Imagine having the power of a large language model (LLM) right on your phone or laptop, capable of complex tasks like booking flights, scheduling meetings, or even controlling smart home devices. This future is closer than you think, but there's a catch: LLMs are resource-intensive, and running them efficiently on edge devices like smartphones is a major challenge. They often struggle to manage the many 'tools' (like APIs and functions) they need to interact with, leading to slow performance and battery drain. Researchers are tackling this problem head-on, and a new approach called 'Less-is-More' offers a clever solution. Instead of overwhelming the LLM with all possible tools at once, Less-is-More streamlines the process. It first asks the LLM to identify the tools it *thinks* it needs for a given task. Then, using a smart filtering system, it provides the LLM with only the *most relevant* tools. This targeted approach reduces the LLM's cognitive load, allowing it to make faster, more accurate decisions. The results are impressive. In tests on edge devices, Less-is-More significantly boosted the success rate of LLMs completing complex tasks, while also cutting execution time by up to 70% and power consumption by up to 40%. This means faster responses and longer battery life for your AI-powered apps. Imagine asking your phone to “Find a nearby Italian restaurant with outdoor seating and book a table for tonight.” Less-is-More makes this type of sophisticated interaction possible, right on your device, without needing to send your data to the cloud. This breakthrough opens exciting possibilities for deploying powerful AI assistants directly onto your devices. While challenges remain, such as handling unexpected errors and adapting to diverse user requests, Less-is-More represents a significant step towards a future where powerful, personalized AI is always at your fingertips.