Fleet of Agents: Coordinated Problem Solving with Large Language Models

While numerous frameworks have been developed to enhance the reasoning abilities of large language models (LLMs), there is a scarcity of methods that effectively balance the trade-off between cost and quality. In this paper, we introduce Fleet of Agents (FoA), a novel and intuitive yet principled framework utilizing LLMs as agents to navigate through dynamic tree searches, employing a genetic-type particle filtering approach. FoA spawns a multitude of agents, each exploring the search space autonomously, followed by a selection phase where resampling based on a heuristic value function optimizes the balance between exploration and exploitation. This mechanism enables dynamic branching, adapting the exploration strategy based on discovered solutions. We conduct extensive experiments on four benchmark tasks, \``Game of 24\'', \``Mini-Crosswords\'', \``WebShop\'' and \``SciBench\'', utilizing four different LLMs, GPT-3.5, GPT-4, LLaMA3.2-11B, and LLaMA3.2-90B. On average across all tasks and LLMs, FoA obtains an absolute quality improvement of $\simeq 5\%$ while requiring only $\simeq 35\%$ of the cost of previous SOTA methods. Notably, our analyses reveal that (1) FoA achieves the best cost-quality trade-off among all benchmarked methods, and (2) FoA+ LLaMA3.2-11B surpasses the Llama3.2-90B model. FoA is publicly available at [https://github.com/au-clan/FoA](https://github.com/au-clan/FoA).

Large language models (LLMs), like GPT-4 and LLaMA, are powerful tools for solving complex problems. But making them reason well often comes with a trade-off between high costs and good quality. Our work introduces a new framework called Fleet of Agents (FoA), which cleverly balances cost and quality. Instead of relying on a single agent or blindly exploring many paths, FoA uses an approach inspired by "genetic" or "evolutionary" algorithms. FoA spawns many small agents to explore possible solutions and then selects the most promising ones to continue, much like nature, which favors the fittest.We tested FoA using various LLMs on tasks such as mathematical puzzles, crosswords, question answering, and online shopping. Across all tasks, FoA consistently resulted in better solution quality while substantially reducing the computational cost compared to existing methods.This means FoA helps AI systems reason more effectively and efficiently, making them more accessible, practical, and sustainable for a wide range of applications. We have made FoA publicly available so others can use and build on it.