Researchers have proposed a new approach called Divergent-Convergent Reasoning (DCR) to improve the performance of Large Language Models (LLMs). DCR consists of two phases: an exploration phase that generates multiple candidate solutions and a convergent reconciliation phase. The authors show that even a single reconciliation step can improve accuracy, especially when the correct answer is in the minority. They also introduce recursive DCR, which allocates additional test-time compute based on disagreements among exploration outputs. This approach outperforms fixed-compute baselines while using less compute on average. The researchers analyze disagreement via a training-free dispersion metric and find that higher disagreement is associated with larger accuracy improvements from reconciliation. --- Why it matters: This matters to engineers and researchers in AI because it provides new insights into how to improve the performance of Large Language Models at test time, potentially leading to more accurate and efficient reasoning. The proposed approach also reveals emerging scaling laws for agentic LLM systems, which could have significant implications for their development. Source: https://arxiv.org/abs/2608.15303