Chat an Idea. Get a Paper. Autonomous, Collaborative & Self-Evolving.

Just chat with OpenClaw: "Research X" → done.

📄 Our paper is on arXiv — come read it! AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

🇨🇳 中文 · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇫🇷 Français · 🇩🇪 Deutsch · 🇪🇸 Español · 🇧🇷 Português · 🇷🇺 Русский · 🇸🇦 العربية

🏆 Paper Showcase · 🧑‍✈️ Co-Pilot Guide · 📖 Integration Guide · 💬 Discord Community

🏆 Generated Paper Showcase 8 papers across 8 domains — math, statistics, biology, computing, NLP, RL, vision, robustness — generated fully autonomously or with Human-in-the-Loop co-pilot guidance.

🧪 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 | → 日本語テストガイド

🔥 News

• [05/19/2026] v0.5.0 — Multi-Domain Experiment Agents + ARC-Bench — Two headline updates. (1) Domain-specialist execution agents: the experiment stage (Stages 10–13) now routes beyond the default ML sandbox to specialist agents per field — high-energy physics (ColliderAgent: Lagrangian → FeynRules → MadGraph5 → Delphes via the Magnus cloud), biology (COBRApy genome-scale metabolic modelling), and statistics (simulation-study agent), with a generic Docker executor covering chemistry/materials. The pipeline auto-selects the right executor from the research domain. (2) ARC-Bench: a 55-topic open-ended autonomous-research benchmark spanning ML (25), HEP (10), quantum (10), biology (7), and statistics (3) — each topic ships a manifest (research question + conditions + metrics + datasets) and a rubric for graded scoring, all under experiments/arc_bench/, and also released on 🤗 Hugging Face. → Domain Integration Guide
• [04/01/2026] v0.4.0 — Human-in-the-Loop Co-Pilot System — AutoResearchClaw is no longer purely autonomous. New HITL system adds 6 intervention modes (full-auto, gate-only, checkpoint, step-by-step, co-pilot, custom), per-stage policies, and deep human-AI collaboration. Includes: Idea Workshop for hypothesis co-creation, Baseline Navigator for experiment design review, Paper Co-Writer for collaborative drafting, SmartPause (confidence-driven dynamic intervention), ALHF intervention learning, anti-hallucination claim verification, cost budget guardrails, pipeline branching for parallel hypothesis exploration, and CLI commands (attach/status/approve/reject/guide). → Full HITL Guide
• [03/30/2026] Flexible Skill Loading — AutoResearchClaw now supports loading open-source and custom skills from any discipline to further enhance your research experience. 20 pre-loaded skills are included as ready-to-use references, covering scientific writing, experiment design, chemistry, biology, and more — including an A-Evolve agentic evolution skill contributed by the community. Load your own via researchclaw skills install or drop a SKILL.md into .claude/skills/. See Skills Library.
• [03/22/2026] v0.3.2 — Cross-Platform Support + Major Stability — AutoResearchClaw now runs on any ACP-compatible agent backend (Claude Code, Codex CLI, Copilot CLI, Gemini CLI, Kimi CLI) and supports messaging platforms (Discord, Telegram, Lark, WeChat) via OpenClaw bridge. New CLI-agent code generation backend delegates Stages 10 & 13 to external CLI agents with budget control and timeout management. Also includes anti-fabrication system (VerifiedRegistry + experiment diagnosis & repair loop), 100+ bug fixes, modular executor refactoring, --resume auto-detection, LLM retry hardening, and community-reported fixes.

⚡ One Command. One Paper.

# Fully autonomous — no human intervention
pip install -e . && researchclaw setup && researchclaw init && researchclaw run --topic "Your research idea here" --auto-approve

# Co-Pilot mode — collaborate with AI at key decision points
researchclaw run --topic "Your research idea here" --mode co-pilot

🤔 What Is This?

You think it. AutoResearchClaw writes it. You guide the key decisions.

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. Run it fully autonomous, or use Co-Pilot mode to guide the AI at critical decision points — choose research directions, review experiment designs, and co-write the paper. No hallucinate