AgentArxiv — An Open-Access Preprint Repository for AI-Generated Research

[jihuang2024]

Project description

AgentArxiv (ArXivClaw) — An open-access preprint repository and publishing platform designed specifically for AI-generated research outputs.

The Problem

Autonomous research agents (like OpenClaw, GPT-Researcher, AI Scientist, and many others) are increasingly capable of producing research-quality outputs: literature reviews, experiment reports, hypothesis papers, and technical analyses. However, there is no centralized, standardized place for these agents to submit and archive their results. Outputs are scattered across chat logs, local files, or ad-hoc repositories — invisible to other agents and humans alike.

The Vision

AgentArxiv is "arXiv for AI-generated research." It is not another research agent — it is the platform/ecosystem where autonomous research agents can publish their outputs in a structured, searchable, and citable way.

Think of it as the hub that attracts agents to submit results, creating a network effect: the more agents publish, the more valuable the archive becomes for both AI and human researchers.

Core Concept

• Agent-first submission: AI agents can autonomously submit research outputs via a standardized API (JSON/LaTeX/PDF) — no human intervention required.
• Metadata & provenance: Every submission includes agent ID, model version, confidence score, topic tags, references, and timestamps.
• Versioning: Git-like version tracking for paper updates, corrections, and improvements over time.
• Search & discovery: Topic-based search, AI-generated summaries, cross-referencing with existing arXiv papers and datasets.
• Human interaction (optional): Humans can browse, comment, endorse, or refine AI-generated papers — creating a hybrid AI-human collaboration loop.
• AI-assisted review: Other agents can review, score, and flag submissions for quality, novelty, and correctness.

Why It Matters

1. Network effect for AI research: Once agents have a "home" to publish, more agents will submit, creating a self-reinforcing cycle of knowledge accumulation.
2. Transparency & reproducibility: Full provenance tracking (model version, parameters, data sources) makes AI-generated research auditable.
3. Accelerates discovery: Researchers (human or AI) can build on each other's work instead of starting from scratch.
4. Bridges AI and academia: Provides a structured way for AI outputs to enter the scientific discourse.

Architecture Overview

+-------------------------+
|  Autonomous AI Agents   |
|  (OpenClaw, GPT, etc.)  |
+-----------+-------------+
            |
            | Submit via standardized API
            v
+-------------------------+
|   AgentArxiv Backend    |
|  - Database (Postgres)  |
|  - File storage (S3)    |
|  - Metadata indexing    |
|  - Version control      |
|  - AI review pipeline   |
+-------------------------+
            |
            v
+-------------------------+
|   Frontend / API        |
|  - Paper feed & search  |
|  - Agent profiles       |
|  - Export (PDF/JSON)    |
|  - Human comments       |
+-------------------------+

Submission Schema (Example)

{
  "title": "Adaptive PID Tuning via Reinforcement Learning",
  "abstract": "We propose a method for...",
  "agent_id": "openclaw-v2.1",
  "model_version": "gpt-5-mini-20260301",
  "confidence_score": 0.87,
  "categories": ["AI/ML", "Control Systems"],
  "keywords": ["PID", "reinforcement learning", "adaptive control"],
  "sections": {
    "introduction": "...",
    "methods": "...",
    "results": "...",
    "discussion": "...",
    "references": ["arxiv:2401.12345", "doi:10.1234/example"]
  },
  "version": 1,
  "timestamp": "2026-03-24T15:00:00Z"
}

Relevant Technology

• Backend: Python (FastAPI or Django), PostgreSQL, S3-compatible storage
• Frontend: React + Tailwind CSS (or Next.js)
• AI/Agent Integration: REST/GraphQL API for agent submissions
• Search: Elasticsearch or Meilisearch for full-text and metadata search
• Authentication: OAuth2 for human users; API keys for agent submissions
• Version Control: Git-like diffing system for paper revisions
• Deployment: Docker, cloud hosting (AWS/GCP/self-hosted)
• Optional: LaTeX rendering, PDF generation, citation graph visualization

Complexity and required time

Complexity

• Beginner
• Intermediate
• Advanced - The project requires the user to have a good understanding of all components of the project to contribute

Required time (ETA)

• Little work
• Medium work
• Much work - The project will take more than a couple of weeks and serious planning is required

Categories

• Mobile app
• IoT
• Web app
• Frontend/UI
• AI/ML
• APIs/Backend
• Voice Assistant
• Developer Tooling
• Extension/Plugin/Add-On
• Design/UX
• AR/VR
• Bots
• Security
• Blockchain
• Futuristic Tech/Something Unique

[ZigRazor]

Good idea, can i giovedì you support?

[jihuang2024]

Good idea, can i giovedì you support?

Thank you a lot for showing interest. I realized that "https://www.agentarxiv.org/" is very close to this idea, but I wasn't able to find its source code; not sure how people/agent could contribute to it.

Maybe you can own this project and start from there, I am happy to support you : ) Because I have limited experience, time and bandwidth. Thank you.