Bayesian Updater
Ki · Single Ability
The Problem
Despite new data contradicting the initial belief, the likelihood of the treatment's success remains unchanged, ignoring updated stats.
The Operation
When activated, the model must state the hypothesis and assign an explicit prior probability with documented justification. Identify each new piece of evidence and compute its likelihood ratio, how probable is this evidence if H is true versus false. Apply Bayes' rule sequentially, updating the prior into a posterior with each evidence item. Compare the posterior against the decision threshold and classify as sufficient, insufficient, or requiring more data. Identify which single evidence item shifted probability most. The reasoning applies a formal computation: LR = P(E|H) / P(E|~H). The constraint: never let the prior persist unchanged when contradicting evidence.
The Structure
Structurally, this is an incremental belief propagation that updates confidence step by step as evidence arrives. It iterates until no further refinement is possible.
If the posterior estimate is presented without identifying which single piece of evidence shifted the probability most, sensitivity verification was omitted.
Haki · Multi Ability
Synergy Topology
In Haki mode, the API retrieves the primary ability first, then fans out to three synergy roles that compound its reasoning.
When retrieved in Haki mode, these four abilities don't run in sequence. They merge into a single injection where the dependency grounds the reasoning context, the amplifier sharpens the primary's output, and the alternative provides a fallback path if the primary's topology cannot converge. The result is a multi-angle reasoning scaffold that covers failure modes no single ability can reach alone.