A research assistant framework that transforms human research ideas into complete research reports and code repositories. Designed to complement human researchers rather than replace them.
Research Team Structure
- PhD Agent: Research planning and literature review lead
- Postdoc Agent: Expert guidance and methodology refinement
- ML Engineer: Code implementation and technical development
- Professor Agent: Quality evaluation and research direction
How It Works
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Literature Review
- Semantic search across research papers
- Contextual understanding of related work
- Synthesis of key findings and gaps
- Automatic citation management
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Experimentation
- Collaborative experimental design
- Iterative code development and testing
- Results analysis and validation
- Documentation of findings
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Report Writing
- Academic paper structure
- Integration of results and literature
- LaTeX formatting and figure generation
- Citation and reference management
Operation Modes
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Autonomous Mode
- Self-directed research workflow
- Internal peer review process
- Continuous quality monitoring
- Independent decision-making
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Co-Pilot Mode
- Human-AI collaboration
- Regular feedback checkpoints
- Adjustable interaction levels
- Responsive to researcher guidance
Key Tools
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MLE-Solver
- Machine learning code generation
- Self-improving algorithms
- Iterative refinement process
- Error detection and correction
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Paper-Solver
- Research synthesis
- Academic writing
- Results visualization
- Format compliance
External Integrations
- arXiv for literature access
- Hugging Face for ML models
- Python environment for experiments
- LaTeX for document preparation
Resources