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https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-23-atir-towards-audio-text-interleaved-contextual/ 这篇论文旨在解决现有音频-文本检索方法无法处理查询和文档中音频与文本交错出现（如多轮对话、混合输入）的局限性。为此，作者定义了音频-文本交错上下文检索（ATIR）任务，并构建了一个包含约8.8万对样本的大规模基准。为解决直接应用多模态大语言模型（MLLM）时音频token冗余导致的效率和精度问题，论 https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-21-omni-embed-audio-leveraging-multimodal-llms-for/ Tue, 21 Apr 2026 00:00:00 +0000 https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-21-omni-embed-audio-leveraging-multimodal-llms-for/ 这篇论文旨在解决当前音频-文本检索模型在**真实、多样化用户查询**下性能下降的问题。作者指出，现有基准测试（如AudioCaps, Clotho）依赖描述性标题式查询，与真实世界中简短、多变的搜索行为（如问题、命令、关键词、排除性查询）存在巨大差距。为此，论文提出了两大核心贡献：1) **Omni