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This paper introduces SAGE, Semantic Audio Generative Encoder: a compact variational autoencoder that shapes its latent by distilling embeddings from a pretrained audio-text model, combining high reconstruction fidelity, state-of-the-art semantic structure, and fast inference.
We show that a quantized model that keeps its classification accuracy still changes $14$ to $46\%$ of its top-1 retrieval results, and that aggregate ranking metrics reveal only part of this damage. We tie this failure to the gap between the two highest model scores and use that gap to decide when to trust a quantized...
It is concluded that tail-shape estimation in the LLM toxicity-evaluation setups the authors examined is more fragile than the recent literature suggests, and recommends the protocol as a starting point for tail-index claims in similar setups.
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