这篇论文发现了世界模型选动作的隐性陷阱：候选越多反而越容易选错。ASAR用相邻集合重建的方法，在75个机器人任务上提升了最多28%的成功率，值得搞机器人控制的看看。
论文揭示基于采样和潜在世界模型的控制器存在“提议过度生成”问题：当候选动作序列池从72增加到288，可行性从0.375降至0.062（位置目标）、0.344降至0.031（位置加偏航目标），即使大池中始终包含可行序列。作者提出相邻集动作重建(ASAR)方法，通过测量低代价序列的密度并从相邻集重建完整动作。在Carry and Release评估的75个查询中，Kernel ASAR在72、144、288个提议下分别将事件完成成功率提升28.0、24.0、18.7个百分点（潜在成本下）及18.7、20.0、17.3个百分点（轨迹可达性成本下）。
Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models
Controllers based on sampling and latent world models assign a predicted terminal cost to each candidate action sequence, choose the minimum, execute its first action block, and replan. This rule can fail even when the terminal cost perfectly and accurately reflects the true task objective in the physical world. Residual prediction error can give an infeasible sequence an anomalously low cost, and a larger proposal pool gives such errors more chances to outrank feasible alternatives. We call this conditional failure proposal overgeneration. In Cube candidate execution audits, increasing the total proposal budget from 72 to 288 reduces the feasibility of selection by minimum latent cost from .375 to .062 for position targets and from .344 to .031 for targets defined by position and yaw, although every larger pool contains a feasible sequence. We introduce Adjacent Set Action Reconstruction (ASAR). Among proposals with low cost, ASAR measures density from standardized early action prefixes and reconstructs a full sequence from an adjacent set with a light anchor from the sequence with minimum cost. On a Carry and Release evaluation set of 75 queries, Kernel ASAR improves event completion success over matching selection by 28.0, 24.0, and 18.7 percentage points under latent cost and by 18.7, 20.0, and 17.3 points under a trajectory reachability cost at 72, 144, and 288 proposals. Analysis of finite proposal pools characterizes selection risk from the lower tail, separation by a related radius support statistic, and sequence containment under an explicit local feasibility condition.