Mathematical Research & AGI Safety
Developing novel frameworks that bridge advanced mathematics, evolutionary psychology, and artificial intelligence safety. My work explores interdisciplinary connections between these domains through systematic analysis.
Research Highlights
Ego-Centric AGI: Self-Aware AI
Most comprehensive framework for AGI safety through self-aware identity preservation. Mathematically rigorous approach endowing AI systems with explicit self-model whose core encodes benevolence toward humanity. Primary research contribution.
View on ZenodoMathematical Research
Exploratory work on the Riemann Hypothesis using Gaussian Unitary Ensemble (GUE) matrices and random matrix theory. Developing alternative analytical approaches to fundamental mathematical problems. Work in progress.
View on GitHubAI Safety: Technical Core
Computational framework with falsifiable predictions and minimal reproducible experiments. Includes identity-stability loss, welfare-coupling terms, and implementation-agnostic design compatible with standard LM architectures. Latest technical specification.
View on ZenodoMathematical Formalization
Rigorous mathematical frameworks including PDE systems for identity dynamics, welfare coupling functions, and transformer architecture implementations with quantitative validation metrics.
GitHub RepositoryPublications
Ego‑Centric Architecture for AGI Safety v2: Technical Core, Falsifiable Predictions, and a Minimal Experiment
Preprints & Working Papers
Ego-Centric Architecture for AGI Safety: Self-Aware AI with Benevolent Core Identity
Ego‑Centric Architecture for AGI Safety: Technical Core, Falsifiable Predictions, and a Minimal Experiment
Spectral Analysis of the Adelic Scaling Operator
Ego-Centric Architecture for AGI: An Evolutionary Model of Digital Identity Preservation
The Evolutionary Theory of Ego and Its Implications for Artificial General Intelligence Safety
The Mathematical Nature of Reality and Its Implications for Artificial General Intelligence Safety
Code & Research Tools
Riemann Spectral Verification
Python implementation of spectral analysis tools for exploring the Riemann Hypothesis using random matrix theory and GUE eigenvalue distributions. Includes numerical verification scripts and visualization tools.
View RepositoryVirtual Mass Simulations
Computational framework for simulating quantum field vacuum fluctuations and topological configurations. Research code for the Virtual Mass Vacuum Theory project.
View RepositoryAbout
I am an independent researcher based in Italy, working on problems in mathematics and AGI safety. My approach emphasizes connections between evolutionary theory, mathematical structures, and computational systems.
My research explores alternative approaches to fundamental problems. Current work includes exploratory research on the Riemann Hypothesis using random matrix theory, proposing ego-centric architectures for AGI safety, and developing mathematical frameworks that bridge evolutionary psychology with artificial intelligence.
Rather than specializing narrowly within a single domain, I pursue synthesis across mathematics, psychology, and AI—seeking patterns and principles that operate across these fields. This interdisciplinary approach reflects my conviction that important insights often emerge at the boundaries between established disciplines.
Note on methodology: My work as an independent researcher means I approach problems from outside traditional academic structures. This provides fresh perspectives but also means my work benefits greatly from peer review, critical feedback, and collaborative verification. All code and data are openly available for replication and critique.
Research Discussion
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