🤖 AI Summary
AI scientist systems face diminished statistical rigor and elevated false discovery risk due to dynamic hypothesis testing. Method: This paper proposes a structured, functional-programming–based assurance framework centered on a novel Research Monad and declarative scaffolding mechanism. Implemented as a Haskell embedded domain-specific language (eDSL) with a monad transformer stack, it enforces online false discovery rate (FDR) correction, strict data isolation, and state consistency constraints *during* LLM-generated code execution—thereby eliminating data leakage and multiple-comparison bias at the architectural level. Contribution/Results: Evaluated across 2,000 simulated hypothesis tests and end-to-end scientific case studies, the framework significantly improves statistical robustness and reproducibility of automated discoveries, establishing a formal, trustworthy foundation for AI-driven scientific research.
📝 Abstract
Sequential statistical protocols require meticulous state management and robust error handling -- challenges naturally suited to functional programming. We present a functional architecture for structural enforcement of statistical rigor in automated research systems (AI-Scientists). These LLM-driven systems risk generating spurious discoveries through dynamic hypothesis testing. We introduce the Research monad, a Haskell eDSL that enforces sequential statistical protocols (e.g., Online FDR (false discovery rate) control) using a monad transformer stack. To address risks in hybrid architectures where LLMs generate imperative code, we employ Declarative Scaffolding -- generating rigid harnesses that structurally constrain execution and prevent methodological errors like data leakage. We validate this approach through large-scale simulation (N=2000 hypotheses) and an end-to-end case study, demonstrating essential defense-in-depth for automated science integrity.