# Beyond Binary Instrument QA: Probing Instrument Grounding in Music Audio-Language Models

arXiv: https://arxiv.org/abs/2606.31338 · HTML: https://mlsanity.com/paper/2606.31338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 25 of 25)

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- [A Tutorial on Deep Learning for Music Information Retrieval](https://mlsanity.com/paper/1709.04396/) (2017)
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## Abstract (arXiv)

Recent music audio-language models achieve high accuracy on instrument question-answering benchmarks, but it remains unclear whether this reflects robust audio grounding or benchmark-specific shortcuts. In this paper, we introduce an OpenMIC-derived diagnostic benchmark sequence for instrument grounding in music audio-language models, extending binary instrument-presence QA to genre-prior-reduced examples, confusable instrument discrimination, longer audio context, and temporal localization. Across these settings, high binary QA accuracy often fails to predict model behavior: models can exhibit option-position bias, confusable-instrument errors, and temporal response bias. These results suggest that instrument grounding should be evaluated with multi-axis diagnostic benchmarks rather than a single aggregate accuracy.