The Big Picture
Break complex wearable queries into separate user requests and small, typed tasks; a dedicated query agent then pulls only the exact evidence each task needs, keeping answers traceable while using much less context and improving trust and transparency.
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The Evidence
Decompose a multi-part wearable question into distinct intents and ordered tasks so each sub-request keeps its own evidence and time constraints. Use a intent recognition and task decomposition to turn natural-language time phrases into exact dates, fetch structured records, and hand only task-relevant evidence to analysis or advice agents. Compared with feeding the whole record and question into one model, this approach keeps retrieval context much smaller while maintaining similar retrieval accuracy and raising mean trustworthiness and transparency scores (actionability improved inconsistently).
Data Highlights
1Built and tested on a synthetic dataset of 10,000 virtual users
2Each user’s record spans 30 days (March 1–30, 2026) in the evaluation set
3All methods used the same foundation model (deepseek-v4-pro) at temperature 1.0; the task-oriented approach reduced query-stage token consumption while keeping retrieval accuracy comparable
What This Means
Engineers building conversational health assistants or analytics tools — the framework shows how to keep answers traceable and limit what you feed into a language model. Product and technical leaders evaluating safe, auditable health features will find the explicit intent/task structure useful for logging, governance, and review. Researchers working on grounded language interfaces can adopt the explicit intent–task representation to study evidence flow and component-level behavior.
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Key Figures
Fig 1: Figure 1: Overview of the task-oriented multi-agent framework. A composite query is decomposed into isolated intents and typed tasks with explicit dependencies. Specialized agents execute the tasks, and the terminal intent results are aggregated into the final response.
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Learn MoreConsiderations
Evaluation used a synthetic, one-month dataset and automatic scoring, so real-world generalization is untested. The study compares the full system against a direct-model baseline but does not provide component-level ablations to isolate which parts drive gains. Actionability (practical next steps for users) did not consistently improve, so human expert review and user studies are needed before production use.
Methodology & More
Complex questions about wearable data often bundle several requests, repeated intents, and vague time phrases. Representing a user query as a sequence of distinct intents, each broken into typed tasks with explicit predecessor links, preserves which evidence feeds which downstream analysis and keeps repeated requests separate. A Manager Agent performs intent recognition and task decomposition; tasks are mapped deterministically to specialized agents. The Query Agent resolves natural-language time expressions to exact dates, reads structured records using those indices, and returns only the task-relevant textual evidence to later agents.
The setup was validated on a synthetic dataset of 10,000 virtual users with 30 days of data per user. Using the same underlying language model for fair comparison, the task-oriented framework achieved comparable structured retrieval accuracy to a baseline that ingests the full record, but constructed much smaller query-stage contexts (fewer tokens) by returning only needed evidence. Mean Trustworthiness and Transparency scores favored the framework, while Actionability did not improve consistently. Results suggest that explicit intent organization and evidence scoping help make wearable-data answers more traceable and more trustworthy, but real-world testing and component-level analysis are needed before deployment.
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Credibility Assessment:
Single-author arXiv preprint with no listed affiliation or citation/h-index signals — little identifiable credibility information.