개요
AutoResearchClaw는 다중 지능을 통한 협업을 통해 어떤 아이디어로부터 과학 논문을 생성하는 완전히 자동화된 연구 파이프라인입니다. 사람은 여러 대형 언어 모델의 배경을 통합하고 참조 검증, 실험 실행 및 필드 전용 지능과 같은 도구를 지원합니다.
README 미리보기
\n \n\n\nChat an Idea. Get a Paper. Autonomous, Collaborative & Self-Evolving.\n\n\n\n\n Just chat with OpenClaw: "Research X" → done.\n\n\n\n 📄 Our paper is on arXiv — come read it! AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration\n\n\n\n \n\n\n\n\n \n \n \n \n \n \n \n \n\n\n\n 🇨🇳 中文 ·\n 🇯🇵 日本語 ·\n 🇰🇷 한국어 ·\n 🇫🇷 Français ·\n 🇩🇪 Deutsch ·\n 🇪🇸 Español ·\n 🇧🇷 Português ·\n 🇷🇺 Русский ·\n 🇸🇦 العربية\n\n\n\n 🏆 Paper Showcase · 🧑✈️ Co-Pilot Guide · 📖 Integration Guide · 💬 Discord Community\n\n\n---\n\n\n\n\n\n\n\n🏆 Generated Paper Showcase\n8 papers across 8 domains — math, statistics, biology, computing, NLP, RL, vision, robustness — generated fully autonomously or with Human-in-the-Loop co-pilot guidance.\n\n\n\n\n\n---\n\n> **🧪 We're looking for testers!** Try the pipeline with your own research idea — from any field — and [tell us what you think](docs/TESTER_GUIDE.md). Your feedback directly shapes the next version. **[→ Testing Guide](docs/TESTER_GUIDE.md)** | **[→ 中文测试指南](docs/TESTER_GUIDE_CN.md)** | **[→ 日本語テストガイド](docs/TESTER_GUIDE_JA.md)**\n\n---\n\n## 🔥 News\n- **[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)