Latent world models are increasingly used for control and goal-conditioned planning, yet assessing whether their learned representations are useful for planning usually requires slow, planner-coupled simulator evaluation with CEM or similar planners. Such evaluation is black-box and model-complexity-dependent: under the same protocol, different world models may require minutes to hours per checkpoint. In this work, we propose ATM, an Action-Consistency Transfer Matrix for diagnosing whether latent transitions preserve action semantics relevant to planning. ATM compares action information in real encoded transitions and model-predicted transitions through lightweight post-hoc probes, producing an interpretable matrix that reveals representation quality, transition-domain inconsistency, and failure modes without simulator rollout. It can also be collapsed into a simple screening score for within-task ranking across checkpoints, variants, and world models. When the true success gap is non-trivial, ATM achieves highly reliable pairwise ranking, while reducing minutes-to-hours CEM evaluation to seconds-level transition analysis, yielding more than 100x speedup in our setup. We further introduce AITS, showing that action-identifiability is not only diagnostic but also a useful training signal for improving downstream planning without changing the planner.

翻译：隐式世界模型越来越多地被用于控制与目标条件规划，然而评估其学得的表征是否对规划有效，通常需要结合CEM或类似规划器进行缓慢的、与规划器耦合的仿真器评估。这种评估是黑箱且依赖于模型复杂度的：在同一协议下，不同世界模型每个检查点可能需要几分钟到几小时的评估时间。本研究提出ATM，一种用于诊断隐式状态转移是否保留与规划相关的动作语义的动作一致性迁移矩阵。ATM通过轻量级事后探针比较真实编码转移和模型预测转移中的动作信息，生成一个可解释的矩阵，无需仿真器回滚即可揭示表征质量、转移域不一致性以及故障模式。该矩阵还可简化为一个简单的筛选分数，用于跨检查点、变体及世界模型的同任务内排序。当真实成功差距不可忽略时，ATM能实现高度可靠的成对排序，同时将分钟到小时级别的CEM评估降至秒级的转移分析，在我们的设置中实现超过100倍的加速。我们进一步引入AITS，表明动作可辨识性不仅具有诊断价值，还能作为有效的训练信号，在不改变规划器的情况下改进下游规划。