[2603.04142] A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series

Computer Science > Machine Learning arXiv:2603.04142 (cs) [Submitted on 4 Mar 2026] Title:A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series Authors:Davide Gabrielli, Paola Velardi, Stefano Faralli, Bardh Prenkaj View a PDF of the paper titled A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series, by Davide Gabrielli and 3 other authors View PDF HTML (experimental) Abstract:Continuous physiological monitoring is central to emergency care, yet deploying trustworthy AI is challenging. While LLMs can translate complex physiological signals into clinical narratives, it is unclear how agentic systems perform relative to zero-shot inference. To address these questions, we present Vivaldi, a role-structured multi-agent system that explains multivariate physiological time series. Due to regulatory constraints that preclude live deployment, we instantiate Vivaldi in a controlled, clinical pilot to a small, highly qualified cohort of emergency medicine experts, whose evaluations reveal a context-dependent picture that contrasts with prevailing assumptions that agentic reasoning uniformly improves performance. Our experiments show that agentic pipelines substantially benefit non-thinking and medically fine-tuned models, improving expert-rated explanation justification and relevance by +6.9 and +9.7 points, respectively. Contrarily, for thinking models, agentic orchestration often degrades explanation quality, including a 14-point...