AI 中文总结
本文提出损失条件状态执行方法，通过分组下置信界限决定是否执行世界模型提案，证明其降低期望损失，并在M4和京东库存数据上验证了有效性。
AI 中文摘要
我们引入了损失条件状态执行，这是一种模型无关的方法，用于决定是执行世界模型的固定可行提案还是保留当前状态。然而，仅凭预测信息量并不能确定更新是否会降低下游损失。发生排名可以接近完美，而持久性仍是唯一的绝对损失贝叶斯行动。两种转移定律也可以共享发生信息和条件方差，却要求相反的绝对损失决策。我们将状态可移动性形式化为存在降低损失的可行修正，并将其与特定提案的收益区分开来。我们的方法从预测分布构建特定损失的可行提案，并在独立校准单元上评估其相对于持久性的分组有界损失增益。仅在具有正联合下置信界限的组中执行提案。对于固定提案和具有有界单元损失的组，我们证明当校准单元是从目标总体独立同分布抽取时，每个被接受的组以高概率具有比持久性更低的期望损失。在公开预测和动作条件动力学基准上的实验显示了支持的更新以及认证与覆盖之间的权衡。在28,684个保留的M4月度序列上，该方法对14.0%的序列执行提案，并实现有界损失0.588，而持久性为0.599，始终执行提案为0.621。两种比较的配对95%自助置信区间均低于零。在中国领先的电子零售商京东的六种不健康库存类型的约束预测中，强发生排名信号与基于损失的持久性偏好共存，说明了为什么事件可预测性和状态执行必须分开评估。
英文摘要
We introduce loss-conditioned state execution, a model-agnostic method that decides whether to execute a world model's fixed feasible proposal or retain the current state. Predictive informativeness alone, however, does not establish whether an update will reduce downstream loss. Occurrence ranking can approach perfection while persistence remains the unique absolute-loss Bayes action. Two transition laws can also share occurrence information and conditional variance yet require opposite absolute-loss decisions. We formalize state movability as the existence of a loss-reducing feasible correction and distinguish it from the benefit of a particular proposal. Our method constructs a loss-specific feasible proposal from a predictive distribution and evaluates its groupwise bounded-loss gain over persistence on independent calibration units. The proposal is executed only in groups with a positive simultaneous lower confidence bound. For fixed proposals and groups with bounded unit losses, we prove that every accepted group has lower expected loss than persistence with high probability when calibration units are i.i.d. draws from the target population. Experiments on public forecasting and action-conditioned dynamics benchmarks show supported updates and a trade-off between certification and coverage. On 28,684 held-out M4 Monthly series, the method executes the proposal for 14.0% of series and achieves bounded loss 0.588, compared with 0.599 for persistence and 0.621 for always executing the proposal. The paired 95% bootstrap intervals for both comparisons lie below zero. In constrained forecasting of six unhealthy-inventory types from JD$\mbox{.}$com, a leading e-retailer in China, strong occurrence-ranking signal coexists with a loss-based preference for persistence, illustrating why event predictability and state execution must be evaluated separately.
