QUEST: A RAG-based Planning Memory to Augment Task Solving of LLM-based Cognitive Agents

AAMAS 2026 Workshop EMAS Submission46 Authors

Keywords: planning, memory-augmented planning, llm-based agents

Abstract: Agents based on Large Language Models (LLMs) are increasingly used as autonomous entities across various domains, but they still face persistent planning challenges, such as hallucinations and the generation of infeasible plans when relying solely on LLMs' pre-trained data for domain-specific tasks. This study proposes QUEST, a novel framework that leverages Retrieval-Augmented Generation (RAG) to integrate memory-augmented techniques into agent planning. QUEST operates in two phases: an offline construction phase that indexes knowledge bases using LLM-generated questions and summarized descriptions, and an online phase that employs a structured two-step retrieval process via agentic tools. We evaluate QUEST in a fictional text-based proof-of-concept scenario through an ablation study. Using an LLM-as-a-Judge paradigm with a dual-metric evaluation, Rule-Level Compliance and Holistic Safety, we assess the generated plans against baseline and standard RAG setups. The results suggest that QUEST can improve aspects of plan generation in this setting, showing higher levels of constraint adherence and relatively stable evaluator behavior compared to the baselines. These findings provide preliminary evidence that integrating structured knowledge representations may support more reliable agent planning, highlighting directions for further investigation in more complex and diverse environments.

Paper Type: Regular paper

Demo: No, we do not plan to present a demo.

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Submission Number: 46