LLMCWM

This is the official code repository for the paper "Language Agents Meet Causality -- Bridging LLMs and Causal World Models"

Figure 2: Illustration of the first roll-out step with the Causal World Model. The image $\mathbf{X}^0$ and action description $L^0$ are encoded into initial latent representations. The CRL module then disentangles these representations, and the transition model predicts the next state. The causal mapper transforms the disentangled causal representation of the next state into the estimated causal variables $\mathbf{\hat{C}}^1$. Finally, the state descriptor $s$ generates a natural language description $\ell^1$ of the next state.