Retrospection Anchor
Ki · Single Ability
The Problem
The sales forecast drifted from reality, showing optimism bias without recalibration against actual quarterly results.
The Operation
This cognitive operation forces the model to extract all prior predictions and compare each against its actual outcome. Classify drift patterns: identify whether predictions systematically overshoot or undershoot. If bias is detected, compute a correction buffer and constrain future estimates by applying it. Verify the buffer reduces drift on past predictions. If no outcome data exists, flag as unanchored and demand uncertainty bounds. The reasoning applies a formal computation: signed drift = predicted - actual; per forecast. The constraint: never issue forecast without anchoring historical accuracy.
The Structure
Under the hood, the reasoning follows an anchor-drift-correct cycle that detects when reasoning has drifted from its reference point. Execution repeats until the reasoning locks onto a stable conclusion.
If a prediction's accuracy is never compared to its actual outcome, retrospective anchoring was not applied.
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