Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards transient acoustic events and clinical context; (ii) limited data availability, exacerbated by severe class imbalance. To bridge these gaps, we present Resp-Agent, an autonomous multimodal system orchestrated by a novel Active Adversarial Curriculum Agent (Thinker-A$^2$CA). Unlike static pipelines, Thinker-A$^2$CA serves as a central controller that actively identifies diagnostic weaknesses and schedules targeted synthesis in a closed loop. To address the representation gap, we introduce a Modality-Weaving Diagnoser that weaves EHR data with audio tokens via Strategic Global Attention and sparse audio anchors, capturing both long-range clinical context and millisecond-level transients. To address the data gap, we design a Flow Matching Generator that adapts a text-only Large Language Model (LLM) via modality injection, decoupling pathological content from acoustic style to synthesize hard-to-diagnose samples. As a foundation for these efforts, we introduce Resp-229k, a benchmark corpus of 229k recordings paired with LLM-distilled clinical narratives. Extensive experiments demonstrate that Resp-Agent consistently outperforms prior approaches across diverse evaluation settings, improving diagnostic robustness under data scarcity and long-tailed class imbalance. Our code and data are available at https://github.com/zpforlove/Resp-Agent.

翻译：基于深度学习的呼吸听诊目前面临两个根本性挑战：(i) 固有信息损失，即将信号转换为频谱图会丢弃瞬态声学事件和临床上下文；(ii) 有限的数据可用性，且严重的类别不平衡加剧了这一问题。为弥合这些差距，我们提出了Resp-Agent，这是一个由新型主动对抗课程智能体（Thinker-A$^2$CA）协调的自主多模态系统。与静态流程不同，Thinker-A$^2$CA作为中央控制器，主动识别诊断弱点并在闭环中调度有针对性的合成。为解决表征差距，我们引入了一种模态编织诊断器，它通过战略性全局注意力与稀疏音频锚点，将电子健康记录数据与音频令牌编织在一起，从而同时捕获长程临床上下文和毫秒级瞬态信息。为解决数据差距，我们设计了一种流匹配生成器，它通过模态注入适配纯文本大语言模型，将病理内容与声学风格解耦，以合成难以诊断的样本。作为这些工作的基础，我们引入了Resp-229k，这是一个包含22.9万条录音并配有大语言模型提炼的临床叙述的基准语料库。大量实验表明，Resp-Agent在多种评估设置下持续优于先前方法，在数据稀缺和长尾类别不平衡条件下提升了诊断鲁棒性。我们的代码和数据可在 https://github.com/zpforlove/Resp-Agent 获取。