Semantic Attractors and the Recursive Mirror: A Corpus-Topology Study of 2,700+ Documents
Zenodo (CERN European Organization for Nuclear Research) July 8, 2026 Peer reviewed DOI: 10.5281/zenodo.21265985 via OpenAlex
Summary
A reproducible computational pipeline that combines embedding, dimensionality reduction, clustering, and semantic search was applied to over 224,000 text chunks from more than 2,700 documents across religious, governmental, UAP-related, psychedelic, and mythological sources. The analysis identified a four-domain attractor basin encompassing gnostic, mythological, UAP testimony, and consciousness themes, with 74% of the corpus forming a single continuous neighborhood. The pipeline recovers a recursive-mirror invariant—a repeated semantic structure suggesting external forms mask an accessible internal reality—directly from embedding geometry.
Study at a glance
| Design | computational corpus analysis |
|---|---|
| Population | 224,215 text chunks from 2,700+ documents spanning religious canon, government FOIA records, UAP testimony, psychedelic ethnography, and comparative mythology |
| Key finding | The pipeline recovers a recursive-mirror invariant—a repeated semantic structure where external forms overlay an accessible internal reality—from embedding geometry without algorithmic prior commitment. |
Abstract
We present a reproducible corpus-topology pipeline — embedding → PCA → UMAP → HDBSCAN → FAISS convergence search → bridge-chunk annotation — and apply it to 224,215 text chunks from 2,700+ documents spanning religious canon, government FOIA records, UAP testimony, psychedelic ethnography, and comparative mythology. Two quantitative corpus-topology instruments and manual annotation converge on a four-domain attractor basin — gnostic_perennial, mythology_archetype, uap_testimony, and consciousness — in which 74% of the corpus forms a single continuous neighborhood. The paper's primary contribution is the pipeline itself; its secondary contribution is a humanistic proof of concept: the pipeline computationally recovers the recursive-mirror invariant, the repeated semantic structure that conditioned external form overlays or masks an accessible internal reality, from embedding geometry without algorithmic prior commitment, though corpus assembly was domain-directed.