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UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
Jingbo Xu, Jianjie Fang, Zhuohang Li +9
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively. Although these models learn shared physical dynam…
Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning
Gangyi Zhang, Junjie Meng, Letian Zhang +6
Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressivel…
IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training
Rongze Tang, Jianjie Fang, Zhaolu Wang +8
World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausible interactions. Existing appr…
CAER: Causal Action Effect Reweighting for World Model Training
Jianjie Fang, Xvyuan Liu, Ziyou Wang +9
World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of how scenes evolve after agen…
DensityKV: Density-Guided KV Cache Compression for Long Video Generation
Wenqu Zhao, Xuemin Chi, Xin Zhang +6
Autoregressive video diffusion models enable streaming generation through sliding-window attention, but each generated block is conditioned on previously generated content, causing…
TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction
Lei Jin, Yiding Ma, Xin Zhang +3
World Action Models (WAMs) combine future-state prediction with robot action generation, but existing approaches largely rely on visual futures. Visual prediction captures scene st…
WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learning
Haisheng Su, Zongdai Liu, Xin Jin +13
World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. However, existing WAMs are constraine…
Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
Shuailei Ma, Jiaqi Liao, Xinyang Wang +24
Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inheren…
Worldscape-MoE: A Unified Mixture-of-Experts World Model for Scalable Heterogeneous Action Control
Jianjie Fang, Yongyan Xu, Ziyou Wang +13
World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, fo…
Dreaming when Necessary: Advancing World Action Models with Adaptive Multi-Modal Reasoning
Yinzhou Tang, Jingbo Xu, Yu Shang +4
World Action Models (WAMs) offer a promising approach to embodied intelligence, yet existing methods rely heavily on video prediction as action priors and lack adaptive multimodal…
WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation
Baining Zhao, Jiacheng Xu, Weicheng Feng +13
Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D environments. We argue that aerial V…
WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models
Yu Shang, Zhuohang Li, Yiding Ma +18
While world models have emerged as a cornerstone of embodied intelligence by enabling agents to reason about environmental dynamics through action-conditioned prediction, their eva…
Aerial World Model for Long-horizon Visual Generation and Navigation in 3D Space
Weichen Zhang, Peizhi Tang, Xin Zeng +12
Unmanned aerial vehicles (UAVs) have emerged as powerful embodied agents. One of the core abilities is autonomous navigation in large-scale three-dimensional environments. Existing…