Designing Human Oversight in Autonomous Scientific Discovery

AI systems are increasingly capable of autonomous scientific discovery (ASD), from execution-grounded research automation to test-time learning that discovers state-of-the-art solutions. Yet effective research still requires human judgment to set direction, resolve ambiguity, and ensure oversight. This paper presents a structured AI-human collaborative workflow that complements ASD by embedding human oversight at every stage of AI-assisted research. The workflow operates as an iterative loop in which an agentic coding assistant reads the full project context, engages the user through structured brainstorming to resolve ambiguities, executes approved plans with progress tracking, and employs cross-family model review for validation. We instantiate this workflow in a web-based ASD system that implements a six-stage supervised pipeline for end-to-end research paper generation with human decision authority at critical junctures. We map each workflow phase to a category of human oversight—oversight by design, oversight by review, and oversight by memory—and argue that human-oversight workflows and ASD systems serve complementary roles: automated systems optimize solutions within well-defined search spaces, while oversight workflows ensure those solutions serve the right goals and accumulate institutional knowledge across sessions.