NAACA uses a neuro-inspired oscillatory working memory to gate attention in audio language models, raising AudioQwen's average precision from 53.5% to 70.6% on XD-Violence while cutting unnecessary calls.
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2026 2verdicts
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PlanRAG-Audio introduces a planning-based retrieval-augmented generation approach that lets large audio language models handle long recordings by selectively retrieving query-relevant information rather than processing entire audio sequences.
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NAACA: Training-Free NeuroAuditory Attentive Cognitive Architecture with Oscillatory Working Memory for Salience-Driven Attention Gating
NAACA uses a neuro-inspired oscillatory working memory to gate attention in audio language models, raising AudioQwen's average precision from 53.5% to 70.6% on XD-Violence while cutting unnecessary calls.
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PlanRAG-Audio: Planning and Retrieval Augmented Generation for Long-form Audio Understanding
PlanRAG-Audio introduces a planning-based retrieval-augmented generation approach that lets large audio language models handle long recordings by selectively retrieving query-relevant information rather than processing entire audio sequences.