Newest papers

PrismWAM: Decoupling State Representations in World Action Models

Tianyu Xin, Wanqi Zhong, Bowen Hu, Zhangchi Guo, Lijie Wang, Yiming Wang, Haoran Liao, Heyang Yu, Xiaoyu Tian, Zhiding Yu, Yiming Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26564 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

世界のアクションモデルは、広範囲(例えば、テクスチャーと色)から、物理的に構成されている方法(例えば、占領とポーズ)まで、世界の見た目(例えば、テクスチャーと色)がどのように見えるかを切り離すべきであると主張するプレートレイアウトビデオモデルから、通常、流れのデータを含む隠れたエンベッディングから世界を表します。この目的のために、我々は、WAMの状態を構成するべきであると主張する $\textb{WAM}の未調査の質問を提起します。世界状態は、世界がアクションの下でどのように進化するかを分離するべきであると主張します:前者はセマンティックな文脈をキャプチャーし、後者は物

Machine translation (Meta M2M100); the English original follows.

Abstract

World Action Models (WAMs) typically represent the world through latent embeddings from pretrained video models, which entangle heterogeneous aspects of the world, from how it looks (e.g., texture and color) to how it is physically configured (e.g., occupancy and pose). This raises an underexplored question: $\textbf{what should constitute the state of a WAM?}$ We argue that the world state should separate how the world appears from how the world evolves under action: the former captures semantic context, while the latter is governed by physical state. To this end, we propose $\textbf{PrismWAM}$, which decouples physics from semantics while jointly modeling state and action. PrismWAM factorizes an embodied task into a $\mathrm{Semantic\ Planning}\rightarrow\mathrm{Physical\ Rollout}\rightarrow\mathrm{Executable\ Action}$ pipeline: given only the initial RGB observation, semantic planning learns to predict the final goal image, while physical rollout predicts the fine-grained evolution toward this goal in depth space; executable actions are then generated from the depth rollout. Without any embodied action pretraining or additional data augmentation, PrismWAM achieves strong manipulation performance in both simulation and the real world. Notably, PrismWAM even surpasses a broad range of models with large-scale embodied action pretraining (e.g., OpenWAM-$α$). More broadly, we reframe World Action Models from modeling how the world looks to how it responds to action, paving the way for action-centric world models grounded in occupancy, flow, tactile sensing, proprioception, and beyond.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Action (physics), Embodied cognition, Semantics (computer science), Artificial intelligence, Decoupling (probability)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange