BALMS: Benchmarking Agentic LLMs for Longitudinal Mental Health Sensing

2026-08-27

AI Summary

为解决心理健康评估的连续监测问题，本文提出BALMS基准，利用大语言模型处理可穿戴设备数据，进行长期心理状态预测与解释。

Abstract

Mental health assessment relies on episodic self-report scales, which convert subjective states such as stress into numerical scores but provide only sparse snapshots of wellbeing. Wearable devices offer longitudinal behavioral and physiological signals for continuous, low-burden monitoring. Recent LLM-driven personal-health agents enable natural language queries over wearable signals, but mainly handle short-term, retrieval-based lookups (e.g., highest step count over a week). They do not evaluate whether agents can reason over long-term signals to predict wellbeing scores paired with evidence-grounded rationales. To address this gap, we introduce BALMS, the first systematic benchmark of LLM-based agentic systems for longitudinal mental health sensing. BALMS spans 3 real-world longitudinal datasets, 2 task families (closed-form wellbeing-score prediction and rationale generation auto-graded by an LLM-as-Judge), 3 agentic paradigms evaluated across 5 open- and closed-source LLM backbones. We find that zero-shot agents rarely outperform a simple mean baseline, except with stronger backbones or compact, semantically meaningful features. Chain-of-thought prompting improves reasoning-oriented backbones, but does not guarantee temporal grounding or numerical correctness. Together with more analysis on efficiency and temporal scaling, BALMS highlights the need for longitudinal mental health agents that selectively retrieve history, ground temporal evidence, and reason over interpretable behavioral features.

Problem

• mental health assessment
• longitudinal signals
• wellbeing prediction
• evidence-grounded rationales
• agentic LLMs

Innovation

• Longitudinal Mental Health Sensing
• Agentic LLMs
• Chain-of-thought Prompting
• Temporal Evidence Grounding