The dominant paradigm for Audio-Text Retrieval (ATR) relies on dual-encoder architectures optimized via mini-batch contrastive learning. However, restricting optimization to local in-batch samples creates a fundamental limitation we term the Gradient Locality Bottleneck (GLB), which prevents the resolution of acoustic ambiguities and hinders the learning of rare long-tail concepts. While external knowledge injection can break this bottleneck, it often triggers a problem called Representation-Drift Mismatch (RDM), where a static knowledge base becomes misaligned with evolving encoders, degrading guidance into noise. To address these intertwined challenges, we propose the Adaptive Self-improving Knowledge (ASK) framework. ASK breaks the GLB via multi-grained knowledge injection and mitigates RDM through a dynamic refinement strategy that synchronizes the knowledge base with the model. Additionally, an adaptive reliability weighting scheme is employed to filter retrieval noise based on cross-modal consistency. Extensive experiments across multiple benchmarks demonstrate that ASK consistently achieves new state-of-the-art performance across various backbones.

翻译：音频文本检索（ATR）的主流范式依赖于通过小批量对比学习优化的双编码器架构。然而，将优化限制于局部批次样本会导致一个根本性限制，我们称之为梯度局部瓶颈（GLB），这阻碍了声学歧义性的解决以及罕见长尾概念的学习。虽然外部知识注入可以打破这一瓶颈，但往往会引发一个称为表征漂移失配（RDM）的问题，即静态知识库与不断演化的编码器之间产生不对齐，从而将指导退化为噪声。为应对这些相互交织的挑战，我们提出了自适应自提升知识（ASK）框架。ASK通过多粒度知识注入打破GLB，并采用动态精化策略来缓解RDM，使知识库与模型同步更新。此外，还引入了一种基于跨模态一致性的自适应可靠性加权机制，以过滤检索噪声。在多个基准上的大量实验表明，ASK能够在各种骨干网络上持续取得新的最优性能。