AriGraph: Learning Knowledge Graph World Models with Episodic Memory for
  LLM Agents
Authors: name: Petr Anokhin, name: Nikita Semenov, name: Artyom Sorokin, name: Dmitry Evseev, name: Mikhail Burtsev, name: Evgeny Burnaev

Advancements in generative AI have broadened the potential applications of
Large Language Models (LLMs) in the development of autonomous agents. Achieving
true autonomy requires accumulating and updating knowledge gained from
interactions with the environment and effectively utilizing it. Current
LLM-based approaches leverage past experiences using a full history of
observations, summarization or retrieval augmentation. However, these
unstructured memory representations do not facilitate the reasoning and
planning essential for complex decision-making. In our study, we introduce
AriGraph, a novel method wherein the agent constructs a memory graph that
integrates semantic and episodic memories while exploring the environment. This
graph structure facilitates efficient associative retrieval of interconnected
concepts, relevant to the agent's current state and goals, thus serving as an
effective environmental model that enhances the agent's exploratory and
planning capabilities. We demonstrate that our Ariadne LLM agent, equipped with
this proposed memory architecture augmented with planning and decision-making,
effectively handles complex tasks on a zero-shot basis in the TextWorld
environment. Our approach markedly outperforms established methods such as
full-history, summarization, and Retrieval-Augmented Generation in various
tasks, including the cooking challenge from the First TextWorld Problems
competition and novel tasks like house cleaning and puzzle Treasure Hunting.