AURAL-RL
AURAL performs adaptive reasoning in continuous latent space before generating an answer. This checkpoint uses Qwen2.5-Omni-7B with compression factor c = 4, chunk size m = 10, four Gaussian components, and a covariance rank of 16.
Paper · Code and training · AuralReason
Quick start
Use Python 3.12, Linux, and an NVIDIA GPU. Install the AURAL package:
git clone https://github.com/JensenYX/AURAL.git
cd AURAL
python -m pip install -e .
python -m pip install flash-attn==2.8.3 --no-build-isolation
python -m aural.infer --model YuxiangW/AURAL --audio speech.wav
An audio clip can be accompanied by an instruction:
python -m aural.infer --audio speech.wav --text "Describe the emotion in the speaker's voice."
For text input:
python -m aural.infer --text "What is 17 + 25?"
The AURAL loader reads the backbone, joint latent head, processor, and reasoning configuration from this repository. Generation uses latent and EOL temperatures of 1, a maximum of 100 latent states, and greedy answer decoding. The output is text.
Files
model-*.safetensors: Qwen2.5-Omni backbone weights.latent_policy.safetensors: the joint low-rank Gaussian mixture head.aural_config.json: latent dimensions, compression, chunk size, and prompt.- Tokenizer and processor files, including the speaker dictionary required by the Qwen2.5-Omni loader.
The code repository includes SFT, RL, custom-data formats, and training commands.
Citation
@article{wang2026aural,
title={AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models},
author={Wang, Yuxiang and others},
journal={arXiv preprint arXiv:2610.01560},
year={2026}
}