research-survey

Automating a literature survey requires integrating semantic search, arXiv web discovery, PDF parsing, and parallel subagent reading. This pipeline orchestrates those steps to download and analyze papers, ultimately generating a structured markdown report for review.

Finding related work with a local vec-db involves semantic search over indexed top-venue papers to gain quick context, supplemented by web search. This approach broadens coverage beyond indexed sources to find related work and emerging methods for your topic.

Generating a structured markdown report for a literature review requires deep-reading papers via parallel subagents to produce comparative analyses. The process anchors insights to your research idea, identifying gaps, connections, and differentiation opportunities for your work.

Yes, you can use parallel subagents to deep-read research papers. The pipeline downloads primary source PDFs and deploys these subagents to read papers and generate structured analyses, summarizing findings into a comprehensive markdown report.

Yes, the pipeline requires a research topic and a target note path. Providing these inputs allows the survey pipeline to run end-to-end and output the structured markdown report directly to your specified location for further synthesis.

A full literature survey pipeline is needed for topics requiring comprehensive coverage from arXiv papers to related work and emerging methods. It is essential when you need to parse PDFs and identify research gaps rather than just finding quick references.