PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship
Cancer survivors face elevated rates of depression, anxiety, and general emotional distress, yet the precise moments they most need support are often the moments when self-report is sparse, a phenomenon we term the diary paradox. Passive smartphone sensing offers a continuous, unobtrusive alternative, but prior sensing-based affect prediction has been limited by an accuracy ceiling, suggesting a bottleneck not only in available data, but in how behavioral signals are interpreted. We present PULSE, a system that shifts from fixed feature pipelines to agentic sensing investigation: LLM agents equipped with eight purpose-built tools autonomously query smartphone sensing data, compare current behavior against personalized baselines, and calibrate inferences through retrieval-augmented population-level comparisons. Rather than receiving pre-formatted feature summaries, agents decide which modalities to inspect, how far back to look, and how deeply to investigate, mirroring hypothesis-driven clinical reasoning. We evaluate PULSE through a 2*2 factorial design crossing reasoning architecture (structured vs. agentic) with data modality (sensing-only vs. with diary) on 50 cancer survivors from a longitudinal study of cancer survivors. Agentic reasoning is the primary driver of performance: agentic multimodal agent achieves balanced accuracy of 0.743 for emotion regulation desire with diary and sensing data, while agentic agents predict intervention availability at 0.713 with passive sensing data only. These results suggest that agentic investigation may be a cornerstone for unlocking the clinical value of passive sensing, advancing the feasibility of proactive just-in-time mental health support.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following and making them essential for real-world,…
Reinforcement LearningInstruction FollowingOpportunistic Single-Photon Time of Flight
Scattered light from pulsed lasers is increasingly part of our ambient illumination, as many devices rely on them for active 3D sensing. In this work, we ask: can these "ambient" light signals be detected and leverag…
A Survey on Agentic Multimodal Large Language Models
With the recent emergence of revolutionary autonomous agentic systems, research community is witnessing a significant shift from traditional static, passive, and domain-specific AI agents toward more dynamic, proactive, …
From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete evidence. We argue that a key limitatio…
Reinforcement LearningCoded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces
In this paper, we focus on radar imaging using active sensing with a single transceiver and reconfigurable intelligent surface (RIS). RISs are arrays with tunable passive phase shifter elements that can modify the propag…
Compressive Sensing