# Task-Aware Answer Preservation under Audio Compression for Large Audio Language Models

arXiv: https://arxiv.org/abs/2605.06631 · HTML: https://mlsanity.com/paper/2605.06631/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2026.

## Built on by (most cited first, 2 of 2)

- [On Task Scope and Information Retention in Source Coding](https://mlsanity.com/paper/2609.37575/) (2026)
- [AnchorPrompt: Self-Distilled Soft Prompts for Robust Audio-Language Models](https://mlsanity.com/paper/2610.00706/) (2026)

## Abstract (arXiv)

Large audio language models (LALMs) increasingly reason over long audio clips, motivating audio compression to reduce memory use and inference latency. However, audio compression can leave the model's overall answer accuracy acceptable while severely degrading accuracy for particular query families. We introduce a framework for auditing a given audio compression method by measuring the increase in an LALM's answer error relative to uncompressed audio. We formalize an acceptance criterion that limits this increase for every required query family and derive a practical protocol for testing compression settings with statistical confidence. Experiments spanning five multiple-choice audio question-answering benchmarks and four LALMs expose compression-induced damage hidden by overall accuracy.