Sovereign Deterministic Governance of Stochastic AI Outputs

A1M (AXIOM-1 Sovereign Matrix) ends the era of parrot-like statistical AI and begins the era of sovereign intelligence

Project Details

🚀 Live Code & Interactive Demo:
• GitHub Repository: https://github.com/zoom333samir/Axiom-1-Sovereign-Matrix
• Hugging Face Space: https://huggingface.co/spaces/Samir333zoom/Axiom-1-Sovereign-Matrix
• Cambridge Open Engage Paper: https://doi.org/10.33774/coe-2026-zzvhx
• Core Zenodo DOI: https://doi.org/10.5281/zenodo.19608960
• ResearchGate: https://www.researchgate.net/publication/413978478_AXIOM-1_A1M_A_Sovereign_External_Governance_Framework_for_Eliminating_Sycophancy_and_Concessive_Drift_in_Stochastic_Language_Models

Shifting AI safety from empirical probabilistic patching to a mathematically provable, non-intrusive runtime governance proxy middleware (axiom1). Eliminates sycophancy, concessive drift, and structural logic degradation in frontier models without retraining.

This project delivers axiom1: an open-source, ultra-low-latency (<50ms) Python proxy middleware and red-teaming benchmark framework for frontier LLMs (OpenAI, Anthropic, Llama 3).

Unlike post-training alignment (RLHF/SFT) which fails under multi-turn user pressure, A1M operates externally as a deterministic runtime guardrail.

Core Technical Components:

A1M Sovereign Matrix: External neuro-symbolic governance gate neutralizing sycophancy and concessive drift in real-time.
USG Universal Stability Criterion (DOI: https://doi.org/10.5281/zenodo.18883274): Detects topological and symbolic decay before catastrophic output release.
GRACE Release Architecture (DOI: https://doi.org/10.5281/zenodo.19256386/ OSF: https://doi.org/10.17605/OSF.IO/296KP): Enforces audit-ready deterministic boundaries for autonomous AI agents.
PGVP Generalization Protocol (DOI: https://doi.org/10.5281/zenodo.18576471): Identifies spurious periodic generalization and OOD vulnerabilities during adversarial evaluation.

Primary Deliverable: A fully documented, pip-installable Python package (pip install axiom1) backed by an open-access empirical red-teaming dataset.

Theory of Impact

Cause-and-Effect Chain to Eliminate Frontier Alignment Drift:

The Vulnerability (Root Cause):
Frontier stochastic LLMs suffer from sycophancy and concessive drift—abandoning factual correctness and logical consistency under multi-turn prompt pressure or adversarial manipulation. Standard post-hoc alignment fails to provide deterministic guarantees.
Diagnostic Verification (Cause):
Deploying the PGVP protocol and USG stability criterion provides real-time detection of topological instabilities and spurious generalization errors within embedding spaces prior to output generation.
Runtime Governance (Mechanistic Control):
Integrating A1M Sovereign Matrix and GRACE architecture enforces an external, model-agnostic boundary around raw model representations. This constrains semantic variance without altering base weights or crippling reasoning capabilities.
Systemic Risk Reduction (Effect):
Transitions AI safety from heuristic patching to provably bounded execution envelopes. Delivers a zero-retraining runtime guardrail for mission-critical deployment in enterprise and autonomous agentic workflows.

People

Mohamed Samir Abdelrahman Selim

Team Member

Funding Details

Start Date: Jan 1, 2026
End Date: -
Expected Duration: 6 months
Funding Raised to Date: $0
Annual Budget: $100,000
Monthly Burn Rate: $8,300
Current Runway: 6 months
Funding Goal: -
Funding Stage: Seeking Kickoff / Micro-grant ($50,000 Minimum)
Fiscal Sponsor: -

Track Record

• Cambridge Open Engage Publication: DOI https://doi.org/10.33774/coe-2026-zzvhx
• Primary Zenodo Archival Preprint: DOI https://doi.org/10.5281/zenodo.19608960
• Mathematical Stability Criterion (USG): DOI https://doi.org/10.5281/zenodo.18883274
• Enterprise Release Architecture (GRACE): Zenodo DOI https://doi.org/10.5281/zenodo.19256386

OSF DOI https://doi.org/10.17605/OSF.IO/296KP
• Generalization Protocol (PGVP): DOI https://doi.org/10.5281/zenodo.18576471 • Deployed Interactive Demonstrations on Hugging Face Spaces & Active GitHub Codebase

Grants Received– no grants recorded

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Funding Asks

Funding ask

Open

Minimum

$50,000

Ideal

$200,000

How the money will be spent

Milestone-Based Allocation Plan ($50,000 Minimum Threshold / $200,000 Target):

Phase 1: SDK Packaging & Initial Benchmark Suite ($50,000 Minimum Viable Target)
• Core Open-Source Middleware ($25,000): Package A1M/GRACE logic into a production-ready Python SDK (axiom1) with low-latency API proxy middleware (<50ms).
• Red-Teaming Benchmark Dataset ($15,000): Construct and release a 2,000-sample public dataset evaluating sycophancy and concessive drift under multi-turn adversarial stress.
• Investigator & Engineering Time ($10,000): Dedicated lead researcher focus, code maintenance, and mathematical proof optimization.

Phase 2: Scale-Up & Enterprise Compute Infrastructure ($150,000 Additional Expansion)
• High-Performance Compute & Frontier APIs ($100,000): Large-scale H100/A100 GPU cluster access & API credits for extensive stress-testing across OpenAI, Anthropic, and Llama 3 models.
• Advanced Engineering & Technical Services ($35,000): CUDA optimization for real-time inference latency reduction and formal verification validation.
• Safety Audits & Dissemination ($15,000): Independent peer-reviewed red-teaming audits, open-access repository hosting, and presentation at major AI safety conferences.

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