Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data
Large language models (LLMs) are increasingly applied to analyzing wearable sensing data, which are long-term, multimodal, and highly personalized. A key challenge is context selection: providing insufficient context limits reasoning, while including all available data leads to inefficiency and degraded generation quality. We propose Wearable As Graph (WAG), a graph-based context retrieval framework that enables query-adaptive reasoning over wearable data with LLMs. WAG organizes wearable metrics and user-specific signals into a personalized knowledge graph, and retrieves a query-conditioned subgraph to support downstream generation. The retrieval process integrates global relationships, ...
| Authors | Zhenyu Lu, Mahyar Abbasian, Amir M. Rahmani |
|---|---|
| Primary category | cs.IR |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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