ForkSCOPE: Charting the Agentic Garden of Forking Paths
Making sense of hundreds of different ways to analyze the same data
When researchers analyze data, they make dozens of small choices—what variables to include, how to handle missing values, which statistical tests to run—and different choices lead to different answers. This paper presents ForkSCOPE, a tool that lets AI generate hundreds of complete analyses automatically, then maps out all these different decision paths in a way humans can actually understand and explore. Instead of forcing analyses into a predetermined category system, ForkSCOPE learns the structure from the analyses themselves, making it possible to scale up without losing insight.
Scientists often disagree about what findings "really mean" because the same dataset can tell different stories depending on analytical choices. ForkSCOPE helps researchers see the full landscape of defensible analyses and spot where small decisions flip conclusions—making it harder to accidentally pick only the results that support a preferred answer. This transparency is especially urgent as AI automates more research; without tools like this, we risk trading human bias for algorithmic black boxes that no one can audit.