This survey asks how far AI Scientist systems are from changing the world, and argues that their impact should be evaluated not by whether they produce plausible papers, but by whether they produce auditable research trajectories that can be inspected, reproduced, and challenged. We first provide a definition of AI Scientists, distinguish them from adjacent scientific AI systems, and organize the field around four core capabilities: knowledge acquisition, hypothesis generation, experimental validation, and evolution. We further synthesize representative systems by autonomy, execution grounding, auditability, human control, and evidence level. The resulting picture shows a field moving from prompt-to-paper automation toward auditable research laboratories, where executable grounding, provenance, and human-governed autonomy become central. We conclude by identifying open challenges toward trustworthy AI-driven science and providing a roadmap for integrating AI Scientists into the future landscape of scientific discovery.

High-quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, stylistic controllability, and efficiency. We present AutoFigure-Edit, an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. By combining long-context understanding, reference-guided styling, and native SVG editing, it enables efficient creation and refinement of high-quality scientific illustrations. To facilitate further progress in this field, we release the video at https://youtu.be/10IH8SyJjAQ, the full codebase at https://github.com/ResearAI/AutoFigure-Edit and provide a live demo for easy access and interactive use at https://autofigure.cc/.