章节结构（全文标题提取）： 1 Introduction 2 Setup 3 The predictor is not the bottleneck 4 The objective saturates, then inverts 5 The pathology is the method’s, and it explains the dissociation 6 The information is present 7 Repair 8 Results 9 Limitations 10 Related work 11 Conclusion References Contributions · Scope · 4.1 This is the observed failure · 5.1 The authors’ own weights · 5.2 Metric quality orders long-horizon planning · 5.3 A correlate, not a cause · 7.3 The better objective is the less accurate one · 7.4 A learned cost must be trained on what the planner scores · 8.1 Planning becomes horizon-independent · 8.2 Where the learned cost loses, and why
Latent world models are judged by how well they predict, so when planning fails at long horizons the natural reading is that the predictor degrades. On a reproduction of LeWorldModel on TwoRoom we show the binding constraint is the planner's objective instead. The predictor is not the limit: its imagined state seventy-five environment steps ahead is still only 0.189 as wrong as assuming the world froze, while the planner never imagines beyond twenty-five. The objective is. Cross-entropy-method planning minimises squared latent distance, which tracks true distance at r = 0.426, saturates by about eighty arena units and decreases beyond a hundred and twenty, so moving away from the goal can lower the cost. The information is present throughout: a ridge probe recovers position from the frozen embedding at R^2 0.9922. The pathology is the method's, not one reimplementation's. It is present in the authors' released weights, and across four checkpoints long-horizon success rank-orders exactly with metric quality and inversely with prediction accuracy. Replacing only the objective, with nothing retrained and no GPU, lifts goals reached at offset 100 from 26.0% to 98.0%, equals the 98.0% at offset 25, and reaches 92.0% under a third of the budget: planning stops depending on the horizon. The best cost is not the most accurate. A head learned from frame separation alone predicts spatial distance worse than a position probe (r = 0.819 against 0.9897) yet plans better, charging 24% more to cross the environment's dividing wall where squared latent distance charges 4% less. It has learned reachability, not proximity.