arXiv:2603.06638v3 Announce Type: replace Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of health time series modalities and tasks, failing to reflect the diverse domains and extensive temporal dependencies inherent in real-world physiological modeling. To bridge these gaps, we introduce HEARTS (Health Reasoning over Time Series), a unified benchmark for evaluating hierarchical reasoning capabilities of LLMs over general health time series. HEARTS integrates
The rapid advancement and adoption of large language models (LLMs) are pushing their applicability into complex, domain-specific tasks like health time series analysis, necessitating robust and unified benchmarks to assess their performance.
This benchmark will accelerate the development and validation of LLMs for critical real-world healthcare applications, potentially transforming medical diagnostics, patient monitoring, and treatment planning through automated reasoning.
The creation of a unified benchmark specifically for hierarchical reasoning in health time series will standardize evaluation, drive LLM development in healthcare, and allow for more direct comparisons of model capabilities in this complex domain.
- · AI researchers in healthcare
- · Healthcare technology companies
- · Patients requiring predictive analytics
- · Developers of foundational LLMs
- · Traditional time series analytics firms
- · LLMs without strong reasoning capabilities
- · Healthcare systems slow to adopt AI
Improved accuracy and reliability of AI-driven healthcare diagnostics and predictions will emerge.
The integration of LLM-powered reasoning will become a standard component of new healthcare IT infrastructure and medical devices.
Personalized medicine, driven by highly accurate health time series analysis, could become more widely accessible and effective, fundamentally changing patient care models.
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Read at arXiv cs.LG