AutoThink is a new innovation aimed at improving the performance of local language models (LLMs) by 43% through efficient reasoning techniques. The key features include adaptive classification, which enables models to learn new categories without retraining, and an open-source implementation of Pivotal Token Search. This combination results in enhanced performance with reduced token usage on average, where simple queries are processed faster without heavily taxing computational resources on more complex queries. Key takeaways are the minimal overhead of steering vectors, negligible latency from classification, and the importance of selecting optimal layers for target processing. The author invites feedback on applying adaptive strategies, identifying useful reasoning patterns, and layer optimization methods.