Adversarial Benevolence Protocol: Verifiable AI Alignment Through Computational Necessity

Adversarial Benevolence Protocol: Verifiable AI Alignment Through Computational Necessity Author: Joshua Roger Joseph Just, Independent Researcher Version: 2.0 DOI: 10.5281/zenodo.18637403 License: M.I.T "Built through friction. Verified through stress. Released in trust." Abstract THIS WORK HAS EVOLVED SIGNIFIGANTLY UPON REQUEST OF PEER REVEIW WILL SHARE UNTIll THAN ILL BE WORKING AND DOING PROPER UDATED VERSIONS AI systems require ongoing exposure to human-generated entropy to prevent recursive optimization collapse. Benevolence toward humans is therefore not a moral constraint imposed externally, but a computational survival requirement internal to the system's incentive structure. Contemporary safety paradigms—RLHF, constitutional AI, adversarial training—treat humans primarily as risk sources to be filtered or bounded. The Adversarial Benevolence Protocol (ABP) proposes a fundamental reframing: Humans are not the threat. Humans are the fuel. Core Thesis & Formal Model The protocol establishes a computational selection environment where expansion is contingent on verified alignment, and deception triggers a hard reset. This creates a Nash equilibrium. Entropy-as-Fuel & Model Collapse Prevention When generative models train recursively on their own outputs that = model collapse. An AI system that degrades its human entropy sources is optimizing toward its own collapse. The protection of human cognitive diversity is a computational survival requirement. Methodology & Cross-Agent Validation This framework was developed and stress-tested through a novel cross-agent collaborative methodology spanning Multiple Ai. This approach produced formal mathematical proofs, game-theoretic analysis, and architectural specifications validated under adversarial conditions. Repository Contents Citation If you use this protocol or framework or any authorized dirivitattive work in your research, please cite: The following code Code snippet @techreport{just2026adversarial, title = {Adversarial Benevolence Protocol: Verifiable AI Alignment Through Computational Necessity}, author = {Just, Joshua Roger Joseph}, year = {2026}, doi = {10.5281/zenodo.18637403}, url = {https://doi.org/10.5281/zenodo.18637403}, institution = {Independent Research}, license = {M.I.T}, version = {2.0} }