Cervical cancer remains a leading cause of cancer-related mortality among females globally, with diagnosis primarily relying on multi-sequence magnetic resonance imaging (MRI). However, existing Multi-modal Large Language Models (MLLMs) struggle with processing 3D multi-sequence inputs due to high computational complexity and inefficient long-sequence modeling. To this end, we present Cervical-RG, which, to the best of our knowledge, this is the first framework that utilizes 3D multi-sequence MRI images for automated report generation. International Federation of Gynecology and Obstetrics (FIGO) staging, which plays a critical role in cervical cancer management, is also incorporated into the report. The workflow consists of (1) image diagnosis generation. (2) Chain of Thought (CoT)-guided FIGO staging with rationale, and (3) cross-stage consistency verification. Meanwhile, the entire pipeline simulates the collaborative diagnostic process of multi-disciplinary experts in clinical practice. Besides, we propose a novel model to handle multi-sequence inputs, comprising a volumetric multi-sequence encoder and a Mamba-Transformer hybrid decoder, which integrates global attention with selective state-space modeling to effectively handle long-range dependencies and spatial relationships. To validate our method, we curate Cervical-MD—a multi-modal dataset comprising 3,137 volumetrically aligned MRI-report pairs across five sequences (ADC, T1CA, T1CS, T2A, T2S), annotated by two radiologists. Experimental results demonstrate state-of-the-art performance in automated cervical cancer report generation. Our codes will be open-sourced soon.