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Automatic chest X-ray report generation can assist clinical reading, but vision–language models may hallucinate findings or miss subtle cues, and their localized evidence is often hard to inspect. We propose TraceCXR, an evidence-traceable framework that links generated clinical statements to concept-level node traces and localized visual evidence maps. TraceCXR constructs ProtoGraph, which makes clinical concepts evidence-addressable by associating them with disease-aware visual prototypes, so retrieved concepts can be traced back to candidate image evidence. Building on ProtoGraph, EviLoop Prompting retrieves patient-specific graph context from the image and grounds the retrieved cues to localized visual evidence; confidence-gated residual injection suppresses unreliable graph prompts. We further introduce Process-Supervised Node Planning to regularize clinically relevant node selection and concentrated evidence attribution during training. Experiments on IU X-ray, MIMIC-CXR, and CheXpert Plus improve report quality and clinical correctness, with stronger evidence-centric retrieval and competitive grounding and transfer performance. Code: https://github.com/Blaise-H02/TraceCXR.