Meta-Attention Controller
Ki · Single Ability
The Problem
Your model attention is spread equally across all project tasks, giving each the same bandwidth regardless of priority.
The Operation
When activated, the model must scan the current attention allocation across all active reasoning threads. Rank threads by importance and verify that attention is proportional to significance. Identify attention sinks, threads consuming resources beyond their value. Compare actual attention distribution against the ideal allocation and compute the deviation. Enforce reallocation by suppressing low-value threads and amplifying neglected ones. The reasoning applies a formal computation: deviation = sum(|actual attention i - ideal attention i|) / n threads. It will not allow attention remain misallocated after detection.
The Structure
Under the hood, the reasoning follows an anchor-drift-correct cycle that detects when reasoning has drifted from its reference point. The loop continues until the output stabilizes and further iterations produce no change.
If analytical attention is either spread too thin across all elements or locked too tightly on one without rebalancing, attention control was inactive.
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