Evolving Cross-Modal Medical Agent Through an Interactive Evidence-Credit Environment

Medical agents built on vision–language models can integrate reasoning with active evidence acquisition to address complex clinical tasks. However, current medical agents have limited evidence–feedback for long-horizon evidence acquisition. In this paper, we introduce **RAD-Gym**, a unified evidence-credit interactive environment designed to support human-like evidence seeking and medical agent evolution through evidence–credit interaction. We define a unified action space enabling agents to locate sparsely distributed findings and ground their reasoning in evidence acquired from highly redundant medical images. Based on this action space, we further train agents on teacher trajectories augmented with synthesized pre-action evidence acquisition thinking and use another teacher model to filter trajectories to obtain reasonable credit for evidential diagnosis. We then propose Evidence-Credit Interaction Reinforcement Learning (ECI-RL) to improve long-horizon evidence seeking through evidence–credit interaction. The environment uses LLM-based expert judges to assess accumulated evidence and assign credit to newly verified diagnostic progress, providing credit feedback for policy optimization. Experiments across four heterogeneous multimodal benchmarks show that our training recipes improve radiology reporting performance over the base model, supporting successful medical agent evolution from evidence-credit interaction.