Reduced False Rejection to 0.000 and Unsafe Acceptances to 0.000 under Correlated Shared Representation Failure (Dynamic Benchmark Suits)

Overview
For those who wanna dive into this deeply
Diagnostic Science Branch (Important)
Benchmark without Diagnostic Science
Benchmark with Diagnostic Sciences (Correlated Failure)
I have been working on a governance first AI architecture for sometime now. It's core hypothesis is how prediction and governance are two seperate computational problems.

Moving forth, I introduced formalization and likewise. In a sense, it seperated the prediction from governance, which goes along the lines of:

M = (X, Phi, F, G, A, W, P, Theta, Pi)

x -> Phi(x)=phi -> {f_i(phi)} -> r_i

where,
s_i = f_i(phi) in [0,1]
r_i = 2s_i - 1 in [-1,1]

where -1 is a complete rejection and +1 is maximum support.

Note this is evidence based, and is for each specialist individually.

And for the governance layer, it goes along the lines of:
z = (g_1(phi),...,g_k(phi)) in R_{>=0}^k

a = A(z), where A is monotone

w_i = W(i, phi, z)

m = max_l p_l(phi) in [0,1]

theta = Theta(a,m) = theta_0 + lambda a - delta m

R(x) = sum_i w_i r_i

Pi(R,theta,a) = 1[a < tau_hard] * 1[R >= theta]

The gi within the z are the diagnostic functions. There's a seperate branch of the given architecture for that where you can find high quality diagnostic functions where quality is defined as how much of a perspective introduction/breadth of scope/influence does a given diagnostic function provides Mavs.

On bounded tabular small benchmarks compared with basic systems like static mean ensembles, MAVS had 144-202 times less unsafe acceptances than them and highest accuracy under high corruption.

However, I just completed two dynamic benchmark experiments which provide way more understanding of the given architecture within a proper research-style system, and without Diagnostic Sciences, MAVS with broad parameters (gi) simply increased its False Rejection to 100% shutting everything down under correlated failure.

However, AFTER using Diagnostic Sciences, we introduced a proper given solution such has Perception Extension theorum and likewise (refer to the docs and the repository directly to see how I addressed this) to decrease the False Rejection to 0 and Unsafe Acceptances also to 0 whilst having a mean reward of ~0.83 ish.

I also believe if we use Diagnostic Sciences, we can perhaps optimize that 0.83 to as close as possible to 1 or even 1 itself maybe, but I haven't experimented that.

Right now, under correlated failure, mavs had 0 false rejections, and 0 unsafe acceptances with an approximately mean reward of ~0.83.

I'd like critique on the situation on what sort of limitations/blind spots do I have in this. The website needs updating to introduce these relatively new benchmarks.