大型语言模型作为准晶体：生成文本中的无重复的一致性

本文提出一种将大型语言模型 (Large Language Models, LLMs) 与准晶体之间的类比。准晶体是表现为全球一致性而无周期重复性的系统，通过局部约束生成。虽然 LLMs 通常以预测准确性、事实性或对齐性进行评估，但这种结构视角表明，它们最具特征性的行为之一是产生内部共振的语言模式。drawing on the history of quasicrystals, which forced a redefinition of structural order in physical systems, the analogy highlights an alternative mode of coherence in generative language: constraint-based organization without repetition or symbolic intent. Rather than viewing LLMs as imperfect agents or stochastic approximators, we suggest understanding them as generators of quasi-structured outputs. This framing complements existing evaluation paradigms by foregrounding formal coherence and pattern as interpretable features of model behavior. While the analogy has limits, it offers a conceptual tool for exploring how coherence might arise and be assessed in systems where meaning is emergent, partial, or inaccessible. In support of this perspective, we draw on philosophy of science and language, including model-based accounts of scientific representation, structural realism, and inferentialist views of meaning. We further propose the notion of structural evaluation: a mode of assessment that examines how well outputs propagate constraint, variation, and order across spans of generated text. This essay aims to reframe the current discussion around large language models, not by rejecting existing methods, but by suggesting an additional axis of interpretation grounded in structure rather than semantics.