AI Research Assistant for the AIVC Literature
Transforms academic papers into a queryable knowledge graph — ask natural-language questions, get cross-paper synthesis powered by a multi-agent RAG system.
The AIVC field is growing faster than any individual researcher can track. Knowledge is fragmented across hundreds of papers spanning scRNA-seq biology, graph neural networks, variational inference, foundation models, and wet lab validation. There is no tool that lets you ask "which methods address batch effects in single-cell data, and how have they evolved?" and get a grounded, cross-paper answer.
CellScout is built on an Orchestrator + Worker multi-agent pattern. The Orchestrator decomposes research queries into subtasks and routes them to specialized agents. Unlike flat RAG systems, CellScout reasons over a knowledge graph — capturing method lineage, dataset usage, and open problems as typed edges between nodes.
Reads papers and extracts structured knowledge — methods, datasets, core claims, code links — into typed graph nodes.
Traverses the knowledge graph to answer cross-paper questions. Finds method evolution chains, dataset overlaps, and research gaps.
Retrieves reference implementations from the knowledge base and adapts them to your pipeline on request.
Decomposes queries, schedules agents, synthesizes results into a coherent research-grade response.
The graph captures biological knowledge structure — not just document chunks. Typed edges enable reasoning that flat vector search cannot do.
Standard RAG retrieves document chunks by vector similarity — it cannot answer "how did method X evolve into method Y" or "which papers share dataset Z and what did they find differently?" The knowledge graph enables graph traversal queries that surface relationships invisible to embedding search. CellScout combines both: vector search for relevance, graph traversal for relational reasoning.