{
  "annotation_id": "c7d8f24b-2c7d-4d59-88f2-0ba0c422d944",
  "date_created": "2026-02-17T05:53:04.772000Z",
  "date_modified": "2026-02-17T05:53:04.772000Z",
  "file_hash": "2263496d8e1319ecbf4bd136365d45ee27b9406a5488602f7a62feb7583440c3",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.05353",
    "authors": [
      "Shovito Barua Soumma",
      "Hassan Ghasemzadeh"
    ],
    "categories": [
      "cs.LG",
      "cs.IT",
      "math.IT"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting",
    "url": "https://arxiv.org/abs/2601.05353",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "20d49810-88e1-4f16-9e8d-2ed51d33aa92",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}