  Large language models (LLMs) have significantly evolved, moving from simple
output generation to complex reasoning and from stand-alone usage to being
embedded into broader frameworks. In this paper, we introduce \emph{Fleet of
Agents (FoA)}, a novel 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 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 experimentally validate FoA using two benchmark tasks, "Game of
24" and "Mini-Crosswords". FoA outperforms the previously proposed
Tree-of-Thoughts method in terms of efficacy and efficiency: it significantly
decreases computational costs (by calling the value function less frequently)
while preserving comparable or even superior accuracy.
