Deterministic Governance & Topological Stability for Frontier AI

Shifting AI safety from empirical probabilistic patching to mathematically provable topological stability bounds and deterministic runtime output governance.

Project Details

What 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)

https://doi.org/10.5281/zenodo.18576471

Theory of Impact

Cause-and-Effect Chain to Mitigate Existential AI Risks:

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

Mohamed Samir Abdelrahman Selim

Team Member

Grants Received– no grants recorded

Add grant

Funding Asks

Funding ask

Open

Minimum

$50,000

Ideal

$200,000

How the money will be spent

Budget Allocation Breakdown ($200,000 USD Target):

High-Performance Cloud Compute Infrastructure (50% - $100,000):

Provisioning enterprise-grade GPU clusters (Nvidia H100 / A100 instances) for dynamic topological stress-testing and runtime governance matrix (A1M/GRACE) evaluation across frontier open-weight models.

Principal Investigator Stipend & Dedicated Time (30% - $60,000):

Sustaining 100% full-time dedicated research, mathematical proof expansion, code maintenance, and framework optimization for 12 months.

Specialized Technical Services & Contractors (12% - $24,000):

Targeted engagement of CUDA optimization engineers and formal verification specialists to minimize execution latency in runtime control loops.

Open-Access Deployment, Benchmarking & Dissemination (8% - $16,000):

Hosting interactive Hugging Face Spaces, dataset infrastructure, open-access repository maintenance, and presentation at international AI safety symposiums.

Discussion

No comments yet. Be the first to share your thoughts.