ProWorld: Progress-Aware Hyperbolic World Models for Long-Horizon Visual Goal Reaching
2026-08-03 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors address a problem in planning tasks where predicting just the next visual step isn't enough to reach long-term goals accurately. They introduce a new idea called "goal-conditioned progress order," which ranks states based on how much they advance toward the goal, capturing the direction of progress better. To handle this, they build ProWorld, a model that uses hyperbolic geometry to organize visual information and improve planning by ensuring steady progress toward goals. Their experiments show that ProWorld outperforms previous methods in various visual goal-reaching tasks.
Visual world modelsGoal-conditioned progressLatent representationsHyperbolic geometryTrajectory planningVisual goal-reachingNext-step predictionMulti-step rolloutEntailment learningProgress-aware planning
Authors
Zihan Liu, Yuzhe Zhuang, Yuanzu Li, Wanshuang Gou, Jiahong Liu, Min Zhou, Menglin Yang
Abstract
JEPA-style visual world models offer an effective paradigm for visual goal planning by predicting future latent representations. Existing methods typically learn local transition consistency through next-step representation prediction. However, in long-horizon tasks, accurate local prediction alone need not ensure sustained progress toward the goal. First, multi-step rollouts can remain locally plausible while drifting away from goal-relevant trajectories. Second, locally similar future states can correspond to substantially different long-term progress, making them difficult to distinguish in a latent space optimized mainly for local consistency. To address these challenges, we introduce goal-conditioned progress order, a relative ordering of states according to how they advance toward a given goal. This order exhibits an asymmetric, coarse-to-fine structure: early states retain broader future possibilities, while later states concentrate on more specific goal-relevant regions. Such a structure is well suited to hyperbolic geometry. Motivated by this observation, we propose ProWorld, a progress-aware hyperbolic visual world model. ProWorld leverages goal-conditioned progress order to organize visual latent-space dynamics, maintains directional progress within trajectories via hyperbolic entailment learning, and mitigates progress ambiguity among locally similar future states via hyperbolic future discrimination. Furthermore, we design a progress-aware planning objective that scores candidate rollouts by jointly considering proximity to the goal and sustained progress across intermediate states. Experiments on four visual goal-reaching tasks demonstrate that ProWorld achieves an average absolute success-rate gain of 9.67 over LeWM. The code will be released after the paper is accepted.