Intern-Atlas: 1M-Paper Methodology Evolution Graph as Research Infrastructure for AI Scientists
Presents Intern-Atlas, a large-scale methodology evolution graph built from 1,030,314 AI papers that automatically identifies method-level entities, infers lineage relationships among methodologies, and captures bottlenecks that drove transitions between successive innovations. The graph contains 9,410,201 semantically typed edges and is designed as living research infrastructure for AI scientists navigating the literature. Accepted at IJCAI-ECAI 2026. Appeared on HuggingFace Daily Papers May 3.
Why it matters
As the AI literature grows exponentially, tools that map the actual evolutionary graph of methods — tracking which ideas gave birth to which — become critical infrastructure for systematic research. Intern-Atlas is a large-scale instantiation of this vision with community endorsement at IJCAI-ECAI 2026.
Importance: 2/5
IJCAI-ECAI 2026 accepted paper on research infrastructure, HF Daily Papers on May 3.