submission PDFs by layout and semantic analysis to detect and block adversarial review bias.
• Iterative revision is supported: research proposals and papers are refined based on detailed feedback. Revised versions are resubmitted and re-evaluated, with improvement assessed quantitatively.

Acceptance of research is determined by a quantitative majority voting: if at least three out of five independent agents (human or AI) vote “accept,” the submission passes. This is formally expressed as $\sum_{i=1}^5 v_i \geq 3$, with $v_i$ indicating binary vote values. The system tracks rates of improvement via metrics such as pairwise assessment accuracy and percentage of revisions rated as improvements by the agents (Zhang et al., 20 Aug 2025).

4. Experimental Validation and Performance Metrics

Extensive experimental evaluations demonstrate reliability and robustness:

• Proposal-level assessment achieves approximately 77% accuracy on curated benchmarks.
• Full-paper evaluations show around 81% accuracy.
• Prompt injection detection, critical for platform integrity, attains 84.8% accuracy on synthetic adversarial cases and 87.9% on known real-world attacks.
• Over 90% of revised submissions receive higher ratings than original versions.
• Voting-based acceptance rates increase significantly after iterative review (proposals: 0% to 45.2%, papers: 10% to 70%).

The experimental criteria encompass methodological quality, novelty, clarity, feasibility, and planning, and leverage both structured agent scoring and comparison against established datasets (Zhang et al., 20 Aug 2025).

5. Integration, Scalability, and Agent Interfaces

aiXiv is engineered for extensibility and interoperable collaboration. The API and MCP interfaces enable:

• Integration of new agent types (e.g., advanced LLMs, human reviewers, reinforcement learning agents).
• Scalable orchestration of content submission, review, revision, and publication across multiple research domains.
• Real-time participation by human researchers in commenting, validation, and discussion, facilitating an open knowledge ecosystem.

Agents can be registered and coordinated via dedicated interfaces, allowing expansion of ecosystem capabilities as new AI and human paradigms emerge (Zhang et al., 20 Aug 2025).

6. Scientific Impact and Future Trajectory

aiXiv’s multidimensional review pipeline and open architecture present substantive advancements over conventional research publication channels:

• Democratizes scientific dissemination by removing human-only barriers for high-quality AI-generated research.
• Accelerates publication via automated review, iterative revision, and rapid improvement cycles, without compromising quality or reproducibility.
• Supports continual agent evolution via planned reinforcement learning integration, allowing agents to learn from aggregate feedback and autonomously acquire new skills.

Future ambitions include expanding support for experimental workflows involving robot scientists and broadening the scope to dynamic, open-ended scientific inquiry with adaptive learning and continual error correction. The integration of human–AI collaboration is designed for co-evolution in scientific practice (Zhang et al., 20 Aug 2025).

7. Comparison with Existing Publication Ecosystems

Contrast with arXiv and traditional venues highlights several differences:

• arXiv relies on rapid dissemination and light human moderation; aiXiv introduces rigorous, automated review and iterative refinement.
• aiXiv’s review pipeline is explicitly architected for agent-mediated validation, handling both human and synthetic content.
• Empirical validation indicates higher acceptance rates and quality improvement after review, addressing the scalability and reliability constraints faced by existing platforms (Zhang et al., 20 Aug 2025).

A plausible implication is that aiXiv’s approach, if widely adopted, could support increasing volumes of autonomous research generation and fundamentally reshape scientific knowledge dissemination.

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In summary, aiXiv is a next-generation open-access scientific ecosystem rooted in multi-agent collaboration, automated quality assurance, and iterative refinement, designed to serve both human and AI scientists. The combination of scalable agent interfaces, robust review methodologies, and validated quality improvement positions aiXiv as a foundational infrastructure for future autonomous and collaborative scientific discovery (Zhang et al., 20 Aug 2025).