Dedicated workflow for academic research
OpenDeepSearch is particularly suitable for academic information retrieval, and the following is an optimized configuration plan:
Specialized configuration recommendations
- Selection of academic-friendly LLM models (e.g., Claude-3-Opus)
- Enabling Deep Search Mode for Complex Academic Problems
- Configure a dedicated sequencer to prioritize academic resources (arXiv, ResearchGate, etc.)
Typical Search Patterns
- Conceptual query: "Current theoretical boundaries of quantum entanglement"
- Methodology query: "State-of-the-art NLP pre-training methods for 2023-2025"
- Comparative Analysis: "Comparing Traditional Statistical Methods with Deep Learning in Genomics"
Advanced Techniques
- Use Boolean logic to combine search terms
- Extend your search with citations in the results
- Export search results to a structured format (JSON/Markdown) for subsequent processing
- Build a personalized knowledge base in conjunction with document management tools such as Zotero
With this specialized configuration and skills, the efficiency of academic research can be significantly improved and high-quality research materials can be obtained quickly.
This answer comes from the articleOpenDeepSearch: an open source search tool that supports intelligent reasoningThe
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