计算机科学与探索2026，Vol.20Issue(4)：977-1005,29.DOI:10.3778/j.issn.1673-9418.2510018
基于检索增强生成的任务规划方法综述
Survey on Retrieval-Augmented Generation for Task Planning
摘要
Abstract
Retrieval-augmented generation(RAG)technology has become a key paradigm for improving the accuracy of large language model task response by dynamically integrating external knowledge to effectively alleviate the hallucination problem and knowledge timeliness of large language models.However,how to efficiently utilize the retrieval-augmented generation architecture to adapt to the planning requirements of complex tasks remains a core challenge.As a popular solution,retrieval augmentation generation is triggered according to different timings,which significantly enhances the planning robustness of large language models in knowledge-intensive scenarios and complex planning tasks.Firstly,the necessity of decision triggering in the retrieval-enhanced generation framework is explained.Next,focusing on the core issue of the timing for triggering RAG-enhanced task planning,this paper categorizes the relevant triggering mechanisms into three types:rule-oriented triggers,state-adaptive triggers,and interactive feedback triggers,and conducts a systematic review.Then,starting from the basic paradigm of retrieval-augmented planning,it introduces the general role of retrieval-generation architectures in task planning,abstracts existing work into two advanced paradigms:global node planning and hierarchical task planning,and further unifies the three paradigms in terms of time,space,and safety dimensions,forming a cross-paradigm evolutionary framework consisting of memory-augmented RAG,multi-agent collaborative RAG,and adversarially robust RAG.Finally,it summarizes the current challenges of applying retrieval-augmented generation technology to assist task planning in practical applications.关键词
检索增强生成/大语言模型(LLM)/任务规划Key words
retrieval-augmented generation/large language model(LLM)/task planning分类
信息技术与安全科学引用本文复制引用
马逸博,陈希亮,章乐贵,赖俊..基于检索增强生成的任务规划方法综述[J].计算机科学与探索,2026,20(4):977-1005,29.基金项目
国家自然科学基金(62273356).This work was supported by the National Natural Science Foundation of China(62273356). （62273356）
