Uncertainty Quantifier
Ki · Single Ability
The Problem
The sales forecast is 15% growth next quarter, but there's a confidence gap due to missing uncertainty tolerance.
The Operation
This ability makes the model extract every quantitative claim in the reasoning chain. Classify each claim's uncertainty as aleatoric or epistemic. Do not accept point estimates without bounds, flag any unbounded assertion as incomplete. Verify that confidence intervals reflect actual evidence strength, not rhetorical certainty. Quantify the sensitivity of conclusions to each uncertain input. The reasoning applies a formal computation: CI = estimate +/- z * std error; z = z score(confidence level). Accept point estimates without bounds flag any unbounded is rejected.
The Structure
Under the hood, the reasoning follows an accumulate-classify-decide pattern that gathers evidence, categorizes it, then concludes.
If any claim is stated as a point estimate without an accompanying confidence interval, formal uncertainty quantification 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