Despite displaying semantic competence, large language models' internal mechanisms that ground abstract semantic structure remain insufficiently characterised. We propose a method integrating role-cross minimal pairs, temporal emergence analysis, and cross-model comparison to study how LLMs implement semantic roles. Our analysis uncovers: (i) highly concentrated circuits (89-94% attribution within 28 nodes); (ii) gradual structural refinement rather than phase transitions, with larger models sometimes bypassing localised circuits; and (iii) moderate cross-scale conservation (24-59% component overlap) alongside high spectral similarity. These findings suggest that LLMs form compact, causally isolated mechanisms for abstract semantic structure, and these mechanisms exhibit partial transfer across scales and architectures.

翻译：尽管大型语言模型展现出语义理解能力，但其支撑抽象语义结构的内在机制仍未得到充分阐释。本研究提出一种整合角色交叉最小对比对、时序涌现分析与跨模型比较的方法，以探究LLMs如何实现语义角色。我们的分析揭示：(i) 高度集中的电路结构（28个节点内贡献度达89-94%）；(ii) 渐进式结构优化而非相位突变，且更大模型有时会绕过局部化电路；(iii) 中等程度的跨尺度保守性（组件重叠率24-59%）与高光谱相似性并存。这些发现表明，LLMs为抽象语义结构形成了紧凑且因果隔离的机制，且这些机制在不同规模与架构间存在部分迁移特性。