AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
<|reference_start|>AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents: Advancements in the capabilities of Large Language Models (LLMs) have created a promising foundation for developing autonomous agents. With the right tools, these agents could learn to solve tasks in new environments by accumulating and updating their knowledge. Current LLM-based agents process past experiences using a full history of observations, summarization, 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 and updates a memory graph that integrates semantic and episodic memories while exploring the environment. We demonstrate that our Ariadne LLM agent, consisting of the proposed memory architecture augmented with planning and decision-making, effectively handles complex tasks within interactive text game environments difficult even for human players. Results show that our approach markedly outperforms other established memory methods and strong RL baselines in a range of problems of varying complexity. Additionally, AriGraph demonstrates competitive performance compared to dedicated knowledge graph-based methods in static multi-hop question-answering.<|reference_end|>
@article{anokhin2024arigraph:,
    title={AriGraph: Learning Knowledge Graph World Models with Episodic Memory for
  LLM Agents},
    author={Petr Anokhin, Nikita Semenov, Artyom Sorokin, Dmitry Evseev, Mikhail
  Burtsev, Evgeny Burnaev},
    journal={arXiv preprint arXiv:2407.04363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04363},
    primaryClass={cs.AI}
}