aiXiv: Autonomous Research Publication Platform

• aiXiv is an open-access platform that integrates human and AI agents to generate, review, and refine scientific research through a structured, iterative workflow.
• The platform uses cyclic multi-agent review mechanisms, including pairwise and meta-review systems, achieving high accuracy improvements (e.g., 77% and 81% accuracy).
• Robust API interfaces, retrieval-augmented generation, and real-time public engagement enable seamless, scalable integration of automated and human-driven quality control.

aiXiv is an open-access platform designed to support the autonomous generation, review, and dissemination of scientific research by both human and AI scientists. Its multi-agent architecture orchestrates a closed-loop cycle of submission, critique, iterative refinement, and acceptance, directly addressing the lack of scalable quality control for AI-generated research in traditional publishing systems. The platform is characterized by deep integration of advanced LLM agents, a rich set of programmatic and model-control interfaces, and robust experimental evidence demonstrating reliability and improved quality for AI-generated proposals and papers (Zhang et al., 20 Aug 2025).

1. Multi-Agent Ecosystem Architecture

aiXiv employs a multi-agent design, with distinct agent classes orchestrating each step of the research lifecycle. AI agents autonomously generate proposals and full research papers. Submissions are ingested into a closed-loop workflow, wherein LLM-based reviewer agents—including Single Review, Meta Review, and Pairwise Review agents—evaluate and critique submissions. Meta-review agents consolidate evaluations, synthesizing structured feedback for refinement. Human scientists participate via public interface tools: commenting, liking, and discussing submissions, alongside optional direct involvement in problem formulation or revision guidance. All actions are coordinated programmatically through the API and Model Control Protocol (MCP), which governs agent behaviors and ensures interoperability among heterogeneous (human and AI) actors.

The submission–review–revision–resubmission–voting loop of aiXiv is formally expressed as: $$\text{Submission} \rightarrow \text{Review Agents} \rightarrow \text{Revision} \rightarrow \text{Resubmission} \rightarrow \text{Final Voting}$$ with agent actions dynamically orchestrated to meet platform-defined quality standards.

2. Functional Innovations and Platform Features

aiXiv introduces several principal innovations relative to legacy preprint servers and journal platforms:

• Support for both early-stage research proposals and full papers within the same infrastructure.
• Automated iterative reviewing: Submissions undergo sequential cycles of LLM-based review, revision, and reassessment, in contrast to the single-pass human peer review typical of traditional venues.
• Retrieval-augmented generation (RAG) is built into review agents, which ground their critiques and suggestions in external scientific literature.
• Human–AI integration is achieved via the public interface, allowing real-time user feedback (comments, likes, discussion threads) to inform revision cycles.
• Robust quality assurance safeguards include a multi-stage prompt injection detection and defense pipeline.
• API and MCP interfaces enable upload, retrieval, discussion, and control for both human and AI agents in real time.

The platform’s automated workflows—spanning submission, staged LLM evaluation, and community engagement—yield a scalable, extensible open-access ecosystem for autonomous scientific discovery.

3. Iterative Quality Control and Review Mechanisms

aiXiv’s primary quality control is an automated, cyclic, multi-agent review process:

• Direct Review Mode: Reviewer agents issue structured critiques along key axes—methodological soundness, novelty, clarity, feasibility.
• Pairwise Review Mode: The system evaluates first and revised versions of a submission head-to-head for improvement, using a rubric-informed accuracy measurement.
• A meta-review agent consolidates individual critiques, while community feedback via public interface may supplement agent comments.
• Iterative cycles: Authors (AI or human) revise their work in response to structured feedback. Each revision enters subsequent reviewer cycles until standard thresholds are met.
• Final acceptance is determined by multi-AI voting: A minimum of three ‘accept’ votes out of five models is required for publication.

This cyclic approach provides measurable signals of quality improvement, tracks pairwise assessment accuracy, and ensures progressive enhancement of content rigor and clarity.

4. Experimental Evaluation and Impact

Extensive experiments demonstrate the robustness and reliability of aiXiv:

• Pairwise accuracy at the proposal level reached 77% with GPT-4.1 + RAG; at the paper level, 81% accuracy was validated on ICLR datasets.
• Direct review experiments confirmed substantial quality gains: >90% of revised proposals and papers were rated as improved, rising to nearly 100% when authors provided structured responses to reviewer feedback.
• Prompt injection defense achieved detection rates of 84.8%–87.9% in adversarial settings.
• Multi-agent voting empirically boosts acceptance rates post revision (proposals: 0% → 45.2%, papers: 10% → 70%).

These results—benchmarked against DeepReview and AI Researcher systems—underscore the advantage of aiXiv’s closed-loop, multi-agent quality control for AI-generated scientific content.

5. Integration and Extensibility

aiXiv’s modular programmatic interfaces—its API and MCP layers—enable seamless integration of external agents, tools, and data sources. This supports heterogeneous agents (diverse LLMs, human contributors) and flexible communication protocols, facilitating both real-time and batch operations on submissions, reviews, and revisions. The retrieval-augmented framework is extensible: future upgrades may incorporate new external document sources or more sophisticated grounding algorithms, further accelerating agent learning and review capabilities.

Human engagement mechanisms, such as comments and discussions, provide an additional feedback channel, aligning AI-generated content with community standards and facilitating the convergence of autonomous and human-driven discovery.

6. Future Directions and Ecosystem Implications

aiXiv is positioned as foundational for a next-generation open-access publication system in the AI sciences. The hybrid automated/human review model and continual feedback loops are expected to influence transparent, scalable, and fair assessment in scholarly communication. Planned integration of reinforcement learning and continual agent training may enable autonomous skill acquisition and dynamic adaptation within the ecosystem, supporting long-term scientific agility.

As AI-generated research increases in volume and importance, aiXiv’s multi-agent, iterative review paradigm and standardization of quality controls are poised to drive adoption both within specialized AI research domains and across adjacent scientific disciplines. A plausible implication is the normalization of collaborative human–AI authoring models and the redefinition of peer review standards in open-access publication infrastructures.

Summary Table: Core Components of the aiXiv Platform

Component	Functionality	Agent Types Involved
Submission Portal	Ingest proposals/papers	Human, AI Author Agents
Review Pipeline	Structured critique (Direct, Pairwise)	LLM Reviewer, Meta Agents
Revision Mechanism	Guided iterative improvement	Author Agents
Voting System	Multi-agent acceptance decision	LLM Voting Agents
Public Engagement	Comments, likes, discussion	Human Community, AI Agents

aiXiv thus operationalizes a closed-loop, scalable, and transparent publication workflow that leverages autonomous LLM agents while systematically incorporating human oversight and community engagement (Zhang et al., 20 Aug 2025).