Large language models (LLMs) have gained prominence for their ability to handle complex reasoning tasks, transforming applications from chatbots to code-generation tools. These models are known to benefit significantly from scaling their computation during inference, often producing higher accuracy by dedicating more resources to hard problems. However, this approach brings along considerable drawbacks. Longer processing times and higher computing costs make it challenging to scale such solutions in real-world settings, where responsiveness and affordability are crucial. As technology advances toward more intelligent systems, there is a growing need to explore how LLMs can become not only smarter but also more…