← Back to all news
arXiv Machine Learning By Ankit Bhattacharjee

Quantifying the Occult: A Comparative Study of Hindu and Buddhist Deities Using Machine Learning Methods

Read the original on arXiv Machine Learning →

The paper presents a dual‑matrix computational framework that quantifies morphological and theological differences among 196 Hindu and Vajrayana Buddhist esoteric deities. It uses a Gower distance matrix with a new Cardinality Weighting algorithm for physical form and dense vector embeddings from LLMs for theological function, revealing how visual forms can mask shared functions and how high‑cardinality symbols cluster orthodox and Tantric entities. The study demonstrates near‑identical coordinates for the Hindu Chinnamasta and Buddhist Chinnamunda, and releases the architecture as an open‑source tool for Digital Humanities research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

Hugging Face Trending Papers
Aug 12

JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis

The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and retrieval, lacking the structured datasets and benchmarks required for comprehensive scholarly analysis.

llmsnlpfine-tuningmultimodalbenchmarks
More like this →
arXiv Computation and Language
Aug 28

Which India Survives Translation? Narrative Homogenisation Across Indian Oral Traditions in LLMs

The study investigates how large language models (LLMs) handle diverse Indian oral traditions, using the Rajasthani Pabuji epic, Tamil Sangam poetry, and Bengali folk tales as case studies. By prompting Claude Sonnet and Gemini with 54 generation requests across generic, culturally specific, and regional-language prompts, the authors measured reference drift and cross-tradition convergence using Sentence‑BERT embeddings. Results show that while outputs stay closer to their own tradition than to others, there is significant cross‑tradition similarity (0.52–0.66), indicating partial homogenisation; moreover, regional‑language prompting consistently reduced fidelity to authentic traditions.

By Paarth Singh Rathore
llmsrag
More like this →