While transformer models exhibit strong capabilities on linguistic tasks, their complex architectures make them difficult to interpret. Recent work has aimed to reverse engineer transformer models into human-readable representations called circuits that implement algorithmic functions. We extend this research by analyzing and comparing circuits for similar sequence continuation tasks, which include increasing sequences of digits, number words, and months. Through the application of circuit analysis techniques, we identify key sub-circuits responsible for detecting sequence members and for predicting the next member in a sequence. Our analysis reveals that semantically related sequences rely on shared circuit subgraphs with analogous roles. Overall, documenting shared computational structures enables better prediction of model behaviors, identification of errors, and safer editing procedures. This mechanistic understanding of transformers is a critical step towards building more robust, aligned, and interpretable language models.

通过对序列相似性任务的电路分析与比较，我们揭示了语义相关的序列依赖于具有类似角色的共享电路子图，并且共享计算结构的记录有助于更好地预测模型行为、识别错误和更安全的编辑过程，这对于构建更健壮、对齐和可解释的语言模型是一个关键步骤。

定位Transformer中的跨任务序列延续电路