Large Language Models (LLMs) have redefined complex task automation with exceptional generalization capabilities. Despite these advancements, state-of-the-art methods rely on single-strategy prompting, missing the synergy of diverse reasoning approaches. No single strategy excels universally, highlighting the need for frameworks that fuse strategies to maximize performance and ensure robustness. We introduce the Select, Mix, and ReinvenT (SMaRT) framework, an innovative strategy fusion approach designed to overcome this constraint by creating balanced and efficient solutions through the seamless integration of diverse reasoning strategies. Unlike existing methods, which employ LLMs merely as evaluators, SMaRT uses them as intelligent integrators, unlocking the "best of all worlds" across tasks. Extensive empirical evaluations across benchmarks in reasoning, planning, and sequential decision-making highlight the robustness and adaptability of SMaRT. The framework consistently outperforms state-of-the-art baselines in solution quality, constraint adherence, and performance metrics. This work redefines LLM-driven decision-making by pioneering a new paradigm in cross-strategy calibration, unlocking superior outcomes for reasoning systems and advancing the boundaries of self-refining methodologies.

翻译：大型语言模型（LLM）凭借其卓越的泛化能力，重新定义了复杂任务的自动化。尽管取得了这些进展，现有最先进方法仍依赖于单一策略提示，未能利用多种推理方法的协同效应。没有任何单一策略能在所有场景中表现最优，这凸显了需要能够融合多种策略以最大化性能并确保鲁棒性的框架。我们提出了选择、混合与重塑（SMaRT）框架，这是一种创新的策略融合方法，旨在通过无缝整合多样化的推理策略来创建平衡且高效的解决方案，从而突破这一限制。与现有仅将LLM用作评估器的方法不同，SMaRT将其用作智能集成器，从而在各类任务中实现“博采众长”。在推理、规划和序列决策等多个基准测试上进行的大量实证评估，突显了SMaRT的鲁棒性和适应性。该框架在解决方案质量、约束遵循和性能指标方面持续优于最先进的基线方法。这项工作通过开创跨策略校准的新范式，重新定义了LLM驱动的决策过程，为推理系统解锁了更优的结果，并推动了自优化方法的前沿边界。