大型语言模型作为准晶体：生成文本中无重复的连贯性
Large Language Models as Quasi-crystals: Coherence Without Repetition in Generative Text
AI总结：
本文提出将大型语言模型类比为准晶体，强调其生成文本中基于约束的连贯性而非重复，并引入结构评估作为补充现有语义评估的新视角。
摘要：
本文提出了一种将大型语言模型（LLMs）与准晶体进行类比解释的论述，准晶体是通过局部约束生成的系统，表现出全局连贯性而无周期性重复。虽然LLMs通常以预测准确性、事实性或对齐性来评估，但这种结构性视角表明，它们最具特征性的行为之一是产生内部共振的语言模式。借鉴准晶体的历史，它迫使物理系统中结构秩序的定义被重新定义，这一类比突显了生成语言中另一种连贯性模式：基于约束的组织，无需重复或符号意图。我们建议不要将LLMs视为不完美的代理或随机近似器，而是将其理解为准结构化输出的生成器。这一框架通过强调形式连贯性和模式作为模型行为的可解释特征，补充了现有的评估范式。尽管这一类比有其局限性，它为探索在意义涌现、部分或不可达的系统中连贯性如何产生和评估提供了概念工具。为支持这一观点，我们借鉴了科学哲学和语言哲学，包括基于模型的科学表征、结构实在论和意义推断主义观点。我们进一步提出了结构评估的概念：一种评估模式，检查输出如何在生成文本的跨度中传播约束、变化和秩序。本文旨在重新构建当前围绕大型语言模型的讨论，不是通过拒绝现有方法，而是通过提出一个基于结构而非语义的额外解释轴。
英文摘要：
This essay proposes an interpretive analogy between large language models (LLMs) and quasicrystals, systems that exhibit global coherence without periodic repetition, generated through local constraints. While LLMs are typically evaluated in terms of predictive accuracy, factuality, or alignment, this structural perspective suggests that one of their most characteristic behaviors is the production of internally resonant linguistic patterns. 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.
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