arXiv:2606. 28751v1 Announce Type: new Abstract: We propose a path-space formulation of prediction in AI world models.
arXiv:2606. 20104v1 Announce Type: cross Abstract: Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions.
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
arXiv:2603. 16689v2 Announce Type: replace Abstract: Next-token predictors often appear to develop internal representations of the latent world and its rules.
arXiv:2606.27504v2 Announce Type: replace Abstract: World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only...
arXiv:2607. 22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control.
DA‑WAM is a framework that integrates predictive representation learning, action‑conditioned future modeling, and trajectory scoring into a single decision‑making objective for autonomous driving. It uses an online encoder with a stable momentum target to keep future representations aligned with the driving task, generating a distinct future latent for each trajectory candidate. A future‑latent‑conditioned scorer evaluates these latents, with expert‑matched trajectories supervised by observed futures and safety‑critical hard negatives providing additional guidance, achieving state‑of‑the‑art results on NAVSIM‑v1 and NAVSIM‑v2.
The paper presents an end‑to‑end JEPA world model that enhances latent prediction with inverse dynamics and state alignment to improve goal‑conditioned robotic planning. By preventing latent collapse and grounding representations in physical configuration, the model achieves top success rates on tasks such as TwoRoom, PushT, and OGBench‑Cube, outperforming the baseline LeWorldModel. Ablation studies confirm that state alignment consistently boosts planning success over inverse dynamics alone across all four benchmark tasks.
arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of this requirement: Vision-Language-Action (VLA) models exploit VLM priors for semantic reasoning, while World Action Models (WAMs) provide future-aware prediction through generative world modeling.
arXiv:2606. 09311v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM).
arXiv:2609.05834v1 Announce Type: new Abstract: World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the...
arXiv:2607. 11270v1 Announce Type: cross Abstract: Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities.