A learned world model provides a powerful physical intuition for evaluating future states. But its effectiveness in continuous control also depends critically on how candidate actions are generated for model-based planning. Rather than solely asking how accurately a model can simulate the future, we ask: which candidate actions are worth evaluating in the first place? Existing planners typically search arbitrarily or use expert demonstrations only to initialize a sampling mean, discarding the expert's state-conditioned confidence. Properly guiding this search requires a robust action prior, yet current approaches often rely on independent visual encoders or large-scale VLMs to obtain one. We argue that this architectural bloat is unnecessary: the exact same data - and the learned representations of the world model itself - inherently encode the agent's action intuition. We introduce PRISM, a task-agnostic framework that extracts both from a single dataset while maintaining strict architectural simplicity. Building on a standard JEPA-style latent world model, PRISM attaches a lightweight MLP directly to its frozen encoder to predict a state-conditioned Gaussian prior. At plan time, PRISM fuses this prior into the planner's sampling distribution via a precision-weighted Product-of-Gaussians update. This parameter-free, closed-form integration steers the sampling process, making the prior confident where it is and ceding control where it is not. PRISM improves success rates by 35 percentage points over vanilla world-model-based MPC on Cube and 32 percentage points on PushT, without introducing significant inference overhead.

翻译：摘要：学习到的世界模型能为评估未来状态提供强大的物理直觉。但它在连续控制中的有效性同样关键地取决于如何为基于模型的规划生成候选动作。我们关注的并非仅仅是模型模拟未来的精确度，而是：首先哪些候选动作值得评估？现有规划器通常任意搜索，或仅使用专家演示来初始化采样均值，丢弃了专家与状态相关的置信度。适当引导这一搜索需要稳健的动作先验，然而当前方法往往依赖独立的视觉编码器或大规模VLM来获取。我们认为这种架构冗余是不必要的：完全相同的数据——以及世界模型自身学到的表征——内在地编码了智能体的动作直觉。我们提出PRISM，一个任务无关的框架，从单个数据集中提取两者，同时保持严格的架构简洁性。基于标准JEPA风格的潜在世界模型，PRISM将轻量级MLP直接附加到其冻结编码器上，以预测状态条件的先验高斯分布。在规划时，PRISM通过精度加权的产品高斯更新将该先验融合到规划器的采样分布中。这种无参数、闭式的集成引导采样过程，使先验在确信的地方发挥主导，在不确信的地方让渡控制。与基于普通世界模型的MPC相比，PRISM在Cube任务上成功率提升35个百分点，在PushT任务上提升32个百分点，且未引入显著推理开销。