Researchers propose SimulRAG, a framework that uses scientific simulators to improve the accuracy of long-form scientific question answering. This is achieved by grounding generation in external sources and selectively verifying claims with simulator evidence. The authors also release a benchmark dataset for evaluating the performance of such systems. Experiments show that SimulRAG improves informativeness and factuality compared to existing methods. --- Why it matters: This matters because it addresses the issue of hallucination in long-form scientific question answering, which can lead to inaccurate or inconsistent claims. By using simulators to verify claims, researchers can increase trustworthiness and accuracy in AI-generated explanations. Source: https://arxiv.org/abs/2509.25459