  Large Language Models (LLMs) have emerged as powerful tools in artificial
intelligence, especially in complex decision-making scenarios, but their static
problem-solving strategies often limit their adaptability to dynamic
environments. We explore the enhancement of reasoning capabilities in LLMs
through Temperature Tree ($T^2$) prompting via Particle Swarm Optimization,
termed as $T^2$ of Thoughts ($T^2oT$). The primary focus is on enhancing
decision-making processes by dynamically adjusting search parameters,
especially temperature, to improve accuracy without increasing computational
demands. We empirically validate that our hybrid $T^2oT$ approach yields
enhancements in, single-solution accuracy, multi-solution generation and text
generation quality. Our findings suggest that while dynamic search depth
adjustments based on temperature can yield mixed results, a fixed search depth,
when coupled with adaptive capabilities of $T^2oT$, provides a more reliable
and versatile problem-solving strategy. This work highlights the potential for
future explorations in optimizing algorithmic interactions with foundational
language models, particularly illustrated by our development for the Game of 24
and Creative Writing tasks.
