Clinical diagnosis education requires students to master both systematic reasoning processes and comprehensive medical knowledge. While recent advances in Large Language Models (LLMs) have enabled various medical educational applications, these systems often provide direct answers that could reduce students’ cognitive engagement and lead to fragmented learning. Motivated by these challenges, we propose DDxTutor, a framework that follows differential diagnosis principles to decompose clinical reasoning into teachable components. It consists of a structured reasoning module that analyzes clinical clues and synthesizes diagnostic conclusions, and an interactive dialogue framework that guides students through this process. To enable such tutoring, we construct DDxReasoning, a dataset of 933 clinical cases with fine-grained diagnostic steps verified by doctors. Our experiments demonstrate that fine-tuned LLMs achieve strong performance in generating structured teaching references and conducting interactive diagnostic tutoring dialogues. Human evaluation by medical educators and students validates the framework’s potential and effectiveness for clinical diagnosis education. Our project is available at https://github.com/med-air/DDxTutor.

Recent progress in Large Language Models (LLMs) has opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS). However, existing LLM-driven NAS approaches rely heavily on prompt engineering and domain-specific tuning, limiting their practicality and scalability across diverse tasks. In this work, we propose LM-Searcher, a novel framework that leverages LLMs for cross-domain neural architecture optimization without the need for extensive domain-specific adaptation. Central to our approach is NCode, a universal numerical string representation for neural architectures, which enables cross-domain architecture encoding and search. We also reformulate the NAS problem as a ranking task, training LLMs to select high-performing architectures from candidate pools using instruction-tuning samples derived from a novel pruning-based subspace sampling strategy. Our curated dataset, encompassing a wide range of architecture-performance pairs, encourages robust and transferable learning. Comprehensive experiments demonstrate that LM-Searcher achieves competitive performance in both in-domain (e.g., CNNs for image classification) and out-of-domain (e.g., LoRA configurations for segmentation and generation) tasks, establishing a new paradigm for flexible and generalizable LLM-based architecture search.

The evolution of text-to-image (T2I) generation techniques has introduced new capabilities for information visualization, with the potential to advance knowledge democratization and education. In this paper, we investigate how T2I models can be adapted to generate educational health knowledge contents, exploring their potential to make healthcare information more visually accessible and engaging. We explore methods to harness recent T2I models for generating health knowledge flashcards—visual educational aids that present healthcare information through appealing and concise imagery. To support this goal, we curated a diverse, high-quality healthcare knowledge flashcard dataset containing 2,034 samples sourced from credible medical resources. We further validate the effectiveness of fine-tuning open-source models with our dataset, demonstrating their promise as specialized health flashcard generators. Our code and dataset are available at: https://github.com/med-air/HealthCards.