Accurate forecasting of blood glucose from CGM is essential for preventing dysglycemic events, thus enabling proactive diabetes management. However, current forecasting models treat blood glucose readings captured using CGMs as a numerical sequence, either ignoring context or relying on additional sensors/modalities that are difficult to collect and deploy at scale. Recently, LLMs have shown promise for time-series forecasting tasks, yet their role as agentic context extractors in diabetes care remains largely unexplored. To address these limitations, we propose GlyRAG, a context-aware, retrieval-augmented forecasting framework that derives semantic understanding of blood glucose dynamics directly from CGM traces without requiring additional sensor modalities. GlyRAG employs an LLM as a contextualization agent to generate clinical summaries. These summaries are embedded by a language model and fused with patch-based glucose representations in a multimodal transformer architecture with a cross translation loss aligining textual and physiological embeddings. A retrieval module then identifies similar historical episodes in the learned embedding space and uses cross-attention to integrate these case-based analogues prior to making a forecasting inference. Extensive evaluations on two T1D cohorts show that GlyRAG consistently outperforms state-of-the art methods, achieving up to 39% lower RMSE and a further 1.7% reduction in RMSE over the baseline. Clinical evaluation shows that GlyRAG places 85% predictions in safe zones and achieves 51% improvement in predicting dysglycemic events across both cohorts. These results indicate that LLM-based contextualization and retrieval over CGM traces can enhance the accuracy and clinical reliability of long-horizon glucose forecasting without the need for extra sensors, thus supporting future agentic decision-support tools for diabetes management.

翻译：基于连续血糖监测（CGM）数据进行准确的血糖预测对于预防血糖异常事件至关重要，从而能够实现主动的糖尿病管理。然而，当前的预测模型将CGM捕获的血糖读数视为数值序列，要么忽略上下文信息，要么依赖难以大规模收集和部署的额外传感器/模态。近年来，大语言模型（LLM）在时间序列预测任务中显示出潜力，但其作为糖尿病护理中主动上下文提取器的作用在很大程度上仍未得到探索。为了应对这些局限性，我们提出了GlyRAG，一种上下文感知的检索增强预测框架，它能够直接从CGM轨迹中获得对血糖动态的语义理解，而无需额外的传感器模态。GlyRAG采用LLM作为情境化代理来生成临床摘要。这些摘要通过一个语言模型进行嵌入，并在一个采用交叉翻译损失以对齐文本与生理嵌入的多模态Transformer架构中，与基于分块的血糖表示进行融合。随后，一个检索模块在学习到的嵌入空间中识别相似的历史事件，并在进行预测推断之前，利用交叉注意力机制整合这些基于案例的类比。在两个1型糖尿病队列上的广泛评估表明，GlyRAG始终优于最先进的方法，实现了高达39%的均方根误差（RMSE）降低，并在基线基础上进一步降低了1.7%的RMSE。临床评估显示，GlyRAG将85%的预测置于安全区域，并在两个队列中预测血糖异常事件方面实现了51%的改进。这些结果表明，基于LLM的上下文情境化以及对CGM轨迹的检索，可以在无需额外传感器的情况下，提高长时程血糖预测的准确性和临床可靠性，从而为未来用于糖尿病管理的主动决策支持工具提供支持。