Researchers have developed a new method called Self-Speculation for Reasoning Models (SSR) to speed up large language models' reasoning processes. SSR uses the model's own chain-of-thought responses at different levels of detail to speculate and verify answers, allowing it to accept longer draft prefixes and reduce latency on tasks like voice assistants or coding agents. The method is particularly effective on structured and long-form generation tasks, with a relative improvement of up to 24.1% in total generation latency for certain models. --- Why it matters: This matters because large language models are increasingly used in applications where speed is crucial, such as voice assistants and coding agents. SSR's ability to reduce latency can improve user experience and make these systems more efficient. Source: https://arxiv.org/abs/2608.20359