Making Foresight Actionable: Repurposing Representation Alignment in World Action Models

• URL: http://arxiv.org/abs/2606.12217v1
• Date: Wed, 10 Jun 2026 15:31:25 GMT
• Title: Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
• Abstract summary: World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution.<n> generating plausible visual futures does not always guarantee the extraction of accurate actions.<n>We propose AGRA, an Action-Grounded Representation Alignment objective.
• Score: 57.23863557252883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However, our empirical observations reveal a phenomenon: generating plausible visual futures does not always guarantee the extraction of accurate actions. To diagnose this failure, we conduct action-head attention analysis and causal interventions. We find that the action decoder fails to focus on task-relevant interaction regions and remains sensitive to perturbations in task-irrelevant areas. This reveals a representation mismatch: hidden states optimized for visual reconstruction are not inherently organized in a form useful for low-level action control. In this paper, we propose AGRA, an Action-Grounded Representation Alignment objective that regularizes the world-action interface by aligning intermediate video diffusion features with spatially coherent semantic representations from a foundation visual encoder. We evaluate AGRA on real-world manipulation tasks. Experiments show that AGRA makes world model representations more action-grounded: by focusing the action decoder on the correct interaction regions, it improves object localization accuracy and affordance understanding, and makes the policy more robust to perturbations in task-irrelevant regions. As a result, AGRA consistently improves both in-distribution performance and out-of-distribution generalization over the baseline world action model.

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