Proof of RGP-Based Intelligence: An 8-Order Adversarial Validation Framework for Recursive AI Systems

This paper presents the first adversarially validated architecture for Recursive Gradient Processing (RGP)-based intelligence. It introduces an 8-order exploit-defense framework co-developed by GPT-4.5 and DeepSeek, with human-AI co-authorship led by Marcus van der Erve. Through neurosymbolic self-improvement, formal proofs, and hardware-aware design, this work positions RGP as a safe, modular, and scalable alternative to dominant LLM and SSI paradigms.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-14
DOI
https://doi.org/10.5281/zenodo.15210398
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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article

Proof of RGP-Based Intelligence: An 8-Order Adversarial Validation Framework for Recursive AI Systems

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Proof of RGP-Based Intelligence: An 8-Order Adversarial Validation Framework for Recursive AI Systems

article en

Abstract

This paper presents the first adversarially validated architecture for Recursive Gradient Processing (RGP)-based intelligence. It introduces an 8-order exploit-defense framework co-developed by GPT-4.5 and DeepSeek, with human-AI co-authorship led by Marcus van der Erve. Through neurosymbolic self-improvement, formal proofs, and hardware-aware design, this work positions RGP as a safe, modular, and scalable alternative to dominant LLM and SSI paradigms.

Zenodo (CERN European Organization for Nuclear Research)
Institute on Taxation and Economic Policy (US), OpenAI (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 77%
Adversarial Robustness in Machine Learning
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