Imagine searching for the secret of aging in a jungle of scattered scientific papers, each using different terms and ideas. What if we could build a living, explorable map of every credible aging theory, linked to its evidence, ready for both scientists and AI to use?
Aging theory literature is incredibly diverse and sparse. There's no single keyword or database that covers it all.
Theories spread across multiple databases, using inconsistent terminology and diverse conceptual frameworks
Theories range from molecular mechanisms to system-level processes, requiring multi-scale analysis
No comprehensive database exists—theories must be extracted from full-text papers, not just abstracts
Systematically collect, classify, and structure all scientific theories of aging—creating the most comprehensive, queryable knowledge base for aging research. A living, explorable map ready for both scientists and AI to use.
Starting broad, then narrowing focus: maximize recall → fast filtering → precise extraction
This funnel approach—broad to narrow—is essential for handling the wild diversity and complexity of aging theory research. We prioritize recall first, then progressively apply more sophisticated (and expensive) filters to ensure we don't miss important theories while maintaining cost efficiency.
Intelligent multi-agent orchestration for systematic knowledge extraction
Querier Agent
AI-driven query expansion to maximize recall across diverse sources
Collector Agent
Multi-source retrieval with intelligent parsing and quality metrics
Classification Agent
Chain-of-thought reasoning for precise paper classification
Normalization Agent
Multi-stage extraction, validation, and mechanism-based clustering
Retrieval Agent
Semantic search with advanced RAG for scalable question answering
Building the largest structured knowledge base of aging theories