Large language models (LLMs) have shown impressive abilities, but complex reasoning, especially in relational scenarios like kinship or spatial understanding, remains a significant challenge. Think about solving a family riddle or navigating a complex city—tasks humans handle with relative ease. LLMs often stumble with these multi-step relational puzzles. A new research paper introduces "Path-of-Thoughts" (PoT), a novel framework that could change this. Instead of treating reasoning as a single, complex problem, PoT breaks it down into digestible steps: graph extraction, path identification, and reasoning. Imagine creating a map of all the relationships within the problem, highlighting the relevant connections, and then using those highlighted pathways to find the solution. That's essentially what PoT does. First, it extracts essential information like entities and their relationships from the text, creating a graph—a kind of cognitive map. Then, it pinpoints the specific reasoning pathways within that graph that connect to the question. Finally, it uses either the LLM or a symbolic reasoning engine to deduce the answer based on those pathways. This approach allows PoT to outperform existing methods, achieving up to a 21.3% improvement in accuracy on benchmark datasets. More importantly, this method provides greater robustness to errors. LLMs, like humans, sometimes make mistakes. By following multiple potential reasoning paths, PoT can avoid getting derailed by incorrect information, leading to more reliable results. This is a significant step towards making LLMs more reliable and robust reasoners. While promising, PoT also highlights ongoing challenges, particularly the need for higher-quality datasets for training and testing. Inconsistent or incomplete data can hinder accurate evaluation, underlining the need for more robust benchmarking in relational reasoning. The future of LLMs hinges on unlocking their full reasoning potential. PoT offers a promising new direction, paving the way for LLMs to tackle increasingly complex and nuanced real-world problems.