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arxiv: 2604.16342 · v1 · submitted 2026-03-15 · 💻 cs.HC

SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent

Hansoo Lee , Yoonjae Cho , Sonya S.Kwak , Rafael A. Calvo This is my paper

Pith reviewed 2026-05-15 12:14 UTC · model grok-4.3

classification 💻 cs.HC
keywords sleep monitoringLLM agentssensor datawearablespersonalized healthdata-action gaphuman-computer interactiongrounded AI responses
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The pith

SAGE grounds LLM sleep responses in personal sensor metrics to raise trust and actionability.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes SAGE to close the data-action gap that leaves users with wearables but no clear way to act on their numbers. It builds a normalized, queryable time-series store from continuous sleep, physiological, and activity readings. The engine then lets an LLM answer questions or issue selective alerts by pulling exact periods, personal baselines, and metric comparisons instead of general knowledge. If this grounding works, responses become traceable and personalized enough that users can actually change behavior rather than just view dashboards.

Core claim

SAGE normalizes continuous sleep, physiological, and activity data from sensors into a queryable time-series layer that supports selective system-initiated monitoring for meaningful deviations from personal baselines and user-initiated natural-language questions translated into executable database queries, thereby grounding LLM outputs in precise period, comparison, and metric data to enhance personalization, traceability, and trust.

What carries the argument

The queryable time-series layer that converts raw sensor streams into structured, retrievable records for LLM queries.

If this is right

  • Notifications trigger only on deviations from an individual's own baseline, reducing unnecessary alerts.
  • Natural-language questions receive answers that reference exact time windows and personal comparisons.
  • LLM outputs become directly traceable to the user's own recorded metrics.
  • The same grounding layer can support both proactive monitoring and on-demand Q&A within one agent.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same normalization and query approach could extend to other continuous health signals such as heart-rate variability or step patterns.
  • If traceability improves perceived reliability, users might share more data or sustain use longer than with current apps.
  • Combining the time-series layer with simple rule checks could create hybrid agents that stay both flexible and safe.

Load-bearing premise

That grounding LLM outputs in personal sensor data will meaningfully increase user trust and help close the data-action gap.

What would settle it

A controlled study measuring whether participants using the grounded SAGE agent show higher trust scores and larger sleep-behavior changes than participants using an ungrounded LLM or a static dashboard.

Figures

Figures reproduced from arXiv: 2604.16342 by Hansoo Lee, Rafael A. Calvo, Sonya S.Kwak, Yoonjae Cho.

Figure 1
Figure 1. Figure 1: SAGE Architecture for Sleep Care Agents: Bridges [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Intent Routing Logic: A LangGraph state machine that classifies inputs into chat or data paths to prevent hallucinations [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example of messaging interactions of the SAGE-based conversational sleep care agent [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

Sleep is vital for health, yet access to data alone does not guarantee improvement. While wearables and health apps enable tracking, users face a "Data-Action Gap," struggling to interpret metrics and translate them into action. Current interventions fail to bridge this: static dashboards lack context, rule-based agents rely on rigid scripts, and LLM-agents lack grounding in personal data, causing trust issues. We propose SAGE (Sensor-Augmented Grounding Engine) for an LLM-powered sleep care agent. SAGE normalizes continuous sleep, physiological, and activity data from the sensors into a queryable time-series layer. It supports (1) selective system-initiated monitoring that triggers notifications only upon detecting meaningful deviations against personal baselines to reduce alert fatigue, and (2) user-initiated Q&A where natural language is translated into executable database queries. By ensuring responses are grounded in precise period, comparison, and metric data, SAGE aims to enhance personalization, traceability, and trust, articulating a novel design space for evidence-based messaging in sleep care.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes SAGE (Sensor-Augmented Grounding Engine), an architectural component for LLM-powered sleep care agents. It normalizes continuous sleep, physiological, and activity sensor data into a queryable time-series layer that supports (1) selective, deviation-triggered notifications against personal baselines to reduce alert fatigue and (2) translation of natural-language queries into executable database queries, with the goal of grounding LLM outputs in precise period, comparison, and metric data to improve personalization, traceability, and trust while closing the data-action gap.

Significance. If the proposed grounding mechanisms were implemented and empirically validated, the work could meaningfully advance human-AI interaction in personal health by providing a concrete design pattern for evidence-based messaging that current static dashboards and ungrounded LLM agents lack.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'ensuring responses are grounded in precise period, comparison, and metric data' will enhance personalization, traceability, and trust (and close the data-action gap) is presented as an aim without any prototype, simulation, user study, or even qualitative walkthrough demonstrating measurable effects on trust or behavior.
  2. [Proposed Approach] Proposed system description: The selective notification mechanism depends on detecting 'meaningful deviations' against personal baselines, yet no definition, threshold, or algorithm for identifying such deviations is supplied; this detail is load-bearing for the claim of reduced alert fatigue.
minor comments (1)
  1. A diagram showing the data flow from raw sensors through the normalized time-series layer to the LLM query translator would substantially improve clarity of the architecture.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and insightful comments, which help clarify the scope and strengthen the presentation of SAGE as a proposed architectural component. We address each major comment below, indicating planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'ensuring responses are grounded in precise period, comparison, and metric data' will enhance personalization, traceability, and trust (and close the data-action gap) is presented as an aim without any prototype, simulation, user study, or even qualitative walkthrough demonstrating measurable effects on trust or behavior.

    Authors: We acknowledge that the manuscript presents SAGE as a design proposal without empirical validation or a qualitative walkthrough. The central claims are grounded in the architectural rationale and prior literature on data-action gaps in health tracking, rather than measured outcomes. To address this, we will revise the abstract to explicitly frame the work as a proposed design pattern and add a new subsection to the Discussion that includes illustrative scenarios (e.g., a sample user query and corresponding grounded response) along with a proposed evaluation framework for future studies measuring trust and behavior. This clarifies the current scope while outlining paths to validation. revision: partial

  2. Referee: [Proposed Approach] Proposed system description: The selective notification mechanism depends on detecting 'meaningful deviations' against personal baselines, yet no definition, threshold, or algorithm for identifying such deviations is supplied; this detail is load-bearing for the claim of reduced alert fatigue.

    Authors: We agree that the absence of a concrete definition for 'meaningful deviations' is a significant omission that weakens the selective notification claim. In the revised manuscript, we will expand the Proposed Approach section with a precise specification: deviations are computed via a 30-day rolling z-score against the user's personal baseline (mean and SD) for metrics such as sleep efficiency and total sleep time, triggering notifications only when |z| exceeds 1.5. We will also note that thresholds can be further tuned per user. This addition directly supports the reduced alert fatigue argument with an explicit, implementable algorithm. revision: yes

Circularity Check

0 steps flagged

No circularity: purely architectural proposal with no derivations or fitted results

full rationale

The manuscript is a system design proposal describing SAGE's normalization layer, deviation-triggered notifications, and NL-to-query translation. No equations, parameters, predictions, or derivations appear anywhere. The statement that grounding 'aims to enhance personalization, traceability, and trust' is presented as a design goal rather than a result derived from inputs or self-citations. No self-citation chains, uniqueness theorems, or ansatzes are invoked. The architecture is self-contained and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The proposal rests on domain assumptions about user behavior and AI trust without new evidence or parameters.

axioms (1)
  • domain assumption Grounding LLM responses in personal sensor data will enhance personalization, traceability, and trust
    Central aim stated in the abstract without supporting data
invented entities (1)
  • SAGE no independent evidence
    purpose: Normalizes sensor data into queryable time-series layer for LLM sleep care agent
    Newly proposed system architecture

pith-pipeline@v0.9.0 · 5485 in / 1104 out tokens · 41476 ms · 2026-05-15T12:14:54.182369+00:00 · methodology

discussion (0)

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