展示HN:Σ-流形宣言

2作者: mihend22 天前原帖
该项目探讨了*文本的线性结构*与其*情感美学影响*之间的联系。我们识别出*五种基本关系*,标记为*A–E*。每种关系代表了*主客体*的转变,即视角和能动性的变化。 随着文本的延长,这些关系形成了*序列*——并且在无限的组合空间中,经验上出现了*八种稳定模式(Σ₁–Σ₈)*。每种模式与特定的*语义和情感领域*相关联——如宣泄、英雄、冥想、幽默等。 这使我们能够通过*结构命令*来指导大型语言模型(LLM),而不是通过语义提示(“写一个关于……的故事”)。例如,生成一个遵循序列Σ₅(悲剧对位)的叙事。您可以在这里直接实验这些原型:[叙事生成器](<a href="https:&#x2F;&#x2F;a2tg9zwayjuqzcpdznklve.streamlit.app&#x2F;~&#x2F;+&#x2F;#narrative-generator" rel="nofollow">https:&#x2F;&#x2F;a2tg9zwayjuqzcpdznklve.streamlit.app&#x2F;~&#x2F;+&#x2F;#narrative-...</a>)或[通过Python](<a href="https:&#x2F;&#x2F;github.com&#x2F;mihendr&#x2F;Echoes-of-autonomy&#x2F;blob&#x2F;main&#x2F;TEMA_REMA_6.py" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mihendr&#x2F;Echoes-of-autonomy&#x2F;blob&#x2F;main&#x2F;TEMA...</a>)。 有趣的是,这些文本进程似乎与*音乐和声*之间存在平行关系。例如,如果将A–E映射到和声功能(I, IV, V, vi, ii),叙事序列就像情感“和弦进行”——其中意义流动、调节并解决。 生成文本的连贯性不仅来自于句法,还来自于LLM围绕这些变化关系构建的*联想场*。当被要求“切换主题”时,它自发地从<i>诗人</i>转变为<i>作家</i>,保持了美学的连续性,而非随机性。 这甚至可能暗示了*儿童如何习得语言*:首先感知结构转换的<i>旋律</i>,然后将其映射到概念和情感上。这种方法最终可能适用于*训练神经系统*,其中意义被视为<i>流动</i>——而不是固定的表征。 [完整文本](<a href="https:&#x2F;&#x2F;medium.com&#x2F;@mihend_80107&#x2F;%CF%83-manifold-manifest-e35ca1a96aec" rel="nofollow">https:&#x2F;&#x2F;medium.com&#x2F;@mihend_80107&#x2F;%CF%83-manifold-manifest-e3...</a>)
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This project explores the connection between *the linear structure of text* and its *emotional-aesthetic impact*. We identify *five fundamental relations* between consecutive sentences — labeled *A–E*. Each represents a shift of *subject-object*, i.e., a transformation of perspective and agency.<p>When texts grow longer, these relations form *sequences* — and from the infinite combinatorial space, *eight stable patterns (Σ₁–Σ₈)* emerge empirically. Each pattern correlates with a distinct *semantic and emotional field* — cathartic, heroic, meditative, humorous, and so on.<p>This allows us to instruct an LLM not through semantic prompts (“write a story about…”), but through *structural commands* — e.g., generate a narrative following sequence Σ₅ (Tragic Counterpoint). You can experiment with these archetypes directly here: [Narrative Generator](<a href="https:&#x2F;&#x2F;a2tg9zwayjuqzcpdznklve.streamlit.app&#x2F;~&#x2F;+&#x2F;#narrative-generator" rel="nofollow">https:&#x2F;&#x2F;a2tg9zwayjuqzcpdznklve.streamlit.app&#x2F;~&#x2F;+&#x2F;#narrative-...</a>) or [via python](<a href="https:&#x2F;&#x2F;github.com&#x2F;mihendr&#x2F;Echoes-of-autonomy&#x2F;blob&#x2F;main&#x2F;TEMA_REMA_6.py" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mihendr&#x2F;Echoes-of-autonomy&#x2F;blob&#x2F;main&#x2F;TEMA...</a>)<p>Interestingly, there appears to be a parallel between these textual progressions and *musical harmony*. For example, if A–E are mapped to harmonic functions (I, IV, V, vi, ii), the narrative sequences behave like emotional “chord progressions” — where meaning flows, modulates, and resolves.<p>Coherence in the generated text arises not only from syntax, but from the *associative field* that the LLM constructs around these shifting relations. When asked to “switch subjects,” it spontaneously moves from <i>Poet</i> to <i>Writer</i>, preserving aesthetic continuity rather than randomness.<p>It might even hint at how *children acquire language*: by first sensing the <i>melody</i> of structural transitions, before mapping them to concepts and emotions. Such a method could eventually apply to *training neural systems*, where meaning is learned as <i>flow</i> — not as fixed representation. [Full text](<a href="https:&#x2F;&#x2F;medium.com&#x2F;@mihend_80107&#x2F;%CF%83-manifold-manifest-e35ca1a96aec" rel="nofollow">https:&#x2F;&#x2F;medium.com&#x2F;@mihend_80107&#x2F;%CF%83-manifold-manifest-e3...</a>)