aiming-lab / AutoResearchClaw

Chat an Idea. Get a Paper. Fully Autonomous. Just chat with OpenClaw : "Research X" → done. 🇨🇳 中文 · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇫🇷 Français · 🇩🇪 Deutsch · 🇪🇸 Español · 🇧🇷 Português · 🇷🇺 Русский · 🇸🇦 العربية 📖 Integration Guide · 💬 Discord Community --- > **🧪 We're looking for testers!** Try the pipeline with your own research idea — from any field — and tell us what you think. Your feedback directly shapes the next version. **→ Testing Guide** | **→ 中文测试指南** --- 🔥 News • **[03/17/2026]** v0.3.0 — **MetaClaw Integration** — AutoResearchClaw now supports MetaClaw cross-run learning: pipeline failures → structured lessons → reusable skills, injected into all 23 stages. **+18.3%** robustness in controlled experiments. Opt-in ( ), fully backward-compatible. See Integration Guide. • **[03/16/2026]** v0.2.0 — Three multi-agent subsystems (CodeAgent, BenchmarkAgent, FigureAgent), hardened Docker sandbox with network-policy-aware execution, 4-round paper quality audit (AI-slop detection, 7-dim review scoring, NeurIPS checklist), and 15+ bug fixes from production runs. • **[03/15/2026]** v0.1.0 — We release AutoResearchClaw: a fully autonomous 23-stage research pipeline that turns a single research idea into a conference-ready paper. No human intervention required. --- ⚡ One Command. One Paper. --- 🤔 What Is This? **You think it. AutoResearchClaw writes it.** Drop a research topic — get back a full academic paper with real literature from OpenAlex, Semantic Scholar & arXiv, hardware-aware sandbox experiments (GPU/MPS/CPU auto-detected), statistical analysis, multi-agent peer review, and conference-ready LaTeX targeting NeurIPS/ICML/ICLR. No babysitting. No copy-pasting. No hallucinated references. 📄 paper_draft.md Full academic paper (Introduction, Related Work, Method, Experiments, Results, Conclusion) 📐 paper.tex Conference-ready LaTeX (NeurIPS / ICLR / ICML templates) 📚 references.bib Real BibTeX references from OpenAlex, Semantic Scholar and arXiv — auto-pruned to match inline citations 🔍 verification_report.json 4-layer citation integrity + relevance verification (arXiv, CrossRef, DataCite, LLM) 🧪 experiment runs/ Generated code + sandbox results + structured JSON metrics 📊 charts/ Auto-generated condition comparison charts with error bars and confidence intervals 📝 reviews.md Multi-agent peer review with methodology-evidence consistency checks 🧬 evolution/ Self-learning lessons extracted from each run 📦 deliverables/ All final outputs in one folder — compile-ready for Overleaf The pipeline runs **end-to-end without human intervention**. When experiments fail, it self-heals. When hypotheses don't hold, it pivots. When citations are fake, it kills them. --- 🚀 Quick Start Output → — compile-ready LaTeX, BibTeX, experiment code, charts. 📝 Minimum required config --- 🧠 What Makes It Different | Capability | How It Works | |-----------|-------------| | **🔄 PIVOT / REFINE Loop** | Stage 15 autonomously decides: PROCEED, REFINE (tweak params), or PIVOT (new direction). Artifacts auto-versioned. | | **🤖 Multi-Agent Debate** | Hypothesis generation, result analysis, and peer review each use structured multi-perspective debate. | | **🧬 Self-Learning** | Lessons extracted per run (decision rationale, runtime warnings, metric anomalies) with 30-day time-decay. Future runs learn from past mistakes. | | **📚 Knowledge Base** | Every run builds structured KB across 6 categories (decisions, experiments, findings, literature, questions, reviews). | | **🛡️ Sentinel Watchdog** | Background quality monitor: NaN/Inf detection, paper-evidence consistency, citation relevance scoring, anti-fabrication guard. | --- 🦞 OpenClaw Integration **AutoResearchClaw is an OpenClaw-compatible service.** Install it in OpenClaw and launch autonomous research with a single message — or use it standalone via CLI, Claude Code, or any AI coding assistant. 🚀 Use with OpenClaw (Recommended) If you already use OpenClaw as your AI assistant: **That's it.** OpenClaw handles , , config setup, and pipeline execution automatically. You just chat. 💡 What happens under the hood • OpenClaw reads → learns the research orchestrator role • OpenClaw reads → understands installation and pipeline structure • OpenClaw copies → • Asks for your LLM API key (or uses your environment variable) • Runs + • Returns the paper, LaTeX, experiments, and citations 🔌 OpenClaw Bridge (Advanced) For deeper integration, AutoResearchClaw includes a **bridge adapter system** with 6 optional capabilities: Each flag activates a typed adapter protocol. When OpenClaw provides these capabilities, the adapters consume them without code changes. See for full details. ACP (Agent Client Protocol) AutoResearchClaw can use **any ACP-compatible coding agent** as its LLM backend — no API keys required. The agent communicates via acpx, maintaining a single persistent session across all 23 pipeline stages. | Agent | Command | Notes | |-------|---------|-------| | Claude Code | | Anthropic | | Codex CLI | | OpenAI | | Gemini CLI | | Google | | OpenCode | | SST | | Kimi CLI | | Moonshot | 🛠️ Other Ways to Run | Method | How | |--------|-----| | **Standalone CLI** | | | **Python API** | | | **Claude Code** | Reads — just say *"Run research on [topic]"* | | **OpenCode** | Reads — same natural language interface | | **Any AI CLI** | Provide as context → agent auto-bootstraps | --- 🔬 Pipeline: 23 Stages, 8 Phases > **Gate stages** (5, 9, 20) pause for human approval or auto-approve with . On rejection, the pipeline rolls back. > **Decision loops**: Stage 15 can trigger REFINE (→ Stage 13) or PIVOT (→ Stage 8), with automatic artifact versioning. 📋 What Each Phase Does | Phase | What Happens | |-------|-------------| | **A: Scoping** | LLM decomposes the topic into a structured problem tree with research questions | | **A+: Hardware** | Auto-detects GPU (NVIDIA CUDA / Apple MPS / CPU-only),…