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Mizar: A 159M-Parameter Audio-Language Model for Audio Understanding

Paper recorded by Signals 4 on 2026-09-23 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.CL · 自然语言处理 · first seen 2026-09-24

Abstract

Audio-language models (ALMs) integrate acoustic perception with the knowledge encoded in language models, enabling contextual understanding of auditory events. Making these capabilities practical on devices with limited memory and computation motivates our focus on small ALMs with fewer than 200M parameters. We introduce a recipe that brings together architecture, data, and three-stage training to

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#3 most recent of 239 cs.CL papers we have recorded · ↑ newer: Digital diglossia: Arabic between X and Facebook · ↓ older: Computation Over Geometry: Meaning Identity Is Computed, Not Shipped i
Cite this page: Mizar: A 159M-Parameter Audio-Language Model for Audio Understanding: the #3 most recent of 239 cs.CL papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mizar-a-159m-parameter-audio-language-model-for-audio-understanding.html
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