Imagine a team of AI specialists, each with unique skills, working together to answer your questions with incredible speed and accuracy. That's the promise of multi-agent large language models (LLMs). But how do you orchestrate this AI dream team effectively? New research explores a groundbreaking approach called Adaptive Question Answering (AQA), transforming how LLMs collaborate. Traditional methods often involve complex systems that can be computationally expensive, especially when a simpler approach would suffice. AQA tackles this challenge by treating question answering like a game of strategy. Using a technique called a 'contextual multi-armed bandit,' AQA learns which LLM strategy works best for different types of questions. It acts like a dynamic manager, assigning the right AI specialist to the task, whether it's a simple lookup or a complex reasoning problem. This intelligent delegation not only boosts accuracy but also saves precious processing time and resources. The research demonstrates that AQA effectively maps questions to the best-suited answering strategies, learning which AI agents should collaborate and when. It's like having an AI project manager that constantly refines its strategy for optimal teamwork. This breakthrough has the potential to revolutionize question answering systems, creating more efficient and adaptable AI assistants. The future of AI lies in collaboration, and AQA provides a fascinating glimpse into how we can make that vision a reality.