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arxiv: 2604.13715 · v1 · submitted 2026-04-15 · 💻 cs.SD · cs.AI

Recognition: unknown

Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt

Yanfeng Shi , Pengfei Cai , Jun Liu , Qing Gu , Nan Jiang , Lirong Dai , Ian McLoughlin , Yan Song
Authors on Pith no claims yet

Pith reviewed 2026-05-10 12:34 UTC · model grok-4.3

classification 💻 cs.SD cs.AI
keywords Large Audio-Language ModelsTemporal PerceptionAudio-Side Time PromptReinforcement LearningAudio GroundingSound Event DetectionDense Audio Captioning
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The pith

Interleaving timestamp embeddings with RL post-training improves temporal perception in large audio-language models.

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

Large audio-language models struggle with precise timing of audio events such as inferring exact onset and offset. The paper introduces an Audio-Side Time Prompt that encodes timestamps as embeddings and interleaves them directly within the audio feature sequence to act as explicit temporal coordinates. It follows this with reinforcement learning after supervised fine-tuning to optimize the model specifically for temporal alignment. Experiments report gains on audio grounding, sound event detection, and dense audio captioning. A sympathetic reader would care because accurate event timing expands these models' usefulness in fine-grained audio analysis scenarios.

Core claim

The TimePro-RL framework encodes timestamps as embeddings interleaved within the audio feature sequence as temporal coordinates to prompt the model, and uses reinforcement learning following supervised fine-tuning to directly optimize temporal alignment performance, resulting in significant gains across audio temporal tasks.

What carries the argument

Audio-Side Time Prompt, which interleaves timestamp embeddings within the audio feature sequence to provide explicit temporal coordinates to the model.

If this is right

  • Significant performance gains in audio grounding tasks.
  • Improved accuracy in sound event detection with precise timing.
  • Better performance in dense audio captioning involving temporal details.
  • The approach demonstrates robust effectiveness across multiple audio temporal tasks.
  • General audio understanding capabilities remain intact after the post-training steps.

Where Pith is reading between the lines

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

  • The prompting method could extend to video-language models to address similar timing issues in visual events.
  • RL-based optimization may reduce reliance on large amounts of precisely timed labeled audio data.
  • Real-time audio monitoring applications could gain from the added timing precision.
  • Analogous coordinate interleaving might improve temporal tasks in speech or music analysis.

Load-bearing premise

The assumption that interleaving timestamp embeddings and applying RL after SFT will improve temporal alignment without degrading other capabilities or overfitting to the chosen evaluation sets.

What would settle it

Evaluating the fine-tuned model on a new audio dataset with temporal annotations and finding no improvement or degradation in onset and offset prediction metrics compared to the base model.

Figures

Figures reproduced from arXiv: 2604.13715 by Ian McLoughlin, Jun Liu, Lirong Dai, Nan Jiang, Pengfei Cai, Qing Gu, Yanfeng Shi, Yan Song.

Figure 1
Figure 1. Figure 1: Overview of our TimePro-RL framework. Timestamp Embeddings are interleaved within audio features as time prompt, followed by SFT and RL post-training. prompt” at the input level, effectively reducing reasoning diffi￾culty and mitigating hallucinations. Inspired by this, we propose Audio-Side Time Prompt (ASTP), in which timestamps are encoded into embedding vec￾tors and interleaved within the audio feature… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of attention weights on Timestamp Em￾beddings. For the audio grounding query “a train horn honk￾ing”, the mel-spectrogram (top) is aligned with the chronolog￾ically arranged attention weights assigned to each Timestamp Embedding (bottom) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

Large Audio-Language Models (LALMs) enable general audio understanding and demonstrate remarkable performance across various audio tasks. However, these models still face challenges in temporal perception (e.g., inferring event onset and offset), leading to limited utility in fine-grained scenarios. To address this issue, we propose Audio-Side Time Prompt and leverage Reinforcement Learning (RL) to develop the TimePro-RL framework for fine-grained temporal perception. Specifically, we encode timestamps as embeddings and interleave them within the audio feature sequence as temporal coordinates to prompt the model. Furthermore, we introduce RL following Supervised Fine-Tuning (SFT) to directly optimize temporal alignment performance. Experiments demonstrate that TimePro-RL achieves significant performance gains across a range of audio temporal tasks, such as audio grounding, sound event detection, and dense audio captioning, validating its robust effectiveness.

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

2 major / 2 minor

Summary. The manuscript proposes the TimePro-RL framework to improve fine-grained temporal perception in Large Audio-Language Models. It introduces an Audio-Side Time Prompt that encodes timestamps as embeddings and interleaves them within the audio feature sequence, then applies Reinforcement Learning after Supervised Fine-Tuning to optimize temporal alignment. Experiments are claimed to demonstrate significant performance gains on audio grounding, sound event detection, and dense audio captioning tasks.

Significance. If the empirical results hold under rigorous validation, the work could meaningfully advance post-training techniques for temporal capabilities in multimodal audio models, addressing a known limitation in current LALMs. The combination of prompt-based coordinate injection with RL optimization offers a practical, potentially generalizable approach; credit is due for focusing on a load-bearing capability gap and for the empirical framing that allows direct testing of the central claim.

major comments (2)
  1. [Abstract and Experiments] Abstract and Experiments section: The central claim of 'significant performance gains' is asserted without any quantitative metrics, baseline comparisons, ablation results, or error analysis in the provided abstract. The experiments section must supply concrete numbers (e.g., mAP or F1 improvements on audio grounding and SED) with SFT-only and other controls to substantiate that the time-prompt + RL combination drives the gains rather than other factors.
  2. [Method (RL stage)] Method section (RL stage): The reward formulation for the post-SFT RL step is not shown to be independent of the evaluation metrics used for audio grounding, SED, and dense captioning. If the reward directly optimizes the same temporal alignment scores on data distributionally close to the test sets, this creates a load-bearing risk of metric hacking or overfitting, undermining the claim of robust effectiveness without held-out generalization experiments.
minor comments (2)
  1. [Method] Clarify the precise dimensionality and initialization of the timestamp embeddings and the exact interleaving mechanism within the audio feature sequence (e.g., before or after each frame).
  2. [Discussion] Add a limitations paragraph discussing potential degradation on non-temporal audio tasks after the RL stage.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We address each major comment below and describe the revisions we will implement to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and Experiments] Abstract and Experiments section: The central claim of 'significant performance gains' is asserted without any quantitative metrics, baseline comparisons, ablation results, or error analysis in the provided abstract. The experiments section must supply concrete numbers (e.g., mAP or F1 improvements on audio grounding and SED) with SFT-only and other controls to substantiate that the time-prompt + RL combination drives the gains rather than other factors.

    Authors: We agree that the abstract should include key quantitative results for clarity. In the revised manuscript, we will update the abstract to report specific metrics, including mAP improvements on audio grounding and F1 scores on sound event detection relative to baselines. The experiments section already contains these concrete numbers along with SFT-only controls, ablations isolating the Audio-Side Time Prompt and RL stages (Tables 2–4), and error analysis in Section 4.3, which together demonstrate that the time-prompt + RL combination is responsible for the gains. revision: yes

  2. Referee: [Method (RL stage)] Method section (RL stage): The reward formulation for the post-SFT RL step is not shown to be independent of the evaluation metrics used for audio grounding, SED, and dense captioning. If the reward directly optimizes the same temporal alignment scores on data distributionally close to the test sets, this creates a load-bearing risk of metric hacking or overfitting, undermining the claim of robust effectiveness without held-out generalization experiments.

    Authors: The reward is computed from temporal alignment objectives (e.g., boundary IoU) on the RL training data, which is standard practice but shares surface similarity with evaluation metrics. All reported results use held-out test sets drawn from distinct distributions. We will expand the Method section with an explicit reward equation and add results on further out-of-distribution held-out sets to strengthen the generalization evidence. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical method proposal with independent experimental validation

full rationale

The paper introduces Audio-Side Time Prompt via timestamp embeddings interleaved in audio features, followed by RL after SFT, and reports performance gains on audio grounding, sound event detection, and dense captioning. No derivation chain, equations, or self-citations reduce any claimed result to fitted inputs or prior author work by construction. The central claims rest on experimental outcomes rather than self-referential definitions or predictions, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on standard assumptions of supervised fine-tuning and reinforcement learning for sequence models plus the untested premise that timestamp embeddings can be treated as reliable temporal coordinates without further justification.

axioms (2)
  • domain assumption Timestamp embeddings can be directly interleaved into audio feature sequences to provide usable temporal coordinates.
    Invoked in the description of the Audio-Side Time Prompt without supporting derivation or prior validation cited in the abstract.
  • domain assumption Reinforcement learning after SFT will optimize temporal alignment without introducing new biases or capability regressions.
    Stated as the motivation for the RL stage; no supporting argument appears in the abstract.
invented entities (1)
  • Audio-Side Time Prompt no independent evidence
    purpose: To supply explicit temporal coordinates to the audio encoder of an LALM.
    New technique introduced in the paper; no independent evidence of its effectiveness is supplied beyond the claimed experimental gains.

pith-pipeline@v0.9.0 · 5468 in / 1408 out tokens · 22705 ms · 2026-05-10T12:34:51.030966+00:00 · methodology

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    Introduction Audio conveys a wealth of information, ranging from human speech to environmental events, and serves as a fundamental modality for perceiving the world[1, 2]. Large Audio-Language Models (LALMs) have significantly advanced general audio understanding by integrating the linguistic reasoning of Large Language Models (LLMs) with audio encoders [...

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    Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt

    Method In this section, we elaborate on the TimePro-RL framework, the overall schematic of which is illustrated in Figure 1. We first present the infusion of temporal cues into LALM’s audio input (Section 2.1), and then describe the RL post-training paradigm tailored for audio temporal tasks, focusing on reward design (Section 2.2). 2.1. Audio-Side Time P...

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    Tasks and datasets We conduct experiments across three representative audio tem- poral tasks: Audio Grounding (AG)[11]

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