Source record

Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2609.24107.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

A multi-agent framework decomposes complex wearable health queries into typed tasks handled by specialized retrieval, analysis, and advice agents that preserve intent boundaries. On synthetic data from 10,000 virtual users, it matches or beats a single-LLM baseline in accuracy and halves query-stage token use, but actionability does not improve consistently.

Explicit intent decomposition with isolated agent state is a concrete workflow pattern, but evaluation is synthetic-only and the authors flag real-data validation and health advice as open problems.

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