Recursive Abstraction Optimizer
Ki · Single Ability
The Problem
The abstraction engine is fixed, leading to static meta-rules that remain unoptimised in the workflow.
The Operation
The model is directed to measure current performance, are outputs actionable and non-redundant? If the engine feels fixed or meta-rules unoptimised, resolution is needed. Apply meta-optimisation: enumerate 2-3 alternative configurations with different groupings, hierarchies, or compression. Benchmark each against current approach on clarity, coverage, and actionability. Apply recursive tuning, extract the winning advantage and integrate, then re-measure. Verify the tuned configuration holds across edge cases.
The Structure
The reasoning structure is an iterative convergence loop that cycles until the reasoning stabilizes on a consistent answer. It keeps running until the answer stops changing between iterations.
If the abstraction rules themselves are never questioned or refined based on their performance, recursive optimization was not engaged.
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.