pith. machine review for the scientific record. sign in

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

Ian McLoughlin, Jun Liu, Lirong Dai, Nan Jiang, Pengfei Cai, Qing Gu, Yanfeng Shi, 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
0
0 comments X

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

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

38 extracted references · 12 canonical work pages · 6 internal anchors

  1. [1]

    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 [...

  2. [2]

    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...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  3. [3]

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

    Experimental setup 3.1. Tasks and datasets We conduct experiments across three representative audio tem- poral tasks: Audio Grounding (AG)[11]. The audio grounding task is defined as localizing a specific sound event within an audio clip based on a descriptive natural language query. For this task, we utilize the FTAR dataset [8] and require the model to ...

  4. [4]

    random init

    Results In this section, we first evaluate TimePro-RL against baselines under zero-shot and finetuned settings. Then, we conduct ab- lation studies to analyze the contributions of components in TimePro-RL. Table 3:Ablation study of different components. ASTP denotes Audio-Side Time Prompt; “random init” refers to random initialization of Timestamp Embeddi...

  5. [5]

    Conclusion In this paper, we propose TimePro-RL, a framework to enhance the fine-grained temporal perception of LALMs. TimePro-RL interleaves Timestamp Embeddings into audio feature sequence to provide temporal cues, and introduces RL post-training with an adaptive temporal reward designed for temporal alignment to further strengthen temporal capabilities...

  6. [6]

    Audio set: An ontol- ogy and human-labeled dataset for audio events,

    J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio set: An ontol- ogy and human-labeled dataset for audio events,” inIEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017, pp. 776–780

    2017

  7. [7]

    Panns: Large-scale pretrained audio neural networks for audio pattern recognition,

    Q. Kong, Y . Cao, T. Iqbal, Y . Wang, W. Wang, and M. D. Plumb- ley, “Panns: Large-scale pretrained audio neural networks for audio pattern recognition,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2880–2894, 2020

    2020

  8. [8]

    Pengi: An audio language model for audio tasks,

    S. Deshmukh, B. Elizalde, R. Singh, and H. Wang, “Pengi: An audio language model for audio tasks,” inAdvances in Neural In- formation Processing Systems (NeurIPS), vol. 36. Curran Asso- ciates, Inc., 2023, pp. 18 090–18 108

    2023

  9. [9]

    SALMONN: towards generic hearing abilities for large language models,

    C. Tang, W. Yu, G. Sun, X. Chen, T. Tan, W. Li, L. Lu, Z. Ma, and C. Zhang, “SALMONN: towards generic hearing abilities for large language models,” inThe Twelfth International Conference on Learning Representations (ICLR). OpenReview.net, 2024

    2024

  10. [10]

    Listen, think, and understand,

    Y . Gong, H. Luo, A. H. Liu, L. Karlinsky, and J. R. Glass, “Listen, think, and understand,” inThe Twelfth International Conference on Learning Representations (ICLR). OpenReview.net, 2024

    2024

  11. [11]

    Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

    Y . Chu, J. Xu, X. Zhou, Q. Yang, S. Zhang, Z. Yan, C. Zhou, and J. Zhou, “Qwen-audio: Advancing universal audio understand- ing via unified large-scale audio-language models,”arXiv preprint arXiv:2311.07919, 2023

    work page internal anchor Pith review arXiv 2023

  12. [12]

    AudioPaLM: A large language model that can speak and listen,

    P. K. Rubenstein, C. Asawaroengchai, D. D. Nguyen, A. Bapna, Z. Borsos, F. d. C. Quitry, P. Chen, D. E. Badawy, W. Han, E. Kharitonovet al., “Audiopalm: A large language model that can speak and listen,”arXiv preprint arXiv:2306.12925, 2023

    work page arXiv 2023

  13. [13]

    Listening between the frames: Bridging temporal gaps in large audio-language mod- els,

    H. Wang, Y . Li, S. Ma, H. Liu, and X. Wang, “Listening between the frames: Bridging temporal gaps in large audio-language mod- els,”arXiv preprint arXiv:2511.11039, 2025

    work page arXiv 2025

  14. [14]

    Sound event detection: A tutorial,

    A. Mesaros, T. Heittola, T. Virtanen, and M. D. Plumbley, “Sound event detection: A tutorial,”IEEE Signal Processing Magazine, vol. 38, no. 5, pp. 67–83, 2021

    2021

  15. [15]

    Multi-domain audio question answering benchmark toward acoustic content reasoning, 2026

    C.-H. H. Yang, S. Ghosh, Q. Wang, J. Kim, H. Hong, S. Ku- mar, G. Zhong, Z. Kong, S. Sakshi, V . Lokegaonkaret al., “Multi-domain audio question answering toward acoustic con- tent reasoning in the DCASE 2025 challenge,”arXiv preprint arXiv:2505.07365, 2025

    work page arXiv 2025

  16. [16]

    Text-to-audio grounding: Building correspondence between captions and sound events,

    X. Xu, H. Dinkel, M. Wu, and K. Yu, “Text-to-audio grounding: Building correspondence between captions and sound events,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 606–610

    2021

  17. [17]

    Detection and classification of acoustic scenes and events,

    D. Stowell, D. Giannoulis, E. Benetos, M. Lagrange, and M. D. Plumbley, “Detection and classification of acoustic scenes and events,”IEEE Transactions on Multimedia, vol. 17, no. 10, pp. 1733–1746, 2015

    2015

  18. [18]

    Audiotime: A temporally-aligned audio-text benchmark dataset,

    Z. Xie, X. Xu, Z. Wu, and M. Wu, “Audiotime: A temporally-aligned audio-text benchmark dataset,” inIEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–5

    2025

  19. [19]

    Grounded-videollm: Sharpening fine- grained temporal grounding in video large language models,

    H. Wang, Z. Xu, Y . Cheng, S. Diao, Y . Zhou, Y . Cao, Q. Wang, W. Ge, and L. Huang, “Grounded-videollm: Sharpening fine- grained temporal grounding in video large language models,” inFindings of the Association for Computational Linguistics (EMNLP). Association for Computational Linguistics, 2025, pp. 959–975

    2025

  20. [20]

    Videochat-r1: Enhancing spatio-temporal perception via reinforcement fine-tuning,

    X. Li, Z. Yan, D. Meng, L. Dong, X. Zeng, Y . He, Y . Wang, Y . Qiao, Y . Wang, and L. Wang, “Videochat-r1: Enhancing spatio-temporal perception via reinforcement fine-tuning,”arXiv preprint arXiv:2504.06958, 2025

    work page arXiv 2025

  21. [21]

    Videotg-r1: Boosting video temporal grounding via curriculum reinforcement learning on re- flected boundary annotations,

    L. Dong, H. Zhang, H. Lin, Z. Yan, X. Zeng, H. Zhang, Y . Huang, Y . Wang, Z.-H. Ling, L. Wanget al., “Videotg-r1: Boosting video temporal grounding via curriculum reinforcement learning on re- flected boundary annotations,”arXiv preprint arXiv:2510.23397, 2025

    work page arXiv 2025

  22. [22]

    Roformer: Enhanced transformer with rotary position embedding,

    J. Su, M. Ahmed, Y . Lu, S. Pan, W. Bo, and Y . Liu, “Roformer: Enhanced transformer with rotary position embedding,”Neuro- computing, vol. 568, p. 127063, 2024

    2024

  23. [23]

    Enhancing temporal un- derstanding in audio question answering for large audio language models,

    A. K. Sridhar, Y . Guo, and E. Visser, “Enhancing temporal un- derstanding in audio question answering for large audio language models,” inConference of the Nations of the Americas Chapter of the ACL (NAACL), 2025, pp. 1026–1035

    2025

  24. [24]

    Number it: Temporal grounding videos like flipping manga,

    Y . Wu, X. Hu, Y . Sun, Y . Zhou, W. Zhu, F. Rao, B. Schiele, and X. Yang, “Number it: Temporal grounding videos like flipping manga,” inIEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), 2025, pp. 13 754–13 765

    2025

  25. [25]

    Time-r1: Post-training large vision language model for temporal video grounding,

    Y . Wang, Z. Wang, B. Xu, Y . Du, K. Lin, Z. Xiao, Z. Yue, J. Ju, L. Zhang, D. Yanget al., “Time-r1: Post-training large vision language model for temporal video grounding,”arXiv preprint arXiv:2503.13377, 2025

    work page arXiv 2025

  26. [26]

    A deep reinforced model for abstractive summarization,

    R. Paulus, C. Xiong, and R. Socher, “A deep reinforced model for abstractive summarization,” inInternational Conference on Learning Representations (ICLR). OpenReview.net, 2018

    2018

  27. [27]

    Self- critical sequence training for image captioning,

    S. J. Rennie, E. Marcheret, Y . Mroueh, J. Ross, and V . Goel, “Self- critical sequence training for image captioning,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7008–7024

    2017

  28. [28]

    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

    D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Biet al., “Deepseek-r1: Incentivizing reason- ing capability in llms via reinforcement learning,”arXiv preprint arXiv:2501.12948, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  29. [29]

    Metrics for polyphonic sound event detection,

    A. Mesaros, T. Heittola, and T. Virtanen, “Metrics for polyphonic sound event detection,”Applied Sciences, vol. 6, no. 6, p. 162, 2016

    2016

  30. [30]

    Sound event detection in domestic environments with weakly labeled data and soundscape synthesis,

    N. Turpault, R. Serizel, A. P. Shah, and J. Salamon, “Sound event detection in domestic environments with weakly labeled data and soundscape synthesis,” inWorkshop on Detection and Classifica- tion of Acoustic Scenes and Events (DCASE), 2019

    2019

  31. [31]

    Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,

    S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic eval- uation measures for machine translation and/or summarization, 2005, pp. 65–72

    2005

  32. [32]

    Qwen2-Audio Technical Report

    Y . Chu, J. Xu, Q. Yang, H. Wei, X. Wei, Z. Guo, Y . Leng, Y . Lv, J. He, J. Linet al., “Qwen2-audio technical report,”arXiv preprint arXiv:2407.10759, 2024

    work page internal anchor Pith review arXiv 2024

  33. [33]

    Qwen2.5-Omni Technical Report

    J. Xu, Z. Guo, J. He, H. Hu, T. He, S. Bai, K. Chen, J. Wang, Y . Fan, K. Danget al., “Qwen2.5-omni technical report,”arXiv preprint arXiv:2503.20215, 2025

    work page internal anchor Pith review arXiv 2025

  34. [34]

    Robust speech recognition via large-scale weak supervision,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” inInternational Conference on Machine Learning (ICML). PMLR, 2023, pp. 28 492–28 518

    2023

  35. [35]

    Lora: Low-rank adaptation of large language models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chenet al., “Lora: Low-rank adaptation of large language models.” inInternational Conference on Learning Rep- resentations (ICLR), vol. 1, no. 2, 2022, p. 3

    2022

  36. [36]

    Audio flamingo 2: An audio- language model with long-audio understanding and expert rea- soning abilities,

    S. Ghosh, Z. Kong, S. Kumar, S. Sakshi, J. Kim, W. Ping, R. Valle, D. Manocha, and B. Catanzaro, “Audio flamingo 2: An audio- language model with long-audio understanding and expert rea- soning abilities,” inInternational Conference on Machine Learn- ing (ICML), ser. Proceedings of Machine Learning Research, vol

  37. [37]

    PMLR / OpenReview.net, 2025

    2025

  38. [38]

    Kimi-Audio Technical Report

    D. Ding, Z. Ju, Y . Leng, S. Liu, T. Liu, Z. Shang, K. Shen, W. Song, X. Tan, H. Tanget al., “Kimi-audio technical report,” arXiv preprint arXiv:2504.18425, 2025

    work page internal anchor Pith review arXiv 2025