Large language models (LLMs) have shown remarkable progress, but their reasoning abilities often fall short. They sometimes struggle with tasks that humans find easy, revealing a gap between their impressive language skills and true understanding. The SimpleBench benchmark, designed to test real-world reasoning, highlights these limitations. But what if there's a relatively straightforward way to improve LLM reasoning? Researchers explored this question by mimicking human problem-solving strategies, using a multi-step prompting approach with feedback and consistency checks. Instead of asking an LLM for a direct answer, they prompted it to generate reasoning steps iteratively, building a logical chain. This approach was tested with several LLMs, including GPT-4o, Claude 3 Opus, Claude 3.5, and o1-preview. The results were intriguing. This iterative method significantly boosted the performance of baseline models like GPT-4o and Claude 3 Opus, bringing their scores closer to their more advanced counterparts, o1-preview and Claude 3.5, respectively. This suggests that a structured prompting approach can act as a meta-layer of reasoning enhancement, independent of the underlying model architecture. Interestingly, the study revealed different reasoning styles between LLMs. GPT-4o tended to be more exploratory and creative, sometimes overthinking simple problems, while Claude models were more objective and consistent, but less flexible. This difference could be exploited in future systems by combining these approaches, using GPT-4o's creativity to complement Claude’s consistency. The research also highlighted the potential of strategic restarts during the reasoning process. Sometimes, an incorrect initial step can derail the entire chain. By forcing restarts, particularly with more open-ended initial prompts, models can explore alternative paths and find better solutions. The study further emphasizes the importance of context. LLMs often struggle when presented with problems lacking real-world nuance. Giving them the ability to ask clarifying questions, much like humans do, could significantly improve their reasoning. Future research could explore more sophisticated feedback mechanisms, allowing models to not just restart but to generate alternative reasoning branches at the point of divergence. This, along with leveraging Directed Acyclic Graphs (DAGs) for parallel processing and hierarchical abstraction for broader validation, could dramatically enhance reasoning efficiency. Another promising direction is context expansion. Similar to how humans recall relevant knowledge when problem-solving, LLMs could benefit from a system that dynamically broadens the scope of relevant information based on the initial prompt. This could lead to richer and more accurate reasoning chains. Finally, the study suggests that scaling alone may not be enough for achieving true AGI. Targeted scaling focused on reasoning and evaluation capacities, combined with richer datasets designed specifically for these abilities, might be more effective than simply increasing model size. This shift in focus, from parameter expansion to contextual awareness and structured reasoning, offers a promising path towards developing LLMs that can truly reason like humans.