Planning retrieval steps before searching for evidence improves both explanation quality and interpretability—the system can show why it chose specific evidence rather than just providing answers.
A-MAR is an AI system that explains artworks by breaking down questions into structured reasoning steps, then retrieving relevant evidence for each step. Unlike standard AI models that give answers based on internal knowledge, A-MAR shows its work—decomposing art questions into explicit goals, finding supporting evidence, and building explanations step-by-step.