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README.md

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 **StethoLM** is the first audio–language model specialized for cardiopulmonary auscultation, capable of performing instruction-driven clinical tasks across the full spectrum of auscultation analysis. It integrates a cardiopulmonary audio encoder with a medical language model backbone, trained on [StethoBench](https://huggingface.co/datasets/askyishan/StethoBench) — a comprehensive benchmark of 77,027 instruction–response pairs from 16,125 labeled recordings.
 
-> Published at **TMLR 2025**.
+This work is published in the Transactions on Machine Learning Research (TMLR).
 
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 ## Model Description
 
-StethoLM connects a **COLA audio encoder** (EfficientNet-based, pre-trained on cardiopulmonary sounds via [CaReAQA](https://arxiv.org/abs/2501.02225)) to **MedGemma-4B-IT** via a learned MLP prefix projector. The audio is encoded into a short sequence of prefix tokens that are prepended to the text input of the language model. All components — audio encoder, prefix projector, and language model (via LoRA) — are jointly fine-tuned end-to-end.
+StethoLM connects a **COLA audio encoder** (EfficientNet-based, pre-trained on cardiopulmonary sounds via [CaReAQA](https://arxiv.org/abs/2505.01199)) to **MedGemma-4B-IT** via a learned MLP prefix projector. The audio is encoded into a short sequence of prefix tokens that are prepended to the text input of the language model. All components — audio encoder, prefix projector, and language model (via LoRA) — are jointly fine-tuned end-to-end.
 
 **Architecture:**
 - **Audio encoder:** COLA (EfficientNet backbone), pre-trained on cardiopulmonary audio, outputs 1280-dim embeddings; **fine-tuned** during StethoLM training