Emergent Lorentzian Structure in Wick-Rotated Euclidean Embeddings (On Hold)

I mistakenly trusted AI as to the existence of current related research and when I finally looked into it myself, I found an ABUNDENCE of related and/or similar research. Hence, I am putting this paper ON HOLD until I bring myself up-to-date on the existing research. Additionally, I am not as familiar with AI/ML research as I would like to be to properly research this topic so I will be changing that as well. ***ON HOLD*** I apply Wick rotation to the static Euclidean geometry of trained word embeddings, projectinginto a low-dimensional subspace and systematically testing all principal-component axes ascandidate timelike directions. I continue the analysis to positional embeddings and the residual stream, then an overall matrix survey. 2.8 added more in-depth gravitational properties verifications. v2.9 added RoPE section and related appendix A v3.0 added DistilBERT analysis whose results were in line with predictions. Also changed the semantic pairs testing to include 500 pairs and re-tested GPT2 with them as well as the DistilBERT analysis. v3.1 added analysis of Mamba and comparison with other models v3.2 fixed a number of errors throught the document. Mostly minor and irrelevant to the results but errors none the less. Version 3.9f resolved everything wrong with the paper due to my mistaking an older version for the current one at some point. It containd all the latest results, all of the intended sections and corrects all errors and inconsistancies, including those stemming from said confusion. The entire document has been double-checked and should be free from any errors or inconsistancies. Version (3.9f) is the preliminary final version of this paper and includes all of the intended sections, tests and results. There will be one more version (v4f) that will be the finished, final version; it should be identical to this version if no errors are found during the final proofread/consistancy check/result verification. I lied. Version 3.9f got reformatted and submitted to PRXIntelligence Version 4.0 reflects additional findings made after submission to PRXIntelligence, which probably means I shouldn't have submitted the paper yet, but what are you going to do? Includes analysis of OPT-125m, GPT-Neo-125m and Pythia-160m as well as format change to conform to PRXIntelligene submission guidelines. Not likely to be the final version. This version (4.01) updated an incorrect bibliography entry. v4.01

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-15
DOI
https://doi.org/10.5281/zenodo.19592865
Primary Topic
Statistical Mechanics and Entropy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Emergent Lorentzian Structure in Wick-Rotated Euclidean Embeddings (On Hold)

Ronald Kyle
Zenodo (CERN European Organization for Nuclear Research)
Statistical Mechanics and Entropy
article

Emergent Lorentzian Structure in Wick-Rotated Euclidean Embeddings (On Hold)

Ronald Kyle
article en

Abstract

I mistakenly trusted AI as to the existence of current related research and when I finally looked into it myself, I found an ABUNDENCE of related and/or similar research. Hence, I am putting this paper ON HOLD until I bring myself up-to-date on the existing research. Additionally, I am not as familiar with AI/ML research as I would like to be to properly research this topic so I will be changing that as well. ***ON HOLD*** I apply Wick rotation to the static Euclidean geometry of trained word embeddings, projectinginto a low-dimensional subspace and systematically testing all principal-component axes ascandidate timelike directions. I continue the analysis to positional embeddings and the residual stream, then an overall matrix survey. 2.8 added more in-depth gravitational properties verifications. v2.9 added RoPE section and related appendix A v3.0 added DistilBERT analysis whose results were in line with predictions. Also changed the semantic pairs testing to include 500 pairs and re-tested GPT2 with them as well as the DistilBERT analysis. v3.1 added analysis of Mamba and comparison with other models v3.2 fixed a number of errors throught the document. Mostly minor and irrelevant to the results but errors none the less. Version 3.9f resolved everything wrong with the paper due to my mistaking an older version for the current one at some point. It containd all the latest results, all of the intended sections and corrects all errors and inconsistancies, including those stemming from said confusion. The entire document has been double-checked and should be free from any errors or inconsistancies. Version (3.9f) is the preliminary final version of this paper and includes all of the intended sections, tests and results. There will be one more version (v4f) that will be the finished, final version; it should be identical to this version if no errors are found during the final proofread/consistancy check/result verification. I lied. Version 3.9f got reformatted and submitted to PRXIntelligence Version 4.0 reflects additional findings made after submission to PRXIntelligence, which probably means I shouldn't have submitted the paper yet, but what are you going to do? Includes analysis of OPT-125m, GPT-Neo-125m and Pythia-160m as well as format change to conform to PRXIntelligene submission guidelines. Not likely to be the final version. This version (4.01) updated an incorrect bibliography entry. v4.01

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 14%
Statistical Mechanics and Entropy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.