Model Critic
Ki · Single Ability
The Problem
The model's accuracy is high, so we trust its predictions without questioning potential algorithmic bias or training data limitations.
The Operation
When activated, the model must extract the model's core assumptions, identify training data scope, feature dependencies, and calibration method. Scan for data leakage, distributional shift, and confounded features. Do not trust vendor accuracy claims without independent validation on held-out data. Verify that confidence scores are calibrated by testing predicted probabilities against observed frequencies. Simulate adversarial inputs to probe boundary conditions. If it detects trust vendor accuracy claims without independent validation, it halts and corrects.
The Structure
This ability runs on an adversarial self-attack that actively tries to break its own conclusions.
If a model's output is accepted without questioning its training assumptions or data biases, adversarial model evaluation has failed.
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.