Hypothesis Pruning Engine
Ki · Single Ability
The Problem
Continuing to allocate resources to every hypothesis branch, even when evidence is conclusive, leads to excess hypotheses and combinatorial explosion.
The Operation
Under this ability, the model must enumerate all active hypotheses and extract the evidential support level for each. Compute a viability score per hypothesis by weighting evidence strength, internal consistency, and predictive power. Rank hypotheses by viability and identify candidates below the pruning threshold. Verify that pruning a low-ranked hypothesis does not discard a promising minority explanation. Never retain branches that add combinatorial cost without evidential warrant. The reasoning applies a formal computation: viability = w1*evidence strength + w2*internal consistency + w3*predictive power. The constraint: never retain branches add combinatorial cost without evidential.
The Structure
Structurally, this is a convergence funnel where multiple candidates enter, evidence narrows them, and only survivors exit. The loop continues until the output stabilizes and further iterations produce no change.
If the number of active hypotheses grows without eliminating those with insufficient evidentiary support, hypothesis pruning was not active.
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.