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/.

Role-playing agents (RPAs) powered by large language models (LLMs) have been widely utilized in dialogue systems for their capability to deliver personalized interactions. Current evaluations of RPAs mainly focus on personality fidelity, tone imitation, and knowledge consistency, while overlooking emotional fidelity, a key factor that affects user experience. To this end, we propose a benchmark called EmoCharacter to assess emotional fidelity of RPAs in dialogues. EmoCharacter includes two benchmark datasets (single-turn and multi-turn dialogues), three evaluation settings, and six metrics to measure the emotional fidelity between RPAs and the characters they portray. Based on EmoCharacter, we conduct extensive evaluations on RPAs powered by seven widely used LLMs with representative role-playing methods. Our empirical findings reveal that: (1) Contrary to intuition, current role-playing methods often reduce the emotional fidelity of LLMs in dialogues; (2) Enhancing the general capabilities of LLMs does not necessarily improve the emotional fidelity of RPAs; (3) Fine-tuning or In-Context Learning based on real dialogue data can enhance emotional fidelity.