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Radiology report generation (RRG) requires integrating temporal medical images and creating accurate reports. Traditional methods often overlook crucial temporal information. We introduce Libra, a temporal-aware multimodal large language model (MLLM) for chest X-ray (CXR) report generation. Libra combines a radiology-specific image encoder with an MLLM and uses a Temporal Alignment Connector to capture and synthesize temporal information. Experiments show that Libra sets new performance benchmarks on the MIMIC-CXR dataset for the RRG task.