I’m building an open, AI-assisted mathematics research workspace focused on Gaussian random fields, persistent homology and the geometry of short-lived topological features.
The aim is to make the work inspectable: full arguments, exact-source references, reproducible computations and clearly recorded review scopes.
Explore the public site · Try a reproducible example · Cite the work
| Repository | What you’ll find |
|---|---|
| main | Public research introduction, interactive examples and verification tools. |
| Math- | Proofs and their stated assumptions; begin with the reading map. |
| query- | A read-only command-line tool for finding exact sources across the workspace. |
| Universal-Law-Workspace | A map of the repositories and how they fit together. |
The work combines human direction, AI-assisted derivation, adversarial review, exact computation and selected Lean 4 formalizations. Results have specific hypotheses and review histories; those scopes travel with each artifact. “Universal Law” names the research workspace, not a claim that a universal law has been discovered.
For the longer technical account and research background, read the historical research stack essay, preserved unchanged from the previous profile.
Questions or corrections are welcome through the main repository’s issues. The contributor guide explains how to propose a bounded task or review an exact object.