Shifting AI safety from empirical probabilistic patching to mathematically provable topological stability bounds and deterministic runtime output governance.
Shifting AI safety from empirical probabilistic patching to mathematically provable topological stability bounds and deterministic runtime output governance.
Project Details
Updated 08/25/26 · Provided by member · VerifiedWhat the Project Does:
This project establishes a mathematically grounded output governance paradigm and topological stability framework to predict, bound, and eliminate non-deterministic failure modes, catastrophic hallucinations, and spurious periodic generalization errors in frontier deep neural architectures. By replacing empirical probabilistic patching (e.g., RLHF) with formal dynamic system control, it enforces deterministic runtime boundaries on high-capacity models.
Who Is Doing It:
Led by Mohamed Samir Abdelrahman Selim, an independent AI safety and theoretical researcher based in Cairo, Egypt.
ORCID Identifier: https://orcid.org/0009-0005-7722-1275
Current Progress & Open-Science Assets:
The research has progressed from core mathematical proofs to fully deployed open-source code and interactive verification spaces across four foundational frameworks:
A1M (AXIOM-1 Sovereign Matrix) for Governing Output Reliability in Stochastic Language Models
https://doi.org/10.5281/zenodo.19608960
https://huggingface.co/spaces/Samir333zoom/Axiom-1-Sovereign-Matrix
https://github.com/zoom333samir/Axiom-1-Sovereign-Matrix
A Universal Stability Criterion for Symbolic Complex Systems: Detecting Structural Deviation Before Catastrophic Collapse (USG)
https://doi.org/10.5281/zenodo.18883274
Governed Release Architecture for Controlled Excellence (GRACE)
https://doi.org/10.5281/zenodo.19256386
https://doi.org/10.17605/OSF.IO/296KP
Detecting Spurious Periodic Generalization in Neural Networks (PGVP)
Theory of Impact
Updated 08/25/26 · Provided by member · VerifiedCause-and-Effect Chain to Mitigate Existential AI Risks:
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The Vulnerability (Root Cause):
As frontier AI systems gain autonomy in safety-critical domains, their reliance on stochastic generation creates latent, un-bounded distribution shifts. Unconstrained entropy leads to catastrophic hallucinations, structural degradation, and unpredictable alignment failures under extreme semantic stress. -
The Diagnostic Intervention (Cause):
Deploying the Universal Stability Criterion (USG) and Periodic Generalization Verification Protocol (PGVP) provides real-time detection of topological instabilities and spurious periodic errors within high-dimensional embedding spaces before failure manifests at the output layer. -
The Runtime Governance (Mechanistic Control):
Integrating the Axiom-1 Sovereign Matrix (A1M) and GRACE governance architecture enforces provable runtime boundary matrices around model representations. This constrains output variance dynamically without crippling the underlying functional reasoning capability.
People
Updated 09/20/26 · By grantmaking.aiTeam Member
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