LLM test-time compute (or LLM inference) via search has emerged as a promising research area with rapid developments. However, current frameworks often adopt distinct perspectives on three key aspects (task definition, LLM profiling, and search procedures), making direct comparisons challenging. Moreover, the search algorithms employed often diverge from standard implementations, and their specific characteristics are not thoroughly specified. In this survey, we provide a comprehensive technical review that unifies task definitions and provides modular definitions of LLM profiling and search procedures. The definitions enable precise comparisons of various LLM inference frameworks while highlighting their departures from conventional search algorithms. We also discuss the applicability, performance, and efficiency of these methods. For further details and ongoing updates, please refer to our GitHub repository: https://github.com/xinzhel/LLM-Agent-Survey/blob/main/search.md

本研究针对现有大语言模型推理框架在任务定义、大语言模型分析和搜索过程上的不一致性进行了系统性审查，填补了这一研究领域的空白。提出了统一的任务定义和模块化的分析框架，便于对不同推理方法的比较，并强调了与传统搜索算法的差异。研究结果有助于提升大语言模型推理的可比性、性能和效率。

通过搜索进行大语言模型测试时计算的调查：任务、大语言模型分析、搜索算法和相关框架