提出双性操作与分层映射，让修辞模式可量化、可扩展。
Duality-based Mode Operations and Pyramid Multilayer Mapping for Rhetorical Modes
- 基于对偶操作生成组合与泛化修辞模式，拓展修辞体系。
- 引入马尔金修辞比特（MRB），量化表达力增长速度。
- 分层映射降低认知复杂度，适合语言建模与AI推理研究。
修辞模式在学术与非学术写作中均有价值，可作为语言学研究与计算建模的课题。本文提出基于对偶的修辞操作（拆分-合并、正向-反向、扩展-缩减、正交对偶），生成组合与泛化模式，增强跨应用的知识多样性。进一步提出金字塔多层映射框架（含修辞模型层、认知层、认识论层），降低认知复杂度。通过二项式组合与香农熵分析，量化表达多样性与复杂度降低程度。识别出边际修辞比特（MRB），定义可衡量的修辞可扩展参数，表征每阶段表达增长速度。直接熵测表明，分层选择小子集显著降低选择不确定性，优于全集平坦选择。该研究将静态不可测的修辞分类转化为动态可量化的论述设计系统，为未来AI系统从语言符号操作迈向多层次修辞推理结构提供路径，贯通语言学、教学法、学术研究与计算领域。
原文摘要 · Abstract (English)
Rhetorical modes are useful in both academic and non-academic writing, and can be subjects to be studied within linguistic research and computational modeling. Establishing a conceptual bridge among these domains could enable each to benefit from the others. This paper proposes duality-based mode operations (split-unite, forward-backward, expansion-reduction and orthogonal dualities) to expand the set of rhetorical modes, introducing generated modes like combination and generalization, thereby enhancing epistemic diversity across multiple applications. It further presents a pyramid multilayer mapping framework (e.g., three layers from the rhetorical model layer, to cognitive layer, and to epistemic layers) that reduces the resulting cognitive complexity. The degrees of expressive diversity and complexity reduction are quantified through binomial combinatorics and Shannon entropy analysis. A Marginal Rhetorical Bit (MRB) is identified, permitting the definition of a rhetorical-scalable parameter that measures expressive growth speed in bits per stage. A direct entropy measure shows that hierarchical selection over smaller subsets markedly reduces choice uncertainty compared with flat selection across all modes. These considerations appear to transform static and non-measurable rhetorical taxonomies into more dynamic and more measurable systems for discourse design. From this work, it would be possible to identify a pathway for future AI systems to operate not only on language tokens but on layered rhetorical reasoning structures, bridging linguistic, pedagogical, academic, and computational research
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。