Researchers have identified a potential issue in continuous code world models, which are used to generate executable world models for classical planners. The problem arises when an omitted mode is not verified, leading to a sampling-verification danger law. This can result in the planner being pinned at the mode boundary, missing critical events and reducing the attainable return. The study proposes a localization budget that can be used to determine if a model differs significantly from another within a certain region. The researchers also demonstrate that this issue is not limited to 1D clamps and can occur in higher-dimensional regions. They provide evidence from real LLM synthesis, where GPT-5.x was able to repair an omitted 1D clamp in most mode-containing draws but failed to recover the rule on 2D regions. --- Why it matters: This research is important for engineers working on continuous code world models because it highlights a potential flaw in the acceptance process. If not addressed, this issue could lead to suboptimal performance and reduced accuracy of classical planners. Understanding and mitigating this problem can improve the reliability and effectiveness of these systems. Source: https://arxiv.org/abs/2608.17956