LAT-Audio

Overview

LAT-Audio is a large audio-language model designed for precise temporal awareness in long-form audio understanding.

Unlike existing models that degrade on long audio, LAT-Audio introduces a progressive global-to-local reasoning paradigm, enabling models to maintain temporal consistency over audio up to 30 minutes.

The core idea is to first construct a global timeline that captures the temporal-semantic structure of the audio, and then perform task-specific reasoning grounded on this timeline.
During reasoning, LAT-Audio iteratively incorporates audio evidence through a Think-With-Audio Chain-of-Thought (TWA-CoT) process, which significantly reduces:

• temporal hallucination (invalid timestamps)
• timestamp drift (progressive misalignment over time)

Model Description

LAT-Audio formulates long-form audio understanding as a structured reasoning process:

1. Global Timeline Construction
   The model summarizes the audio into a coarse temporal structure.

2. Global-to-Local Reasoning
   Downstream tasks are performed conditioned on the global timeline.

3. Think-With-Audio Chain-of-Thought (TWA-CoT)
   The model iteratively retrieves local audio segments and refines its reasoning with grounded evidence.

This design enables robust temporal reasoning under long-context settings, where conventional direct modeling approaches often fail.

Figure 1: Overall framework of LAT-Audio.

Model Variants

We provide two model variants:

Model	Reasoning	Training Data	Description
LAT-Audio	Yes	LAT-Chronicle	Tool-augmented multi-step reasoning model with global-to-local temporal inference
LAT-Audio-Base	No	LAT-Chronicle + in-house	Direct modeling baseline fine-tuned from Qwen3-Omni with more in-house data, offering faster and simpler inference

Quick Start

Download through Hugging Face

pip install -U "huggingface_hub[cli]"
huggingface-cli download mcshao/LAT-Audio --local-dir ./LAT-Audio
huggingface-cli download mcshao/LAT-Audio-Base --local-dir ./LAT-Audio-Base

For detailed inference methods and examples, please refer to the official repository: 👉 https://github.com/alanshaoTT/LAT-Audio-Repo

Citation

If you find this work useful, please cite:

@article{shao2026lataudio,
  title={Listening with Time: Precise Temporal Awareness for Long-Form Audio Understanding},
  author={Shao, Mingchen and Su, Hang and Tian, Wenjie and Mu, Bingshen and Lin, Zhennan and Fan, Lichun and Luo, Zhenbo and Luan, Jian and Xie, Lei},
  journal={arXiv preprint arXiv:2604.22245},
  year={2026}
}

Contact

For questions, feedback, or collaboration:

📧 mcshao@mail.nwpu.edu.cn