TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health
arXiv:2605.21295v1 Announce Type: new Abstract: Longitudinal passive sensing enables continuous health prediction, yet models often fail under cross-dataset distribution shifts. Traditional ML overfits cohort-specific artifacts, while Large Language Models (LLMs) struggle to reason reliably over long, heterogeneous time-series. We introduce TimeSRL, a two-stage LLM framework that routes predictions through an explicit semantic bottleneck. The model first abstracts raw signals into high-level natural language, then predicts behavioral outcomes from these abstractions alone. This forces the mode
The proliferation of advanced LLMs combined with increasing demand for robust, generalizable health monitoring systems is driving demand for such innovations.
This research addresses a critical limitation of current AI in healthcare: generalization across diverse datasets, paving the way for more reliable and transferable applications in mental health and beyond.
The explicit semantic bottleneck in TimeSRL provides a new architectural approach for LLMs to manage and reason about complex, heterogeneous time-series data more effectively than previous methods.
- · AI in healthcare developers
- · Mental health tech companies
- · Patients receiving remote care
- · LLM researchers
- · Traditional ML approaches in health
- · Narrowly-scoped health AI models
Improved accuracy and reliability of AI-driven mental health predictions from passive sensing.
Accelerated adoption of LLM-based solutions for longitudinal health monitoring across various conditions.
Ethical and regulatory discussions intensify regarding the use of highly generalizable AI for sensitive personal health data.
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Read at arXiv cs.LG