Typical Application Scenarios
In the whole process from literature review to the finished paper, the researcher needs to spend 60% time on repetitive work.CortexON can be realized:
- Reduction in time-consuming literature searches 80%
- Increase data organization efficiency by 5 times
- Chart generation on-the-fly
Specific implementation pathways
- Literature phase::
- Configuring Web Proxy to Crawl Google Scholar/PubMed
- Set search syntax: "intitle:deep learning site:.edu after:2022″
- analysis phase::
- Automatically categorize PDFs (by subject/author/impact factor) with document agents
- Calling Zotero management references in conjunction with API proxies
- writing stage::
- Code Agent to generate LaTeX templates
- Executive Agent coordinates assembly of modules
Parameter Optimization Recommendations
Add in .env:ACADEMIC_MODE=true
MAX_PAPER_PAGES=50
CITATION_STYLE=APA
And create research_workflow.yaml to define academic-specific processes
This answer comes from the articleCortexON: Open source multi-agent AI tool to support automation of complex tasksThe
May not be reproduced without permission:AI productivity tools " How can multi-agent AI be applied to achieve efficiency breakthroughs in academic research scenarios?
