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arXiv · 2601.18798

ELF: A Family of Encoder-Free ECG-Language Models

William Han·Tony Chen·Chaojing Duan·Xiaoyu Song·Yihang Yao·Yuzhe Yang·Michael A. Rosenberg·Emerson Liu·Ding Zhao
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Abstract

ECG-Language Models (ELMs) extend recent advances in Multimodal Large Language Models (MLLMs) to automated ECG interpretation. However, most existing ELMs inherit Vision-Language Model (VLM) design choices and rely on pretrained ECG encoders, introducing substantial architectural and training complexity. Inspired by encoder-free VLMs, we introduce ELF, a family of three encoder-free ELMs that remain competitive with, and often outperform, prior state-of-the-art ELMs across two datasets despite substantially simpler architectures and training pipelines. All code and data are available at github.com/ELM-Research/ECG-Language-Models.

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William Han, Tony Chen, Chaojing Duan, Xiaoyu Song, Yihang Yao, Yuzhe Yang, Michael A. Rosenberg, Emerson Liu, Ding Zhao. 2026-01-05. ELF: A Family of Encoder-Free ECG-Language Models. https://arxiv.org/abs/2601.18798

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