Radiology report generation involves translating visual signals from pixels into precise clinical language. Existing encoder-decoder models often suffer from hallucinations, generating plausible but incorrect medical findings. We propose GraphRAG-Rad, a novel architecture that integrates biomedical knowledge through a novel Latent Visual-Semantic Retrieval (VSR). Unlike traditional Retrieval-Augmented Generation (RAG) methods that rely on textual queries, our approach aligns visual embeddings with the latent space of the Knowledge Graph, PrimeKG. The retrieved sub-graph guides the Visual Encoder and the Multi-Hop Reasoning Module. The reasoning module simulates clinical deduction paths (Ground-Glass Opacity → Viral Pneumonia → COVID-19) before it combines the information with visual features in a Graph-Gated Cross-Modal Decoder. Experiments on the COV-CTR dataset demonstrate that GraphRAG-Rad achieves competitive performance with strong results across multiple metrics. Furthermore, ablation studies show that integrating latent retrieval and reasoning improves performance significantly compared to a visual-only baseline. Qualitative analysis further reveals interpretable attention maps. These maps explicitly link visual regions to symbolic medical concepts, effectively bridging the modality gap between vision and language.

There is a huge demand for information about climate change across all sectors as societies seek to mitigate and adapt to its impacts. However, the volume and complexity of climate information, which takes many formats including numerical, text, and tabular data, can make good information hard to access. Here we use Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to create an AI agent that provides accurate and complete information from the United Kingdom Climate Projections 2018 (UKCP18) data archive. To overcome the problematic hallucinations associated with LLMs, four phases of experiments were performed to optimize different components of our RAG framework, combining various recent retrieval strategies. Performance was evaluated using three statistical metrics (faithfulness, relevance, coverage) as well as human evaluation by subject matter experts. Results show that the best model significantly outperforms a generic LLM (GPT-3.5) and has high-quality outputs with positive ratings by human experts. The UKCP Chatbot developed here will enable access at scale to the UKCP18 climate archives, offering an important case study of using RAG-based LLM systems to communicate climate information.