A recent advancement in large language model (LLM) inference introduces a method called representation consistency (RC) aimed at improving answer aggregation accuracy and coherence. This approach enables the effective combination of multiple candidate responses without requiring intricate changes to existing prompting or sampling techniques. By focusing on the consistency of internal representations across different answers, RC enhances the model's ability to produce more reliable and unified outputs. The method has been detailed in a study published on arXiv, highlighting its potential to streamline inference processes in LLMs. This development aligns with ongoing research efforts to optimize LLM performance during test-time scaling and answer synthesis. The RC method represents a promising direction for improving LLM inference without increasing computational complexity. It contributes to the broader goal of making large language models more dependable and efficient in practical applications.