ABSTRACT As large language models (LLMs) continue to advance, they have demonstrated remarkable performance across a wide range of tasks. However, their application in highly specialized scientific domains—such as quantum physics—remains limited, particularly in educational settings where precision and domain expertise are crucial. Given the abstract nature and mathematical rigor of quantum theory, delivering accurate AI‐assisted support in this field is both technically demanding and educationally valuable. To address these challenges, we propose a retrieval‐augmented generation (RAG) framework that incorporates a domain‐specific knowledge base constructed from authoritative quantum physics textbooks. Our approach introduces customized strategies at both the preretrieval and postretrieval stages to enhance the relevance of retrieved content, mitigate hallucinations, and improve factual accuracy and contextual alignment. This tighter integration between retrieval and generation substantially improves the system's ability to produce reliable and pedagogically meaningful responses. To enable rigorous and systematic evaluation, we present PhysicBench—the first comprehensive benchmark specifically designed for quantum physics question answering in RAG systems. Unlike existing datasets, which are either scarce or ill‐suited to this domain, PhysicBench supports fine‐grained evaluation across key components of the RAG pipeline, including document retrieval, ranking effectiveness, and answer generation accuracy. It fills a critical gap in current resources and provides detailed metrics for performance analysis at each stage. Experimental results show that our proposed system significantly outperforms LLM‐only baselines, highlighting the effectiveness of domain‐aware RAG approaches. Overall, this work contributes both a robust technical solution and a much‐needed benchmark to advance research in AI‐powered education and intelligent question answering within the field of quantum physics.

Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we performed an experiment where a participant interacts with a robot in a non-emergency task to experience its behavior and then chooses whether to follow the robot's instructions in an emergency or not. Artificial smoke and fire alarms were used to add a sense of urgency. To our surprise, all 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to safely exit the way they entered.