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arXiv · 2507.19855

Inducing Causal World Models in LLMs for Zero-Shot Physical Reasoning

Aditya Sharma·Ananya Gupta·Chengyu Wang·Chiamaka Adebayo·Jakub Kowalski
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Abstract

Large Language Models (LLMs), despite their advanced linguistic capabilities, fundamentally lack an intuitive understanding of physical dynamics, which limits their effectiveness in real-world scenarios that require causal reasoning. In this paper, we introduce Causal World Model Induction (CWMI), a novel framework designed to embed an explicit model of causal physics within an LLM. Our approach incorporates a dedicated Causal Physics Module (CPM) and a new training objective called Causal Intervention Loss, encouraging the model to learn cause-and-effect relationships from multimodal data. By training the model to predict the outcomes of hypothetical interventions instead of merely capturing statistical correlations, CWMI develops a robust internal representation of physical laws. Experimental results show that CWMI significantly outperforms state-of-the-art LLMs on zero-shot physical reasoning tasks, including the PIQA benchmark and our newly proposed PhysiCa-Bench dataset. These findings demonstrate that inducing a causal world model is a critical step toward more reliable and generalizable AI systems.

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Aditya Sharma, Ananya Gupta, Chengyu Wang, Chiamaka Adebayo, Jakub Kowalski. 2025-12-26. Inducing Causal World Models in LLMs for Zero-Shot Physical Reasoning. https://arxiv.org/abs/2507.19855

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