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arxiv: 2605.29300 · v1 · pith:4X52YXQ3new · submitted 2026-05-28 · 💻 cs.CL · cs.AI· cs.SD

MusTBENCH: Benchmarking and Advancing Temporal Grounding in Music LLMs

Daeyong Kwon , Qiyu Wu , Shinobu Kuriya , Junghyun Koo , Shuyang Cui , Zhi Zhong , Wei-Hsiang Liao , Hiromi Wakaki
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Yuki Mitsufuji
This is my paper

Pith reviewed 2026-06-29 07:57 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.SD
keywords temporal groundingmusic LLMsLALMsMusTBENCHMusTbenchmarkaudio-language modelstemporal optimization
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The pith

Existing music large audio-language models fail at precise temporal grounding in audio, but a four-stage optimization recipe called MusT delivers significant gains on expert-validated tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates MusTBENCH to test whether LALMs can tie their answers to the right time segments in music clips, using five question-answering tasks checked by music experts. Current models perform poorly, revealing that they often ignore when events like instrument entries or rhythm changes occur. The authors then introduce MusT, which adapts the music encoder, adapts the LLM, adds supervised fine-tuning, and applies RL-based optimization. Experiments show MusT lifts performance over strong baselines on the benchmark. The work treats temporal grounding as a distinct missing skill needed for real music understanding.

Core claim

MusTBENCH measures temporal grounding via five expert-validated QA tasks focused on localized musical events. Existing LALMs struggle with precise alignment between responses and audio timestamps. MusT, a four-stage temporal optimization recipe of music encoder adaptation, LLM adaptation, LLM supervised fine-tuning, and RL-based optimization, produces significant improvements over strong baselines.

What carries the argument

MusT, the four-stage temporal optimization recipe that performs music encoder adaptation, LLM adaptation, supervised fine-tuning, and RL-based optimization to improve timing alignment.

If this is right

  • MusT-trained models produce responses better aligned with specific timestamps in music audio.
  • MusTBENCH becomes a standard test for evaluating future music LLMs on temporal accuracy.
  • Focus shifts to handling localized events such as instrument entries and rhythmic transitions.
  • Temporal grounding is established as a distinct training target separate from general music content understanding.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar timing weaknesses likely appear in non-music audio tasks such as speech or environmental sound understanding.
  • Improved temporal grounding could support downstream uses like music editing tools or synchronized analysis.
  • The benchmark design may need periodic updates to prevent models from overfitting to its specific question patterns.

Load-bearing premise

The five tasks in MusTBENCH, validated by music experts, measure temporal grounding ability without being skewed by question wording or audio clip selection.

What would settle it

A model that scores high on MusTBENCH but still gives incorrect timestamps for the same events when tested on new music clips not used in the benchmark.

Figures

Figures reproduced from arXiv: 2605.29300 by Daeyong Kwon, Hiromi Wakaki, Junghyun Koo, Qiyu Wu, Shinobu Kuriya, Shuyang Cui, Wei-Hsiang Liao, Yuki Mitsufuji, Zhi Zhong.

Figure 1
Figure 1. Figure 1: (Left) MUSTBENCH examples illustrating five types of temporally grounded music reasoning questions. (Right) Performance on MUSTBENCH comparing open-source baselines with MUST across five temporal grounding tasks, showing consistent gains from our approach. Values are normalized. full music track and asked to predict when an instru￾ment or vocal first enters or finally exits. Even on this straightforward ta… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the MUSTBENCH construction pipeline. (A) Timestamped music captions are generated by segmentation, mood-change modeling, feature-grounded captioning, and cross-validation and rewriting. (B) QA pairs are generated from stem-wise MIDI annotations, timestamped music captions, and human annotations. All generated QA pairs are validated with assistance from human music experts to produce the final b… view at source ↗
Figure 3
Figure 3. Figure 3: TSG predictions for onset and offset local [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of the proposed four-stage training pipeline and model architecture. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (A) TSG predictions during GRPO training. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example of timestamped music caption with [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of timestamped music caption with [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Temporal source grounding (TSG) results across baseline models. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Human-assisted validation instruction for LTR and TAD. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Human-assisted validation instruction for GTO. [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Human-assisted validation instruction for MTR. [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
read the original abstract

Recent Large Audio-Language Models (LALMs) have demonstrated promising abilities in understanding musical content. However, whether their responses are grounded in the correct temporal regions of the audio remains underexplored. This limitation is particularly critical for music understanding, where key information often occurs as temporally localized events, such as instrument entries and rhythmic transitions. To address this gap, we introduce MusTBENCH, a music-expert-validated benchmark designed to evaluate temporal grounding in LALMs through five temporally grounded question-answering tasks. To further improve temporal grounding in existing models, we propose MusT, a novel four-stage temporal optimization recipe spanning music encoder adaptation, LLM adaptation, LLM supervised fine-tuning, and RL-based optimization. Experiments on MusTBENCH show that existing LALMs struggle with precise temporal grounding, while MusT brings significant improvements over strong baselines. These results establish temporal grounding as a key missing capability in current LALMs and position MusTBENCH as a challenging benchmark for future research in temporally grounded music understanding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 1 minor

Summary. The paper introduces MusTBENCH, a music-expert-validated benchmark consisting of five temporally grounded question-answering tasks to evaluate precise temporal grounding in Large Audio-Language Models (LALMs) for music. It also proposes MusT, a four-stage optimization recipe (music encoder adaptation, LLM adaptation, supervised fine-tuning, and RL-based optimization) to improve this capability. Experiments indicate that existing LALMs struggle with temporal grounding while MusT yields significant gains over strong baselines.

Significance. If the benchmark construction, validation, and reported gains hold under scrutiny, the work identifies temporal grounding as an underexplored limitation in music LALMs and supplies both an evaluation resource and a training recipe, which could guide subsequent model development in audio-language modeling.

minor comments (1)
  1. [Abstract] Abstract states that MusT brings 'significant improvements' and that existing LALMs 'struggle,' but provides no quantitative metrics, baseline names, or effect sizes, making it impossible to gauge the practical magnitude of the advance from the provided text alone.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of the manuscript and for noting the potential significance of MusTBENCH as an evaluation resource and MusT as a training recipe. The recommendation is listed as uncertain, but the report contains no specific major comments or requests for clarification. We address this below and remain available to provide further details on benchmark construction, expert validation, or experimental results.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and description introduce MusTBENCH as an externally validated benchmark with five tasks and MusT as a four-stage training recipe. No equations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations appear in the text. Claims about LALMs struggling with temporal grounding and MusT improvements are framed as empirical outcomes on the benchmark, without reduction to author-defined inputs by construction. This matches the default non-circular case for benchmark papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only input provides no equations, datasets, or modeling details from which to extract free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5747 in / 1177 out tokens · 24745 ms · 2026-06-29T07:57:10.460341+00:00 · methodology

discussion (0)

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Reference graph

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