发表机构
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出双矩阵计算架构，量化196位印度教与佛教密宗神祇的形态与神学差异，验证“图像伪装”并建模“Atin效应”，揭示跨传统密宗对应关系。
AI 中文摘要
本研究引入了一种双矩阵计算架构，以数学方式量化196位印度教与金刚乘佛教密宗神祇的形态学与神学分歧。物理形态通过离散Gower距离矩阵评估，并辅以新颖的“基数加权”算法；而神学功能则通过由大语言模型（LLM）语义扩展生成的稠密向量嵌入进行映射，该嵌入明确用作合成代理，以缓解循环论证问题。多模态拓扑投影为“图像伪装”提供了算法验证，展示了不同的视觉形式如何在结构上掩盖跨传统的共享功能。此外，我计算建模了“Atin效应”——它同时作为顺序认知偏差的心理观察和机器学习基准——展示了高基数密宗锚点（如维纳琴或断头）如何覆盖系统性神学差异，从而在数学上将正统与坦特罗实体聚类。跨传统空间分析表明，最高密宗表现形式，如印度教的Chinnamasta和佛教的Chinnamunda，在视觉（$D_G = 0.288$）和语义（$D_C = 0.068$）边界上共享几乎相同的数学坐标，表明存在1:1的密宗转移。通过开源此架构，我为数字人文学者和比较神学家提供了一种可扩展的无监督机器学习工具，以严格映射跨定性文化语料库的潜在结构连续性。
英文摘要
This study introduces a dual-matrix computational architecture to mathematically quantify the morphological and theological divergence of 196 Hindu and Vajrayana Buddhist esoteric deities. Physical morphology is evaluated via a discrete Gower distance matrix enhanced by a novel "Cardinality Weighting" algorithm, while theological function is mapped via dense vector embeddings generated from Large Language Model (LLM) semantic expansions, explicitly utilized as a synthetic proxy to mitigate circular reasoning. The multi-modal topological projections provide algorithmic validation of "iconographic camouflage", demonstrating how distinct visual forms structurally obscure shared cross-tradition functions. Furthermore, I computationally model the "Atin Effect" - serving simultaneously as a psychological observation of sequential cognitive bias and a machine learning benchmark - demonstrating how high-cardinality esoteric anchors (e.g., a veena or a severed head) override systemic theological disparities to mathematically cluster orthodox and Tantric entities. Cross-tradition spatial analysis establishes that the highest esoteric manifestations, such as the Hindu Chinnamasta and the Buddhist Chinnamunda, share a near-identical mathematical coordinate across both visual ($D_G = 0.288$) and semantic ($D_C = 0.068$) boundaries, indicating a 1:1 esoteric transfer. By open-sourcing this architecture, I provide a scalable, unsupervised machine learning tool for Digital Humanities scholars and comparative theologians to rigorously map latent structural continuities across qualitative cultural corpora.
CommentsUnder peer review at Digital Scholarship in the Humanities
