Peak-Detector uses instruction-tuned LLMs and a condensed peak-representation of time-series data to achieve robust cross-modal peak detection with self-generated explanations across ECG, PPG, BCG, and BSG signals.
Available: https://arxiv.org/pdf/2511.07425
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Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign
Peak-Detector uses instruction-tuned LLMs and a condensed peak-representation of time-series data to achieve robust cross-modal peak detection with self-generated explanations across ECG, PPG, BCG, and BSG signals.
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Benchmarking Local Language Models for Social Robots using Edge Devices
Benchmarking 25 LLMs on Raspberry Pi hardware shows Granite4 Tiny Hybrid (7B) balances 2.5 tokens/s, 0.90 tokens/J, and 54.6% MMLU while teaching effectiveness does not require high general knowledge scores.