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MAVS-GC (Multi-Adaptive Vetting System – Governance Core) is a governance-first AI architecture built around the hypothesis that explicit governance is a separate computational problem from prediction. Rather than assuming increasingly capable AI systems naturally become safer, MAVS-GC introduces an explicit governance layer between specialist model outputs and real-world decisions, allowing systems to reject, constrain, or escalate uncertain outputs through an auditable decision process.
The goal is to determine whether MAVS-GC generalizes beyond its initial benchmark suite and can serve as a practical governance architecture for safer AI systems under corruption, uncertainty, and failure conditions.
I will achieve this through a structured validation program built on the existing MAVS Research Bible, a 21-chapter roadmap for turning MAVS from an initial architecture into a full research program.
Chapters 1–10 are already complete or substantially completed. They cover:
Mission
Core thesis
Research identity
Literature mapping
Formal foundations roadmap
Theorem program
Governance metrics
Complexity analysis
Synthetic benchmark program
Real benchmark program
These chapters established the foundation, formal structure, synthetic validation, and first empirical benchmark cycle.
The next stage is to execute the generalization program. This includes:
Chapter 11: ablation matrix
Chapter 12: explainability program
Chapter 13: adversarial testing
Chapter 14: cross-domain validation
Later chapters on governance learning, self-governance, scientific discovery, publication, reviewer attack trees, and open-source release strategy
The immediate goal is to test whether MAVS-GC keeps reducing unsafe acceptances across more realistic settings. I plan to construct five cross-domain evaluation suites, with each suite spanning five distinct domains. Each suite will include multiple corruption families, repeated runs, and frontier-model evaluation where possible.
The success condition is not “MAVS wins everything.” The success condition is finding out whether the original effect survives broader testing. If it does, that gives stronger evidence that explicit governance can reduce unsafe AI decisions. If it fails, the negative result is still useful because it tells us where governance-first architectures break.
The majority of the funding will be used to execute the next large-scale validation stage of MAVS-GC.
The primary objective is to construct five industrial-grade evaluation suites, with each suite spanning five distinct domains. Every suite will be designed to evaluate MAVS-GC under different corruption families, uncertainty conditions, and failure scenarios while using the latest frontier AI models, including systems such as OpenAI's GPT series and Anthropic's Claude models where appropriate. The goal is to determine whether the observed reduction in unsafe decisions generalizes beyond the initial benchmark and remains consistent across different problem domains and model families.
Each evaluation suite will simulate realistic deployment conditions rather than isolated academic benchmarks. Together, these suites will cover diverse corruption stages, multiple corruption families, adversarial conditions, repeated evaluation cycles, and comprehensive governance traces to measure whether explicit governance consistently changes failure behavior under industrial-scale settings.
A portion of the funding will also support Chapter 11 (Ablation Matrix) by systematically removing or modifying governance components to identify which mechanisms are responsible for the observed robustness improvements. Additional funding will support adversarial testing, reproducibility studies, independent benchmark expansion, and open-source documentation so the results can be independently verified and reproduced.
Finally, part of the funding will be used to upgrade my current research hardware, which has become a bottleneck for running large-scale experiments, analyzing results, processing datasets, and executing repeated evaluation cycles efficiently.
This is currently a solo independent research project, done my me, namely Saif Malik.
The track record is the work already completed.
I did not begin by asking for funding for an idea. I first built the MAVS-GC roadmap, formalized the architecture, implemented benchmark programs, and completed the first empirical validation cycle.
Completed work includes:
MAVS Research Bible V3, a 21-chapter long-term research roadmap
Foundation Arc covering the mission, thesis, identity, formal objects, metrics, theorem roadmap, and cost model
Chapter 9 synthetic benchmark program
Chapter 10A clean predictive benchmark
Chapter 10B robustness benchmark
Chapter 10C reproducibility and stability benchmark
Public GitHub repositories for the benchmark programs
A concise external research overview document
Corrected final robustness numbers and benchmark highlights
The most important empirical result so far is Chapter 10B. Under high corruption, MAVS-GC maintained 85.30% predictive accuracy with 0.45% unsafe acceptance, while the evaluated ensemble baselines had 43.24% accuracy with 67.61% unsafe acceptance. Under specialist failure, MAVS-GC maintained 89.95% accuracy with 1.35% unsafe acceptance.
Chapter 10A was important because it prevented overclaiming. MAVS-GC was competitive under clean conditions, but did not demonstrate universal predictive superiority. Chapter 10C added the stability story, showing stronger stability preservation under corruption than under clean conditions.
This means the project is no longer at the “idea” stage. The next step is not inventing MAVS-GC. The next step is testing whether the observed behavior generalizes.
The most likely failure mode is that MAVS-GC does not generalize beyond the current benchmark setting.
Possible failure causes:
The current result may be benchmark-specific.
MAVS-GC may work well for tabular corruption but not for frontier-model settings.
The governance mechanisms may reduce unsafe acceptances only by over-rejecting too aggressively.
Some domains may require different diagnostics or threshold policies.
The architecture may need stronger adversarial resistance than the current version provides.
If the project fails, the outcome is still useful. A clean negative result would tell us that the initial MAVS-GC effect does not generalize, or only generalizes under certain conditions. That would narrow the research direction and prevent inflated claims.
The worst useful outcome is: MAVS-GC fails to generalize, but the experiments identify exactly where and why it fails.
The best outcome is: MAVS-GC continues reducing unsafe acceptances across multiple domains, corruption families, and frontier-model settings, giving stronger evidence that explicit governance is a reusable architectural principle for safer AI systems.
I have raised no funding for MAVS-GC so far. The project has been self-driven and built with limited personal resources and available AI tooling.