AI决策的「直觉」和「现实」对不上怎么办
AI做规划时常用一个技巧：在它自己脑内的「潜空间」里，用欧氏距离衡量当前状态离目标有多远，然后选最短路径。但研究者发现一个反直觉的事：AI脑内觉得「很近」的路径，在真实世界里可能根本走不通——潜空间的距离和真实任务进展之间经常对不上。他们给这个问题起了个名字叫「决策-度量对齐」，并设计了一套诊断方法，能精确测出这个偏差有多大。更关键的是，他们找到了修补方法：在训练时额外加入两个「动作导向」的目标头，一个教AI理解动作之间的因果关系，另一个教它根据示范来设定目标。修补后的AI在规划时，潜空间里的距离和真实成功率之间的匹配度大幅提升，收敛更快、成功率更高，但AI对任务本身的「理解能力」(probe scores)却没变。这说明：AI知道「该做什么」和「知道怎么走」是两回事，而后者才是规划的关键。
📄 原文摘要(英文)
JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task variables, however, does not guarantee that this particular cost ranks candidate action sequences by real task progress. We call the latter property decision-metric alignment. We introduce Plan-Real Spearman, which measures latent--real rank agreement on random plans, and CEM-stage Spearman, which measures the same agreement as cross-entropy-method (CEM) search concentrates its proposal. We analyze sufficient conditions under which latent distance preserves real-cost rankings, identifying encoder distortion, terminal rollout error, and candidate margins as the controlling quantities. Guided by the observed empirical alignment gap, DA-LeWM augments LeWM with inverse-dynamics and demonstration-conditioned goal-action heads. Across all our experiments, DA-LeWM accelerates convergence and achieves higher online success than LeWM, while probe scores remain similar. These results show that action-conditioned objectives improve the geometry used by Euclidean-cost, CEM-based latent MPC.