From Evidence Orchestration to Reasoning Internalization for Radiology Report Generation

Radiology report generation (RRG) methods often produce diagnostic findings without explicit clinical evidence, which may lead to factual hallucinations and lesion omissions. Although recent MLLM-based medical agents have begun to acquire clinical evidence through external tools, integrating conflicting clinical observations returned by different tools and mitigating error accumulation caused by early tool decisions remain challenging. Thus, we propose an evidence-informed RRG framework. Specifically, deliberative multi-tool evidence orchestration is first introduced, which employs Monte Carlo Tree Search (MCTS) to explore alternative tool-use trajectories and integrate outputs from different tools into structured clinical evidence. During MCTS decision-making, Counterfactual Search-Control Skill Evolution (CSC-SkillEvo) is further incorporated, which constructs counterfactual supervision signals from tool-use trajectories to refine tool-prioritization and search-termination decisions, thereby enabling holistic clinical evidence orchestration. Finally, we devise Curriculum-Annealed On-Policy Self-Distillation (CA-OPSD), which progressively restricts the clinical evidence accessible to the teacher to construct curriculum-based distillation signals, thereby enabling the RRG model to gradually internalize evidence-supported reasoning capabilities. Experiments on public datasets demonstrate that our framework consistently improves the clinical consistency of generated reports, thereby enhancing the reliability of RRG.