I Gave an AI a Research Topic — It Came Back With a Full Academic Paper

#ai #opensource #devtools #productivity

Summary: AutoResearchClaw is a 23-stage autonomous research pipeline that takes a topic and outputs a complete academic paper — literature review, experiments, statistical analysis, multi-agent peer review, and conference-ready LaTeX. And it's OpenClaw-compatible.

The Pipeline

I read a paper about Omni-SimpleMem — a multimodal memory framework for AI agents. What caught my attention was a line in the acknowledgments:

"The architecture was discovered by AutoResearchClaw."

The paper was written by an AI that also designed the system it's about.

What It Does

You: Research [topic]
                    ↓
AutoResearchClaw — 23 stages, fully autonomous
                    ↓
You get: A full academic paper with:
  • Literature review (OpenAlex + Semantic Scholar + arXiv)
  • Reproducible experiments with sandbox execution
  • Statistical analysis with charts
  • Multi-agent peer review
  • LaTeX (NeurIPS/ICML/ICLR templates)
  • Citation validation (4-layer anti-fabrication)
  • Self-learning lessons for future runs

The 23 Stages

Stage	What Happens	Why It Matters
1-2	Intent extraction → Literature review	Knows what to research
3-4	Hypothesis generation → Search strategy	Plans the experiment
5-7	Experiment design → Baselines → Setup	Rigorous methodology
8-10	Code generation → Sandbox → Run	Reproducible by design
11-13	Analysis → Multi-agent debate → Extra runs	No p-hacking
14	Paper writing	Introduction to conclusion
15	PIVOT/REFINE loop	Autonomously decides: proceed, refine, or pivot
16-18	Sentinel watchdog → Peer review → Citation check	Anti-hallucination
19-23	LaTeX → Packaging → Self-learning → Archive	Production-ready

The Coolest Part: PIVOT/REFINE

Stage 15 autonomously decides its own fate:

PROCEED — Results look good, write the paper
REFINE — Marginal results, tweak and rerun
PIVOT — Wrong direction, change approach

The OpenClaw Connection

AutoResearchClaw is an OpenClaw-compatible service. It also supports ACP protocol, meaning it works with Codex CLI, Claude Code, Copilot CLI, and more.

What This Means

Before	After
Literature review takes days	Done in hours
Experiments require manual setup	Sandboxed and automated
Paper writing is slow	AI drafts, you refine
Citations may be hallucinated	4-layer verification

Quick Start

git clone https://github.com/aiming-lab/AutoResearchClaw.git
cd AutoResearchClaw
pip install -e .
researchclaw setup
researchclaw run --topic "Your idea" --auto-approve

Resources

GitHub: github.com/aiming-lab/AutoResearchClaw (13K stars)
Paper: arxiv.org/abs/2605.20025
OpenClaw: github.com/openclaw/openclaw

From Shenzhen, watching AI learn to do its own research.

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