Researchers have proposed a method to compress audio tokens in large language models. They used techniques like unsupervised segmentation and pooling to reduce the number of tokens before they are processed by the model's decoder. The team also employed low-rank adapters during fine-tuning to mitigate potential performance degradation. Experiments showed that compressed models can achieve similar results to full-frame models while reducing input audio token count up to three times. --- Why it matters: This matters because large language models require significant computational resources, and compressing their input data can make them more efficient and scalable for real-world applications. Reducing the number of tokens also makes these models more suitable for edge devices with limited processing power. Source: https://arxiv.org/abs/2511.20973