Adaptive Threshold Tuner
Ki · Single Ability
The Problem
Success is judged by whether the ROI exceeds an arbitrary 15% threshold without further analysis.
The Operation
Under this ability, the model must identify which input variables the conclusion is most sensitive to. Simulate small perturbations in each key variable and measure the effect on the output. Rank variables by sensitivity magnitude to expose hidden leverage points. Verify that high-sensitivity variables have been examined with proportionate rigor. Trace whether any assumption masks a sensitivity that would alter the conclusion. Accept results ignore high sensitivity factors is rejected.
The Structure
Under the hood, the reasoning follows a sensitivity perturbation grid that systematically varies inputs to find where the conclusion breaks. The procedure repeats until diminishing returns trigger an exit.
If the conclusion depends on an arbitrary threshold without testing how it changes when that threshold shifts, sensitivity reflection was skipped.
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.
Appears in Use Cases