  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.
