Meta-Memory Curator
Ki · Reasoning
The Problem
Your model document retains every data point from the past five years without relevance scoring, all context is treated as equally important regardless of the current strategic question.
The Operation
This ability makes the model scan all context elements and classify each as relevant, marginal, or noise using meta indexing. Apply eviction policy, filter out noise to reduce distortion. Apply promotion rule, rank retained context by informativeness and weight high-value items above low-value ones. Trace how each retained element influences the conclusion. Never allow irrelevant context to distort reasoning. The reasoning applies a formal computation: context weight = informativeness rank * relevance to goal. Allow irrelevant context distort reasoning is rejected.
The Structure
Structurally, this is a convergence funnel where multiple candidates enter, evidence narrows them, and only survivors exit. It keeps running until the answer stops changing between iterations.
If all available context is retained at equal weight without filtering out information irrelevant to the current question, context curation has failed.
Haki · Reasoning-Multi
Cross-Domain Suppression
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, the primary ability is augmented with failure guards extracted from 3 abilities in different cognitive domains. Each guard blocks a specific reasoning failure the primary alone wouldn't catch. A self-check forces verification before output. The result is cross-domain coverage that no single ability can reach alone.