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技术目前面临两大根本性挑战：（i）固有的信息损失，即信号转换为频谱图时会丢弃瞬态声学事件与临床上下文；（ii）有限的数据可用性，且因严重的类别不平衡问题而加剧。为弥合这些差距，我们提出了Resp-Agent——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，能够主动识别诊断薄弱环节，并以闭环方式调度针对性合成任务。为解决表征差距，我们引入了模态编织诊断器，该模块通过战略性全局注意力与稀疏音频锚点，将电子健康记录（EHR）数据与音频标记进行编织，从而同时捕获长程临床上下文与毫秒级瞬态特征。针对数据差距，我们设计了流匹配生成器，通过模态注入技术适配纯文本大语言模型（LLM），实现病理内容与声学风格的解耦，以合成难以诊断的样本。作为这些工作的基础，我们构建了Resp-229k基准语料库，包含22.9万条录音及其经LLM提炼的临床叙述。大量实验表明，Resp-Agent在多种评估场景中均持续优于现有方法，显著提升了在数据稀缺和长尾类别不平衡条件下的诊断鲁棒性。我们的代码与数据公开于https://github.com/zpforlove/Resp-Agent。