A Survey of AI Scientists

TL;DR

This survey reviews the evolution of AI systems acting as autonomous scientists, introducing a six-stage framework to understand their development, challenges, and future directions in transforming scientific discovery.

Contribution

It presents a unified six-stage methodological framework for autonomous scientific systems and synthesizes recent advancements from foundational modules to scalable, collaborative AI scientists.

Findings

Field evolved from foundational modules to integrated systems

Current frontier focuses on scalability, impact, and human-AI collaboration

Identifies challenges in robustness and governance

Abstract

Artificial intelligence is undergoing a profound transition from a computational instrument to an autonomous originator of scientific knowledge. This emerging paradigm, the AI scientist, is architected to emulate the complete scientific workflow-from initial hypothesis generation to the final synthesis of publishable findings-thereby promising to fundamentally reshape the pace and scale of discovery. However, the rapid and unstructured proliferation of these systems has created a fragmented research landscape, obscuring overarching methodological principles and developmental trends. This survey provides a systematic and comprehensive synthesis of this domain by introducing a unified, six-stage methodological framework that deconstructs the end-to-end scientific process into: Literature Review, Idea Generation, Experimental Preparation, Experimental Execution, Scientific Writing, and Paper Generation. Through this analytical lens, we chart the field's evolution from early Foundational Modules (2022-2023) to integrated Closed-Loop Systems (2024), and finally to the current frontier of Scalability, Impact, and Human-AI Collaboration (2025-present). By rigorously synthesizing these developments, this survey not only clarifies the current state of autonomous science but also provides a critical roadmap for overcoming remaining challenges in robustness and governance, ultimately guiding the next generation of systems toward becoming trustworthy and indispensable partners in human scientific inquiry.