ResearcharXiv cs.AI

AGI Maze as a Benchmark Framework for World-Modeling Agents

#artificial intelligence#benchmark#world-modeling#language models#mazes

English

The paper introduces AGI Maze, a benchmark framework designed for world-modeling agents, highlighting the limitations of large language models (LLMs) in representing environments. It presents grid-based maze tasks that require agents to learn and utilize world state representations, demonstrating initial evaluations where LLMs struggle to solve even simple mazes despite improved performance with a baseline agent using message history as memory.

中文

本文介绍了AGI Maze，一个旨在为世界建模代理提供基准框架的工具，强调了大型语言模型（LLMs）在表示环境方面的局限性。它呈现了需要代理学习和利用世界状态表示的网格迷宫任务，展示了LLMs在解决简单迷宫方面的初步评估，尽管使用消息历史作为内存的基线代理提高了性能。