Despite the importance of open-ended event forecasting for risk management, current LLM-based methods predominantly target only the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. To bridge this gap, we advance open-ended event forecasting from pinpoint forecasting to scatter forecasting by introducing the proxy task of hypothesis generation. This paradigm aims to generate an inclusive and diverse set of hypotheses that broadly cover the space of plausible future events. To this end, we propose SCATTER, a reinforcement learning framework that jointly optimizes inclusiveness and diversity of the hypothesis. Specifically, we design a novel hybrid reward that consists of three components: 1) a validity reward that measures semantic alignment with observed events, 2) an intra-group diversity reward to encourage variation within sampled responses, and 3) an inter-group diversity reward to promote exploration across distinct modes. By integrating the validity-gated score into the overall objective, we confine the exploration of wildly diversified outcomes to contextually plausible futures, preventing the mode collapse issue. Experiments on two real-world benchmark datasets, i.e., OpenForecast and OpenEP, demonstrate that SCATTER significantly outperforms strong baselines. Our code is available at https://github.com/Sambac1/SCATTER.

翻译：尽管无限制事件预测对风险管理至关重要，但当前基于大语言模型的方法主要聚焦于最可能的结果，忽略了真实世界事件固有的不确定性。为弥补这一不足，我们通过引入假设生成这一代理任务，将无限制事件预测从精确点预测推进到发散式预测。该范式旨在生成涵盖广泛且多样化的假设集，全面覆盖可能未来事件的空间。为此，我们提出SCATTER框架——一种联合优化假设包容性与多样性的强化学习方法。具体而言，我们设计了一种新颖的混合奖励机制，包含三个组成部分：1）衡量与观测事件语义一致性的有效性奖励；2）鼓励采样结果内部差异的组内多样性奖励；3）促进跨不同模式探索的组间多样性奖励。通过将经过有效性门控的得分整合到总体目标中，我们将多样化结果的探索限制在上下文合理的未来情景内，从而防止模式坍塌问题。在OpenForecast和OpenEP两个真实世界基准数据集上的实验表明，SCATTER显著优于强基线方法。我们的代码开源在https://github.com/Sambac1/SCATTER。