摘要：前沿语言模型能够在一次前向推理中解决数年前足以构成研究贡献的推理问题，但在持续数小时的任务中却会失败：它们会遗忘早先的决策、宣称尚未完成的工作已经完成，或偏离目标。我们将这一现象称为“地平线鸿沟”（horizon gap），并综述了通过系统性种子文献采集获得、经披露的 26.8% 误收录过滤后，再由定向补充扩展的 1,547 篇 arXiv 论文（2024—2026 年）。我们厘清了三种经常被混为一谈的属性：长时程（任务属性：所需步骤数）、长上下文（模型属性：token 容量）以及长期记忆（系统属性：跨步骤/会话的持久性）。我们将语料库组织为贯穿长时程任务生命周期的六个类别——规划、记忆、执行、训练、评估以及基础/安全——并与一个描述“时程由何处承载”的维度交叉：上下文内、单个任务内但超出上下文，或跨任务持续存在。我们发现，在所有类别中都存在相同模式：随着时程延长，仅基于结果的信号会变得缺乏信息量；而该领域的应对方式——无论是过程奖励模型、信用分配还是轨迹级诊断——都是构建更密集的步骤级信号。我们始终将批判性和诊断性文献视为一等研究脉络，认为若将批评与方法割裂，往往会把同一篇论文拆分到不同章节。最后，我们提出若干开放的测量问题：如何分解模型能力与执行框架能力，如何管理同时用于训练和评估的过程级信号中的相关偏差，以及长时程可靠性是否存在可泛化的预测理论。

Abstract:Frontier language models solve reasoning problems in a single forward pass that would have been research contributions years ago, yet fail at multi-hour tasks: losing track of earlier decisions, declaring half-finished work done, or drifting from goals. We call this the horizon gap and survey 1,547 arXiv papers (2024-2026) collected via systematic seed harvest with a disclosed 26.8% bleed filter, extended by targeted supplementation. We disambiguate three routinely conflated properties: long-horizon (task property: required steps), long-context (model property: token capacity), and long-term memory (system property: persistence across steps/sessions). We organize the corpus into six categories tracking a long-horizon task's lifecycle -- planning, memory, execution, training, evaluation, and foundations/safety -- crossed with an axis capturing where horizons are carried (within-context, within-task-beyond-context, or cross-task-persistent). Across all categories, we find the same pattern: outcome-only signals grow uninformative as horizons lengthen, and the field's response -- whether process reward models, credit assignment, or trajectory-level diagnostics -- manufactures denser step-level signals. We treat critical and diagnostic literature as first-class threads throughout, arguing that segregating critique from method would routinely split single papers across chapters. We close by naming open measurement problems: decomposing model versus harness capability, managing correlated bias in process-level signals used for both training and evaluation, and whether long-horizon reliability admits general predictive theory.