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AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention.
Features
- Automatically generates full academic papers from a research topic or idea.
- Retrieves real references from arXiv and Semantic Scholar with citation verification.
- Runs automated experiments with generated code in a sandbox environment.
- Uses multi-agent debate and peer review to refine hypotheses and results.
- Produces conference-ready LaTeX papers compatible with templates like NeurIPS and ICML.
- Includes a multi-stage research pipeline covering literature review, experimentation, analysis, and publication.
Project Samples
