), but they hadn't opened it for use at that time. However, today I saw the open-source project launched by the Data Science team of the University of Hong KongAI-Researcheras an open-source alternative to Google'sAI Co-ScientistAI-Researcher.
Project address: https://github.com/HKUDS/AI-Researcher/tree/main
I tried running it, but it didn't work, and I feel the code is still incomplete. Waiting for updates from the official side later.
What is AI-Researcher?
AI-Researcher is a fully automatic research system driven by large language models (LLMs) with the following core features:
🎯 Fully autonomous: Achieves end-to-end automation of the research process. 🔄 Seamless collaboration: One-stop handling from research conception to paper publication. 🧠 Advanced AI integration: Adopts cutting-edge AI technology to effectively improve research efficiency. 🚀 Accelerates scientific innovation: Significantly simplifies the scientific exploration process.
Two user input modes
AI-Researcher provides two different research initiation modes to meet various research needs:
Detailed conceptualization mode: Researchers submit specific research ideas and requirements, and the system automatically plans the research scheme accordingly. Reference literature mode: Without pre-setting specific research goals, researchers only need to upload reference literature, and the system automatically generates innovative research points and conducts in-depth research.
Key functions and integration
AI-Researcher integrates multiple key research functions to create a complete research ecosystem:
📚 Literature review: Automatically completes comprehensive analysis of existing research. 📊 Creative generation: Efficiently organizes and proposes new research directions. 🧪 Algorithm design and implementation: Quickly converts research ideas into practical algorithms. 💻 Algorithm validation and optimization: Automatically performs algorithm testing, performance evaluation, and optimization. 📈 Result analysis: Provides in-depth data interpretation and research insights. ✍️ Paper writing: Automatically generates high-quality complete academic papers.
✨ How AI-Researcher works
🔄 End-to-end research automation system
AI-Researcher comprehensively automates the entire research lifecycle through an integrated pipeline. The system organizes research activities across three strategic stages:
Literature review and creative generation 📚💡
🔍 Resource collector: Automatically collects comprehensive research materials from major academic databases (such as arXiv, IEEE Xplore, ACM Digital Library, Google Scholar), code platforms (such as GitHub, Hugging Face), and open datasets systematically. 🧠 Resource filter: Screens out high-impact papers, well-maintained code implementations, and benchmark datasets through quality indicators (such as citation counts, code maintenance status, data integrity) and relevance assessments. 💭 Creative generator: Through in-depth analysis of the screened high-quality resources, systematically proposes novel research directions, automatically evaluates the limitations of existing methods, maps emerging technology trends, and explores unknown research areas.
New algorithm design, implementation, and validation 🧪💻
Design → Implementation → Validation → Optimization
📝 Design phase: Focuses on concept development, proposing algorithm ideas and theoretical foundations, planning implementation strategies, ensuring that the proposed solutions are innovative and practically feasible. ⚙️ Implementation phase: Converts abstract concepts into concrete code implementations, develops functional modules, establishes test environments and infrastructure required for validation. 🔬 Validation phase: Conducts systematic experiments, evaluates algorithm performance, collects metrics and records all findings, ensuring that the algorithm implementation strictly meets actual needs. 🔧 Optimization phase: Iteratively optimizes based on validation results, identifies optimization space, improves code efficiency, and implements necessary improvements.
Paper writing ✍️📝
📄 Writing agent: Automatically generates complete academic papers, integrating research ideas, motivations, newly designed algorithm frameworks, and validated performance. Uses a hierarchical writing method to generate precise and clear papers.
🚀 This fully automated system completely eliminates the need for human intervention throughout the research lifecycle, making scientific discovery from conception to publication seamless and effortless, ideal for researchers to achieve their goals efficiently.
🔬 Comprehensive benchmarking suite
We have developed a comprehensive and standardized evaluation framework to objectively assess the academic capabilities and research quality of AI researchers. Key innovations include:
👨🔬 Expert-level benchmarks: Uses papers written by human experts as benchmarks to ensure high-quality assessment standards. 🌈 Multi-domain coverage: Covers four major research fields: computer vision (CV), natural language processing (NLP), data mining (DM), and information retrieval (IR). 🌐 Fully open source: Publicly discloses the methods and processes for constructing benchmarks, including processed datasets, data collection pipelines, and processing codes, ensuring transparent evaluations. 📊 Comprehensive evaluation metrics: Uses a hierarchical systematic approach; key metrics include innovativeness, comprehensiveness of experiments, strength of theoretical foundation, depth of result analysis, and writing quality.
🚀 This benchmark suite provides an objective framework for evaluating research automation capabilities, continuously evolving to meet the needs of the research community.
🌟 Simple and easy-to-use AI research assistant
AI-Researcher offers barrier-free, simple research automation experience, helping users focus on innovation:
🌐 Support for multiple LLMs: Easily integrates mainstream language model providers such as Claude, OpenAI, Deepseek, etc. 📚 Easy start for research: Just provide a list of relevant papers, and AI-Researcher automatically completes subsequent work without uploading files or complex configurations. 🧠 Extremely low domain threshold: Automatically identifies research gaps, proposes innovative methods, and executes the entire research process. 📦 Ready-to-use: Can be used immediately without complex configurations, quickly starting the research process.