Imagine a group of brilliant minds tackling a complex problem, bouncing ideas off each other, and collectively arriving at a solution far superior to anything they could achieve alone. That’s the power of brainstorming, and researchers are now discovering that it can significantly boost the reasoning abilities of large language models (LLMs). LLMs, despite their impressive capabilities, can sometimes get stuck in a single perspective, leading to biased or incorrect results. This new research explores how introducing a "brainstorming" process among multiple LLMs can unlock their full potential. The idea is simple yet effective: instead of relying on a single LLM, a group of models with diverse strengths and weaknesses work together, sharing their reasoning processes and iteratively refining their answers. Like a team of human experts, each LLM brings a unique perspective to the table, challenging assumptions and uncovering insights that might otherwise be missed. The results are remarkable. Experiments across various reasoning tasks, from logical puzzles to factual questions, show that brainstorming significantly improves accuracy. In some cases, the combined intelligence of smaller LLMs through brainstorming even rivals the performance of much larger, more computationally expensive models. This discovery has exciting implications for the future of AI. It suggests that we can achieve greater intelligence not just by building bigger models, but by fostering collaboration and knowledge sharing among diverse AI agents. This approach could lead to more robust, reliable, and efficient AI systems, paving the way for breakthroughs in fields like scientific discovery, problem-solving, and decision-making. While the research is still in its early stages, it offers a tantalizing glimpse into the power of collective intelligence in the world of AI. The challenge now is to refine these brainstorming techniques, explore different collaboration strategies, and unlock the full potential of this exciting new frontier in AI research.