Despite their tremendous success in many applications, large language models often fall short of consistent reasoning and planning in various (language, embodied, and social) scenarios, due to inherent limitations in their inference, learning, and modeling capabilities. In this position paper, we present a new perspective of machine reasoning, LAW, that connects the concepts of Language models, Agent models, and World models, for more robust and versatile reasoning capabilities. In particular, we propose that world and agent models are a better abstraction of reasoning, that introduces the crucial elements of deliberate human-like reasoning, including beliefs about the world and other agents, anticipation of consequences, goals/rewards, and strategic planning. Crucially, language models in LAW serve as a backend to implement the system or its elements and hence provide the computational power and adaptability. We review the recent studies that have made relevant progress and discuss future research directions towards operationalizing the LAW framework.

翻译：尽管大型语言模型在许多应用中取得了巨大成功，但由于其推理、学习和建模能力的固有局限，它们在各种（语言、具身及社会）场景中往往难以实现一致的推理与规划。在这篇立场论文中，我们提出了一种新的机器推理视角——LAW，它将语言模型、智能体模型与世界模型的概念相连接，以实现更鲁棒且更通用的推理能力。具体而言，我们提出世界模型和智能体模型是更好的推理抽象，引入了类似人类审慎推理的关键要素，包括对世界及其他智能体的信念、对后果的预期、目标/奖励以及战略规划。关键在于，LAW框架中的语言模型作为实现系统或其组件的后端，从而提供计算能力与适应性。本文回顾了近期取得相关进展的研究，并讨论了实现LAW框架可行化的未来研究方向。