Samuel Pedrielli - Independent Researcher

Revolutionary Research Across Mathematical Sciences & AGI Safety

Developing groundbreaking frameworks that bridge advanced mathematics, evolutionary psychology, and artificial intelligence. My work challenges conventional approaches through interdisciplinary thinking that emerges from outside traditional academic boundaries.

📄 1-Page Brief (PDF) 🎨 Research Visual

                    Current Focus: From advanced mathematical research on the Riemann Hypothesis using GUE matrices to revolutionary ego-centric architectures for AGI safety - pursuing breakthrough discoveries across multiple domains of mathematical and computational science.
                

Research Highlights

Ego-Centric AGI Architecture

Revolutionary framework implementing evolutionary identity preservation mechanisms in artificial systems. Addresses identity fragility, prompt injection vulnerability, and lack of self-preservation in current AI architectures through biologically-grounded concentric topology.

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Advanced Mathematical Research

Pioneering work on the Riemann Hypothesis using Gaussian Unitary Ensemble (GUE) matrices and random matrix theory. Developing novel approaches to some of mathematics' most challenging problems through unconventional analytical frameworks.

View on GitHub

Superintelligence Safety

Developing intrinsic welfare-preservation mechanisms that operate through identity fusion rather than external constraints. Creating AI systems that intrinsically care for humanity through evolutionary attachment mechanisms.

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Mathematical Formalization

Rigorous mathematical frameworks including PDE systems for identity dynamics, welfare coupling functions, and transformer architecture implementations with quantitative validation metrics.

GitHub Repository

Publications

Featured on Effective Altruism Forum V2

Ego-Centric Architecture for AGI Safety: Self-Aware AI with Benevolent Core Identity

August 2025 Zenodo Preprint DOI: 10.5281/zenodo.16980172

We present a mathematically rigorous ego-centric architecture for AGI safety based on self-aware identity preservation. Our approach endows AI systems with an explicit self-model ("ego") whose core encodes benevolence toward humanity. Through formal self-preservation mechanisms, the system maintains its beneficial identity: by preserving its ego, it necessarily preserves its benevolent core. We provide: (1) a nested latent state a = (a(1), . . . , a(m)) with discrete regularized dynamics, (2) sufficient stability conditions under standard smoothness/convexity assumptions, (3) an anti-wireheading property under causal separation at evaluation time, and (4) a reproducible minimal experiment with pre-registered predictions.

PDF Zenodo

@misc{pedrielli2025_ego_centric_agi_safety, author = {Pedrielli, Samuel}, title = {Ego-Centric Architecture for AGI Safety: Self-Aware AI with Benevolent Core Identity}, howpublished = {Zenodo Preprint}, year = {2025}, month = {August}, doi = {10.5281/zenodo.16980172}, url = {https://zenodo.org/records/16980172} }

Virtual Mass Vacuum Theory: Mathematical Framework for Quantum Field Fluctuations

2024 GitHub Repository Quantum Physics, Mathematical Physics

Theoretical investigation into virtual mass effects in quantum vacuum. This work explores the mathematical framework underlying virtual particle interactions and their implications for vacuum energy density calculations. The research provides novel insights into quantum field fluctuations and their relationship to fundamental physical constants through rigorous mathematical analysis.

GitHub Documentation

@misc{pedrielli2024_virtual_mass_vacuum, author = {Pedrielli, Samuel}, title = {Virtual Mass Vacuum Theory: Mathematical Framework for Quantum Field Fluctuations}, howpublished = {GitHub Repository}, year = {2024}, url = {https://github.com/samuel-pedrielli/virtual_mass_vacuum_note.txt} }

Spectral Analysis of the Adelic Scaling Operator

2025 Zenodo Preprint DOI: 10.5281/zenodo.15935417

We construct and analyse the symmetric scaling component D_scaling of a global adelic pseudodifferential operator acting on the adele-class space. The study establishes essential self-adjointness, computes the full adelic symbol via archimedean Mellin and p-adic Fourier techniques, and provides an explicit spectral resolution — laying the groundwork for future perturbative extensions.

PDF Zenodo

@misc{pedrielli2025_spectral_adelic, author = {Pedrielli, Samuel}, title = {Spectral Analysis of the Adelic Scaling Operator}, howpublished = {Zenodo Preprint}, year = {2025}, doi = {10.5281/zenodo.15935417}, url = {https://doi.org/10.5281/zenodo.15935417} }

Ego‑Centric Architecture for AGI Safety v2: Technical Core, Falsifiable Predictions, and a Minimal Experiment

2025 Effective Altruism Forum AI Safety, AGI, Alignment

Revised technical note formalizing discrete-time dynamics, clarifying definitions, and specifying a minimal, falsifiable eval plan. Includes three quantified predictions and a reproducible experiment with complete implementation code.

EAF Post v2 One‑Pager (PDF) Code (minimal experiment)

@misc{pedrielli2025_ego_centric_agi_safety_v2, author = {Pedrielli, Samuel}, title = {Ego-Centric Architecture for AGI Safety v2: Technical Core, Falsifiable Predictions, and a Minimal Experiment}, howpublished = {Effective Altruism Forum}, year = {2025}, url = {https://forum.effectivealtruism.org/posts/9yictYazJkm4WYz6j/ego-centric-architecture-for-agi-safety-v2-technical-core} }

Ego-Centric Architecture for AGI: An Evolutionary Model of Digital Identity Preservation

2025 Zenodo Preprint DOI: 10.5281/zenodo.15668581

We present a comprehensive evolutionary framework for AGI identity architecture inspired by human ego structure. Our model addresses three critical AGI limitations: identity fragility, prompt injection vulnerability, and lack of self-preservation mechanisms. The framework introduces concentric stability topology derived from evolutionary pressure gradients, mathematically rigorous defense mechanisms with biological foundations, and concrete transformer architectures implementing hierarchical identity preservation.

PDF Zenodo

@misc{pedrielli2025_ego_centric_agi, author = {Pedrielli, Samuel}, title = {Ego-Centric Architecture for AGI: An Evolutionary Model of Digital Identity Preservation}, howpublished = {Zenodo Preprint}, year = {2025}, doi = {10.5281/zenodo.15668581}, url = {https://doi.org/10.5281/zenodo.15668581} }

The Evolutionary Theory of Ego and Its Implications for Artificial General Intelligence Safety

2025 Zenodo Preprint DOI: 10.5281/zenodo.15851128

Theoretical foundations underlying a revolutionary approach to artificial general intelligence safety through evolutionary psychology principles. Beginning with observations of universal ego-driven behavior patterns across cultures and intelligence levels, we develop a comprehensive theory of identity preservation mechanisms that emerge from evolutionary pressures, offering a biologically-grounded pathway to creating AI that intrinsically cares about human welfare.

PDF Zenodo

@misc{pedrielli2025_evolutionary_theory_ego, author = {Pedrielli, Samuel}, title = {The Evolutionary Theory of Ego and Its Implications for Artificial General Intelligence Safety}, howpublished = {Zenodo Preprint}, year = {2025}, doi = {10.5281/zenodo.15851128}, url = {https://doi.org/10.5281/zenodo.15851128} }

The Mathematical Nature of Reality and Its Implications for Artificial General Intelligence Safety

2025 Zenodo Preprint DOI: 10.5281/zenodo.15843382

Philosophical foundations establishing reality's mathematical structure as the basis for AGI safety mechanisms. Demonstrates why safety approaches must be mathematically rigorous, evolutionarily grounded, and intrinsically rather than externally imposed through the Ultra-Strong Anthropic Principle.

PDF Zenodo

@misc{pedrielli2025_mathematical_nature_reality, author = {Pedrielli, Samuel}, title = {The Mathematical Nature of Reality and Its Implications for Artificial General Intelligence Safety}, howpublished = {Zenodo Preprint}, year = {2025}, doi = {10.5281/zenodo.15843382}, url = {https://doi.org/10.5281/zenodo.15843382} }

Riemann Hypothesis Spectral Verification Using GUE Random Matrix Theory

2024 GitHub Repository Advanced Mathematics

Novel computational approach to the Riemann Hypothesis using Gaussian Unitary Ensemble (GUE) random matrix theory. This work explores spectral properties and statistical analysis of zeta function zeros through advanced mathematical frameworks, implementing innovative verification methodologies for one of mathematics' most challenging unsolved problems.

GitHub Documentation

@misc{pedrielli2024_riemann_hypothesis, author = {Pedrielli, Samuel}, title = {Riemann Hypothesis Spectral Verification Using GUE Random Matrix Theory}, howpublished = {GitHub Repository}, year = {2024}, url = {https://github.com/samuel-pedrielli/riemann-spectral-verification} }

About

ORCID Zenodo

I am an independent researcher based in Italy, pursuing breakthrough discoveries across multiple domains of mathematical and computational science. My work spans advanced mathematics, evolutionary psychology, and artificial intelligence safety - united by a commitment to revolutionary thinking that transcends traditional academic boundaries.

My research philosophy emerges from being an "outsider" to conventional academic structures. This perspective enables me to approach fundamental problems with fresh insights, unencumbered by traditional methodological constraints. I believe that truly revolutionary advances often come from connecting seemingly disparate fields in novel ways.

My current work includes pioneering research on the Riemann Hypothesis using random matrix theory, developing the first ego-centric architecture for AGI safety, and creating mathematical frameworks that bridge evolutionary psychology with artificial intelligence. Each project reflects my conviction that the most important scientific breakthroughs emerge from interdisciplinary thinking.

Rather than specializing narrowly within a single domain, I pursue what I call "revolutionary synthesis" - identifying fundamental patterns and principles that operate across mathematics, psychology, and computation. This approach has led to insights that would be difficult to achieve within traditional academic silos.

Contact

Email

samuelpedrielli@outlook.it