Structured knowledge of method evolution, not just citations, is essential infrastructure for AI agents doing research. This graph enables machines to understand how innovations emerge and build upon each other, unlocking automated idea evaluation and generation.
Intern-Atlas is a structured database of how AI research methods evolve and build on each other, extracted from over 1 million papers. Unlike traditional citation networks, it explicitly maps methodological relationships—showing which techniques led to which innovations and why—making it queryable for AI research agents and enabling automated discovery of new research directions.