RUNES：结合符号规则与残差网络的可解释事件序列预测
What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules
一篇把可解释性做进事件预测的论文：RULES 学出人能看懂的规则，RUNES 再叠残差网络补准确率，还能看到每条预测背后哪些规则在起作用。
论文提出 RULES，一种从事件序列数据中直接学习顺序规则的神经符号模型，在合成数据上能准确还原真实规则。RULES 在真实数据上也能发现紧凑且可解释的规则集合。作者进一步将 RULES 与残差网络结合，构成 RUNES（Rule Network for Event Sequences），把规则以加性方式与残差网络组合。RUNES 在保持有竞争力的预测准确率同时，能透明展示哪些规则被激活、残差如何修正预测。
What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules
In real-world applications, being able to explain why a prediction is made is often as important - if not more so - than achieving high accuracy. However, existing methods for sequential event prediction typically prioritize accuracy at the expense of interpretability. Neuro-symbolic approaches offer a promising direction to improve interpretability by learning symbolic rules, but extending them to next-event prediction is challenging due to the presence of temporal dependencies. We introduce RULES, a neuro-symbolic model that learns sequential rules directly from data, and combine it with a residual network to form RUNES (Rule Network for Event Sequences). RULES accurately recovers ground truth rules from synthetic data and discovers compact, interpretable rule sets on real data. RUNES combines these rules additively with a residual network, achieving competitive accuracy while providing transparent access to active rules and how residuals adjust predictions.
