Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data

• URL: http://arxiv.org/abs/2605.18763v1
• Date: Fri, 10 Apr 2026 13:13:17 GMT
• Title: Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data
• Abstract summary: Wearable As Graph (WAG) organizes wearable metrics and user-specific signals into a personalized knowledge graph.<n>WAG retrieves a query-conditioned subgraph to support downstream generation.<n>We evaluate WAG on over 10,000 data-grounded queries from real-world wearable datasets.
• Score: 4.084164541001164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, capturing prior knowledge and population- and individual-level patterns via hierarchical Bayesian modeling, with local relationships that reflect short-term signal deviations. A query openness signal further controls retrieval breadth. We evaluate WAG on over 10,000 data-grounded queries from real-world wearable datasets. Across LLM-based and human evaluations, WAG achieves an approximately 70% win rate over baseline and standard RAG methods, demonstrating the effectiveness of structured, query-adaptive context retrieval for LLM-driven analysis of wearable data.

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