Latent Goal Prediction from Language for Model-Based Planning

Planning with world models is bottlenecked by compounding prediction errors and the difficulty of defining optimizable goals. Visual targets provide precise local gradients but poor distant guidance, while language is flexible yet limited by noisy cross-modal alignment or dependence on large generative models unsuited for the high-sampling nature of model-based planning. To address these challenges, we introduce Latent Goal Prediction from Language (LAGO), a framework that predicts both sequence

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ethics.ai (26 May 2026), “Latent Goal Prediction from Language for Model-Based Planning,” evidence record 3647, https://ethics.ai/record/3647 (originally published by arXiv).

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